marjolein zee - nro...502802-l-os-zee processed on: 3_25_2016 marjolein zee eeeeeeeeeee aansluitend...
TRANSCRIPT
502802-L-os-Zee502802-L-os-Zee502802-L-os-Zee502802-L-os-Zee Processed on: 3_25_2016Processed on: 3_25_2016Processed on: 3_25_2016Processed on: 3_25_2016
Marjolein Zee
eeeeeeeeeee
Aansluitend bent u van hartewelkom op de receptie.
eeeeeeeeee:Britt [email protected] [email protected]
Mar
jole
in Z
ee
eee
e eee
eeee
ee eeee
eeeeeeeeeeee
eeeee
ee eeeeeeeeeee
ee
voor het bijwonen van de openbare verdediging
van het proefschrift
eeee eeeeeee ee eeeeeeee eeeeeeee eeeeeee eeeeeeeeeeeee
op dinsdag 24 mei 201614:00 in de Agnietenkapel,
Oudezijds Voorburgwal 231te Amsterdam.
eeee eeeeeee ee eeeeeeeeeeeeeeee eeeeeee eeeeeeeeeeeee
Marjolein Zee
eeeeeeeeeee
Aansluitend bent u van hartewelkom op de receptie.
eeeeeeeeee:Britt [email protected] [email protected]
Mar
jole
in Z
ee
eee
e eee
eeee
ee eeee
eeeeeeeeeeee
eeeee
ee eeeeeeeeeee
ee
voor het bijwonen van de openbare verdediging
van het proefschrift
eeee eeeeeee ee eeeeeeee eeeeeeee eeeeeee eeeeeeeeeeeee
op dinsdag 24 mei 201614:00 in de Agnietenkapel,
Oudezijds Voorburgwal 231te Amsterdam.
eeee eeeeeee ee eeeeeeeeeeeeeeee eeeeeee eeeeeeeeeeeee
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
FROM GENERAL
TO STUDENT-SPECIFIC TEACHER SELF-EFFICACY
Marjolein Zee
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
This research project was supported by grant no. 411-12-036 of the Netherlands Organisation for Scientific Research (NWO). This dissertation was sponsored by Stichting Kohnstamm Fonds. Cover design by B. E. Hakvoort Layout by M. Zee ISBN: 978-94-028-0141-5 NUR: 841 Copyright © 2016 M. Zee
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
FROM GENERAL
TO STUDENT-SPECIFIC TEACHER SELF-EFFICACY
ACADEMISCH PROEFSCHRIFT
Ter verkrijging van de graad van doctor
aan de Universiteit van Amsterdam
op gezag van de Rector Magnificus
prof. dr. D. C. van den Boom
ten overstaan van een door het College voor Promoties ingestelde commissie,
in het openbaar te verdedigen in de Agnietenkapel
op dinsdag 24 mei 2016, te 14:00 uur door
MARJOLEIN ZEE
geboren te Hoorn
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
PROMOTIECOMMISSIE:
Promotor: Prof. dr. P. F. de Jong
Universiteit van Amsterdam
Co-promotor: Dr. H. M. Y. Koomen
Universiteit van Amsterdam
Overige leden: Prof. dr. T. T. D. Peetsma
Universiteit van Amsterdam
Prof. dr. F. J. Oort
Universiteit van Amsterdam
Prof. dr. G. J. J. M. Stams
Universiteit van Amsterdam
Prof. dr. A. E. M. G. Minnaert
Rijksuniversiteit Groningen
Prof. dr. K. Verschueren
Katholieke Universiteit Leuven
Faculteit der Maatschappij- en Gedragswetenschappen
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CONTENTS
_________________________________________________________________________
CHAPTER 1 General introduction 9
CHAPTER 2 Teacher self-efficacy and its effects on classroom processes, 19
student academic adjustment and teacher well-being:
A Synthesis of 40 Years of Research
CHAPTER 3 Inter- and intra-individual differences in teachers’ self-efficacy: 79
A multilevel factor exploration
CHAPTER 4 Teachers’ self-efficacy in relation to individual students with a 115
variety of social-emotional behaviors: A multilevel investigation
CHAPTER 5 Students’ disruptive behavior and the development of teachers’ 145
self-efficacy: The role of teacher-perceived closeness and conflict
in the student–teacher relationship
CHAPTER 6 General discussion 175
Summary 191
Samenvatting (Summary in Dutch) 195
References 199
Dankwoord (Acknowledgments in Dutch) 219
About the author 223
List of publications 224
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
“Homework is a breeze. Cooking is a pleasant diversion. Putting up a retaining wall is a
lark. But teaching is like climbing a mountain.”
– Fawn M. Brodie
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
9
CHAPTER 1
GENERAL INTRODUCTION
_________________________________________________________________________
“As an elementary school teacher, I’ve come across many tough and challenging classes,
but they didn’t warn me for this one. The first school period was simply hell. Sophie has
symptoms of ADHD and ODD, and she’s just uncontrollable, constantly testing me.
She’s not the only one, though. Of the 16 students in my class, five have serious conduct
problems. There have been days that I left with a sore throat, almost feeling like a third-
year student teacher again.”
– Teacher Relationship Interview with Anna, a fourth-grade teacher
The realities of today’s elementary classrooms, where children with various backgrounds,
needs, and (dis)abilities are educated side by side, make a strong appeal to teachers’ ability to
organize and execute their daily teaching tasks. Since the inception of Dutch national policies
geared toward inclusive and appropriate education (Ministry of Education, Culture and Science,
2014)1, teachers are increasingly required to design individualized education plans to fit the
learning needs of all students, and to provide the behavioral, social, and emotional supports
that help these students participate in all aspects of school life (Derriks, Ledoux, Overmaat, &
van Eck, 2002; Schram, van der Meer, & van Os, 2012; Smeets & Rispens, 2008; van Gennip,
Marx, & Smeets, 2007). Catering for appropriate education and adequately dealing with a
diverse student body is, however, not always as straightforward as it may seem. According to
recent national reports, about half of Dutch regular elementary teachers do not believe
themselves capable of dealing with students who differ in behavior and educational needs,
despite having all kinds of valuable teaching knowledge, skills, and expertise (e.g., Smeets et al.,
2013; Smeets, Blok, & Ledoux, 2013; Smeets, Ledoux, Regtvoort, Felix, & Mol Lous, 2015).
This seeming discrepancy between teachers’ actual competencies on the one hand and their
ultimate behaviors, feelings, and actions on the other has spurred many educational
researchers, both in the Netherlands and beyond, to investigate the concept of teacher self-efficacy.
1 Appropriate Education Act, August 2014 [Wet Passend Onderwijs, augustus 2014].
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 1
10
Teacher self-efficacy (TSE), or teachers’ beliefs in their capabilities to “organize and execute
the courses of action required to produce given attainments” (Bandura, 1997, p. 3), has
nowadays been increasingly considered as one of the central determinants of teachers’ thought
processes, motivation, affective states, and actions (Bandura, 1977, 1986, 1997; Tschannen-
Moran, Woolfolk Hoy, & Hoy, 1998; Woolfolk Hoy, Hoy, & Davis, 2009). A vast body of
evidence has suggested that highly self-efficacious teachers are generally likely to perceive
difficult students as less challenging, to take more adequate approaches to improving their
students’ behaviors and performances in class, and to bend over backwards to ensure they
succeed (e.g., Almog & Schechtman, 2007; Brownell & Pajares, 1999; Caprara, Barbaranelli,
Steca, & Malone, 2006; Dunn & Rakes, 2011; Martin & Sass, 2010). Less self-efficacious
educators, like Anna in the beginning of this chapter, have frequently been demonstrated to
impair their own functioning by magnifying the severity of possible stressors in the classroom,
avoiding difficult teaching tasks, and settling for mediocre results (Bandura, 1997; Hamre,
Pianta, Downer, & Mashburn, 2008). Unfortunately, such inefficacious trains of thought may
typically pay off in performance failures and negative changes in students’ academic adjustment
and teachers’ well-being (e.g., Brouwers, Evers, & Tomic, 2001; Caprara, Barbaranelli,
Borgogni, & Steca, 2003; Klassen & Chui, 2010; Klassen et al., 2013).
To understand why elementary teachers at times are able to translate their knowledge into
proficient action and in other cases somehow fail to orchestrate and sustain the skills,
motivation, and effort required for meeting the goals of appropriate education, it seems
important to gain insight into TSE in relation to individual students with different behaviors
and needs. Such knowledge is crucial for a comprehensive understanding of teachers’ dealings
with diversity in the classroom, yet currently lacking due to several conceptual and
methodological issues. The overarching goal of the present dissertation, therefore, is to take
stock of the current state of theory and research on teacher self-efficacy, and address several
major challenges the field of TSE is facing at present.
To set the context for this dissertation, the first section of this General Introduction provides a
brief overview of Bandura’s (1977, 1986, 1997) social-cognitive theory. This theory of human
agency has since long been considered as the dominant framework for studying teachers’ sense
of self-efficacy. Based on the basic tenets of this framework, several challenges in the field of
TSE are subsequently brought to the fore that seem to have hampered its breadth of influence
and practical usefulness to both educational researchers and practitioners alike. In closing this
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
GENERAL INTRODUCTION
11
chapter, a brief outline is provided of how these theoretical, empirical, and methodological
challenges will be addressed in the remaining chapters of this dissertation.
FOUNDATIONAL TENETS OF TEACHER SELF-EFFICACY
Teachers’ sense of self-efficacy has generally been embedded in the concepts of Bandura’s
(1977, 1986, 1997) social-cognitive theory. With this framework, Bandura has advanced a view
of human agency that accords a prominent role to both environmental events and thought
processes in human adaptation and change. Put another way, the social aspect of social-
cognitive theory adheres to the nowadays common notion that human functioning is, in
essence, deeply embedded in social conditions (cf. Bandura, 1997; Bronfenbrenner & Morris,
1998; Ryan & Deci, 2002; Sameroff & Fiese, 2000). What this idea basically indicates is that the
environments in which people operate, including family homes, schools, and workplaces, and the
persons with whom they interact on a daily basis, may offer enabling resources or impose
constraints for their behaviors and actions in given domains of functioning. Specific to the
context of teaching, for instance, studies have documented a myriad of factors positively
contributing to teachers’ classroom achievements, such as decision latitude, social support
from parents and colleagues, and students’ interest in their schoolwork (Bakker, Hakanen,
Demerouti, & Xanthopoulou, 2007; Cheung, 2008; Raudenbusch, Rowan, & Cheung, 1992;
Tschannen-Moran & Woolfolk Hoy, 2007). Other features, including changing school policies,
deficient equipment, and disruptive student behavior in class, have frequently been marked as
the sources of challenge elementary teachers usually report (e.g., Fernet, Guay, Senécal, &
Austin, 2012; Roehrig, Pressley, & Talotta, 2002; Smeets et al., 2015). In this sense, the
classroom environment may stand as teachers’ primary venue for learning about what they can
do in given teaching domains.
It is not to say, however, that teachers should be considered as simply automated conveyers of
environmental constraints or resources. Rather, they are generally believed to actively
contribute to their own development and everyday functioning by exercising some control
over their thoughts, feelings, and actions (Bandura, 1977, 1986). This is where the cognitive part
of social-cognitive theory comes in. According to Bandura (1997), all humans possess a set of
internal personal attributes, the most important of which are self-efficacy beliefs, that enable
them to choose particular courses of action from among other alternatives to attain the goals
they wish to pursue in a given domain. For example, elementary school teachers with
substantial instructional expertise may feel effective and confident in implementing
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 1
12
differentiated instruction and act accordingly in obedient and orderly classrooms. At the same
time, however, these teachers may shy away from the same activity when they sense the
classroom environment surmounts their skills and capabilities to perform the task. By
exercising this self-influence, teachers may thus operate generatively and proactively, and not
only reactively, to chart the contours of their environment (Bandura, 2001).
Taken together, then, how teachers cognitively interpret social constraints and resources in
class may inform and alter their behaviors and actions, the results of which may subsequently
lead to changes in both the classroom environment and teachers’ self-efficacy. This is the
foundation of Bandura’s (1997) model of triadic reciprocal causation, within which personal
internal factors, behavior, and environmental influences work in concert to influence human
agency. Social-cognitive theory thus seems to adhere to and extend such other seminal
frameworks as bio-ecological theory (Bronfenbrenner & Morris, 1996), dynamic systems
theory (Sameroff & Fiese, 2000), and self-determination theory (Ryan & Deci, 2002), by
emphasizing personal cognitions, and highly particularized self-efficacy beliefs in particular, as
the core mechanisms of human agency.
Bandura’s social-cognitive model has, perhaps owing to its intuitive appeal, made increasingly
marked inroads into the literature on teacher self-efficacy. Since its inception in the late 1970s,
numerous conceptualizations of TSE have come onto the scene (cf. Dellinger, Bobbett,
Olivier, & Ellett, 2008; Gibson & Dembo, 1984; Labone, 2004; Tschannen-Moran et al., 1998),
and theoretical models incorporating a variety of antecedents and consequences have been
devised and (sometimes) tested (e.g., Tschannen-Moran et al., 1998; Woolfolk Hoy et al., 2009;
Wyatt, 2016). Evidently, these theoretical and empirical efforts have contributed a great deal to
our understanding of the potential role of teachers’ self-efficacy beliefs in shaping their
behaviors in class. Yet, some of the key conceptions behind the self-efficacy construct are far
from being fully explored, thereby potentially limiting the theoretical and practical utility of
TSE and its breadth of influence.
CHALLENGES REGARDING THE NATURE AND CONSEQUENCES OF TSE
Perhaps one of the most pressing questions that has been asked frequently since Bandura
introduced his social-cognitive model is which particular teaching behaviors, classroom
processes, and student learning outcomes may be affected by teachers’ self-efficacy beliefs.
This seemingly simple question is not an easy one to address, however, since the field of
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
GENERAL INTRODUCTION
13
teacher self-efficacy has been dominated by different research traditions that may sometimes
even be divided in itself (e.g., Henson, 2002; Tschannen-Moran & Woolfolk Hoy, 2001; Wyatt,
2014). For instance, the initial research on TSE attempted to verify that teachers’ general self-
efficacy beliefs were powerfully related to their own effectiveness and their students’
performance (e.g., Armor et al., 1977; Gibson & Dembo, 1984). Somewhat confusingly,
though, this strand of research was not only inspired by Bandura’s social-cognitive scheme, but
also by Rotter’s (1966) locus of control theory, which centers on causal beliefs about the
relationship between actions and outcomes (i.e., outcome expectations), instead of personal
capability beliefs (i.e., self-efficacy). Accordingly, a considerable amount of research that allegedly
concentrated on TSE may actually have examined a different construct, thereby confounding
the theoretical base on which Bandura’s construct is essentially built (e.g., Tschannen-Moran et
al., 1998; Wyatt, 2014).
Next to this initial research tradition, other sets of investigations rapidly began to emerge as
well. Some of these appeared to be mainly educational in nature, employing social-cognitive,
self-determination, or classroom-based frameworks to investigate associations of (domains of)
TSE with a wide range of teacher practices and classroom processes, most of which were
investigated in isolated studies (e.g., Emmer & Hickman, 1991; Goddard & Goddard, 2001;
Guo, McDonald Connor, Yang, Roehring, & Morrison, 2012; Martin & Sass, 2010). Other,
more recent studies emerged from the psychological field of stress and well-being, examining
TSE and other self-referent processes (e.g., self-esteem, self-concept, and competence) in
relation to such factors as burnout, retention and attrition, job commitment, and satisfaction
(e.g., Brouwers & Tomic, 2000; Klassen & Chiu, 2010, 2011; Skaalvik & Skaalvik, 2007, 2010).
By concentrating on such related views of TSE, these studies might have overlooked the
construct’s full complexity and context-specific nature, or referred to entirely different things
(Bandura, 1997).
In conclusion, then, the various research traditions that developed over the past forty-odd
years seem to have resulted in a massive body of work on TSE and its consequences that is
both fragmented and conceptually confused (cf. Klassen, Tze, Betts, & Gordon, 2011; Wyatt,
2014). To improve the applicability of this current literature to educational practice, an
integrative, Bandura-based framework to synthesize the literature on TSE and its associations
with a range of adjustment outcomes at different levels of classroom ecology seems, therefore,
to be needed. Without such a perspective, it seems near impossible to yield an accurate,
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 1
14
theoretical representation of the nature of TSE, and to understand the separate findings
emerging from each prevailing research tradition.
CHALLENGES REGARDING THE MEASUREMENT OF TSE
Given the fragmented and conceptually confused foundation of the TSE literature, it is
perhaps not surprising that challenges regarding the measurement of TSE have also been at the
forefront of much of the work in the field. Markedly, this attention to measurement started as
soon as the very first teacher self-efficacy measure (Rand scale; Armor et al., 1977) came onto
the scene. This scale only consisted of two plain, rather unanalytical items that, as it turned out
later, bore a closer resemblance to Rotter’s (1966) idea of locus of control than Bandura’s self-
efficacy theory. As such, great concern began to arise about the length of self-efficacy scales,
their scale reliability and validity, and their relevance to Bandura’s social-cognitive scheme
(Tschannen-Moran & Woolfolk Hoy, 2001; Woolfolk Hoy et al., 2009). In an attempt to
improve the psychometric properties of the Rand measure and to give allegiance to Bandura’s
theoretical notions, various researchers therefore started to develop new instruments to
measure TSE. Of these instruments, the more general Teacher Efficacy Scale (TES; Gibson &
Dembo, 1984) and domain-specific Teacher Sense of Efficacy Scale (TSES; Tschannen-Moran &
Woolfolk Hoy, 2001) have, by far, been the most popular.
Regrettably, though, both instruments by no means seem to plumb the depths of the teacher
self-efficacy belief system. The TES, for instance, seems to treat teachers’ sense of self-efficacy
simply as a general construct, defined at the classroom-level of analysis, thereby obscuring potential
variation in teachers’ self-percepts of efficacy across teaching tasks and domains (Bandura,
1997; Tschannen-Moran et al., 1998; Wheatley, 2005). In addition, the TSES, despite being
“superior to previous measures of teacher efficacy in that it has a unified and stable factor
structure” (Woolfolk Hoy & Burke Spero, 2005, p. 354), has partly failed to take account of the
social part of Bandura’s social-cognitive theory. Specifically, the TSES is, in the first place,
somewhat limited with regard to the domains of teaching and learning it aims to examine. In
the standard version of this instrument, teachers are usually presented with 24 items portraying
tasks regarding instructional strategies, classroom management, and student engagement
(Tschannen-Moran & Woolfolk Hoy, 2001). Although these teaching domains are certainly
representative of teachers’ daily activities, they may not fully reflect the breadth of teachers’
activities. Indeed, other responsibilities, including teachers’ responsiveness to children’s social-
emotional needs and their regard for students’ perspectives, have also been acknowledged to
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
GENERAL INTRODUCTION
15
be crucial areas of teaching and learning (cf. Hamre, Hatfield, Pianta, & Jamil, 2014; Pianta, La
Paro, & Hamre, 2008). Such areas may be particularly important to advance understanding of
teachers’ perceived ability to deal with a diverse student population. Therefore, a good,
consensually shared conceptual analysis of what it takes for teachers to succeed in their job is
currently needed to identify additional domains of teaching and learning across which TSE can
vary (Bandura, 1997).
In the second place, the TSES tends to examine teachers’ self-percepts of efficacy at
inappropriate levels of specificity. According to Bandura (1997, 2006), the specificity of
teachers’ self-efficacy beliefs can vary on a number of different dimensions, including the
domains of functioning, the task demands, and the characteristics of the persons toward whom
teachers’ behavior is directed. Evidently, the TSES deserves credit for capturing a range of
tasks and responsibilities within different domains of teaching and learning at the classroom-
level of analysis. Yet, the persons toward whom teachers’ behaviors and actions are directed
have largely gone unheeded in this instrument. This is remarkable, given that Tschannen-
Moran and colleagues (1998) seem to be well aware of the highly context-specific nature of
TSE: “Teachers feel efficacious for teaching particular subjects to certain students in specific
settings, and they can be expected to feel more or less efficacious under different
circumstances” (pp. 227-228). Hence, major progress in understanding how TSE operates in a
model of triadic reciprocal causality can be made only if these beliefs are explicitly measured in
terms of particularized capability judgments that may vary under different levels of task
demands within given teaching domains (i.e., domain-specificity), and across different persons
toward whom teachers’ behavior is directed (i.e., student-specificity). Such measures may be more
practically relevant in that they may reveal in which teaching areas TSE may be beneficial or
problematic, and toward which particular students they feel efficacious. In addition,
instruments that are both domain- and student-specific gauge the nature of the teacher self-
efficacy construct in ways that it may better reflect Bandura’s social-cognitive frame.
CHALLENGES REGARDING THE FORMATION AND DEVELOPMENT OF TSE
One last major challenge is the general lack of understanding about the various sources of TSE
and underlying processes through which such sources may become instructive to teachers’ self-
efficacy beliefs across time. Based on Bandura’s triadic reciprocal model (1986, 1997), it is
reasonable to presume that teachers’ self-efficacy beliefs are mainly derived from rich,
reciprocal interactions with their immediate environment over extended periods of time. These
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 1
16
teacher-environment interactions may, according to Bandura (1997), provide various types of
information that are relevant for judging personal capabilities, including classroom mastery
experiences, modeled attainments, performance feedback, and affective states. In line with this
assertion, a handful of research on the sources of TSE has provided modest evidence that
teachers are likely to build a healthy sense of general self-efficacy when they are usually
satisfied with their past classroom performances, are able to cope with psychological stressors,
and when parents and colleagues express faith in their capabilities (e.g., Bandura, 1997;
Cheung, 2008; Klassen & Chui, 2010; Ross, Cousins, & Gadalla,1996; Ruble, Usher, &
McGrew, 2011; Salanova, Llorens, & Schaufeli, 2011; Tschannen-Moran & McMaster, 2009;
Tschannen-Moran & Woolfolk Hoy, 2007). Moreover, studies on the development of TSE
(e.g., Brouwers et al., 2001) disclosed a feedback loop in which teachers’ affective state
predicted their self-efficacy beliefs for classroom management and vice versa.
Perhaps due to the above-noted lack of domain- and student-specific TSE measures, virtually
none of these investigators have yet considered teachers’ encounters with individual students
as the primary conduit through which teachers may gain access to efficacy-relevant information
and build their TSE. This lack of attention to student–teacher interactions and –relationships
in the TSE literature is noteworthy, as individual students’ idiosyncratic behaviors, feelings, and
needs in relation to their teachers may provide the most important evidence of whether
teachers can muster whatever it takes to succeed with the child (Pianta, Hamre, & Stuhlman,
2003). Empirical evidence from Spilt and Koomen (2009) has suggested, for instance, that
teachers judge themselves as angrier and less self-efficacious in relation to individual students
who display disruptive behavior in class. Other studies have spawned some evidence that poor
relationships with students may lead to increases in emotional vulnerability in teachers, and
may result in feelings of professional and personal failure (e.g., Hamre et al., 2008; Newberry &
Davis 2008; O’Connor 2008; Spilt et al., 2011; Yeo, Ang, Chong, Huan, & Quek, 2008). In
light of these findings, it seems important to update, expand, and improve the available
information on the sources of TSE, by shifting the focus to individual students’ behaviors and
characteristics, and exploring how teachers may derive their self-efficacy beliefs from their
relationships with these children over time.
ADDRESSING THE CHALLENGES OF THE TEACHER SELF-EFFICACY LITERATURE
In summary, ever since Bandura presented his seminal theory on human agency, educationists
have explored the construct of TSE in multiple ways such that we currently know much more
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
GENERAL INTRODUCTION
17
about teachers’ capability to give shape to their actions in class and motivate and regulate their
execution. Yet, the absence of a clear understanding of the nature, sources, and consequences
of TSE, and psychometrically sound instruments that give full allegiance to Bandura’s ideas
may have prevented the field from moving forward (cf. Henson, 2001; Tschannen-Moran et
al., 1998; Wheatley, 2005; Wyatt, 2014). As such, it seems difficult to identify useful research-
based insights about TSE that may help teachers better deal with a diverse student body and
meet the goals of appropriate education. The remaining chapters of the present dissertation,
therefore, aim to address the current challenges in the field of TSE in four different ways.
Starting out at the most general, classroom-level of analysis, the second chapter of this dissertation
specifically aims to address current challenges regarding the nature of TSE and its
consequences for a range of outcomes at various levels of classroom ecology. Inspired by the
realization that the field of TSE still reflects a corpus of relatively fragmented and conceptually
confused empirical work, a process-oriented model of TSE is proposed that largely resembles
the CLASS, one of today’s leading frameworks for research on classroom processes (Pianta &
Hamre, 2009; Pianta et al., 2008). The CLASS highlights three domains of student–teacher
interactions, including instructional support, classroom organization, and emotional support,
that are nowadays considered to be the most germane to teachers’ functioning and students’
development (see Downer, Sabol, & Hamre, 2010). For this reason, this triad of domains was
used heuristically to organize and synthesize the body of empirical work on TSE and its
consequences, and to suggest new directions for the field.
Based on these recommendations as well as those of Bandura (1997, 2006), the focus
subsequently shifts to the student-level of analysis in Chapter 3. Central to this chapter is the aim
to advance understanding of the multifaceted nature of teachers’ sense of self-efficacy in upper
elementary school (Grades 3 to 6). To this end, a new teacher self-efficacy scale, based on
Tschannen-Moran and Woolfolk Hoy’s (2001) TSES was developed and evaluated. This new
instrument attempted, first, to address challenges regarding the domains across which teachers’
self-efficacy beliefs may fluctuate, by adding a fourth teaching domain to the original TSES.
Second, the original TSES was adapted to the student-specific level to gain insight into
potential variations in teachers’ self-efficacy beliefs across individual students from their
classrooms. The specifics of the new domain- and student-specific measure, as well as its
association with the TSES at the general, classroom-level are also described in Chapter 3.
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 1
18
Fully refraining from teachers’ self-efficacy beliefs at the classroom-level of analysis, Chapter 4
aims to explore individual students’ background characteristics and social-emotional behaviors
as sources of upper elementary teachers’ domain- and student-specific self-efficacy beliefs. Here,
the predictive value of individual students’ internalizing, externalizing, and prosocial behaviors
are investigated, as is the moderating role of teachers’ perceived classroom misbehavior and
years of teaching experience on their student-specific self-efficacy beliefs.
The issue of the formation and development of domain- and student-specific TSE is carried
further as Chapter 5 aims to explore a theoretical model within which teachers' perceptions of
closeness and conflict in the student–teacher relationship are hypothesized to form the
intermediary mechanisms by which individual students’ disruptive behavior may affect
teachers’ student-specific self-efficacy over time. Theoretical and empirical knowledge in this
direction may help educational researchers and practitioners identify levers to increase teachers’
self-efficacy toward individual students with different behaviors and needs, and thereby
improve these students’ classroom experiences and academic adjustment.
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
19
CHAPTER 2 TEACHER SELF-EFFICACY AND ITS EFFECTS ON CLASSROOM PROCESSES, STUDENT
ACADEMIC ADJUSTMENT AND TEACHER WELL-BEING:
A SYNTHESIS OF 40 YEARS OF RESEARCH
_________________________________________________________________________
This study integrates 40 years of teacher self-efficacy (TSE) research to explore the
consequences of TSE for the quality of classroom processes, students’ academic adjustment,
and teachers’ psychological well-being. Via a criteria-based review approach, 165 eligible
articles were included for analysis. Results suggest that TSE shows positive links with students’
academic adjustment, patterns of teacher behavior and practices related to classroom quality,
and factors underlying teachers’ psychological well-being, including personal accomplishment,
job satisfaction, and commitment. Negative associations were found between TSE and
burnout factors. Last, a small number of studies indicated indirect effects between TSE and
academic adjustment, through instructional support, and between TSE and psychological well-
being, through classroom organization. Possible explanations for the findings and gaps in the
measurement and analysis of TSE in the educational literature are discussed.
_________________________________________________________________________ Zee, M., & Koomen, H. M. Y. (2016). Teacher self-efficacy and its effects on classroom processes, student academic adjustment and teacher well-being: A synthesis of 40 years of research. Review of Educational Research. Advance online publication. doi:10.3102/0034654315626801
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 2
20
INTRODUCTION
Ever since the seminal work of the Rand corporation in the late 1970s (Armor et al., 1976),
studies on teacher self-efficacy (TSE) have been popping up like daisies in a spring field
(Klassen, Tze, Betts, & Gordon, 2011). This increase of research can be largely ascribed to the
notion that TSE beliefs, or teachers’ self-referent judgments of capability, are relevant for a
range of adjustment outcomes at different levels of classroom ecology. Using various measures
and definitions, studies imply that teachers with an assured sense of self-efficacy set the tone
for a high-quality classroom environment by planning lessons that advance students’ abilities,
making efforts to involve them in a meaningful way, and effectively managing student
misbehavior (Chacon, 2005; Woolfolk, Rosoff, & Hoy, 1990). Next to affecting the classroom
quality, TSE has also been found to exert influence over student and teacher outcomes. On the
student side, TSE has shown some links to academic achievement, motivation, and self-
efficacy (Midgley, Feldlaufer, & Eccles, 1989; Thoonen, Sleegers, Peetsma, & Oort, 2011; Ross,
1992). On the teacher side, positive TSE beliefs have been demonstrated to result in improved
psychological well-being in terms of higher levels of job satisfaction and commitment, and
lower levels of stress and burnout (Aloe, Amo, & Shanahan, 2014; Collie, Shapka, & Perry,
2012; Klassen & Chui, 2011).
The broad range of multileveled consequences of TSE speaks to the growing complexity of
this construct since its introduction some four decades ago. Still, consensus has not yet been
reached about which particular role TSE plays at different levels of classroom ecology. Most
reviews and critiques of the TSE literature have predominantly focused on key conceptual and
methodological issues surrounding research on teachers’ capability beliefs, or have proposed
alternative paradigms and frameworks to broaden and clarify this construct (e.g., Henson,
2002; Klassen et al., 2011; Labone, 2004; Tschannen-Moran, Woolfolk Hoy, & Hoy, 1998;
Wheatley, 2005; Woolfolk Hoy, Hoy, & Davis, 2009; Wyatt, 2014). Only two authors have
extended this scope, also putting emphasis on the potential consequences of TSE (Ross, 1998;
Woolfolk Hoy et al., 2009). Although these two reviews have been extensive, they have been
either narrative in nature, or could not yet cover the substantial body of evidence on the
consequences of TSE that has been published in the last decade. Consequently, they essentially
fail to render a more systematic and updated account of TSE and its consequences. The purpose
of the present review study, therefore, is to provide an up-to-date, critical review of forty years
of research on TSE and its consequences for the quality of classroom processes, students’
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
NATURE AND CONSEQUENCES OF TEACHER SELF-EFFICACY
21
academic adjustment, and teachers’ psychological well-being. We aim to go beyond previous
reviews by not only focusing on outcomes related to teaching and learning, but also on
teachers’ welfare. Thereby, we provide a more detailed description of TSE and its
consequences across grades.
Before discussing the main findings and conclusions, we will first provide an overview of the
foundational tenets of TSE to elucidate the complexities surrounding its meaning and
measurement. Inspired by the seminal work of Woolfolk Hoy and colleagues (2009) and
Pianta, La Paro, and Hamre (2008), we will next describe a process-oriented model of TSE.
This model is used heuristically to synthesize empirical research to explore the consequences of
TSE for outcomes at different levels of classroom ecology, and to reveal potential gaps in our
current understanding of this complex construct.
THEORY AND MEASUREMENT OF TEACHER SELF-EFFICACY
The foundational tenets of TSE have, historically, fallen between the two stools of locus of
control (Rotter, 1966) and social-cognitive theory (Bandura, 1977). As with other social-
psychological frameworks, the emphasis in these two theories is on human agency – the idea that
individuals are able to exercise control over actions that affect their lives (e.g., Bandura, 1986,
1997). Rotter’s (1966) attribution-based theory of locus of control is probably one of the best
known examples of this viewpoint. Drawing on previous empirical work, Rotter
conceptualized locus of control as a generalized expectancy for control of reinforcement that
individuals develop in relation to their environment (e.g., Rotter, 1966). Individuals, Rotter
assumed, generally differ in their perceptions of whether outcomes are contingent upon sheer
luck, fate, or others (external control), or a result of their own actions (internal control). Such
perceptions are considered to be largely determined by person-environment transactions that
reinforce individuals’ actions, such as receiving a reward after successful task performance.
These reinforcers, in turn, may serve as (dis)incentives for particular behaviors in future
situations (Rotter, 1966). Evidently, those who believe their environment to be responsive to
their actions – and hence develop a more internal locus of control – are the most likely to
become “happy, healthy, wealthy, and wise” (Lachman, 2006, p. 283).
Over the years, Rotter’s theory has laid the groundwork for many studies and scales, including
the first measure of TSE in the 1970s (see Tschannen-Moran & Woolfolk Hoy, 2001). Using
locus of control as a conceptual base, Rand researchers (Armor et al., 1976; Berman &
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 2
22
McLaughlin, 1977) formulated two simple items to assess teachers’ beliefs about their abilities
to bring about positive student change above the effects of child and environmental features.
Although these items only constituted a small part of the Rand studies, they have been
relatively important in laying an empirical foundation for inquiry into students’ achievement
gains. Thereby, this two-item instrument rapidly gained momentum toward new and more
comprehensive measures of TSE in relation to student outcomes (e.g., Dellinger, 2005;
Tschannen-Moran & Woolfolk Hoy, 2001). Among those instruments, Teachers’ Locus of
Control (Rose & Medway, 1981), Responsibility for Student Achievement (Guskey, 1981), and
the Webb Efficacy Scale (Ashton, Olejnik, Crocker, & McAuliffe, 1982) can be considered the
most prominent.
As refinements of the original Rotter-based construct started to appear, however, so a number
of issues regarding their relevance to TSE began to arise. These issues came barely one year
after the efforts of the Rand Corporation, with the work of Bandura (1977, 1986, 1997).
Building strongly on Rotter’s theory, Bandura argued that individuals’ behaviors are not only
influenced by generalized expectancies for control but also by these individuals’ perceived
capabilities, or self-efficacy, to perform those behaviors in particularized domains. To reinforce
this assertion, Bandura (1977) made a distinction between response-outcome expectancies and
self-efficacy expectations. Generally, response-outcome expectancies refer to individuals’
estimates “that a given behavior will lead to certain outcomes” (Bandura, 1977, p. 193). These
outcome expectancies can be assumed to be operationally equivalent to Rotter’s construct, as
they both determine whether the social environment is perceived to be reactive to personal
actions or not (see Kirsch, 1985). With self-efficacy expectations, Bandura seems to go beyond
such perceived environmental contingencies. He argued that although persons may know that
certain achievements result in desired outcomes, this information becomes virtually useless
when they lack the beliefs they have the abilities to produce such actions. For instance,
teachers’ judgment that scaffolding may increase student learning (outcome expectation) can act as
a motivator to making significant use of this teaching strategy. Yet scaffolding strategies are
unlikely to be initiated unless teachers believe they have the skills and capabilities to selectively
support their students where needed (self-efficacy). Thus, for Bandura (1997), personal self-
efficacy beliefs seem to be the most important cause of human behavior. As the predictor of
outcome expectancies, they help persons decide which courses of action they ought to pursue
and whether to persist in the face of environmental adversities. Also, they determine how
persons interpret their thoughts, actions, and emotions in given situations.
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
NATURE AND CONSEQUENCES OF TEACHER SELF-EFFICACY
23
Bandura’s addition to Rotter’s theory has had an enormous impact on TSE research. In the
first place, most researchers have, since his writings, underscored the need to differentiate
between self-efficacy and outcome expectancies (e.g., Dellinger, Bobbett, Olivier, & Ellett,
2008; Gibson & Dembo, 1984). Of particular note are the early efforts of Gibson and Dembo
(1984), who have performed much work in this area. In an attempt to develop a new measure
of TSE, they found modest evidence for two independent factors that assumedly resembled
self-efficacy and response-outcome expectancies. These factors, which were labelled as
personal teaching efficacy (PTE) and general teaching efficacy (GTE), respectively, have been
confirmed and used by many researchers until the late 1990s (e.g., Emmer & Hickman, 1991,
Hoy & Woolfolk, 1993; Riggs & Enochs, 1990; Soodak & Podell, 1993). After that time, the
popularity of Gibson and Dembo’s Teacher Efficacy Scale (TES) has faded somewhat, due to
issues with the construct and content validity of the general teaching efficacy factor (e.g.,
Pajares, 1997; Tschannen-Moran & Woolfolk Hoy, 2001; Woolfolk & Hoy, 1990).
A second consequence of Bandura’s socio-cognitive framing is that scholars started to
conceptualize TSE as task or situation-specific rather than generalized, as Rotter does. By moving
away from the idea that self-efficacy is an omnibus trait, it is acknowledged that TSE beliefs
may vary according to different types of tasks, students, and circumstances in class (Ross,
Cousins, & Gadalla, 1996; Tschannen-Moran et al., 1998). Such particularized self-efficacy
scales have been argued to have higher predictive validity, due to the variations in TSE that
occur across different tasks and domains (Bandura, 1997). The majority of the current
instruments and conceptualizations of TSE are therefore based on the breadth of teachers’ role
in the classroom and not solely on student outcomes. In the often used Teachers’ Sense of
Efficacy Scale (TSES; Tschannen-Moran & Woolfolk Hoy, 2001), for instance, TSE is treated
as a task-specific, three-dimensional construct reflecting instructional strategies, classroom
management, and student engagement. This Bandura-based instrument – developed in reaction
to the partial invalidity of Gibson and Dembo’s TES – has been described as “superior to
previous measures of teacher efficacy in that it has a unified and stable factor structure”
(Woolfolk Hoy & Burke Spero, 2005, p. 354). Indeed, investigators using either the 24-item or
12-item TSES have reported satisfactory reliability and construct validity evidence for this
instrument, across grades and several countries (e.g., Klassen et al., 2009; Tschannen-Moran &
Woolfolk Hoy, 2001).
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 2
24
In addition to the TSES-dimensions, other educational researchers have developed separate
self-efficacy scales for literacy (Tschannen-Moran & Johnson, 2011), science (Riggs & Enochs,
1990), inclusive practices (Malinen et al., 2013), technology (Sang, Valcke, van Braak, &
Tondeur, 2010), and discipline (Brouwers & Tomic, 2000), or extended the scope of TSE to
the organizational (Friedman & Kass, 2002) or cultural domain (Siwatu, 2007). Together, these
studies recognize that TSE is reflected in multiple specific components of teachers’ profession,
and that the strength of TSE can fluctuate between teaching tasks, roles, students, and over
time.
CONSEQUENCES OF TEACHER SELF-EFFICACY
Thus far, a mounting body of theoretical and empirical work has demonstrated the complex
ways in which TSE may affect outcomes at different levels of classroom ecology. In the late
1970s, student-level investigations first started to appear, focusing on TSE as a potential direct
determinant of students’ achievement and motivation. This research focus was evidently
encouraged by the Rand studies (e.g., Armor et al., 1976), which specifically hypothesized high
TSE to be beneficial for student learning. Subsequent investigators in the 1980s and beyond
have complemented this earlier work, turning their attention to direct consequences of TSE at
the teacher-level. Spurred by Bandura’s (1986) notion that self-efficacy not only affects
behaviors and actions but also thoughts and feelings, these researchers have opened new lines
of inquiry on factors associated with teachers’ psychological well-being (e.g., Klassen & Chiu,
2010, 2011; Skaalvik & Skaalvik, 2007).
Aside from investigators that posited a direction of causal influence from TSE to student-level
and teacher-level outcomes, classroom-oriented studies have suggested that TSE might rather
have an indirect effect on such outcomes. The idea behind this assumption is that TSE, as a
personal characteristic, mainly affects student and teacher outcomes through patterns of
teacher behavior and practices that define the quality of the classroom environment (Guo,
McDonald Connor, Yang, Roehring, & Morrison, 2012; Midgley et al., 1989; Woolfolk Hoy &
Davis, 2005). Gibson and Dembo (1984), for instance, underscored that highly self-efficacious
teachers “persist longer, provide a greater academic focus in the classroom, and exhibit
different types of feedback than teachers who have lower expectations concerning their ability
to influence student learning" (p. 570).
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
NATURE AND CONSEQUENCES OF TEACHER SELF-EFFICACY
25
To add to the complex nature of TSE, other researchers additionally believe that TSE
judgments may act on raising the classroom quality by exerting reciprocal influences over
teachers’ feelings of well-being and personal accomplishment (e.g., Bandura, 1997; Brouwers,
Evers, & Tomic, 2001; Goddard, Hoy, & Woolfolk Hoy, 2004). Such personal emotions and
cognitions are believed to inform and alter future TSE beliefs and accompanying behaviors,
which, in turn, affect both the classroom environment and student performance (Goddard et
al., 2004). This system of triadic reciprocal causality (Bandura, 1986), in which the classroom
environment, teachers’ behavioral patterns, and their cognitions influence each other
dynamically, calls attention to the need for critical exploration of the role that the quality of
classroom processes plays in the relationships among TSE, students’ academic adjustment, and
teachers’ well-being.
A HEURISTIC FRAME TO LINK TSE TO OUTCOMES AT VARIOUS LEVELS OF CLASSROOM ECOLOGY
Recently, Woolfolk Hoy and colleagues (2009) developed a process-oriented framework that
may help researchers to advance understanding of the complex ways in which TSE affects
outcomes at various levels of classroom ecology. In this global framework, TSE is suggested to
have various types of consequences for a range of classroom processes at both student and
teacher levels, including instructional actions, behavioral expectations, and emotional
classroom dynamics. As such, these types of consequences resemble the theoretically driven
and empirically supported classroom quality framework of Pianta and colleagues (CLASS;
Pianta & Hamre, 2009; Pianta et al., 2008). Today, the CLASS is one of the leading frameworks
for research on the quality of classroom processes, not least because of its emphasis on teacher
supports and practices related to the well-established major domains of instructional support,
classroom organization, and emotional support.
The domain of instructional support generally reflects the degree to which teachers are able to
advance students’ meta-cognitive skills, apply their thinking to real-world situations, scaffold
for struggling students, and expand on their understanding (Hamre & Pianta, 2010; Pianta et
al., 2008). The domain of classroom organization includes teaching practices such as providing
clear directions, rules, and expectations, focusing students’ attention toward learning
objectives, and preventing instances of misconduct (Pianta et al., 2008). Emotional support,
finally, comprises such interpersonal and affective classroom dynamics as student–teacher
relationships, teachers’ sensitivity, and regard for student perspectives (Hamre & Pianta, 2010).
Over time, these efficacy-influenced processes are presumed to mainly affect students’
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 2
26
academic adjustment (Hamre & Pianta, 2010; Woolfolk Hoy et al., 2009). Yet, given Bandura’s
model of triadic reciprocal causation, TSE may also have consequences for teachers’ well-
being, either directly or indirectly.
As the triad of domains of the CLASS-framework specifically encompass teacher supports and
practices, they may be helpful in identifying and further organizing the various classroom
processes that are at play in the association between TSE and student and teacher outcomes.
Inspired by this framework, we therefore propose a heuristic model for presenting the results
of our review study. Using this model, we will first review empirical studies on the direct
associations among TSE and the quality of classroom processes (i.e., instructional support,
classroom organization, and emotional support), students’ academic adjustment (i.e., student
motivation and academic achievement), and teachers’ well-being (e.g., burnout, stress, job
satisfaction, commitment, and attrition and retention). Second, we will explore evidence on the
mediating role of classroom processes on the association between TSE and student adjustment
and teacher well-being. Figure 1 provides an overview of these hypothesized relationships.
FIGURE 1
Heuristic Model of Teacher Self-Efficacy in Relation to Classroom Processes, Academic Adjustment, and
Teacher Well-Being
Quality of classroom processes: - Instructional support - Classroom organization - Emotional support
Teachers’ self-efficacy - Global self-efficacy - Domain-specific self-efficacy
Students’ academic adjustment: - Academic achievement - Student motivation
Teachers’ well-being: - Positive (job satisfaction, commitment, coping, retention) - Negative (burnout, stress, attrition)
Note. Solid lines symbolize expected associations that will be examined in the present review. Hypothesized reciprocal effects (dashed lines) displayed in the model will not be part of this study.
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
NATURE AND CONSEQUENCES OF TEACHER SELF-EFFICACY
27
METHOD
LITERATURE SEARCH
To comprehensively identify relevant studies on the consequences of TSE, we used a criteria-
based review approach to search articles from 1976 to March 2014. This time span was set as
research on TSE only started to increase after the work of the Rand Corporation (e.g., Armor
et al., 1976). Potentially eligible articles were collected iteratively from the Internet databases of
PsycInfo, ERIC, and Google Scholar in three stages. During the first stage, we employed an a
priori scoping search to define separate sets of key words to locate articles referring to TSE in
relation to the dimensions of the classroom-based framework. To operationalize TSE, we only
included such subject terms as “teach* self-efficacy”, “teach* efficacy”, “academic optimism”,
“teach* sense of (self-)efficacy”. Other types of self-beliefs, including locus of control, self-
concept, self-worth, self-esteem, perceived competence, and outcome expectations, were not
entered as search terms, as these beliefs, rather than cognitive judgments of capability, reflect
affective reactions (Bandura, 1997). In the second stage, the TSE search terms were
subsequently combined with key words referring to classroom quality, students’ academic
adjustment, and teachers’ psychological well-being, using the Boolean operators “AND” and
“OR”. A search of these descriptors lead us to detect tens of thousands of journal articles,
dissertations, book chapters, and conference proceedings. Therefore, in the third stage, we
further limited the source type to empirical, English language articles published in peer-
reviewed journals. To be included, full-text versions had to be available. After adding these
restrictions, separate searches for each dimension of the framework in relation to TSE were
performed, producing 768 results for classroom processes, 910 results for students’ academic
adjustment, and 710 results for teachers’ well-being.
INCLUSION CRITERIA
There were five criteria for the inclusion of publications in our review. First, each empirical
article was required to specifically focus on preservice or inservice teachers’ individual self-efficacy. As
such, studies investigating self-efficacy beliefs of principals, teaching assistants, mentors, or
school counselors were not included in this review. Likewise, articles were excluded if they
reported solely on collective or school-level (aggregated) TSE. Second, all articles had to
address a direct or indirect relationship between TSE and at least one factor associated with
students’ academic adjustment, teachers’ well-being, or hypothesized classroom processes.
Accordingly, we only reported on direct and indirect effects of TSE, leaving out potential
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 2
28
interaction terms of TSE and other variables described in the selected studies, as well as
associations in which TSE served as a mediator. Third, studies were considered relevant if they
used quantitative empirical data. Although we initially included qualitative data as well, the
small body of retrieved qualitative research appeared to be diverse in terms of study focus,
sample size, teaching context, design, and primary study outcomes. These large differences
made it difficult to compare and critically appraise these articles, and to determine their
relevance in light of the quantitative studies included in the review. Also, most qualitative
research dealt with a specific study population, thereby potentially limiting their generalizability
and influence. It is for this reason that we decided to focus on quantitative empirical research,
and excluded qualitative work from analysis. Fourth, quantitative studies were required to use
psychometrically sound, Bandura-based instruments to identify TSE. This criterion resulted,
among others, in the exclusion of articles using only Gibson and Dembo’s (1984) general
teaching efficacy scale, or other instruments measuring outcome expectancies. Last, the
samples of the studies were allowed to include special education, preschool, elementary school,
high school, and higher education teachers and/or students. Hence, no limits were set with
regard to school type.
Irrelevant papers were removed, and appropriate articles were identified based on information
provided in the abstract or, when the abstract was not available, the title. In case of doubt, the
full text was consulted. Key journals and references of articles that met our criteria were
subsequently hand-searched, to locate additional studies. The application of these criteria
ultimately resulted in 165 articles for analysis.
RESULTS
Following the heuristic model provided in Figure 1, this section provides a thematic analysis of
the literature on TSE. As the reviewed studies generally evaluated various combinations of
variables, the consequences of TSE for each of the outcomes are discussed separately. Table 1
offers a framework for these main outcome domains and their dimensions, and summarizes
the number of studies and mean sample size per dimension. First, consequences of TSE on
teacher behaviors and practices that define instructional, classroom organizational, and
emotional aspects of classroom processes are discussed. The results for each of these major
classroom domains, categorized into subthemes, are displayed in Appendix 1. Next, studies
investigating the direct and indirect links between TSE and student adjustment are reviewed,
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
NATURE AND CONSEQUENCES OF TEACHER SELF-EFFICACY
29
including academic achievement and motivation. These studies are reported in Appendix 2.
Studies on the direct and indirect association between TSE and teachers’ well-being are
discussed last. The results for each aspect of teachers’ well-being can be found in Appendix 3.
Please note that we do not provide a standardized, statistical overview of the strength of
relationships between TSE and student and teacher outcomes in the classroom, due to the
heterogeneous nature of the reviewed studies in terms of sample sizes, analytical methods and
rigor, and instruments used.
TABLE 1
Overview of Study Outcomes, Domains, and Dimensions
Note. QCP = Quality of classroom processes; SAA = Students’ academic adjustment, TWB = Teachers’ well-being.
Main outcome Domain Dimension N studies Mdn sample size (range) QCP Instructional Overall Instructional support 8 166 (19 – 631) Support Support for literacy and math 9 94 (40 – 346) Implementation of
instructional practices 8 79 (20 – 537)
Classroom Organization
Classroom behavior management
17 182 (33 – 983)
Inclusive practices and referral decisions
14 188 (55 – 1,623)
Instructional management 26 302 (8 – 2,132) Emotional
Support Overall emotional climate 3 67 (49 – 1,043)
Student-teacher relationships 7 152 (75 – 597) Regard for student
perspectives 3 96 (75 – 336)
SAA Students’ Overall achievement 8 222 (80 – 2,184) achievement Math achievement 4 307 (19 – 1,329) Literacy achievement 6 67 (20 – 1,075) Achievement in other subjects 4 214 (18 – 450) Students’
motivation Motivational behaviors, beliefs 11 80 (58 – 1,329)
TWB Negative well-
being Teacher burnout 23 404 (49 – 2,249)
Teacher stress and coping 6 109 (30 – 479) Positive well-
being Teacher satisfaction 21 366 (30 – 1,212)
Teacher commitment 12 726 (109 – 26,257) Teacher attrition and retention 9 192 (66 – 1,214)
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 2
30
CONSEQUENCES OF TSE FOR CLASSROOM PROCESSES – INSTRUCTIONAL SUPPORT
Scholars suggest that the instructional behaviors, practices, and strategies teachers employ to
encourage students’ cognitive development may, in part, be determined by their self-efficacy
(e.g., Tschannen-Moran & Woolfolk Hoy, 2001). This possibility was explored in 25 survey
studies (see Appendix 1). Of these studies, more than half used samples with less than 100
teachers. Moreover, around 70% of the 25 reviewed articles on instructional support relied on
simple correlations and global measures of TSE, making it difficult to determine whether
particular domains of TSE have similar patterns of effects on teachers’ instructional support.
Given these methodological choices, it is perhaps not surprising that teachers’ overall levels of
instructional support, and particularly those of preservice teachers, have not been found to be
affected by their self-efficacy (Guo, Piasta, Justice, & Kadaverek, 2010; Pakarinen et al., 2010).
Yet there are indications that inservice TSE contributes to a range of general instructional
practices. Among others, these include process-oriented instruction and differentiation, the
number of goal changes made, the ability to connect to students’ lives and employ effective
teaching strategies, and their choices of differentiated instructional strategies supporting
inclusive education (Allinder, 1995; Martin, Sass, & Schmitt, 2012; Thoonen, Sleegers, Oort,
Peetsma, & Geijsel, 2011; Wertheim & Leyser, 2002; Weshah, 2012). Rigorous structural
equation modeling (SEM) results from Geijsel, Sleegers, Stoel, and Kruger (2009), furthermore,
showed that efficacious teachers frequently engage in professional learning activities, such as
keeping up to date with the profession, trying out new approaches to improve their practices,
and changing their practice to promote process-oriented student learning.
INSTRUCTIONAL SUPPORT FOR LITERACY AND MATH
Besides overall support, nine studies (see Appendix 1) specifically considered the importance
of TSE for the employment of strategies to maximize students’ literacy and mathematics
development. Regarding math, Brown (2005) pointed out that early childhood teachers’ self-
efficacy did not result in more observed mathematics instructional practices, although self-
efficacious teachers did rate the importance of math higher than colleagues without such
beliefs. Largely similar, longitudinal results from Holzberger, Philipp, and Kunter (2013)
showed that TSE is unrelated to students’ subsequent assessment of the level of cognitive
activation or individual learning support. Instead, a reverse effect of instructional quality on
TSE was revealed, with students’ experience of cognitive activation and teachers’ ratings of
classroom management predicting subsequent TSE (ibid.). This is in line with Bandura’s notion
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
NATURE AND CONSEQUENCES OF TEACHER SELF-EFFICACY
31
of triadic reciprocal causation, suggesting that teachers’ instructional support and TSE may
affect one another reciprocally.
The small-scale correlational studies focalizing literacy reveal that efficacious preservice teachers
generally have more knowledge of using expository text as a reading tool (Yildirim & Ates,
2012). Yet these self-efficacious preservice teachers have not been found to use more reading
strategies than less efficacious educators while teaching their students to read (Haverback,
2009). Efficacy in inservice teachers, in contrast, has been shown to contribute both to the quality
of their instructional (literacy) support, and the instructional literacy environment in preschool
(Guo, Sawyer, Justice, & Kaderavek, 2013; Justice, Mashburn, Hamre, & Pianta, 2008).
Language modeling, one key dimension of instructional support, was unrelated to early
childhood teachers’ self-efficacy (Justice et al., 2008).
In three small, cross-sectional studies, the relationship between domain-specific TSE beliefs
and the use of various language strategies in high school was investigated (Chacon, 2005;
Eslami & Fatahi, 2008; Yilmaz, 2011). In the largest study (N = 104), TSE for engagement,
classroom management, and instruction all correlated positively with communication- and
grammar-oriented strategies, but did not affect teachers’ preference for one specific type of
strategy (Chacon, 2005). A study by Yilmaz (2011; N = 54) failed to replicated these results.
Eslami and Fatahi (2008; N = 40) only found positive correlations between dimensions of TSE
and communicatively oriented language strategies. Together, these results suggest that the
consequences of TSE for teachers’ instructional (literacy) support become more evident when
teachers gain experience.
IMPLEMENTATION OF INSTRUCTIONAL PRACTICES
Despite the effectiveness of instructional practices for students’ development, not all teachers
feel capable of implementing and using such practices in class. More specifically, teachers with
high general self-efficacy have been demonstrated to perceive the implementation of new
instructional methods as more important and congruent with their own practices. They
experience less self-survival, task, and impact concerns, and more pedagogic conceptual
change, irrespective of grade (Ghaith & Shaaban, 1999; Ghaith & Yaghi, 1997; Lee, Cawthon,
& Dawson, 2013). Turning to domain-specific TSE, Dunn, Airola, Lo, and Garrison (2013)
found that TSE for data-driven decision making is positively related to collaboration concerns,
suggesting that efficacious teachers more often work with colleagues to improve and increase
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 2
32
use of data-driven decision making in class. Even in the context of physical education, high-
school TSE in teaching daily lesson plans and student-centered teaching styles has been found
to positively affect teachers’ attitude and intention toward curriculum implementation
(Gorozidis & Papaioannou, 2011).
Next to attitudes toward implementation, results of three intervention studies show that
efficacious teachers are likely to more frequently implement and use subject-specific
instructional practices in class (Cantrell & Hughes, 2008; Eun & Heining-Boynton, 2007;
Lakshmanan, Heath, Perlmutter, & Elder, 2011). Cantrell and Hughes (2008) explored the
relationship between TSE and implementation of a content literacy approach among sixth- and
ninth-grade teachers. They found that TSE before implementation was correlated with
teachers’ observed implementation at the start of the content literacy program, but not after,
suggesting TSE to be more important during the initial implementation phase. Likewise, Eun
and Heining-Boynton (2007) were interested in the effects of an English-as-a-Second-
Language Professional Development Program on TSE and classroom practices of K-12
teachers. They revealed that teachers with high self-efficacy were more likely to use the
instructional knowledge and skills acquired from professional-development programs than
educators with low self-efficacy.
Regarding science teaching, standards-based professional development programs have been
shown to have potential to positively affect TSE and, consequently, teachers’ implementation
of reformed science teaching in upper elementary classrooms (Lakshmanan et al., 2011).
Although these studies imply that TSE may be crucial to teachers’ implementation fidelity,
some caution is warranted when making inferences from these results. Specifically, the sample
sizes of these three intervention studies were relatively small (N = 22, 79, and 90, respectively),
and control groups were not included in these investigations.
CONSEQUENCES OF TSE FOR CLASSROOM PROCESSES – CLASSROOM ORGANIZATION
Classroom organization is generally perceived as a domain of classroom processes related to
how well teachers manage students’ behavior and instructional time, and provide lessons and
materials that maximize learning opportunities (Pianta et al., 2008). Within this domain, three
particular dimensions can be distinguished that account for links between TSE and classroom
processes: behavior management, inclusive practices and referral decisions, and instructional
management. Statistical analyses in research that investigated the links between TSE and these
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
NATURE AND CONSEQUENCES OF TEACHER SELF-EFFICACY
33
dimensions (n = 55; see Appendix 1) mostly included application of simple correlations,
regression methods, or analysis of variance (37 studies). Studies employing multilevel analysis
(4 studies), SEM (12 studies) or longitudinal analysis (2 studies) were less common. Although
some investigations focused on specific student populations (e.g., students with emotional
and/or behavioral difficulties; Almog & Shechtman, 2007; Liljequist & Renk, 2007; Yoon,
2004), most studies did not include corrections for confounding by students’ or teachers’
personal characteristics.
CLASSROOM BEHAVIOR MANAGEMENT
The notion that TSE shows links with the ability to organize and manage students’ behavior
and time is particularly consistent with small-scale, correlational research on teachers’ ability to
cope with students’ social-emotional behavior (n = 4). For instance, Lilejequist and Renk
(2007) found that preservice teachers with a high sense of personal self-efficacy report higher
levels of control over externalizing behavior, but seem more bothered by students’
internalizing behavior than teachers with a low sense of self-efficacy. Moreover, in elementary
school, self-efficacious inservice teachers have been shown to cope better with different problem
behaviors, including low achievement, social rejection, shyness, disobedience, hostility,
hyperactivity (Almog & Shechtman, 2007), and students’ bullying behavior (Yoon, 2004).
Conversely, when teachers’ efficacy is hampered by student behavior, they may develop a
critical attitude toward their own teaching abilities (Lambert, McCarthy, O'Donnell, & Wang,
2009).
Potentially, teachers’ perceived ability to cope with challenging students may partly determine
which classroom management behaviors, strategies, and styles they ultimately adopt.
Considering the preservice context, general TSE has been demonstrated to be beneficial to lesson
presenting, questioning, and classroom management behaviors (Saklofske, Michayluk, &
Randhawa, 1988). Preservice teachers with high personal and classroom management efficacy
have also been found to use more positive strategies (i.e., increasing desirable student behavior)
and external strategies (i.e., referring a disruptive student) than poorly efficacious teachers
(Emmer & Hickman, 1991). Whether or not these behavior management strategies are
effective depends on these teachers’ TSE as well (Wertheim & Leyser, 2002).
Some cross-sectional studies suggest that the positive consequences of TSE for preservice
teachers’ classroom management styles and strategies likely extend to the inservice context (e.g.,
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 2
34
Dussault, 2006; Morris-Rothschild & Brassard, 2006). In elementary school and beyond, TSE
has been shown to positively contribute to teachers’ behavior, instructional, people, and overall
classroom management strategies (Abuh-Tineh, Khasawneh, & Khalaileh, 2011) and their
proactive approaches to managing student–teacher conflict. These include, among others, their
integrating, compromising, and obliging styles (Morris-Rothschild & Brassard, 2006). In high
school, TSE has furthermore been positively linked to several organizational citizenship
behaviors of teachers, such as altruism, courtesy, and conscientiousness (Bogler & Somech,
2004; Dussault, 2006; Ngidi, 2012). The only study that took a longitudinal approach to
studying TSE did not find any association between TSE and classroom management across
time, despite reporting significant correlations (Holzberger et al., 2013). Thus, these studies
generally imply that proactive behavioral management strategies are most likely to be employed
by teachers with high self-efficacy. These modest findings are consistent across grade and
context.
Complementing the corpus of research on classroom management strategies and behaviors,
primarily older research (n = 4) from the 1990s underscores that TSE might influence teachers'
beliefs about student control (Hoy & Woolfolk, 1990; Martin & Sass, 2010; Woolfolk & Hoy,
1990; Woolfolk et al., 1990). Woolfolk and Hoy (1990) investigated preservice teachers’ efficacy
in relation to their beliefs about control and motivation. Initially, their findings indicated that
TSE was unrelated to teachers’ pupil control ideology and motivational beliefs. Canonical
correlations, however, revealed that TSE was positively related to a control orientation that
rejects teacher control of students but accepts the schools' control of teachers. Two other
studies indicated that although preservice teachers tend to become more custodial and
controlling in their orientations (Hoy & Woolfolk, 1990), more experienced sixth-and seventh-
grade teachers may adopt a less custodial pupil control ideology when their self-efficacy is high
(Woolfolk et al., 1990). A more recent study of Martin and Sass (2010) discovered that teachers
who express high self-efficacy beliefs for instructional strategies, engagement, and classroom
management generally take a less directive approach in implementing tactics to manage
instruction, irrespective of grade. Probably, TSE is associated with more humanistic attitudes
about classroom control, at least for more experienced elementary school teachers.
INCLUSIVE PRACTICES AND REFERRAL DECISIONS
Several studies (n = 6; see Appendix 1) focusing on teachers’ attitudes toward inclusive practices
have revealed that elementary school teachers with resilient self-efficacy beliefs perceive
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
NATURE AND CONSEQUENCES OF TEACHER SELF-EFFICACY
35
themselves as more successful in teaching students with disabilities (Brownell & Pajares, 1999)
and feel more comfortable in accepting responsibility for these students’ difficulties (Brady &
Woolfson, 2008). Moreover, self-efficacious teachers have been shown to be less anxious
about (Soodak, Podell, & Lehman, 1998), and to have more positive attitudes toward inclusive
education and socio-cultural diversity than inefficacious teachers (Ahsan, Sharma, & Deppeler,
2012; Gao & Mager, 2011; Malinen et al., 2012). These associations, ranging from .11 to .36
(Mdn = .20), hold across grades, and in samples of preservice and inservice teachers.
Next to the indication that self-efficacious teachers are more tolerant toward problematic
students, they may also be less likely to exclude such students from their class. Eight studies
(see Appendix 1) have identified teachers’ referral decisions as potential consequences of TSE
in elementary school, although the results are somewhat mixed. Four studies failed to provide
support for the assumption that TSE relates to decisions to refer children to special education
(Egyed & Short, 2006; Soodak & Podell, 1993; Tejeda-Delgado, 2009), or exclude them from
school (Gibbs & Powell, 2012). Results from one longitudinal study even suggested that having
low self-efficacy in the fall may result in a reduction in subsequent student referrals to the
student support team (Pas, Bradshaw, Hershfeldt, & Leaf, 2010).
Contrary to these five studies, some findings from earlier research indicated that (special
education) teachers who express high self-efficacy beliefs are less likely to perceive children as
problematic, refer them for special education placement, or seek referral or consultation
assistance (Hughes, Barker, Kemenoff, & Hart, 1993; Meijer & Foster, 1988). Teachers with
high TSE have also been found to be more likely to accept interventions suggested by
consultants (DeForest & Hughes, 1992). Note, however, that these studies used less rigorous
methods than the five studies that failed to establish relations between TSE and teachers’
referral decisions. Moreover, teachers’ decision to refer may not only denote students’
placement in special education, but also to teachers’ ability to identify special needs. Hence,
both the study’s methodological characteristics, and the potential ambiguity in the meaning of
teachers’ referral decisions may explain these mixed findings.
INSTRUCTIONAL MANAGEMENT
It is commonly believed that students learn more when teachers make use of tools that actively
involve and cognitively inspire students in class (e.g., Hamre & Pianta, 2010). Consistent with
this notion, various instructional learning formats as consequences of TSE have been
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 2
36
considered in seven studies. The existing body of strictly correlational evidence suggests that
preservice teachers with high self-efficacy for engagement, instructional strategies, and
classroom management are likely to use a learner-centered and constructivist approach in their
teaching, while teachers with low efficacy prefer to use traditional learning formats (e.g., Dunn
& Rakes; 2011; Temiz & Topcu, 2013). Also, high- and low-efficacy preservice teachers have
been shown to differ in time spent in whole class versus small-group instruction and use of
criticism (Gibson & Dembo, 1984). The beneficial value of TSE for preservice teachers’
learner-centered approaches coincides with research performed in the elementary school
context and beyond, suggesting that high efficacy teachers not only hold constructivist learning
conceptions (Eren, 2009), but are also likely to more often use learner-centered, constructivist
instruction (Nie, Tan, Liau, Lau, & Chua, 2013; Ngidi, 2012) and teach more supplemental
activities (Ransford et al., 2009). Moreover, Nie and colleagues found that TSE may explain
about one third more of the variance in constructivist instruction than in didactic approaches
to teaching. Hence, it is reasonable to suggest that high TSE leads to more student-centered,
constructivist approaches to instruction among pre- and inservice teachers. These types of
learning formats are likely to make the most of students’ interest, engagement, and ability to
learn.
Classroom goal structures
TSE has been identified as a potential factor in the management of students’ behavior in
multiple lines of research (e.g., Abuh-Tineh et al., 2011; Morris-Rothschild & Brassard, 2006).
Yet, consideration has also been given to the role that self-referent thoughts play in the
management of students’ self-regulation skills. This particular strand of investigations has been
largely inspired by the theoretical underpinnings of classroom goal theory. Situated within a
socio-cognitive view of motivation, proponents of this theory posit that teachers, through their
use of instructional strategies, establish classroom goal structures that are believed to underlie
students’ own goal orientations and subsequent motivation for learning (Meece, Anderman, &
Anderman, 2006). In six studies (see Appendix 1) a case is made for considering TSE as one of
the mechanisms behind such classroom goal structures, three of which focus on the preservice
context (Capa-Aydin, Sungur, & Uzuntiryaki, 2009; Eren, 2009, 2012). Together, these
investigators have found an assortment of self-regulation strategies among teachers who
experience high self-efficacy. These include teachers’ goal setting, intrinsic interest, mastery and
performance goal orientations, self-instruction, emotional control, self-evaluation, self-reaction,
help-seeking, planned effort and persistence, leadership aspirations, and aspects of their
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
NATURE AND CONSEQUENCES OF TEACHER SELF-EFFICACY
37
professional development. Similarly, globally efficacious inservice teachers tend to more
frequently create mastery goal structures in their classroom than less efficacious teachers, both
in the elementary and secondary grades (Cho & Shim, 2013; Deemer, 2004; Midgley,
Anderman, & Hicks, 1995).
Studies on domain-specific TSE (e.g., Rubie-Davies, Flint, & McDonald, 2011) show less
conclusive results, however. Outcomes of one study suggest that only teachers’ self-efficacy for
instructional strategies leads to a more mastery-focused classroom environment (Ciani,
Summers, & Easter, 2008). In two other studies (Rubie-Davies et al., 2011; Wolters &
Daugherty, 2007), teachers who felt efficacious in both their instructional and engaging
strategies were found to employ instructional strategies thought to foster students’ mastery
goal orientations. Unexpectedly, TSE for classroom management showed negative correlations
with mastery goal structures in both studies, although this effect was likely due to a suppressor.
Overall, TSE was not related to performance goal strategies, except for Cho and Shim’s (2013)
research, in which TSE was identified as a positive predictor of both teachers’ mastery and
performance approach goals. Thus, it can be suggested that TSE is positively associated with
mastery approaches. Whether TSE is also a stable predictor of performance approach goals
and other self-regulation strategies has yet to be clarified.
Technology use in the classroom
One specific instructional tool that helps teachers to differentiate in the classroom and has
potential to provide learning environments that relate to students’ world, is technology use
(Godfrey, 2001). Therefore, it is perhaps not surprising that in a growing corpus of research (n
= 10) the link between TSE and teachers’ technology use has been considered. The focus of
most of this research has been on preservice teachers (e.g., Sang et al., 2010; Teo, 2009). These
researchers have used large samples and structural equation models in cross-sectional designs
and their findings generally suggest that (computer) TSE may positively affect teachers’
perceived usefulness, ease of use, and attitude toward computer use (Wong, Teo, & Russo,
2012) which, in turn, may contribute to their prospective use of technology in class (Chen,
2010; Sang et al., 2010). Similarly, Teo (2009) assessed preservice teachers’ computer self-
efficacy along dimensions of basic teaching skills, advanced teaching skills, and technology for
pedagogy, and revealed that TSE for teaching skills and for technology for pedagogy served as
predictors of traditional and constructivist use of computers and technology.
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 2
38
In the (upper) elementary and secondary grades, computer TSE – either general or specific –
may positively impact teachers’ attitude toward technology (Rohaan, Taconis, & Jochems,
2012), their attitude toward web-based instruction (Lee & Tsai, 2010), and their motivation to
use web-based professional development (Kao, Wu, & Tsai, 2011). In two studies (Mueller,
Wood, Willoughby, Ross, & Specht, 2008; Vannatta & Fordham, 2004) the positive
relationship between TSE and classroom technology use could not be confirmed. Last,
Ahmad, Basha, Marzuki, Hisham, and Sahari (2010) assessed direct and indirect effects of
faculty member’s computer self-efficacy on technology use. Their results indicated that
computer self-efficacy was an important determinant in affecting faculty members’ use of
computer technology. Thus, for technology and computer use in the classroom to move
forward, teachers need to perceive themselves as self-efficacious, especially in using computers
and technology.
CONSEQUENCES OF TSE FOR CLASSROOM PROCESSES – EMOTIONAL SUPPORT
Central to any conceptualization of classroom processes is teachers’ capability to establish
caring relationships with students, acknowledge their opinions and feelings, and create settings
in which students feel secure to explore and learn (Pianta et al., 2008). Although TSE may
potentially be involved in such social-emotional classroom dynamics, this possibility has only
been considered in 13 studies (see Appendix 1), focusing on overall emotional climate,
student–teacher relationship quality, or regard for student perspectives.
OVERALL EMOTIONAL CLIMATE IN THE CLASSROOM
In three large cross-sectional studies, observational methods were used to measure overall
emotional climate in preschool and Grade 5 (Guo et al., 2010; 2012; Pakarinen et al., 2010).
According to one of these studies (Guo et al., 2012), fifth-grade teachers with high self-efficacy
are more likely than poorly efficacious educators to create a supportive environment
characterized by warmth, responsiveness, enthusiasm, teacher support, and effective use of
instructional time. In the preschool context, however, consensus has not been reached on
whether highly self-efficacious teachers provide their students with higher levels of emotional
support (Guo et al., 2010; Pakarinen et al., 2010).
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
NATURE AND CONSEQUENCES OF TEACHER SELF-EFFICACY
39
STUDENT-TEACHER RELATIONSHIP QUALITY
Turning to interpersonal student–teacher relationships, seven correlational studies have shown
mixed results as to whether high TSE increases the quality of teachers’ relationship with
individual students (e.g., de Jong et al., 2013; Mashburn, Hamre, Downer, & Pianta, 2006).
These ambiguous findings may partly be due to the studies’ selected outcome variables and
methods of data analysis. Generally, TSE did not appear to be correlated with the quality of
student–teacher relationships when total scores of teacher-perceived relationship quality were
used and the nested structure of the data was not taken into account (Chung, Marvin, &
Churchill, 2005; Hardré & Sullivan, 2008). In another Dutch study among preservice teachers, de
Jong et al. (2013) also failed to establish significant links between Tschannen-Moran and
Woolfolk Hoy’s (2001) dimensions of TSE and relationship quality, in terms of control and
affiliation. Notably, reliability coefficients of two self-efficacy dimensions in this study were
below the threshold of .70. Only in Jimmieson, Hannam, and Yeo’s (2010) study, TSE has
been shown to be a positive correlate of child-perceived student–teacher relationships in
elementary and middle school.
Researchers have also made a case for the usefulness of focusing on the unique, dyadic
student–teacher relationship dimensions of closeness and conflict. First, using a large sample
and a multilevel design, Mashburn et al. (2006) found that self-efficacious teachers were more
likely to have close, but not less conflictuous relationships with regular preschool students.
When explicitly focusing on problematic students, however, Hamre, Pianta, Downer, and
Mashburn (2008) found less efficacious preschool teachers to be experiencing higher degrees
of conflict with their students than would be expected based on their judgments of students’
problem behaviors. Last, Yoon (2002) failed to confirm that high TSE is associated with high-
quality relationships. In her research of K-5 teachers, TSE only accounted for 2% of the total
variance in student–teacher conflict and closeness.
REGARD FOR STUDENT PERSPECTIVES
Also part of teachers’ emotional support is their willingness and ability to place emphasis on
students’ interests, motivations, and points of view (Pianta et al., 2008). In two cross-sectional
studies of Hardré and Sullivan (2008, 2009), high school teachers with a healthy sense of self-
efficacy for diagnosing motivational problems were found to take such perspectives into
account by using relevance, value, and internally focused strategies that emphasize
interpersonal relatedness with their students and making the educational content more
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 2
40
meaningful to them. However, teachers’ usage of autonomy supportive interpersonal styles was
not predicted by TSE. Other conclusions were reached by Leroy, Bressoux, Sarrazin, and
Trouilloud (2007), who found that fifth-grade teachers’ self-efficacy was positively related to
the creation of an autonomy supportive climate. Thus, focusing on slightly different outcome
variables, these three studies reveal that teachers’ regard for student perspectives is positively
predicted by TSE.
SYNTHESIS OF RESULTS
Taken together, results from studies on the consequences of TSE for classroom processes
indicate that high-efficacy teachers, and especially those with more experience, tend to
effectively cope with a range of problem behaviors, use proactive, student-centered classroom
behavior strategies and practices, and establish less conflictuous relationships with students.
Contrary to preservice teachers, tenured educators who believe in their capabilities also use
more diverse instructional strategies, differentiate more frequently, change their goals
according to students’ needs, and are more positive about the implementation of such
instructional strategies. Probably, more experienced educators with high self-efficacy may have
become more sensitized to students’ signals, needs, and expectations, and are thereby better
able to provide them with adequate supports in class. Yet studies revealing a curvilinear
association between TSE and experience indicated this beneficial by-effect of experience may
not last (e.g., Klassen & Chui, 2010; Wolters & Daugherty, 2007). This underscores the
importance of longitudinal studies investigating the development of TSE over teachers’
careers.
CONSEQUENCES OF TSE FOR STUDENTS’ ACADEMIC ADJUSTMENT
The most common consequence of TSE for student outcomes was based on students’
achievement, including overall school grades, literacy and math performance, and achievement
in some other subjects. Of the 23 reviewed studies (see Appendix 2), most were cross-sectional
in nature, except for four studies that relied on a longitudinal design (Caprara, Barbaranelli,
Steca, & Malone, 2006; Guo et al., 2010, 2012; Midgley et al., 1989). In another three studies,
hierarchical regression techniques were used to account for the nested data structure (Guo et
al., 2010; Jimmieson et al., 2010; Reyes et al., 2012). The sample sizes of the studies varied
extensively, ranging from less than 20 (Allinder, 1995; Ross, 1992) to more than 2,000 (Caprara
et al., 2006). Despite the idea that TSE fluctuates across teaching tasks, none of the reviewed
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
NATURE AND CONSEQUENCES OF TEACHER SELF-EFFICACY
41
investigations used domain-specific instruments to measure the construct of teacher self-
efficacy. Given the divergences in sample size and methodological rigor, which may potentially
have an impact on the parameters estimated, the consequences of TSE for students’ academic
adjustment should be interpreted with caution.
OVERALL ACHIEVEMENT
In several studies (see Appendix 2), TSE has been assessed in relation to students’ overall
academic performance, only one of which did not find an association between TSE and
achievement, as measured by school type (Chong, Klassen, Huan, Wong, & Kates, 2010). In
the elementary school context, studies by Chang (2011) and Woolfolk Hoy, Hoy, and Kurz
(2008) explored the association between Academic Optimism – a latent variable comprising
TSE – and students’ achievement scores, and uncovered a substantial positive relationship.
Next to achievement scores, perceived achievement and academic climate have been shown to be
positively affected by TSE in middle school, although the reported coefficient was small
(Chong et al., 2010; Jimmieson et al., 2010). Consistent findings were also reported in four
studies conducted in high school, indicating that students whose teachers had higher TSE
benefited more in terms of their academic achievement than students whose teachers had a
lower sense of efficacy (Caprara et al., 2006; Hardré et al., 2006; Mohamadi & Asadzadeh,
2012; Mojavezi & Poodineh Tamiz, 2012). Caprara et al. (2006) proposed a longitudinal model
with TSE as a positive contributor to students’ achievement when previous levels of
achievement are controlled for. Hence, these studies imply that TSE may predict students’
overall performance in elementary, middle, and high school. Note, however, that coefficients
are generally modest (Mdn = .27), ranging from .02 to .78.
MATH ACHIEVEMENT
Moving to the consequences of TSE for students’ math performance, there are four empirical
studies to suggest that teachers with high self-efficacy are more likely to facilitate students to
develop their mathematical competence than teachers with low self-efficacy (Allinder, 1995;
Hines, 2008; Midgley et al., 1989; Throndsen & Thurno, 2013). When considering students’
grade level, however, these findings are less straightforward. For instance, Midgley et al. (1989)
revealed that within-year changes in students' perceived performance in mathematics during the
transition from elementary to middle school were positively predicted by TSE, although the
coefficients across years were nonsignificant. Moreover, Throndsen and Turno (2013) found a
small but positive correlation between TSE and math performance across Grades 2 and 3.
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 2
42
When investigating the associations between TSE and math achievement separately for each
grade, significant correlations were only found for second-graders. Given that students’ math
achievement across years does not seem to vary as a function of TSE, it is possible that other
teacher characteristics, including knowledge, skills, and experience, may be more important to
students’ math achievement than teachers’ sense of self-efficacy.
LITERACY ACHIEVEMENT
Compared to other subjects, the associations between TSE and students’ literacy in elementary
school and beyond are relatively inconsistent in the literature (n = 6; see Appendix 2).
Coefficients range from .02 to .76, with the highest value emerging from Cantrell, Almasi,
Carter, and Rintamaa’s (2013) study. Together with Guo et al. (2012), these researchers are the
only ones to demonstrate that TSE is related to students’ literacy outcomes in Grades 5, 6 and
9. Guo et al. (2010), in a study on preschool, and Heneman, Kimball, & Milanowski (2008) and
Reyes et al. (2012) in studies on elementary teachers, failed to confirm the hypothesized
association between TSE and students’ reading and writing achievement. Moreover, teachers’
predictions of students’ success in reading has not been shown to depend on their levels of
self-efficacy in elementary and middle school (Tournaki & Podell, 2005).
It is interesting to note that children’s literacy outcomes are less often predicted by TSE than
their achievement in other subjects, and math in particular. This failure of prediction might,
amongst others, be due to the studies’ methodological characteristics. Studies focusing on
literacy have more often relied on student measures of achievement, compared to other
subjects. Thereby, they accounted for overestimation of coefficients due to shared source
variance, which probably reduced the impact of TSE on students’ literacy outcomes to non-
significance. Also, teachers’ perceptions of student achievement have been shown to be
generally more biased than test scores or grades (Kuncel, Credé, & Thomas, 2005).
Consequently, self-efficacious teachers may also judge their students’ performance to be higher
than their actual achievements, thereby biasing the results of studies in which the same
informants were used. This issue of common source variance, and the inclusion of more
reliable achievement measures, may warrant further consideration.
ACHIEVEMENT IN OTHER SUBJECTS
Four studies were identified that focused specifically on subjects other than literacy or math
(Angle & Moseley, 2010; Lumpe, Czerniak, Haney, & Beltyukova, 2012; Ross, 1992; Ross,
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
NATURE AND CONSEQUENCES OF TEACHER SELF-EFFICACY
43
Hogaboam-Gray, & Hannay, 2001). All studies, except for the investigation of Ross et al.
(2010), were performed in the upper elementary grades and beyond, using cross-sectional
designs and revealing only small associations (Mdn = .13). Regarding students’ science
achievement, Lumpe and colleagues (2012) showed that when fourth- and sixth-grade teachers
feel more efficacious in teaching science, their students’ science scores are higher as well.
Together with outcome expectations and hours of professional development, however, TSE in
this study only explained 4% of the total variance in students’ science performance. In the
context of history, Ross (1992) examined links between TSE, contact with assigned coaches,
and students’ history achievement in a sample of Grades 7 and 8 history teachers. Teachers
who interacted more with their coaches and felt more confident about their teaching abilities
were more likely to positively affect their students’ achievement than colleagues who had less
contact with their coaches or perceived themselves as less self-efficacious. Notably, both
constructs accounted for 57% of the variance in students’ achievement. Angle and Moseley
(2010) aimed to unravel differences between the self-efficacy beliefs of high school biology
teachers whose students’ biology achievement either exceeded or fell below the state
proficiency level. Their results indicated that students’ biology achievement did not vary
according to how confident teachers were in teaching biology. One study investigating
computer literacy (Ross et al., 2001) turned its focus toward very young children. Following up
on the seminal study of Midgley et al. (1989), these authors distinguished an upward trajectory,
in which students moved from a poorly efficacious to a highly efficacious teacher, and a
downward trajectory, comprising students who shifted in exactly the opposite direction. They
found that students developed their computer skills most in the upward trajectory, when
students moved from a lower- to a higher-efficacy teacher. Also, TSE was directly related to
students’ advanced computer skills, accounting for a total of 7% of the variance in computer
skills. This handful of studies is thus indicative of associations between TSE and achievement
in subjects other than literacy and math.
MOTIVATION
In 11 studies (see Appendix 2), TSE was assessed in relation to students’ motivation, including
such aspects as student engagement, intrinsic and extrinsic motivation, academic expectations,
self-efficacy, goal orientations, and school investment. Characteristic of these studies is that
they had relatively small samples and were all cross-sectional in nature, except for the seminal
study of Midgley et al. (1989). In half of studies the authors analyzed their data in multilevel
(four studies) or SEM-designs (two studies), or used more traditional methods such as
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 2
44
regression analysis. Associations between TSE and aspects of students’ motivation ranged
from .13 to .79, with the highest associations found for total motivation and attitude scores.
The existing literature suggests that students’ motivation may be more consistently predicted
by their teachers’ self-efficacy than their academic achievement (e.g., Thoonen et al., 2011b).
During the elementary school years, students have been found to be more engaged in the
classroom (Reyes et al., 2012) and academically efficacious (Ross et al., 2001) when their
teachers’ self-efficacy is high. In one rigorous Dutch study of Thoonen et al. (2011b), however,
teachers’ self-efficacy was found to be unrelated with aspects of students’ motivation, including
academic self-efficacy, mastery or performance-avoidance goals, intrinsic motivation, and
school investment. This is perhaps not surprising, given that most reliability estimates for the
motivational constructs used in this study were below the threshold of .70.
Similar to their elementary peers, middle-schoolers have been demonstrated to benefit from
self-efficacious teachers in terms of higher levels of school satisfaction and confidence in their
achievement at school, lower levels of psychological distress, and positive views about future
learning opportunities (Jimmieson et al., 2010). Additionally, the longitudinal study of Midgley
et al. (1989) lends support for the idea that TSE is associated with students’ motivation during
the transition from upper elementary to middle school. Specifically, students who shifted from
high- to low-efficacy mathematics teachers had lower expectancies and perceived
performances, and higher perceptions of task difficulty than students who were taught by
highly efficacious teachers after the transition. Furthermore, variations in TSE beliefs before
and after the transition to middle school were more important to low-achieving than to high-
achieving students' motivational beliefs.
High schoolers whose teachers feel generally self-efficacious have been shown to display
higher amounts of observed on-task behavior, increased engagement, effort and (intrinsic)
motivation for school, more positive attitudes toward learning, and better opinions about their
teacher (Hardré et al., 2006; Mojavezi & Poodineh Tamiz, 2012; Robertson & Dunsmuir,
2013). Other studies show that TSE may not only be directly related to teacher perceptions of
their students’ emotional engagement, but also indirectly affect their behavioral and emotional
engagement through influence (van Uden, Ritzen, & Pieters, 2013). Turning to domain-specific
TSE beliefs in high school, Hardré and Sullivan (2008) noted that teachers’ efficacy beliefs for
motivating and diagnosing were not associated with their perceptions of students’ academic
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
NATURE AND CONSEQUENCES OF TEACHER SELF-EFFICACY
45
motivation and approaches to learning. However, a later, separate study (Hardré & Sullivan,
2009), indicated that TSE for motivating and diagnosing were predictive of teachers’
perceptions of student motivation, explaining almost a quarter of the variance. Thus, across
educational stages and different countries, TSE seems to be a fairly robust predictor of a
variety of motivational factors.
INDIRECT CONSEQUENCES
In only three relatively large studies did the authors look at the mediating role of classroom
processes in the association between TSE and students’ adjustment (Guo et al., 2012; Thoonen
et al., 2011b; van Uden et al., 2013). Regarding achievement, Guo and colleagues (2012)
pointed toward an indirect relationship of TSE with fifth-graders’ literacy outcomes, via
teachers’ support for learning. Focusing on aspects of motivation, TSE has been shown to
affect specific teacher practices, including interpersonal behavior, process-oriented instruction,
and cooperative learning. These practices, in turn, may raise students’ emotional and behavioral
engagement (van Uden et al., 2013), and well-being in school (Thoonen et al., 2011b). Thus,
this small research base provides initial evidence that teacher practices defining the classroom
quality, and especially those related to emotional support, may canalize the effect of TSE on
student outcomes. This finding corroborates prior evidence that points to the benefits of
emotionally supportive teacher behaviors for encouraging students’ learning, engagement, and
enjoyment in their learning tasks (Reyes et al., 2012; Rimm-Kaufman & Chui, 2007).
SYNTHESIS OF RESULTS
Overall, studies suggest that TSE is modestly associated with students’ academic adjustment in
elementary school and beyond. Yet, aspects of students’ motivation, and total motivation
scores in particular, seem to be more consistently predicted by TSE than their academic
achievement. Assumedly, students’ motivation may be partly considered a factor determining
the quality of classroom processes, and therefore, more proximal to TSE than academic
performance (e.g., Woolfolk Hoy et al., 2009). Regarding achievement, TSE appears less
important for middle and high schoolers’ achievement in various subjects than for the
attainment of elementary school children. This is perhaps not surprising, as high schoolers are
taught by many teachers and see each teacher for only a small proportion of the school day
(Midgley et al., 1989). Consequently, older students may be less affected by teachers’ efficacy
than younger children. Prior work of Guskey (1981) also revealed that secondary school
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 2
46
teachers feel less responsible for the successes and failures of their students than do elementary
teachers. Probably, teachers’ classroom mastery experiences may affect secondary school
teachers’ self-efficacy less than elementary teachers’ capability beliefs, thereby reducing the
impact of TSE on student achievement.
CONSEQUENCES OF TSE FOR TEACHERS’ PSYCHOLOGICAL WELL-BEING
TEACHER BURNOUT
One imperative in understanding teachers’ psychological well-being is to gain insight into how
levels of burnout in teachers are buffered or exacerbated by factors such as their self-efficacy.
Indeed, almost one third of the reviewed articles (n = 22) on teachers’ well-being are
concerned with teacher burnout (see Appendix 3). Overall, the studies on this topic were
conducted in a wide variety of cultural and national settings, typically had larger sample sizes
than TSE research on classroom quality and students’ adjustment, and used more advanced
statistical methods, including multilevel and (longitudinal) SEM. These studies may extend,
therefore, the generalizability and adequacy of teachers’ self-efficacy, and advance
understanding of its causal pathways.
Associations between TSE and both overall burnout levels (range = –.17 to –.63; Mdn = –.36)
and specific dimensions of burnout (range = –.09 to –.76; Mdn = –.25 for emotional
exhaustion; range = .13 to .75; Mdn = .36 for personal accomplishment; range = –.16 to –.60;
Mdn = –.33 for depersonalization) have been fairly consistent across studies. In the preservice
context, teachers with high levels of self-efficacy for instructional strategies and classroom
management have generally been shown to be less likely to feel emotionally exhausted and to
depersonalize their students early in their student-teaching experience than less efficacious
student educators (Fives, Hamman, & Olivarez, 2007). Furthermore, self-efficacy for student
engagement and instructional strategies appears to be predictive of higher levels of personal
accomplishment over time (ibid.).
Some insights into how TSE may relate to such aspects of burnout have been provided by
Hultell, Melin, and Gustavsson (2013). Their cluster-analytic results revealed that preservice
teachers’ burnout levels were accompanied by decreases in TSE, low and stable levels of
burnout were reflected by high TSE, stable and high levels of burnout were characterized by
low TSE, and stable and moderate levels of burnout resulted from changing levels of TSE over
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
NATURE AND CONSEQUENCES OF TEACHER SELF-EFFICACY
47
time. Although these findings imply that higher self-efficacy preservice teachers may be more
resistant to burnout, such outcomes may not necessarily extend to their first years of teaching
(Høigaard, Giske, & Sundsli, 2012).
More experienced inservice teachers with high self-efficacy, however, have been found to
report lower levels of overall burnout (Brissie, Hoover-Dempsey, & Bassler, 1988; Egyed &
Short, 2006; Friedman, 2003) and specific burnout dimensions, with negative associations
between TSE and emotional exhaustion and depersonalization, and positive associations
between TSE and personal accomplishment (Egyed & Short, 2006; Friedman, 2003).
Furthermore, the beneficial effects of these capability beliefs continue to be present in
secondary school, as indicated by rigorous, mainly Dutch research (Briones, Taberno, &
Arenas, 2010; Brouwers et al., 2001; Brudnik, 2009; Evers, Brouwers, & Tomic, 2002; Evers,
Tomic, & Brouwers, 2005). In line with Bandura’s (1997) notion of reciprocal determinism,
support has even been found for a feedback loop in which low levels of TSE reinforce
teachers’ burnout and vice versa (Brouwers et al., 2001). Focusing on specific burnout
dimensions, TSE has a longitudinal negative effect on depersonalization, and a synchronous
positive effect on personal accomplishment (Brouwers & Tomic, 2000). It should be noted,
however, that Brouwers and Tomic’s results also pointed to a reversed direction of
relationships between TSE and emotional exhaustion. This underscores the importance of
longitudinal research that advances knowledge of the definitive causation of TSE on burnout
dimensions.
Culturally diverse studies concerned with all grade levels did not deviate much from the
abovementioned pattern of results (e.g., Avanzi et al., 2013). In Norway, negative relationships
were found between TSE and overall burnout scores (Skaalvik & Skaalvik, 2007) and
emotional exhaustion and depersonalization (Skaalvik & Skaalvik, 2010). Similarly, Avanzi et al.
(2013) found that Italian teachers’ efficacy was negatively correlated with student- and work-
related burnout. Researchers from the United States suggest that teachers have a higher sense
of personal accomplishment and feel less emotionally exhausted when they have confidence in
their abilities to actively engage their students and to handle student misbehavior (Martin et al.,
2012; Shyman, 2010; Tsouloupas et al., 2010). These findings substantiate those of Schwarzer
and colleagues (Schwarzer & Hallum, 2008; Schwarzer, Schmitz, & Tang, 2000; So-kum Tang,
Au, Schwarzer, & Schmitz, 2001), who generally found the same pattern of relationships as
studies discussed above for Syrian, Chinese, and German teachers. So-kum Tang et al. (2001)
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 2
48
furthermore found TSE to be directly and indirectly related to the mental health of teachers,
through their burnout. In one longitudinal study, it was demonstrated that changes in Canadian
teachers’ self-efficacy were negatively related to changes in their levels of emotional exhaustion
and depersonalization, and positively related to changes in their sense of personal
accomplishment (Fernet, Guay, Senecal, & Austin, 2012).
Indirect consequences of TSE on teacher burnout
In two studies (Doménech-Betoret, 2009; Martin et al., 2012) the focus has been on the
potential indirect consequences of teachers’ self-efficacy for their levels of burnout. Martin et
al. (2012), for instance, showed that TSE for student engagement relates positively to
instructional practices, which, in turn, predict the amount of student stressors in the classroom
and increases teachers’ personal accomplishment. Moreover, Doménech-Betoret (2009) found
that difficulties related to the classroom (i.e., student diversity and misbehavior) mediate the
relationship between TSE and burnout dimensions. Hence, with some exceptions, studies of
the predictive associations between TSE and teacher burnout show a quite consistent pattern
across contexts and grade levels. Practices related to instruction and behavior management
may mediate this relationship.
TEACHERS’ STRESS AND COPING
Six studies (see Appendix 3) investigated the degree to which TSE is related to teachers’ levels
of stress and coping. In these studies, correlations between TSE and stress ranged from .06 to
.50, and between TSE and coping from –.11 to .05. Across grades, the results reported indicate
that teachers with high self-efficacy experience less job-related stress (Barouch Gilbert,
Adesopea, & Schroeder, 2013; Doménech-Betoret, 2006; Robertson & Dunsmuir, 2013), and
fewer student stressors (e.g., concerns regarding student demotivation, or teaching students of
mixed ability). Such student stressors, in turn, may significantly reduce teachers’ dissatisfaction
for their job (Sass, Seal, & Martin, 2011). Next to direct associations, higher class and school
TSE also indirectly reduce the amount of teachers’ perceived tension (Helms-Lorenz, Slof,
Vermue, & Canrinus, 2012). Teachers’ active or passive coping, however, was not found to be
predicted by TSE (Chan, 2008). Instead, school coping resources and TSE have been shown to
buffer the negative effect of stressors on teachers’ burnout (Doménech-Betoret, 2006). Thus,
efficacious teachers do not necessarily seem to have more coping resources, but at least may
experience less stress in their profession. The results from these studies should be interpreted
with caution, as these studies typically included only a limited number of teachers, and, in six
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
NATURE AND CONSEQUENCES OF TEACHER SELF-EFFICACY
49
cases, employed statistical techniques that may be unsuitable for use in smaller samples.
TEACHERS’ JOB SATISFACTION
Next to stress and burnout, few researchers have considered more positive factors underlying
teachers’ psychological well-being as outcomes of TSE (e.g., Caprara et al., 2003, 2006; Collie
et al., 2012; Helms-Lorenz et al., 2012). One of the most common outcomes studied in this
domain is teachers’ job satisfaction (n = 21). Only Avanzi et al. (2013) and Salanova et al.
(2011) have conducted longitudinal studies and can infer the causality that has been posited.
However, more than half of the reviewed investigations have gone beyond commonly used
correlational approaches, fitting complex structural equation models and revealing more
complex patterns of relationships. With some exceptions (Briones et al., 2010; Høigaard et al.,
2012; Lent et al., 2011), investigators considering this consequence of TSE have demonstrated
a stable pattern of results, with coefficients ranging from .10 to .86 (Mdn = .33). In elementary
school (Collie et al., 2012; Helms-Lorenz et al., 2012; Skaalvik & Skaalvik, 2010; Stephanou,
Gkavras, & Doulkeridou, 2013), as well as in middle and high school (Canrinus, Helms-
Lorenz, Beijaard, Buitink, & Hofman, 2010; Caprara et al., 2003, 2006; Tsigilis, Koustelios, &
Grammatikopoulos, 2010), self-efficacious teachers have been found to be more satisfied with
their job and relationships than their less efficacious counterparts. Interestingly, higher efficacy
teachers seem less satisfied with their salary, but are better able to manage this when they are at
the same time satisfied with their relationships at work (Canrinus et al., 2012).
Assessing all grade levels simultaneously, results also point to positive associations between
TSE and job satisfaction (Avanzi et al., 2013; Barouch Gilbert et al., 2013; Collie et al., 2012;
Duffy & Lent, 2009; Klassen et al., 2009; Klassen & Chiu, 2010; Moè, Pazzaglia, & Ronconi,
2010; Viel-Ruma, Houchins, Jolivette, & Benson, 2010) and (indirect) negative associations
between TSE and job discontent (Sass et al., 2011). These effects seem to hold across time,
domains of TSE, and levels of teaching experience (e.g., Blackburn & Robinson, 2008;
Canrinus et al., 2012; Salanova, Llorens, & Schaufeli, 2011; Tsigilis et al., 2010).
Indirect consequences of TSE on teachers’ job satisfaction
Probably, TSE and job satisfaction are part of a more complex dynamic. For instance, factors
such as work conditions, student stressors, personal achievement, and social support have been
shown to mediate the relationship between TSE and job satisfaction (Briones et al., 2010;
Duffy & Lent, 2009; Lent et al., 2011; Sass et al., 2011). These complex direct and indirect
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 2
50
influences, which all encompass socioemotional aspects of teaching, evidently speak to
Bandura’s model of triadic reciprocal causation. Yet, longitudinal studies are needed to
determine the causal ordering of effects.
TEACHERS’ COMMITMENT
In total, 12 articles (see Appendix 3) have investigated the predictive associations between TSE
and teacher commitment, which range from .10 to. 36 (Mdn = .26). Large-scale correlational
studies of preservice teachers’ self-efficacy have indicated that TSE, especially in the domain of
classroom management, is a positive predictor of occupational commitment, irrespective of the
country in which teachers perform their job (Evans & Tribble, 1986; Klassen & Chui, 2011;
Klassen et al., 2013). Also in elementary, middle and high school, higher self-efficacy inservice
teachers have generally been found to feel more professionally, affectively, organizationally,
and occupationally committed than poorly efficacious educators (Barouch Gilbert et al., 2013;
Bogler & Somech, 2004; Canrinus et al., 2010; Caprara et al., 2003; Chan, Lau, Nie, Lim, &
Hogan, 2008, Coladarci, 1992; Ebmeier, 2003; Rots, Aelterman, Vlerick, & Vermeulen, 2007).
Regarding domain-specific TSE, two studies of Ware and Kitsantas (2007, 2011), using the
same vast sample of 26,257 teachers, showed that teachers with high efficacy to enlist
administrative control, make decisions, and control aspects of their classroom operations may
be more committed to teaching. In Klassen and Chui’s (2011) study, only TSE for instructional
strategies was a positive predictor of practicing teachers’ occupational commitment. Thus,
teachers feel more committed to teaching when their self-efficacy is high. These results hold
across countries and various types of commitment, including affective, organizational, and
occupational commitment (e.g., Barouch Gilbert et al., 2013; Klassen & Chui, 2011; Klassen et
al., 2013).
TEACHER ATTRITION AND RETENTION
Seven studies (see Appendix 3) sought to assess TSE in direct relation to teacher attrition and
retention. From these generally small correlational studies, it follows that only preservice
teachers with high self-efficacy intend to remain longer in the profession (Bruinsma & Jansen,
2010). Inservice TSE, irrespective of grade level, has not been found to be directly associated
with teachers’ levels of absenteeism (Imants & Van Zoelen, 1995), intention to leave (Barouch
Gilbert et al., 2013; Høigaard et al., 2012) or stay (Canrinus et al., 2010; Hughes, 2012; Malow-
Iroff, O'Connor, & Bisland, 2007).
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
NATURE AND CONSEQUENCES OF TEACHER SELF-EFFICACY
51
Indirect consequences of TSE on teacher attrition and retention
Despite this lack of direct effects, some indirect effects of TSE on teacher attrition and
retention have been noted. Several studies (Klassen & Chui, 2011; Tsouloupas et al., 2010)
suggest that teachers with poor efficacy for classroom management and instructional strategies
may be more prone to feel emotionally exhausted and uncommitted to their occupation than
educators with high efficacy. This may cause them to leave the profession entirely. In contrast,
teachers’ responsibility to remain in their profession seems to be indirectly predicted by TSE
via their affective commitment and relationship satisfaction (Canrinus et al., 2010). Notably,
for both preservice and inservice teachers, low TSE for classroom management seems to be
the most important trigger to abandon their job. Hence, although the correlational nature of
the aforementioned literature prevents us from making causal inferences, the effect of TSE on
attrition and retention is most likely to be mediated by teachers’ job commitment and
satisfaction.
SYNTHESIS OF RESULTS
Findings pertaining to factors underlying teachers’ psychological well-being seem robust.
Irrespective of pre- or inservice context, grade level, and country, and potentially over time,
self-efficacious teachers may suffer less from stress, emotional exhaustion, depersonalization,
and overall burnout, and experience higher levels of personal accomplishment, commitment,
and job satisfaction. Moreover, emotional and organizational processes in class, such as student
misbehavior and motivation, positive affect, and instructional management, are likely to
function as catalysts in the relationship between TSE and teachers’ burnout and job
satisfaction. Following Spilt, Koomen, and Thijs (2011), this process might be attributable to
teachers’ internalization of experiences with specific students in representational models of
relationships, which not only guide teachers’ emotional responses and well-being in class, but
may also increase or compromise their self-efficacy.
TSE is not directly related to teacher attrition and retention. Rather, teachers with low self-
efficacy seem to experience higher levels of emotional exhaustion and lower levels of
satisfaction and commitment, ultimately leading them to quit their job. These indirect effects
imply that positive feelings of well-being, such as commitment and satisfaction, are the
mechanism through which TSE exerts its influence over teachers’ intention to stay or leave.
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 2
52
Overall, positive aspects of teachers’ psychological well-being can thus be suggested to be
more mutable due to their self-efficacy than negative aspects.
DISCUSSION
The present review is probably the first to provide an up-to-date synthesis of forty years of
research on TSE and its various consequences, using a heuristic model inspired by the process-
oriented view of Woolfolk Hoy et al. (2009) and CLASS-framework of Pianta et al. (2008). In
this section, critical areas of research needed to advance TSE research across lines of inquiry
related to classroom processes, student adjustment, and teacher well-being are discussed.
Additionally, some more general methodological and theoretical challenges to the study of TSE
are considered.
CLASSROOM PROCESSES
Of all domains, aspects of classroom organization were found to be the best represented in the
literature, probably owing to the growing popularity of classroom management TSE and the
numerous attempts made to measure this construct (O’Neill & Stephenson, 2011).
Unfortunately, though, most studies in this field did not seem to inform or effectively build on
one another. Specifically, the range of efficacy-influenced teaching processes associated with
this domain was quite extensive, covering a host of different strategies, behaviors, attitudes,
and decisions in class. Moreover, each of these teacher processes appeared to be investigated
only once or twice in isolated, cross-sectional studies focusing on various student groups or
different grades, and using different measures. Such fragmentation of the field may prevent
researchers from drawing definite conclusions on the links among TSE and aspects of
classroom organization across grade level, specific types of students, and contexts. Hence, the
need for integration among those diverse literatures will continue to be an important area of
research for the years to come.
The heuristic model highlighted the current lack of research on the consequences of TSE for
emotionally supportive classroom processes. This is important, as TSE may be particularly
relevant for the interpersonal aspects of teaching (see Labone, 2004). For instance, past
research suggests that TSE may function as a protective factor against poor student–teacher
relationship quality, such that students with generally self-efficacious teachers are less
vulnerable to developing poor-quality relationships with their teacher, even if their behavior is
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
NATURE AND CONSEQUENCES OF TEACHER SELF-EFFICACY
53
problematic (Hamre et al., 2008; Mashburn et al., 2006; Midgley et al., 1989). From a dyadic
angle, Spilt and Koomen’s (2009) results showed that teachers perceive themselves as less self-
efficacious in relation to individual, disruptive students.
Notably, the absence of the emotional support component of teaching is also evident in the
various instruments that have been developed to measure TSE and its underlying dimensions.
Possibly, the role of teachers in maintaining warm relationships with students and supporting
their basic needs is an aspect of TSE that is difficult to capture by current instruments,
especially when taking a dyadic perspective on such associations. However, as classrooms are
inherently social contexts, and teachers’ socioemotional support is one of the strongest
correlates of student adjustment (e.g., Davis, 2003), researchers should work to increase
awareness of the impact of TSE on emotional support in the classroom. Adapting TSE
instruments to include the emotional domain and broadening the construct of TSE to include
dyadic relationships may be important steps forward to further elucidate this link.
STUDENTS’ ACADEMIC ADJUSTMENT
Next to classroom processes, results bring attention to the need to better understand the role
of TSE in students’ academic adjustment. To date, many scholars have come to claim that TSE
is a particularly powerful predictor of students’ academic adjustment. However, the body of
research looking into this specific relationship is not nearly as large as can be expected from
this general assertion. Of all studies reviewed, only 27 provided insight into the links among
TSE and students’ achievement and motivation. Interestingly, these do not appear to be the
studies that are usually referred to. Specifically, most-cited articles (e.g., Tschannen-Moran et
al., 1998) seem to be theoretical in nature, only assuming possible links between TSE and
students’ academic adjustment. Additionally, the majority of scholars focusing on the link
between TSE and achievement tend to mainly discuss links between TSE and a range of
classroom processes. Although these efficacy-influenced processes are generally presumed to be
supportive of students’ achievement, empirical evidence regarding such complex, indirect links
is still lacking. This potentially suggests a bias in the interpretation of TSE research.
Another challenge in the examination of TSE in relation to students’ adjustment pertains to
the methods of data collection and analysis. Generally, the majority of reviewed studies solely
relied on teacher reports of both TSE and student adjustment, and largely overlooked the fact
that students were not sampled independently from each other. This lack of consideration for
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 2
54
both shared method variance and the dependent nature of the data might have possibly lead to
an overstatement of statistical significance, given that students’ adjustment tends to be more
alike when they share the same teacher. As such, the validity of the results of this work may be
questionable in the absence of methodological rigor. Overall, future researchers should take
account of both teacher and student data, and use multilevel (SEM) designs to elucidate the
relationship between TSE and students’ achievement, both directly and indirectly, through
daily classroom processes.
TEACHERS’ WELL-BEING
There is an abundance of studies on the link between TSE and teachers’ psychological well-
being, but the majority of this research has concentrated on factors hampering teachers’
welfare. Perhaps, the popularity of this research focus is unsurprising, as teaching is considered
one of the most stressful occupations (Johnson et al., 2005). Still, the empirical evidence
included in this review suggests that TSE may be of higher predictive value for positive factors,
such as personal accomplishment, than for dimensions of stress and burnout (see Aloe et al.,
2014). Specifically, high TSE beliefs, and in particular those that go beyond the instructional
domain, seem to help teachers stay motivated, satisfied, and consequently on the job. Further
investigation of the complex interrelationships between TSE, positive feelings of well-being,
and teacher retention may be an important avenue of research that could be pursued.
CONCEPTUAL AND METHODOLOGICAL CHALLENGES
Both theoretically and empirically, one of the major challenges to TSE research is to realize the
full richness of the TSE construct. Theories of self-efficacy suggest that TSE should be
measured in terms of specific beliefs that fluctuate across tasks, domains, and different
contexts. Consistent with the review findings of Klassen et al. (2011), a large proportion of
empirical studies unfortunately failed to use such more complex, multidimensional measures.
Bandura (1997) has cautioned researchers attempting to measure TSE that undifferentiated
self-efficacy scales usually suffer from low predictiveness, as these scales are not distinctly
linked to what they seek to predict. This may explain, in part, why most reported coefficients,
and especially those in the domain of teaching and learning, are only small to moderate.
Studies using multidimensional measures of TSE may allow for the detection of unique self-
efficacy dimensions that may have different patterns of effects on similar outcomes. A good
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
NATURE AND CONSEQUENCES OF TEACHER SELF-EFFICACY
55
starting point is the domain-specific Bandura-based scale of Tschannen-Moran and Woolfolk
Hoy (2001), which, coincidentally, bears a strong resemblance to the CLASS-domains of Pianta
and colleagues (2008). The identification of such dimensions of TSE has potential for
comparing, contrasting, and integrating various lines of TSE research and theory that, to date,
have occupied isolated territories.
Ideally, advancements in the sophistication of measures should be accompanied by greater
methodological and data-analytic rigor. Generally, the quality of reviewed studies varied
considerably in terms of sample size, employed measures of TSE, and complexity of statistical
analyses. Especially where students’ academic adjustment and aspects of classroom processes
were concerned, the host of research primarily relied on simple correlation techniques and total
self-efficacy scores in cross-sectional designs, thereby largely failing to take account of the
highly particularized and potentially fluctuating nature of TSE. In future studies, therefore,
greater emphasis should be placed on longitudinal analyses and (multilevel) structural equation
models, in which differing pathways of influences can be compared, and the temporal
precedence of predictors can be established.
LIMITATIONS
The current review faces some limitations. First, despite careful efforts to systematically search
the literature, it is possible that some studies have been overlooked, or their relevance
unacknowledged. This might especially be the case for unpublished work, or articles that did
not meet our inclusion and exclusion criteria. By paying attention to qualitative papers, non-
English language articles, dissertations, or book chapters, future researchers might be able to
uncover new, and more nuanced patterns of results than this review has provided, and
overcome the traditional limitation of publication bias.
A second limitation pertains to the fact that both TSE and its consequences have been
measured by a variety of different instruments, each of which focuses on slightly different
aspects. This lack of common measures not only makes it difficult to make proper distinctions
between different dimensions of TSE, but also to compare and synthesize its consequences
along the heuristic model. In some cases, this might have resulted in a lack of nuance, or even
a misclassification of effects. Related, the divergences in measurement and conceptualization
of the variables of interest in this review, as well as the sometimes largely varying sample sizes
across the included articles, have impeded comparison of the results of all studies, and
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 2
56
especially those regarding students' academic adjustment. For this reason, we also did not
provide a detailed overview of the strength of relationships between TSE and student and
teacher outcomes in the classroom. Although full-scale meta-analyses of TSE and its
consequences are warranted to further advance the field, this is probably only (partly) possible
for research on teachers’ well-being, which generally relies on similar measures.
Third, as the bulk of selected studies was cross-sectional in nature, this review does not allow
for conclusions about definitive causation of TSE along multiple dimensions. Moreover,
following Bandura’s (1997) notions, TSE is likely to be part of a complex system of triadic
reciprocal causality, in which environmental forces, personal factors, and behaviors influence
one another bi-directionally. Consideration of alternative, potentially reciprocal pathways in
process-oriented models would probably add important insights to the discussion of these
associations. To this end, markers of change in TSE over time are needed in future research.
Fine examples in this respect come from studies on the link between TSE and burnout
(Brouwers et al., 2001; Brouwers & Tomic, 2000), suggesting feedback loops in which low
levels of TSE reinforce teachers’ burnout and vice versa. Both social-cognitive theory and
classroom-based research (e.g., Pianta et al., 2008), may provide guidelines for exploring causal
pathways among TSE, classroom processes, and student adjustment across time, and detecting
potential feedback loops. Both longitudinal and experimental studies, especially in the area of
student performance and classroom processes, may therefore be promising next steps to
considering the causal mechanisms underlying TSE and its consequences for students,
teachers, and classroom processes. Research exploring the development of different patterns
of TSE may eventually reveal warning signs early enough to allow preventative actions.
CONCLUSION
This review aimed to provide an up-to-date, critical review of forty years of research on TSE
and its direct and indirect consequences at different levels of classroom ecology. Although the
evidence tends to corroborate that TSE is relevant for the quality of classroom processes,
students’ adjustment, and teachers’ well-being, further theoretical elaboration and empirical
substantiation of the outcomes of the reviewed studies is needed to move the field forward.
Multidimensional measures, research designs that reveal more complex indirect effects and
potential feedback loops, and further integration between lines of inquiry as suggested by the
heuristic model may be helpful in achieving this goal.
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
AP
PEN
DIX
1
Clas
sroom
Proc
esses
Aut
hor(s
) Co
untry
Ca
tego
ry
Subt
hem
e G
rade
N
TS
E m
easu
re
Ana
lysis
Resu
lts
Abu
h-Ti
neh
et a
l.
(201
1)
Jord
an
CO
SAB
Elem
enta
ry, m
iddl
e, an
d hi
gh sc
hool
56
6 te
ache
rs
TSE
S Co
rrela
tions
TS
E→
Inst
ruct
iona
l Man
agem
ent:
r = .4
2 TS
E→
Beh
avio
r Man
agem
ent:
r = .3
6 TS
E→
Peo
ple
Man
agem
ent:
r = .3
5 TS
E→
Clas
sroo
m M
anag
emen
t Ove
rall:
r =
.47
Ahm
ad e
t al.
(201
0)
Mala
ysia
CO
Co
mpu
ter
Use
U
nive
rsity
73
1 te
ache
rs
New
ly de
velo
ped
scale
SE
M
Com
pute
r TSE→
Tec
hnol
ogy
Use
: β =
.45
Ahs
an e
t al.
(201
2)
Bang
la-de
sh
CO
Inclu
sive
Prac
tices
Pr
eser
vice
con
text
1,
623
teac
hers
TE
IP
Corr
elatio
ns
TSE
(Inc
lusiv
e Pr
actic
es)→
Con
cern
s: r =
.24
TSE
(Inc
lusiv
e Pr
actic
es)→
Atti
tude
s: r =
.20
Alli
nder
(199
5)
USA
IS
IP
E
lemen
tary
scho
ol
(Spe
cial E
duca
tion;
G
3–G
6)
19 te
ache
rs
38 st
uden
ts
TES
AN
COV
A
Hig
h vs.
low
TSE→
Goa
l Am
bitio
usne
ss: M
= 2
0.50
(S
D =
4.8
3); M
= 1
6.88
(SD
= 4
.78)
(ns)
Hig
h vs.
low
TSE→
N In
stru
ctio
nal C
hang
es: M
= .6
7 (S
D =
.26)
; M =
1.0
8 (S
D =
.49)
(ns)
Hig
h vs.
low
TSE→
N G
oal C
hang
es: M
= 1
.17
(SD
=
.67)
; M =
.54
(SD
= .4
8)
Hig
h vs.
low
TSE→
Tim
ing
of C
hang
e: M
= 2
.00
(SD
=
1.6
7); M
= 2
.23
(SD
= 1
.59)
(ns)
Alm
og &
Sh
echt
man
(200
7)
Isra
el
CO
Prob
lem
Beha
vior
s E
lemen
tary
scho
ol
(G1
– G
3)
33 te
ache
rs
TES
Corr
elatio
ns
TSE→
Low
Ach
ievem
ent:
r = .3
3 TS
E→
Shy
ness
: r =
.44
TS
E→
Diso
bedi
ence
: r =
.22
TSE→
Soc
ial R
ejec
tion:
r =
.37
TSE→
Pas
sive
Agg
ress
ion
: r =
.45
TSE→
Impu
lsive
ness
: r =
.30
TSE→
Hos
tility
and
Agg
ress
ion
: r =
.20
TSE→
Hyp
erac
tivity
: r =
.30
Bogl
er &
Som
ech
(200
4)
Isra
el
CO
SAB
Mid
dle
scho
ol
(G7–
G9)
98
3 te
ache
rs
SPE
S Re
gres
sion
TSE→
Org
aniz
atio
nal C
itize
nshi
p Be
havi
or: β
= .3
5
Brad
y &
W
oolfs
on (2
008)
UK
CO
In
clusiv
e Pr
actic
es
Prim
ary
scho
ol
118
teac
hers
TS
ES
(sho
rt)
Regr
essio
n TS
E→
Loc
us o
f Cau
salit
y: β
= .1
9 TS
E→
Con
trolla
bilit
y: β
= -.
09 (n
s) TS
E→
Sta
bilit
y: β
= .1
1 (n
s)
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
Brow
n (2
005)
USA
IS
IS
Mat
h/
Lite
racy
Pr
esch
ool
94 te
ache
rs
TSE
S Co
rrela
tions
TS
E (I
S, C
M, S
E, O
vera
ll)→
Mat
h Be
liefs
: r =
.23;
r =
.2
0; r
= .2
7; r
= .2
5
TSE
(Ove
rall)→
Tea
cher
Beli
efs a
bout
the
Impo
rtanc
e of
Mat
hs: r
= .1
9 (n
s) TS
E (O
vera
ll)→
Mat
h In
stru
ctio
nal P
ract
ices
: r =
.2
4(ns
) Br
owne
ll &
Pa
jares
(199
9)
USA
CO
In
clusiv
e Pr
actic
es
Elem
enta
ry sc
hool
(G
2)
200
teac
hers
N
ewly
deve
lope
d sc
ale
SEM
TS
E→
Suc
cess
in T
each
ing
Stud
ents
with
Disa
bilit
ies:
β =
.39
Capa
-Ayd
in e
t al.
(200
9)
Turk
ey
CO
Goa
l St
ruct
ures
Pr
eser
vice
con
text
1,
218
teac
hers
(T
)TSE
S Ca
noni
cal
corr
elatio
ns
TSE→
Self
-Reg
ulat
ion
Var
iables
: r =
.52
Chac
on (2
005)
Ven
ezue
la IS
IS
Mat
h/
Lite
racy
H
igh
scho
ol
104
teac
hers
TS
ES
(sho
rt)
Corr
elatio
ns
TSE
(IS,
CM
, SE
)→ C
omm
unica
tion
Stra
tegy
: r =
.32;
r =
.26;
r =
.39
TSE
(IS,
CM
, SE
)→ G
ram
mar
Stra
tegy
: r =
.24;
r =
.2
4; r
= .2
4
Cant
rell
&
Hug
hes (
2008
)
USA
IS
Im
ple-
men
tatio
n E
lemen
tary
and
m
iddl
e sc
hool
(G
6 –G
9)
22 te
ache
rs
New
ly de
velo
ped
scale
Co
rrela
tions
TS
E (f
all, s
prin
g)→
Im
plem
enta
tion
Cont
ent L
itera
cy
App
roac
h: r
= .4
8; r
= .3
1 (n
s)
Chen
(201
0)
U
SA
CO
Com
pute
r U
se
Pres
ervi
ce c
onte
xt
206
teac
hers
N
ewly
deve
lope
d sc
ale
SEM
TS
E→
Tec
hnol
ogy
Use
: β =
.45
Cho
& S
him
(2
013)
U
SA
CO
Goa
l St
ruct
ures
E
lemen
tary
, mid
dle,
and
high
scho
ol
211
teac
hers
TS
ES
(sho
rt)
HLM
TS
E→
Mas
tery
Goa
ls fo
r Tea
chin
g: β
= .3
1 TS
E→
Per
form
ance
Goa
ls fo
r Tea
chin
g: β
= .1
7
Chun
g et
al.
(200
5)
USA
E
S ST
R Pr
esch
ool
152
teac
her
608
child
ren
TBS
Regr
essio
n TS
E→
Stu
dent
-Tea
cher
Rel
atio
nshi
p: β
= .1
4 (n
s)
Cian
i et a
l. (2
008)
USA
CO
G
oal
Stru
ctur
es
Seni
or h
igh
scho
ol
156
teac
hers
TS
ES
(sho
rt)
SEM
TS
E-I
S→ M
aste
ry C
lassr
oom
Goa
l Stru
ctur
e: β
= .3
3
Dee
mer
(200
4)
U
SA
CO
Goa
l St
ruct
ures
H
igh
scho
ol
(G9–
G12
) 99
teac
hers
TE
S (a
dapt
ed)
SEM
TS
E→
Mas
tery
Inst
r. Pr
actic
es: β
= .2
6 TS
E→
Per
form
ance
Inst
r. Pr
actic
es: β
= .0
0 (n
s)
DeF
ores
t &
Hug
hes (
1992
) U
SA
CO
Refe
rral
Dec
ision
s E
lemen
tary
scho
ol
(G2–
G4)
10
2 te
ache
rs
TES
A
NO
VA
Lo
w vs
. hig
h TS
E→
Inte
rven
tion
Acc
epta
nce:
F(1,
56)
=
6.7
2
De
Jong
et a
l. (2
013)
Net
her-
lands
E
S ST
R Pr
eser
vice
con
text
12
0 te
ache
rs
TSE
S (s
hort)
H
LM
TSE
-IS→
Con
trol;
Affi
liatio
n: B
= -.
04; B
= -.
03 (n
s) TS
E-C
M→
Con
trol;
Affi
liatio
n: B
=.0
4; B
= -.
04 (n
s) TS
E-S
E→
Con
trol;
Affi
liatio
n: B
= -.
05; B
= .0
6 (n
s)
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
Dun
n &
Rak
es
(201
1)
USA
CO
IL
F Pr
eser
vice
con
text
18
5 te
ache
rs
TSE
S (s
hort)
Co
rrela
tions
TS
E→
Lea
rner
-Cen
tere
d Be
liefs
: r =
.32
Dun
n et
al.
(201
3)
U
SA
IS
Impl
e-m
enta
tion
Kin
derg
arte
n –
high
sc
hool
(K–1
2)
537
teac
hers
N
ewly
deve
lope
d sc
ale, T
SES
SEM
D
ata-
Driv
en D
ecisi
on M
akin
g TS
E→
Impa
ct
Colla
bora
tion
Conc
erns
: β =
.25
Dus
saul
t (20
06)
Cana
da
CO
SAB
Hig
h sc
hool
48
7 te
ache
rs
EA
EE
Co
rrela
tions
TS
E→
Altr
uism
: r =
.34
TS
E→
Cou
rtesy
: r =
.19
TS
E→
Con
scien
tious
ness
: r =
.19
TSE→
Civ
ic V
irtue
: r =
.24
Egy
ed &
Sho
rt (2
006)
U
SA
CO
Refe
rral
Dec
ision
s E
lemen
tary
scho
ol
106
teac
hers
TE
S D
iscr.
analy
sis
No r
esults
are g
iven
(insig
nific
ant)
Em
mer
&
Hick
man
(199
1)
USA
CO
SA
B Pr
eser
vice
con
text
16
1 te
ache
rs
SCM
D
Corr
elatio
ns
TSE
-CM→
Pos
itive
Stra
tegi
es: r
= .3
0 TS
E-C
M→
Red
uctiv
e St
rate
gies
: r =
.00
(ns)
TSE
-CM→
Ext
erna
l Stra
tegi
es: r
= .0
9(ns
) TS
E (P
erso
nal)→
Pos
itive
Stra
tegi
es: r
= .3
2 TS
E (P
erso
nal)→
Red
uctiv
e St
rate
gies
: r =
.11
(ns)
TSE
(Per
sona
l)→ E
xter
nal S
trate
gies
: r =
.20
Ere
n (2
009)
Turk
ey
CO
Goa
l St
ruct
ures
IL
F
Pres
ervi
ce c
onte
xt
374
teac
hers
TE
SPT
Corr
elatio
nsH
LM
Corre
lation
al res
ults:
TSE→
Per
form
ance
- App
roac
h G
oals;
: r =
.19
TSE→
Per
form
ance
- Avo
idan
ce G
oals;
: r =
-.21
TS
E→
Mas
tery
- App
roac
h G
oals;
: r =
.27
TSE→
Mas
tery
- Avo
idan
ce G
oals;
: r =
-.07
(ns)
Regre
ssion
resu
lts:
TSE→
Con
stru
ctiv
ist C
once
ptio
ns: β
= .1
2 TS
E→
Tra
ditio
nal C
once
ptio
ns: β
= -.
03(n
s)
Ere
n (2
012)
Turk
ey
CO
Goa
l St
ruct
ures
Pres
ervi
ce c
onte
xt
396
teac
hers
TS
AO
/TSE
S SE
M
AO→
Plan
ned
Effo
rt: β
= .8
3 A
O→
Plan
ned
Pers
isten
ce: β
= .
48
AO→
Pro
fess
iona
l Dev
elop
men
t Asp
iratio
ns: β
= .7
8 A
O→
Lea
ders
hip
Asp
iratio
ns: β
= .1
4
Esla
mi &
Fat
ahi
(200
9)
Iran
IS
IS
Mat
h/
Lite
racy
H
igh
scho
ol
40 te
ache
rs
New
ly de
velo
ped
scale
Co
rrela
tions
TS
E (I
S, C
M, S
E)→
Gra
mm
ar-O
rient
ed S
trate
gies
: r =
.1
9 (n
s); r
= -.
08 (n
s); r
= -.
04(n
s) TS
E (I
S, C
M, S
E)→
Com
mun
icatio
n-O
rient
ed
Stra
tegi
es: r
= .3
0; r
= .2
5 (n
s); r
= .3
7
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
Eun
& H
einin
g-Bo
ynto
n (2
007)
U
SA
IS
Impl
e-m
enta
tion
Kin
derg
arte
n–hi
gh
scho
ol (K
–12)
90
teac
hers
TE
S (a
dapt
ed)
Regr
essio
n TS
E→
Use
of K
now
ledge
and
Ski
lls: t
(1) =
2.4
35
Gao
& M
ager
(2
011)
USA
CO
In
clusiv
e Pr
actic
es
Pres
ervi
ce c
onte
xt
216
teac
hers
TE
S Co
rrela
tions
TS
E→
Atti
tude
s tow
ard
Child
ren
with
Aca
dem
ic D
isabi
lities
: r =
.29
TSE→
Atti
tude
s tow
ard
Child
ren
with
Phy
sical
Disa
bilit
ies: r
= .1
8 TS
E→
Atti
tude
s tow
ard
Child
ren
with
Beh
avio
ral
Disa
bilit
ies: r
= .2
0 TS
E→
Atti
tude
s tow
ard
Child
ren
with
Soc
ial
Disa
bilit
ies: r
= .2
9 TS
E→
Per
sona
l Beli
efs o
f Div
ersit
y: r =
.35
TSE→
Pro
fess
iona
l Beli
efs o
f Div
ersit
y: r =
.43
Geij
sel e
t al.
(200
9)
The
Net
her-
lands
IS
IP
Elem
enta
ry sc
hool
32
8 te
ache
rs
New
ly de
velo
ped
scale
SE
M
TSE→
Kee
ping
up-
to-d
ate: β
= .3
1 TS
E→
Ref
lectiv
e Pr
actic
e: β
= .3
0 TS
E→
Cha
nged
Pra
ctice
: β =
.25
Gha
ith &
Yag
hi
(199
7)
USA
IS
Im
ple-
men
tatio
n M
iddl
e an
d hi
gh
scho
ol
25 te
ache
rs
TES
AN
OV
A
Low
vs. H
igh
TSE→
Con
grue
nce:
M =
3.0
0 (S
D =
1.
20);
M =
4.6
0 (S
D =
.67)
Lo
w vs
. Hig
h TS
E→
Cos
t: M
= 3
.53
(SD
= 1
.13)
; M =
3.
30 (S
D =
1.4
2) (
ns)
Low
vs. H
igh
TSE→
Diff
iculty
: M =
3.3
3 (S
D =
1.1
1);
M =
2.7
0 (S
D =
1.0
6) (
ns)
Low
vs. H
igh
TSE→
Impo
rtanc
e: M
= 4
.53
(SD
= .7
4);
M =
5.2
0 (S
D =
.42)
Gha
ith &
Sha
aban
(1
999)
Leba
non
IS
Impl
e-m
enta
tion
Unk
now
n 29
2 te
ache
rs
TES
Corr
elatio
ns
TSE→
Self
-Sur
viva
l Con
cern
s: r =
-.14
TS
E→
Impa
ct C
once
rns:
r = -.
17
TSE→
Tot
al Te
ache
r Con
cern
s: r =
-.19
Gib
bs &
Pow
ell
(201
2)
UK
CO
Re
ferr
al D
ecisi
ons
Prim
ary
and
nurs
ery
scho
ols
197
teac
hers
TS
ES
(ada
pted
) Co
rrela
tions
TS
E-C
M→
Fix
ed T
erm
Exc
lusio
ns: r
= -
.14
(ns)
TSE
-IS→
Fix
ed T
erm
Exc
lusio
ns: r
= -.
10 (n
s) TS
E-S
E→
Fix
ed T
erm
Exc
lusio
ns: r
= -.
10 (n
s)
Gib
son
& D
embo
(1
984)
USA
CO
G
oal
Stru
ctur
es
ILF
Pres
ervi
ce c
onte
xt
55 te
ache
rs
(resu
lts a
re
base
d on
8
teac
hers
)
TES
Des
crip
tives
H
igh
vs. L
ow T
SE→
Am
ount
of T
ime
Spen
t in
Inst
ruct
ion:
M =
234
.0 (S
D =
.61.
9);
M =
271
.5 (S
D =
24.
9)
Hig
h vs.
Low
TSE→
Am
ount
of T
ime
Spen
t in
Non
acad
emic
Task
s: M
= 2
10.3
(SD
= 6
4.4)
; M =
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
172.
0 (S
D =
12.
5)
Hig
h vs.
Low
TSE→
Pra
ise p
er C
orre
ct A
nsw
er: M
=
.03
(SD
= .0
3); M
= .0
1 (S
D =
.02)
H
igh
vs. L
ow T
SE→
Crit
icism
per
Inco
rrec
t Ans
wer
: M
= .0
0 (S
D =
.00)
; M =
.04
(SD
= .0
2)
Hig
h vs.
Low
TSE→
Per
siste
nce
per I
ncor
rect
Ans
wer
: M
= .7
5 (S
D =
.37)
; M =
.66
(SD
= .3
4)
Hig
h vs.
Low
TSE→
Lac
k of
Per
siste
nce
per I
ncor
rect
A
nsw
er: M
= .3
8 (S
D =
.11)
; M =
.67
(SD
= .1
2)
Gor
ozid
is &
Pa
paio
anno
u (2
011)
Gre
ece
IS
Impl
e-m
enta
tion
Juni
or h
igh
scho
ol
130
teac
hers
N
ewly
deve
lope
d sc
ale
SEM
TS
E in
Stu
dent
-Cen
tere
d Te
achi
ng S
tyles→
Atti
tude
s to
war
d Im
plem
enta
tion:
β =
.31
TSE
in T
each
ing
Dail
y Le
sson
Plan
s→ In
tent
ion
tow
ard
Impl
emen
tatio
n: β
= .3
1
Guo
et a
l. (2
010)
USA
E
S, IS
E
C IS
Pr
esch
ool
67 te
ache
rs
328
stud
ents
TS
EQ
Co
rrela
tions
TS
E→
Em
otio
nal S
uppo
rt: r
= .1
6 (n
s) TS
E→
Inst
ruct
iona
l Sup
port:
r =
.17
(ns)
Guo
et a
l. (2
012)
USA
E
S E
C E
lemen
tary
Sch
ool
(G5)
1,
043
teac
hers
an
d th
eir
stud
ents
TSE
Q
SEM
TS
E→
Sup
port
for L
earn
ing:
β =
.19
TSE→
Sup
port
for L
earn
ing→
Gra
de 5
Rea
ding
: β =
.0
1
Guo
et a
l. (2
013)
USA
IS
IS
Mat
h/
Lite
racy
Pr
esch
ool
54 te
ache
rs
TSE
Q
Regr
essio
n TS
E→
Inst
ruct
iona
l Sup
port:
β =
.40
Ham
re e
t al.
(200
8)
USA
E
S ST
R Pr
esch
ool
597
teac
hers
2,
282
child
ren
TSE
Q (a
dapt
ed)
HLM
TS
E→
Con
flict
: B =
-.01
Har
dré
& S
ulliv
an
(200
8)
USA
E
S ST
R Pe
rsp.
H
igh
scho
ol
75 te
ache
rs
MSQ
Re
gres
sion
TSE
(Mot
ivat
ing;
Diag
nosin
g)→
Rela
tedn
ess a
nd
Em
otio
nal S
uppo
rt: β
= .2
1 (n
s); β
= .1
7(ns
) TS
E (M
otiv
atin
g; D
iagno
sing)→
Rele
vanc
e an
d V
alue:
β =
.11
(ns);
β =
.37
Har
dré
& S
ulliv
an
(200
9)
USA
E
S E
C H
igh
scho
ol
96 te
ache
rs
MSQ
Re
gres
sion
TSE
(Mot
ivat
ing;
Diag
nosin
g)→
Aut
onom
y Su
ppor
tive
Inte
rper
sona
l Sty
les:
r = -.
02 (n
s); r
= .2
1 (n
s) TS
E (M
otiv
atin
g; D
iagno
sing)→
Inte
rnal
Stra
tegi
es: β
=
.08
(ns);
β =
.59
TSE
(Mot
ivat
ing;
Diag
nosin
g)→
Can
’t In
fluen
ce: β
= -
.36;
β =
.02
(ns)
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
Hav
erba
ck (2
009)
USA
IS
IS
Mat
h/
Lite
racy
Pr
eser
vice
con
text
40
teac
hers
TS
ES
(read
ing)
Co
rrela
tions
TS
E (p
rete
st)→
Rea
ding
Stra
tegy
Use
: r =
.10
(ns)
TSE
(pos
ttest
)→ R
eadi
ng S
trate
gy U
se: r
= -.
12 (n
s)
Hol
zber
ger e
t al.
(201
3)
Ger
man
y CO
, IS
SAB
IS M
ath/
Li
tera
cy
Mid
dle
scho
ol (G
9)
155
teac
hers
3,
483
stud
ents
G
TSE
S (L
ongi
tudi
nal)
SEM
, co
rrela
tions
Corre
lation
al res
ults (
teach
er an
d ch
ild p
ercep
tions
): TS
E→
Cog
nitiv
e A
ctiv
atio
n: r
= .2
1 (n
s); r
= .4
5 TS
E→
Clas
sroo
m M
anag
emen
t: r =
.47;
r =
.31
TSE→
Indi
vidu
al Le
arni
ng S
uppo
rt: r
= .1
6 (n
s); r
=
.24
Long
itudin
al res
ults (
teach
er an
d ch
ild p
ercep
tions
): T1
TSE→
T2
Cogn
itive
Act
ivat
ion:
ns
T1 T
SE→
T2
Clas
sroo
m M
anag
emen
t: ns
T1
TSE→
T2
Indi
vidu
al Le
arni
ng S
uppo
rt: β
= .2
4; n
s T1
Cog
nitiv
e A
ctiv
atio
n→ T
2 TS
E: n
s; β
= .3
3 T1
Clas
sroo
m M
anag
emen
t→ T
2 TS
E: β
= .1
9; β
= .3
3
Hoy
& W
oolfo
lk
(199
0)
USA
CO
SA
B Pr
eser
vice
con
text
19
1 te
ache
rs
TES
Repe
ated
m
easu
res
AN
OV
A
Pre-
and
pos
t Pup
il Co
ntro
l Orie
ntat
ion:
M
prete
st =
50.
83; M
postt
est =
48.
50
Hug
hes e
t al.
(199
3)
USA
CO
Re
ferr
al D
ecisi
ons
Elem
enta
ry sc
hool
55
teac
hers
N
ewly
deve
lope
d sc
ale
AN
OV
A
Uni
varia
te F
for pr
oblem
beha
vior v
ignett
es ar
e not
given
.
Jimm
ieson
et a
l. (2
010)
A
ustra
lia
ES
STR
Elem
enta
ry a
nd
mid
dle
scho
ol
(G5–
G7)
170
teac
hers
3,
057
stud
ents
JE
S H
LM
TSE→
Stu
dent
-Tea
cher
Rel
atio
nshi
p : β
= .2
0
Just
ice e
t al.
(200
8)
USA
IS
IS
Mat
h/
Lite
racy
Pr
esch
ool
135
teac
hers
TS
EQ
(ada
pted
) Re
gres
sion
TSE→
Qua
lity
of L
angu
age
Mod
eling
: B
= .1
3 (n
s) TS
E→
Qua
lity
of L
itera
cy F
ocus
: B
= .6
8
Kao
et a
l. (2
011)
Taiw
an
CO
Com
pute
r U
se
Elem
enta
ry sc
hool
48
4 te
ache
rs
New
ly de
velo
ped
scale
Re
gres
sion
Resu
lts fo
r var
ious p
rofes
siona
l deve
lopme
nt ou
tcome
s: Ba
sic (I
nter
net)
TSE→
Per
sona
l Int
eres
t: β
= .1
6 A
dvan
ced
(Int
erne
t) TS
E→
Occ
upat
iona
l Pro
mot
ion:
β
= .3
2 A
dvan
ced
(Int
erne
t) TS
E→
Pra
ctic
al E
nhan
cem
ent: β
= .1
3 A
dvan
ced
(Int
erne
t) TS
E→
Soc
ial C
onta
ct: β
= .2
2 A
dvan
ced
(Int
erne
t) TS
E→
Soc
ial S
timul
atio
n: β
= .1
9
Laks
hman
an e
t al.
(201
1)
USA
IS
Im
ple-
men
tatio
n E
lemen
tary
scho
ol
(G5–
G8)
79
teac
hers
ST
EBI
G
row
th
Mod
eling
TS
E→
Tea
cher
Ref
orm
: r(ch
ange
of gro
wth)
= .3
5
Lam
bert
et a
l. U
SA
CO
Prob
lem
Elem
enta
ry sc
hool
52
1 te
ache
rs
PSE
- BM
Co
rrela
tions
TS
E-B
M→
Beh
avio
r Pro
blem
s : r
= -.
16
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
(200
9)
Beha
vior
s
Lee
et a
l. (2
013)
USA
IS
Im
ple-
men
tatio
n E
lemen
tary
and
se
cond
ary
scho
ol
40 te
ache
rs
TSE
S (s
hort)
Re
gres
sion
TSE
(Ove
rall)→
Con
cept
ual C
hang
e: F(
1,25
) =.3
59 (n
s) TS
E (P
rimar
y Sc
hool
)→ C
once
ptua
l Cha
nge:
F(1,
9)
=.9
9 (n
s) TS
E (S
econ
dary
Sch
ool)→
Con
cept
ual C
hang
e: F(
1,14
) =
4.7
1
Lee
& T
sai (
2010
)
Taiw
an
CO
Com
pute
r U
se
Elem
enta
ry, m
iddl
e, an
d hi
gh sc
hool
55
8 te
ache
rs
New
ly de
velo
ped
inst
rum
ent
Corr
elatio
ns
TSE
(Web
-gen
eral)→
Atti
tude
tow
ard
Web
-Bas
ed
Inst
ruct
ion:
r =
.46
TSE
(Web
-com
mun
icativ
e)→
Atti
tude
tow
ard
Web
-Ba
sed
Inst
ruct
ion:
r =
.27
TSE
(Web
-Con
tent
Kno
wled
ge)→
Atti
tude
tow
ard
Web
-Bas
ed In
stru
ctio
n: r
= .6
0 TS
E (W
eb-P
edag
ogica
l Con
tent
Kno
wled
ge)→
A
ttitu
de to
war
d W
eb-B
ased
Inst
ruct
ion:
r =
.61
Lero
y et
al.
(200
7)
Fr
ance
E
S Pe
rsp.
E
lemen
tary
scho
ol
(G5)
33
6 te
ache
rs
TES
(Fre
nch)
SE
M
TSE→
Aut
onom
y Su
ppor
tive
Clim
ate: β
= .2
1
Lilje
quist
& R
enk
(200
7)
USA
CO
Pr
oblem
Be
havi
ors
Pres
ervi
ce c
onte
xt
99 te
ache
rs
TES
SEM
TS
E→
Con
trol o
ver E
xter
naliz
ing
Prob
lems:
B =
.22
TSE→
Bot
hers
ome
of In
tern
alizi
ng P
robl
ems:
B =
.11
Mali
nen
et a
l. (2
012)
Finl
and
CO
Incl
usiv
e
Prac
tices
Pr
esch
ool t
o hi
gh
scho
ol
451
teac
hers
TE
IP
SEM
TS
E fo
r Col
labor
atio
n→ A
ttitu
de to
war
d in
clusiv
e pr
actic
es : β
= -.
03 (n
s) TS
E fo
r Inc
lusiv
e Pr
actic
es→
Atti
tude
tow
ard
Inclu
sive
Prac
tices
: β
= .3
6 TS
E fo
r Beh
avio
r Man
agem
ent→
Atti
tude
tow
ard
Inclu
sive
Prac
tices
: β
= .1
2 (n
s)
Mar
tin e
t al.
(201
2)
USA
IS
IP
E
lemen
tary
, mid
dle
and
high
scho
ol
631
teac
hers
TS
ES
SEM
TS
E-E
N→
Inst
ruct
iona
l Man
agem
ent: β
= -.
83
TSE
-EN→
Inst
r. M
an.→
Stu
dent
Beh
avio
r Stre
ssor
s:
β =
-.43
TS
E-E
N→
Inst
r. M
an.→
Per
sona
l Acc
ompl
ishm
ent:
β
= -.
63
Mar
tin &
Sas
s (2
010)
USA
CO
SA
B
Elem
enta
ry, m
iddl
e, an
d hi
gh sc
hool
55
0 te
ache
rs
TSE
S Co
rrela
tions
TS
E-C
M→
Beh
avio
r Man
agem
ent;
Inst
ruct
ion
Man
agem
ent:
r = -.
19; r
= -.
51
TSE
-IS→
Beh
avio
r Man
agem
ent;
Inst
ruct
ion
Man
agem
ent:
r = -.
02 (n
s); r
= -.
52
TSE
-SE→
Beh
avio
r Man
agem
ent;
Inst
ruct
ion
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
M
anag
emen
t: r =
-.12
(ns);
r =
-.65
Mas
hbur
n et
al.
(200
6)
USA
E
S ST
R Pr
esch
ool
210
teac
hers
71
1 ch
ildre
n
TSE
Q (a
dapt
ed)
HLM
TS
E→
Con
flict
: B =
-.0
1 (n
s) TS
E→
Clo
sene
ss: B
= .0
2
Meij
er &
Fos
ter
(198
8)
Net
her-
lands
CO
Re
ferr
al D
ecisi
ons
Elem
enta
ry sc
hool
(G
2)
230
teac
hers
TE
S (D
utch
ad
apte
d ve
rsio
n)
Corr
elatio
ns
TSE→
Pro
blem
Cha
nce:
r = -
.23
TSE→
Ref
erra
l Cha
nce:
r = -
.14
Mid
gley
et a
l. (1
995)
USA
CO
G
oal
Stru
ctur
es
Elem
enta
ry a
nd
mid
dle
scho
ol
(G4–
G7)
158
teac
hers
96
9 st
uden
ts
New
ly de
velo
ped
sclae
Co
rrela
tions
Re
sults
for e
lemen
tary a
nd m
iddle
schoo
l tea
chers
: TS
E→
Tas
k G
oals
for S
tude
nts:
r = .
37; r
= .2
3(ns
) TS
E→
Per
form
ance
Goa
ls fo
r Stu
dent
s: r =
.07(
ns);
r =
-.0
7(ns
) TS
E→
Inst
ruct
iona
l Pra
ctice
s-Ta
sk: r
= .
32;
r = .
21(n
s) TS
E→
Inst
ruct
iona
l Pra
ctice
s-Pe
rfor
man
ce: r
= .0
7 (n
s); r
= -.
20(n
s)
Mor
ris-R
oths
child
&
Bra
ssar
d (2
006
USA
CO
SA
B E
lemen
tary
and
se
cond
ary
scho
ol
283
teac
hers
SC
MD
Re
gres
sion
TSE→
Inte
grat
ing
Style
: β =
.33
TSE→
Obl
igin
g St
yle: β
= .2
4 TS
E→
Com
prom
ising
Sty
le: β
= .2
2 TS
E→
Dom
inat
ing
Style
: β =
.17
(ns)
TSE→
Avo
idin
g St
yle: β
= -.
05 (n
s)
Mue
ller e
t al.
(200
8)
Cana
da
CO
Com
pute
r U
se
Elem
enta
ry a
nd
seco
ndar
y sc
hool
38
9 te
ache
rs
TES
(sho
rt)
Corr
elatio
ns
TSE→
Com
pute
r Use
: r =
.01
(ns)
TSE→
Inte
grat
ion:
r =
-.01
(ns)
TSE→
Com
fort
with
Com
pute
rs: r
= .
02(n
s)
Ngi
di (2
012)
Sout
h A
frica
CO
IL
F E
lemen
tary
, mid
dle,
and
high
scho
ol
280
teac
hers
TS
ES
(sho
rt)
Corr
elatio
ns
AO→
Stu
dent
-Cen
tere
d Te
achi
ng: r
= .
32
AO→
Citi
zens
hip
Beha
vior
: r =
.29
A
O→
Hum
anist
ic M
anag
emen
t: r =
.07
(ns)
AO→
Disp
ositi
onal
Opt
imism
: r =
.30
Nie
et a
l. (2
013)
Sing
apor
e CO
IL
F E
lemen
tary
scho
ol
2,13
9 te
ache
rs
TSE
S (s
hort)
SE
M
TSE→
Con
stru
ctiv
ist In
stru
ctio
n : β
= .6
2 TS
E→
Did
actic
Inst
ruct
ion
: β =
.21
Paka
rinen
et a
l.
(201
0)
Finl
and
IS
EC
IS
Kin
derg
arte
n 49
teac
hers
TE
S (s
hort)
Co
rrela
tions
TS
E→
Em
otio
nal S
uppo
rt: r
= .
23
TSE→
Clas
sroo
m O
rgan
izat
ion:
r =
.11
(ns)
TSE→
Inst
ruct
iona
l Sup
port:
r =
.13
(ns)
Pas e
t al.
(201
0)
USA
CO
Re
ferr
al D
ecisi
ons
Elem
enta
ry sc
hool
(K
–5)
491
teac
hers
9,
795
stud
ents
PT
E
Logi
stic
regr
essio
n TS
E (f
all)→
Ref
erra
l to
stud
ent s
uppo
rt te
am (s
prin
g):
Odd
s rat
io =
.77
TS
E (f
all)→
Ref
erra
l to
spec
ial e
duca
tion
(spr
ing)
:
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
Odd
s rat
io =
.88
(ns)
TSE
(fall
)→ R
efer
ral t
o pr
inci
pal’s
offi
ce (s
prin
g):
Odd
s rat
io =
.94
( ns)
TSE
(fall
)→ In
- sch
ool s
uspe
nsio
n (s
prin
g): O
dds r
atio
=
.81
(ns)
TSE
(fall
)→ O
ut-o
f-sch
ool s
uspe
nsio
n (s
prin
g): O
dds
ratio
= .9
4 ( n
s)
Rans
ford
et a
l. (2
009)
U
SA
IS
ILS
Elem
enta
ry sc
hool
(K
–5)
156
teac
hers
TE
S (a
dapt
ed)
Regr
essio
n TS
E-S
E→
Impl
em..
Dos
age
(ave
rage
): β
= .1
7 (n
s) TS
E-S
E→
Impl
em. D
osag
e (s
uppl
emen
tal):
β =
.19
Roha
an e
t al.
(201
2)
The
Net
her-
lands
CO
Com
pute
r U
se
Elem
enta
ry sc
hool
(G
3–G
6)
354
teac
hers
1,
584
stud
ents
ST
EBI
SE
M
TSE
-IS→
Atti
tude
s tow
ard
Tech
olog
y: β
= .5
3
Rubi
e-D
avie
s et
al. (2
012)
New
Z
ealan
d CO
G
oal
Stru
ctur
es
Elem
enta
ry a
nd
mid
dle
scho
ol
68 te
ache
rs
TSE
S (re
adin
g)
Regr
essio
n TS
E-I
S→ M
aste
ry G
oals;
Per
form
ance
Goa
ls; C
lass-
Leve
l Exp
ecta
tion:
β =
.13
(ns);
β =
-.34
(ns);
β
= .1
1(ns
) TS
E-C
M→
Mas
tery
Goa
ls; P
erfo
rman
ce G
oals;
Clas
s-Le
vel E
xpec
tatio
n: β
= -.
41; β
= .0
1 (n
s); β
= .1
9 (n
s) TS
E-S
E→
Mas
tery
Goa
ls; P
erfo
rman
ce G
oals;
Clas
s-Le
vel E
xpec
tatio
n: β
= .7
1; β
= .1
4 (n
s); β
=- .
02 (n
s)
Sakl
ofsk
e et
al.
(198
8)
USA
CO
SA
B Pr
eser
vice
con
text
87
teac
hers
TE
S Co
rrela
tions
TS
E→
Les
son
Pres
entin
g Be
havi
ors :
r =
.26
TS
E→
Clas
sroo
m M
anag
emen
t Beh
avio
rs: r
= .
23
TSE→
Que
stio
ning
Beh
avio
rs: r
= .
22
Sang
et a
l. (2
010)
Chin
a
Com
pute
r U
se
Pres
ervi
ce c
onte
xt
727
teac
hers
TS
ES,
CSE
SE
M
TSE→
Pro
spec
tive
ICT
Use
: β =
.06
Com
pute
r TSE→
Pro
spec
tive
ICT
Use
: β =
.23
Sood
ak &
Pod
ell
(199
3)
USA
CO
Re
ferr
al D
ecisi
ons
Spec
ial e
duca
tion
167
teac
hers
TE
S (s
hort)
Re
gres
sion
TSE→
Plac
emen
t Jud
gmen
ts: F
= 4
.45
(ns)
TSE→
Ref
erra
l Jud
gmen
ts: F
= 1
.74
(ns)
Sood
ak e
t al.
(199
8)
USA
CO
In
clusiv
e Pr
actic
es
Gen
eral
educ
atio
n 18
8 te
ache
rs
TES
(ada
pted
) Re
gres
sion
TSE→
Anx
iety/
Calm
ness
: F
= 9
.19
Tejed
a-D
elgad
o (2
009)
U
SA
CO
Refe
rral
Des
icion
s E
lemen
tary
scho
ol
(G1–
G5)
16
7 te
ache
rs
TES
AN
OV
A
Zer
o vs.
1,2,
>3
refe
rrals→
TSE
: F(2
, 161
) = 1
.98
(ns)
Tem
iz &
Top
cu
(201
3)
Turk
ey
CO
ILF
Pres
ervi
ce c
onte
xt
101
teac
hers
TS
ES
Corr
elatio
ns
TSE
-IS→
Con
stru
ctiv
ist C
once
ptio
ns: r
= .
87
TSE
-CM→
Con
stru
ctiv
ist C
once
ptio
ns: r
= .
78
TSE
-SE→
Con
stru
ctiv
ist C
once
ptio
ns: r
= .
76
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
TS
E (O
vera
ll)→
Con
stru
ctiv
ist C
once
ptio
ns: r
= .7
8
Teo
(200
9)
Sing
apor
e CO
Co
mpu
ter
Use
Pr
eser
vice
Con
text
1,
094
teac
hers
N
ewly
deve
lope
d sc
ale
SEM
Ba
sic T
each
ing
Skill
s TSE→
Tra
ditio
nal U
se o
f Te
chno
logy
; Con
stru
ctiv
ist U
se o
f Tec
hnol
ogy: β
= .1
7;
β =
.35
Adv
ance
d Te
achi
ng S
kills
TSE→
Tra
ditio
nal U
se o
f Te
chno
logy
; Con
stru
ctiv
ist U
se o
f Tec
hnol
ogy: β
= -
.04
(ns);
β =
-.07
(ns)
Tech
nolo
gy fo
r Ped
agog
y TS
E→
Tra
ditio
nal U
se o
f Te
chno
logy
; Con
stru
ctiv
ist U
se o
f Tec
hnol
ogy: β
= .2
5;
β =
.19
Thoo
nen
et a
l. (2
011)
H
ollan
d IS
IP
E
lemen
tary
scho
ol
(G4–
G6)
62
1 te
ache
rs
3,46
2 st
uden
ts
SSE
SE
M
TSE→
Pro
cess
-Orie
nted
Inst
ruct
ion:
B =
.21
TSE→
Fit
betw
een
Scho
ol a
nd S
tude
nts’
Live
s: B
= .3
5 TS
E→
Coo
pera
tive
Lear
ning
: B =
.30
TSE→
Diff
eren
tiatio
n in
Inst
ruct
ion:
B =
.44
Van
natta
&
Ford
ham
(200
4)
USA
CO
Co
mpu
ter
Use
K
inde
rgar
ten
– hi
gh
scho
ol (K
–12)
17
7 te
ache
rs
TAS
Regr
essio
n TS
E-S
E→
Clas
sroo
m T
echn
olog
y U
se: n
s
Wer
theim
&
Leys
er (2
002)
USA
CO
SA
B IP
Pr
eser
vice
con
text
19
1 te
ache
rs
TES
Corr
elatio
ns
TSE→
Beh
avio
r Man
agem
ent (
Use
; E
ffect
iven
ess)
: r
= .3
1; r
= .2
4 TS
E→
Indi
vidu
alize
d In
stru
ctio
n (U
se;
Eff
ectiv
enes
s):
r = .
39; r
= .
25
TSE→
Diag
nost
ic Te
achi
ng (U
se;
Effe
ctiv
enes
s): r
=
.28;
r =
.21
Wes
hah
(201
2)
Jord
an
IS
IP
Pres
ervi
ce c
onte
xt
106
teac
hers
TE
S (a
dopt
ed)
Corr
elatio
ns
TSE→
Effe
ctiv
e Te
achi
ng P
ract
ices
: r =
.86
Wol
ters
&
Dau
gher
ty (2
007)
USA
CO
G
oal
Stru
ctur
es
Kin
derg
arte
n –
high
sc
hool
(K–1
2)
1,02
4 te
ache
rs
TSE
S H
LM
TSE
-IS→
Mas
tery
Stru
ctur
e; Pe
rfor
man
ce S
truct
ure: β
= .3
1; β
= .0
9 (n
s) TS
E-C
M→
Mas
tery
Stru
ctur
e; Pe
rfor
man
ce S
truct
ure:
β =
-.12
; β =
-.07
(ns)
TSE
-SE→
Mas
tery
Stru
ctur
e; Pe
rfor
man
ce S
truct
ure: β
= .2
1; β
= .0
7 (n
s)
Won
g et
al.
(201
2)
Mala
ysia
CO
Co
mpu
ter
Use
Pr
eser
vice
con
text
30
2 te
ache
rs
New
ly de
velo
ped
scale
SE
M
Com
pute
r TSE→
Per
ceiv
ed E
ase
of U
se: β
= .4
8 Co
mpu
ter T
SE→
Use
fuln
ess: β
= .2
2 Co
mpu
ter T
SE→
Att.
tow
ard
Com
pute
r Use
: β =
.38
Com
pute
r TSE
→ P
erce
ived
Use
→ B
ehav
iora
l In
tent
ion:
β =
.11
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
Woo
lfolk
& H
oy
(199
0)
USA
CO
SA
B Pr
eser
vice
con
text
18
2 te
ache
rs
TES
(Can
onic
al)
Corr
elatio
nsRe
gres
sion
TSE→
Pup
il Co
ntro
l Ide
olog
y: r =
-.04
(ns);
F =
.00
(ns)
TSE→
Mot
ivat
iona
l Orie
ntat
ion:
r =
.05
(ns);
F =
10.
43
TSE→
Bur
eauc
ratic
Orie
ntat
ion:
r =
.18;
F =
.28
(ns)
Woo
lfolk
et a
l. (1
990)
USA
CO
SA
B E
lemen
tary
and
m
iddl
e sc
hool
(G
6–G
7)
55 H
ebre
w
relig
ious
te
ache
rs
TES
Corr
elatio
ns
Regr
essio
n TS
E→
Pup
il Co
ntro
l Ide
olog
y: r =
-.36
; β =
-.13
(ns)
TSE→
Con
trol/
Aut
onom
y O
rient
atio
n: β
= .0
58 (n
s)
Yild
irim
& A
tes
(201
2)
Turk
ey
IS
IS M
ath/
Li
tera
cy
Pres
ervi
ce c
onte
xt
346
teac
hers
N
ewly
deve
lope
d sc
ale
Corr
elatio
ns
TSE→
Kno
wled
ge o
f Usin
g E
xpos
itory
Tex
t: r =
.14
Yilm
az (2
011)
Turk
ey
IS
IS M
ath/
Li
tera
cy
Elem
enta
ry a
nd h
igh
scho
ol
54 te
ache
rs
TSE
S (s
hort)
Co
rrela
tions
TS
E (I
S, C
M, S
E)→
Gra
mm
ar O
rient
ed S
trate
gies
: r =
.08
(ns);
r =
.07
(ns);
r =
.05
(ns)
TSE
(IS,
CM
, SE
)→ C
omm
unica
tion
Orie
nted
St
rate
gies
: r =
.22
(ns);
r =
.17
(ns);
r =
.15
(ns)
Yoo
n (2
002)
USA
E
S ST
R E
lemen
tary
scho
ol
(K–5
) 11
3 te
ache
rs
PSE
-BM
H
LM
TSE
-BM→
Goo
d Re
latio
nshi
p: R
= .1
5 (n
s) TS
E-B
M→
Neg
ativ
e Re
latio
nshi
ps: R
= .3
2 (n
s)
Yoo
n (2
004)
U
SA
CO
SAB
Elem
enta
ry sc
hool
98
teac
hers
PS
E-B
M
Regr
essio
n TS
E-B
M→
Inte
rven
tion
Bully
ing
Beha
vior
s : β
= .2
1
Note
. AO
= A
cade
mic
Opt
imism
; CO
= C
lassr
oom
Org
aniz
atio
n; C
ompu
ter
Use
= C
ompu
ter
and
tech
nolo
gy u
se in
the
class
room
; EC
= E
mot
iona
l Clim
ate;
ES
= E
mot
iona
l Sup
port;
ILF
= I
nstru
ctio
nal L
earn
ing
Form
ats;
Impl
emen
tatio
n =
Im
plem
enta
tion
of in
stru
ctio
nal p
ract
ices;
IP =
Inst
ruct
iona
l Pra
ctice
s; IS
= In
stru
ctio
nal S
uppo
rt; IS
Mat
h/Li
tera
cy =
Instr
uctio
nal s
uppo
rt fo
r mat
h an
d lit
erac
y; G
oal S
truct
ures
= T
each
ers’
class
room
goa
l stru
ctur
es; P
ersp
. = R
egar
d fo
r st
uden
t per
spec
tives
; Pro
blem
Beh
avio
rs =
Abi
lity
to c
ope
with
pro
blem
beh
avio
rs; S
AB
= C
lassr
oom
man
agem
ent s
trate
gies
, atti
tude
s, an
d be
havi
ors;
STR
= S
tude
nt–
teac
her r
elatio
nshi
p qu
ality
. TSE
-IS
= T
SE fo
r ins
truct
iona
l stra
tegi
es; T
SE-C
M =
TSE
for c
lassr
oom
man
agem
ent;
TSE
-SE
= T
SE fo
r stu
dent
eng
agem
ent.
Instr
umen
ts: C
SE; C
ompu
ter S
elf-E
ffica
cy S
cale;
EA
EE
= É
chell
e d'
Aut
o-Ef
ficac
ité d
es E
nseig
nant
s; G
TSE
S =
Gen
eral
Teac
her S
elf-E
ffica
cy S
cale;
JES
= Jo
b E
ffica
cy S
cale;
MSQ
= M
otiv
atin
g St
rate
gies
Que
stio
nnair
e; PS
E-B
M =
Per
sona
l Tea
chin
g E
ffica
cy in
Beh
avio
r Man
agem
ent;
PTE
= P
erso
nal T
each
er E
ffica
cy S
cale;
SCM
D =
Self
-effi
cacy
scale
for C
lassr
oom
Man
agem
ent a
nd D
iscip
line;
SPE
S =
Sch
ool P
artic
ipan
t Em
pow
erm
ent
Scale
; SSE
= S
ense
of S
elf-E
ffica
cy; S
TEBI
= S
cienc
e Te
achi
ng E
ffica
cy B
elief
Ins
trum
ent;
TAS
= T
each
er A
ttrib
ute
Surv
ey; T
BS =
Tea
cher
Beli
efs
Scale
; TE
IP =
Tea
cher
Eff
icacy
for I
nclu
sive
Prac
tice
scale
; TE
S =
Te
ache
r Eff
icacy
Sca
le (re
sults
are
onl
y gi
ven
for P
TE);
TESP
T =
Tea
cher
Eff
icacy
Sca
le fo
r Pro
spec
tive
Teac
hers
; TSA
O =
Tea
cher
Sen
se o
f Aca
dem
ic O
ptim
ism S
cale;
TSE
Q =
Tea
cher
Self
-Effi
cacy
Que
stio
nnair
e; TS
ES
= T
each
er S
ense
of S
elf-E
ffica
cy S
cale;
TTS
ES
= T
urki
sh T
each
ers’
Sens
e of
Eff
icacy
Sca
le.
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
APPE
NDI
X 2
Stud
ents’
Aca
demi
c Adju
stmen
t
Aut
hor(s
) Co
untry
Ca
tego
ry
Them
e G
rade
N
TS
E m
easu
re
Ana
lysis
Resu
lts
Alli
nder
(199
5)
USA
A
A
Mat
h E
lemen
tary
scho
ol
(Spe
cial E
duca
tion;
G
3–G
6)
19 te
ache
rs
38 st
uden
ts
TES
AN
COV
A
Hig
h vs.
low
TSE→
Mat
h A
chiev
emen
t (di
gits
): M
=
57.4
0 (S
D =
13.
06);
M =
48.
06 (S
D =
18.
08)
Hig
h vs.
low
TSE→
Mat
h A
chiev
emen
t (pr
oblem
s): M
=
21.
26 (S
D =
5.1
9); M
= 1
8.02
(SD
= 6
.14)
Ang
le an
d M
osele
y (2
010)
U
SA
AA
Bi
olog
y H
igh
Scho
ol
214
biol
ogy
teac
hers
ST
EBI
A
NO
VA
H
igh
vs. lo
w T
SE→
Bio
logy
Tes
t: M
= 5
5.59
; (SD
=
4.53
); M
= 5
5.68
(SD
= 5
5.51
) (ns
)
Capr
ara
et a
l. (2
006)
It
aly
AA
O
vera
ll Ju
nior
hig
h sc
hool
2,
184
teac
hers
N
ewly
deve
lope
d sc
ale
Long
itudi
nal
SEM
Ti
me
2 TS
E→
Tim
e 3
Aca
dem
ic A
chiev
emen
t: β
= .0
2
Cant
rell
et a
l. (2
013)
U
SA
AA
Li
tera
cy
M
iddl
e sc
hool
(G6)
(H
igh
scho
ol, G
9)
20 te
ache
rs
249
stud
ents
N
ewly
deve
lope
d sc
ale
Regr
essio
n TS
E→
Rea
ding
Ach
ievem
ent (
Gra
de 6
): β
= .7
6
Chan
g (2
011)
Ta
iwan
A
A
Ove
rall
Elem
enta
ry sc
hool
1,
003
teac
hers
E
lemen
tary
Sch
ool
Teac
her A
cade
mic
Opt
imism
Sca
le
SEM
A
cade
mic
Opt
imism
→ A
c. A
chiev
emen
t: β
= .7
8 A
cade
mic
Opt
imism
→ A
c. A
chiev
emen
t→ E
valu
atio
n Sc
ore:
β =
.20
Chon
g et
al.
(201
0)
Sing
apor
e A
A
Ove
rall
Mid
dle
scho
ol
222
teac
hers
TS
ES
(sho
rt)
Logi
stic
regr
essio
n Co
rrela
tions
TSE→
Aca
dem
ic A
chiev
emen
t: eβ
= .7
4 (n
s) TS
E-I
S→ A
cade
mic
Clim
ate:
r = .4
4 TS
E-C
M→
Aca
dem
ic Cl
imat
e: r =
.40
TSE
-SE→
Aca
dem
ic Cl
imat
e: r =
.48
Guo
et a
l. (2
010)
U
SA
AA
Li
tera
cy
Pres
choo
l 67
teac
hers
32
8 st
uden
ts
TSE
Q (a
dapt
ed)
HLM
(lon
gi-
tudi
nal)
TSE→
Lite
racy
(voc
. kno
wled
ge):
t(63)
= .7
27 (n
s);
TSE→
Lite
racy
(prin
t aw
aren
ess)
: t(6
3) =
3.4
5
Guo
et a
l. (2
012)
U
SA
AA
Li
tera
cy
Elem
enta
ry sc
hool
(G
5)
1,04
3 te
ache
rs
and
stud
ents
TS
EQ
SE
M (l
ongi
-tu
dina
l) TS
E→
Lite
racy
: β =
.04
TSE→
Sup
port
for L
earn
ing→
Rea
ding
: β =
.01
Har
dré
& S
ulliv
an
(200
8)
USA
SM
M
oti-
vatio
nal
Stra
tegi
es
Hig
h sc
hool
75
teac
hers
M
SQ
Regr
essio
n M
otiva
tion:
TSE
(Mot
ivat
ing;
Diag
nosin
g)→
Mot
ivat
ion:
β =
.2
1(ns
); β
= .1
4 (n
s) TS
E (M
otiv
atin
g; D
iagno
sing)→
Asp
iratio
ns/F
utur
es:
β =
-.22
(ns);
β =
.08
(ns)
Goa
ls an
d Perc
eived
Abil
ity:
TSE
(Mot
ivat
ing;
Diag
nosin
g)→
Lea
rnin
g G
oals
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
(stu
dent
s): β
= -.
22 (n
s); β
= .0
8 (n
s) TS
E (M
otiv
atin
g; D
iagno
sing)→
Fut
ure
Goa
ls (s
tude
nts)
: β =
.21
(ns);
β =
.09
(ns)
TSE
(Mot
ivat
ing;
Diag
nosin
g)→
Per
ceiv
ed A
bilit
y: β
=
-.29
(ns);
β =
-.03
(ns)
Moti
vatio
nal S
trateg
ies:
TSE
(Mot
ivat
ing;
Diag
nosin
g )→
Rele
vanc
e an
d V
alue:
β =
.11
(ns);
β =
.37
TSE
(Mot
ivat
ing;
Diag
nosin
g)→
Asp
iratio
ns: β
= .1
5 (n
s); β
= .4
0 TS
E (M
otiv
atin
g; D
iagno
sing)→
Can
’t In
fluen
ce: β
= -
.47;
β =
.16(
ns)
TSE
(Mot
ivat
ing;
Diag
nosin
g)→
Rela
tedn
ess a
nd
Em
otio
nal S
uppo
rt: β
= .2
1 (n
s); β
= .1
7(ns
) TS
E (M
otiv
atin
g; D
iagno
sing)→
Ack
now
ledge
Pee
r Pr
essu
re: β
= .1
9 (n
s); β
= .3
1
Har
dré
& S
ulliv
an
(200
9)
USA
SM
M
oti-
vatio
nal
Stra
tegi
es
Hig
h sc
hool
96
teac
hers
M
SQ
Regr
essio
n M
otiva
tion:
TSE
(Mot
ivat
ing;
Diag
nosin
g)→
Mot
ivat
ion:
β =
.52;
β
= -.
03 (n
s) M
otiva
tiona
l Stra
tegies
: TS
E (M
otiv
atin
g; D
iagno
sing)→
Inte
rnal
stra
tegi
es: β
=
.08
(ns);
β =
.59
TSE
(Mot
ivat
ing;
Diag
nosin
g)→
Ack
now
ledge
Pee
r Pr
essu
re: β
= .0
3 (n
s); β
= .3
0 TS
E (M
otiv
atin
g; D
iagno
sing)→
Can
’t In
fluen
ce: β
= -
.36;
β =
.02
(ns)
Har
dré
et a
l. (2
006)
Ta
iwan
A
A, S
M
Ove
rall
(Tas
k)
Mot
i-va
tion
and
Eng
age-
men
t
Hig
h sc
hool
40
4 te
ache
rs
TES
Corr
elatio
ns
TSE→
Stu
dent
Abi
lity:
r =
.27
TSE→
Stu
dent
Eng
agem
ent a
nd E
ffort:
r =
.29
TSE→
Stu
dent
Mot
ivat
ion:
r =
.31
TSE→
Lea
rnin
g G
oals:
r =
.23
TSE→
Per
form
ance
App
roac
h G
oals:
r =
-.10
(ns)
TSE→
Per
form
ance
Avo
idan
ce G
oals:
r =
.09
(ns)
Hen
eman
et a
l. (2
008)
U
SA
AA
Li
tera
cy
Elem
enta
ry sc
hool
1,
075
teac
hers
TSE
S (s
hort)
SE
M
TSE→
Rea
ding
Ach
ievem
ent: β
= .0
2 (n
s)
Hin
es (2
008)
U
SA
AA
M
ath
Mid
dle
scho
ol (G
7)
307
stud
ents
an
d th
eir
TSE
Q (a
dapt
ed)
AN
OV
A
Hig
h vs.
low
TSE→
Mat
h A
chiev
emen
t (te
st 1
): M
=
81.2
3; (S
D =
7.4
2); M
= 7
1.47
(SD
= 9
.43)
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
teac
her
Hig
h vs.
low
TSE→
Mat
h A
chiev
emen
t (te
st 2
): M
=
78.9
0; (S
D =
9.2
9); M
= 7
1.46
(SD
= 8
.59)
H
igh
vs. lo
w T
SE→
Mat
h A
chiev
emen
t (te
st 3
): M
=
82.0
9; (S
D =
10.
11);
M =
69.
01 (S
D =
11.
42)
Jimm
ieson
et a
l. (2
010)
A
ustra
lia
AA
, SM
O
vera
ll W
ell-b
eing
Elem
enta
ry a
nd
mid
dle
scho
ol
(G5–
G7)
170
teac
hers
3,
057
stud
ents
JE
S H
LM
TSE→
Gen
eral
Satis
fact
ion
(stu
dent
s) : β
= .1
7 TS
E→
Psy
chol
ogic
al D
istre
ss (s
tude
nts)
: β
= -.
12
TSE→
Ach
ievem
ent :
β =
.08
TSE→
Opp
ortu
nity
(stu
dent
s) : β
= .1
3
Lum
pe e
t al.
(201
2)
USA
A
A
Scien
ce
Elem
enta
ry sc
hool
(G
4, G
6)
450
teac
hers
ST
EBI
Re
gres
sion
TSE→
Scie
nce
Ach
ievem
ent :
β =
.13
Mid
gley
et a
l. (1
989)
U
SA
AA
, SM
M
ath
Exp
ec-
tanc
ies
Elem
enta
ry a
nd
mid
dle
scho
ol
(G5–
G6)
1,32
9 st
uden
ts
and
their
te
ache
r
New
ly de
velo
ped
scale
Re
gres
sion
Resu
lts ac
ross
two t
ime p
oints:
TS
E→
Exp
ecta
ncie
s: β
= .0
4 (n
s); β
= .0
6;
TSE→
Mat
h Pe
rfor
man
ce: β
= .0
8; β
= .0
7 TS
E→
Tas
k D
iffic
ulty
: β =
-.05
(ns);
β =
-.10
Moh
amad
i &
Asa
dzad
eh (2
012)
Ir
an
AA
O
vera
ll H
igh
scho
ol
284
teac
hers
TS
ES
(sho
rt)
SEM
TS
E→
Aca
dem
ic A
chiev
emen
t: β
= .4
0
Moj
avez
i &
Pood
ineh
Tam
iz
(201
2)
Iran
A
A, S
M
Ove
rall
Atti
tude
s an
d M
oti-
vatio
n
Seni
or h
igh
scho
ol
80 te
ache
rs
120
stud
ents
TS
ES
(long
) Co
rrela
tions
A
NO
VA
Co
rrelat
ional
result
s (M
otiva
tiona
l var
iables
): TS
E→
Mot
ivat
ion
(tota
l): r
= .4
5;
TSE→
Intri
nsic
Mot
ivat
ion:
r =
.39
TSE→
Ext
rinsic
Mot
ivat
ion:
r =
-.09
(ns)
TSE→
Atti
tude
: r =
.79
TSE→
Opi
nion
: r =
.24
AN
OV
A re
sults
(Aca
demi
c ach
ievem
ent):
F(
2,77
) = 8
.40,
p <
.001
.
Reye
s et a
l. (2
012)
U
SA
AA
, SM
Li
tera
cy
Eng
age-
men
t
Elem
enta
ry sc
hool
(G
5–G
6)
63 te
ache
rs
1,39
9 st
uden
ts
AE
S H
LM
TSE→
Eng
lish
Lang
uage
gra
des)
: γ =
.48
(ns)
TSE→
Rea
ding
Ach
ievem
ent (
Eng
agem
ent):
γ =
.22
Robe
rtson
&
Dun
smui
r (20
13)
UK
SM
O
n-Ta
sk
Beha
vior
Se
cond
ary
scho
ol
58 te
ache
rs
TSE
S (s
hort)
H
LM
TSE→
On-
Task
Pup
il Be
havi
or: β
= .4
4
Ross
(199
2)
Cana
da
AA
H
istor
y M
iddl
e sc
hool
(G
7–G
8)
18 te
ache
rs
TES
Regr
essio
n TS
E→
Hist
ory
Ach
ieve
men
t : m
ultipl
e R =
.80
Ro
ss e
t al.
(200
1)
Cana
da
AA
, SM
Co
mp.
Li
tera
cy
Self-
Elem
enta
ry sc
hool
(K
–3)
387
stud
ents
an
d te
ache
rs
New
ly de
velo
ped
scale
Re
gres
sion
Corre
lation
s for
two t
ime p
oints:
TS
E (C
ompu
ter U
se)→
Bas
ic Sk
ills:
r =
-.10
(ns);
r =
-.08
(ns)
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
effic
acy
TSE
(Adv
. Com
pute
r Use
)→ B
asic
Ski
lls:
r = -.
03 (n
s); r
= .2
8 TS
E (C
ompu
ter U
se)→
Adv
ance
d Sk
ills:
r =
.05
(ns);
r =
.22
TSE
(Adv
. Com
pute
r Use
)→ A
dvan
ced
Skill
s:
r = .1
0 (n
s); r
= .2
4 TS
E (C
ompu
ter U
se)→
Self
-eff
icacy
: r =
.15;
r =
.13
TSE
(Adv
. Com
pute
r Use
)→ S
elf-e
ffica
cy: r
= .2
0; .2
8
Thoo
nen
et a
l. (2
011)
H
ollan
d SM
Ta
sk
Mot
iatio
n E
lemen
tary
scho
ol
(G4–
G6)
62
1 te
ache
rs
3,46
2 st
uden
ts
SSE
SE
M
TSE→
Well
-bein
g (c
lass,
scho
ol): β
= .0
2; β
= .0
3 (n
s) TS
E→
Aca
dem
ic E
ffica
cy: β
= -.
01(n
s) TS
E→
Int
rinsic
Mot
ivat
ion:
β =
.00
(ns)
TSE→
Mas
tery
Goa
ls: β
= -.
01 (n
s) TS
E→
Per
form
ance
Avo
idan
ce: β
= -.
01 (n
s) TS
E→
Sch
ool I
nves
tmen
t: β
= -.
01 (n
s)
Thro
ndse
n &
Tu
rmo
(201
3)
Nor
way
A
A
Mat
h E
lemen
tary
scho
ol
(G2–
G3)
52
1 te
ache
rs
9,98
0 st
uden
ts
PALS
(ada
pted
) Co
rrela
tions
TS
E→
Mat
h A
chiev
emen
t (to
tal):
r =
.11;
TS
E→
Mat
h A
chiev
emen
t (G
rade
2):
r = .1
5;
TSE→
Mat
h A
chiev
emen
t (G
rade
3):
r = .0
7 (n
s).
Tour
naki
& P
odell
(2
005)
U
SA
AA
Li
tera
cy
Elem
enta
ry a
nd
mid
dle
scho
ol
384
teac
hers
TE
S A
NO
VA
N
on-si
gnifi
cant
resu
lts w
ere n
ot rep
orted
.
Van
Ude
n et
al.
(201
3)
Hol
land
SM
Eng
age-
men
t Se
cond
ary
scho
ol
((pre
)voc
atio
nal
educ
atio
n)
195
teac
hers
TS
EQ
(ada
pted
) Re
gres
sion
SEM
Re
gressi
on re
sults
: TS
E→
Beh
avio
ral E
ngag
emen
t: β
= .1
3 (n
s) TS
E→
Em
otio
nal E
ngag
emen
t: β
= -.
15
SEM
resu
lts:
TSE→
Influ
ence→
Beh
. Eng
agem
ent: β
= .1
1 TS
E→
Influ
ence→
Em
. Eng
agem
ent: β
= .1
1
Woo
lfolk
Hoy
et
al. (2
008)
U
SA
AA
O
vera
ll E
lemen
tary
scho
ol
(G3–
G4)
18
7 te
ache
rs
TSE
S (s
hort)
Co
rrela
tions
A
cade
mic
Opt
imism
→ A
chiev
emen
t: r =
.24
Note
. Bio
logy
= b
iolo
gy p
erfo
rman
ce; C
omp.
lite
racy
= c
ompu
ter l
itera
cy; H
istor
y =
hist
ory
perf
orm
ance
; Lite
racy
= li
tera
cy p
erfo
rman
ce; M
ath
= m
ath
perf
orm
ance
; Ove
rall
= s
tude
nts’
over
all p
erfo
rman
ce; S
cienc
e =
sc
ience
per
form
ance
; SM
= st
uden
ts’ m
otiv
atio
n (e
.g.,
enga
gem
ent,
on-ta
sk b
ehav
ior,
goal
orien
tatio
ns, s
elf-e
ffica
cy);
TSE
-IS
= T
SE fo
r ins
truct
iona
l stra
tegi
es; T
SE-C
M =
TSE
for c
lassr
oom
man
agem
ent;
TSE
-SE
= T
SE
for s
tude
nt e
ngag
emen
t. In
strum
ents:
AE
S =
Ada
ptiv
e E
ffica
cy S
cale;
JES
= Jo
b E
ffica
cy S
cale;
MSQ
= M
otiv
atin
g St
rate
gies
Que
stion
naire
; PA
LS =
Pat
tern
s of A
dapt
ive
Lear
ning
Sur
vey;
SSE
= S
ense
of S
elf-E
ffica
cy;
STE
BI =
Scie
nce
Teac
hing
Effi
cacy
Beli
ef In
stru
men
t; TE
S =
Tea
cher
Effi
cacy
Sca
le; T
SEQ
= T
each
er S
elf-E
ffica
cy Q
uest
ionn
aire;
TSE
S =
Tea
cher
Sen
se o
f Self
-Effi
cacy
Sca
le.
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
APPE
NDI
X 3
Teac
hers’
Psyc
holog
ical W
ell-B
eing
Aut
hor(s
) Co
untry
Th
eme
Gra
de
N
TSE
mea
sure
A
nalys
is O
utco
mes
A
vanz
i et a
l.(20
13)
Italy
(N
orw
ay)
Burn
out
Satis
f E
lemen
tary
, mid
dle
and
high
scho
ol
(G1–
G10
)
348
Itali
an
teac
hers
; 558
N
orw
egian
te
ache
rs
NTS
ES
SEM
TS
E→
Job
Satis
fact
ion:
r =
.36
TSE→
Bur
nout
(wor
k-re
lated
): r
= -.
26
TSE→
Bur
nout
(stu
dent
-relat
ed):
r =
-.32
Baro
uch
Gilb
ert e
t al.
(201
3)
Dom
i- ni
can
Repu
blic
Stre
ss
Sats
if Co
mm
it A
ttriti
on
Pres
choo
l–hi
gh
scho
ol
109
teac
hers
TS
ES
(sho
rt)
Corr
elatio
ns
Eng
lish
and S
pani
sh-m
ediu
m con
tent t
each
ers:
TSE→
Job
Satis
fact
ion:
r =
.29;
r =
.00
(ns)
TSE→
Job
Stre
ss: r
= -.
40; r
= -.
06
TSE→
Com
mitm
ent:
r = .3
6; r
= .3
2 TS
E→
Inte
ntio
n to
qui
t: r
= -.
18 (n
s); r
= -2
8 (n
s)
Blac
kbur
n &
Rob
inso
n (2
008)
U
SA
Satis
f A
gricu
lture
ed
ucat
ion
80 a
gricu
lture
te
ache
rs
TSE
S (lo
ng)
Corr
elatio
ns
Low,
mod
erate,
and h
igh te
ache
r exp
erien
ce:
TSE
-SE→
Job
Satis
fact
ion:
r =
.54;
r =
.56;
r =
.12
TSE
-IS→
Job
Satis
fact
ion:
r =
-.12
; r =
.84;
r =
.10
TSE
-CM→
Job
Satis
fact
ion:
r =
.57;
r =
.68;
r =
-.52
Bogl
er &
Som
ech
(200
4)
Isra
el
Com
mit
Mid
dle
scho
ol
(G7–
G9)
98
3 te
ache
rs
SPE
S Re
gres
sion
TSE→
Org
aniz
atio
nal C
omm
itmen
t: β
= .1
5 (n
s) TS
E→
Pro
fess
iona
l Com
mitm
ent: β
= .2
9
Brio
nes e
t al.
(201
0)
Spain
Bu
rnou
t Sa
tisf
Seco
ndar
y sc
hool
68
teac
hers
TI
SES
SEM
TS
E→
Em
otio
nal E
xhau
stio
n: β
= -.
20
TSE→
Per
sona
l Ach
ieve
men
t: β
= .4
0 TS
E→
Ach
ievem
ent→
Job
Satis
fact
ion:
β =
.16
TSE→
Sup
port→
Job
Satis
fact
ion:
β =
.08
Briss
ie et
al.
(198
8)
USA
Bu
rnou
t E
lemen
tary
scho
ol
1,21
3 te
ache
rs
TOQ
Re
gres
sion
TSE→
Bur
nout
: β =
-.17
Brou
wer
s & T
omic
(200
0)
Net
her-
lands
Bu
rnou
t Se
cond
ary
scho
ol
558
teac
hers
SC
MD
Lo
ngitu
dina
l SE
M
Onl
y fit
indi
ces a
re g
iven
.
Brou
wer
s et a
l. (2
001)
N
ethe
r-lan
ds
Burn
out
Seco
ndar
y Sc
hool
27
7 te
ache
rs
TISE
S SE
M
Feed
back
loop
: TS
E→
Em
otio
nal E
xhau
stio
n: β
= -.
45 →
D
eper
sona
lizat
ion:
β =
.60 →
Per
sona
l Acc
ompl
ishm
ent:
β =
-.41→
TSE
: β =
.18
Brud
nik
(200
9)
Polan
d Bu
rnou
t Se
cond
ary
Scho
ol
404
teac
hers
G
TSE
S Co
rrela
tions
Co
rrelat
ions a
cross
subje
cts ta
ught
: TS
E→
Em
otio
nal E
xhau
stio
n: r
= -.
23 –
-.63
.
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
TSE→
Neg
. Per
sona
l Acc
ompl
ishm
ent:
r = -.
28 –
-.66
TS
E→
Dep
erso
naliz
atio
n: r
= -.
18 –
-.53
Brui
nsm
a &
Jans
en
(201
0)
Net
her-
lands
Re
tent
ion
Pres
ervi
ce c
onte
xt
198
teac
hers
TS
ES
(ada
pted
) SE
M
TSE→
Inte
ntio
n to
Sta
y: β
= .1
7
Canr
inus
et a
l. (2
012)
N
ethe
r-lan
ds
Satis
f Co
mm
it Re
tent
ion
Seco
ndar
y sc
hool
1,
214
teac
hers
CS
C SE
M
Clas
sroo
m T
SE→
Affe
ctiv
e Co
mm
itmen
t: β
= .1
4 (d
irect)
; β
= .1
2 (in
direct
) Cl
assr
oom
TSE→
Sala
ry S
atisf
actio
n: β
= -.
20 (d
irect)
; β =
.0
7 (in
direct
) Cl
assr
oom
TSE→
Rela
tions
hip
Satis
fact
ion:
β =
.18
Clas
sroo
m T
SE→
Cha
nge
in M
otiv
atio
n: β
= .2
0 (d
irect)
; β
= .1
3 (in
direct
) Cl
assr
oom
TSE→
Res
p. to
Rem
ain: β
= .0
2 (in
direct
) Cl
assr
oom
TSE→
Job
Satis
fact
ion:
β =
.74
Capr
ara
et a
l. (2
003)
It
aly
Com
mit
Satis
f Ju
nior
hig
h sc
hool
72
6 te
ache
rs
New
ly de
velo
ped
scale
SE
M
TSE→
Job
Satis
fact
ion:
β =
.28
TSE→
Org
aniz
atio
nal C
omm
itmen
t: β
= .2
1 (d
irect)
; β =
.0
5 (in
direct
thro
ugh
collec
tive e
ffica
cy)
Capr
ara
et a
l. (2
006)
It
aly
Satis
f Ju
nior
Hig
h Sc
hool
75
teac
hers
N
ewly
deve
lope
d sc
ale
SEM
TS
E→
Job
Satis
fact
ion:
β =
.74
Chan
(200
8)
Chin
a Co
ping
Pr
eser
vive
and
in
serr
vice
con
text
27
3 te
ache
rs
GTS
ES
Regr
essio
n TS
E→
Act
ive
Copi
ng: β
= .0
5 (n
s) TS
E→
Pas
sive
Copi
ng: β
= -.
11 (n
s)
Chan
et a
l. (2
008)
Si
ngap
ore
Com
mit
Prim
ary
and
seco
ndar
y sc
hool
2,
130
prim
ary
1,58
7 se
cond
ary
TSE
S SE
M
TSE→
Com
mitm
ent (
prim
ary
scho
ol): β
= .2
6 TS
E→
Com
mitm
ent (
seco
ndar
y sc
hool
): β
= .2
2
Colad
arci
(199
2)
USA
Co
mm
it E
lemen
tary
and
m
iddl
e sc
hool
(K
–8)
170
teac
hers
TE
S Re
gres
sion
TSE→
Com
mitm
ent: β
= .1
9
Colli
e et
al.
(201
2)
Cana
da
Satis
f Pr
imar
y an
d se
cond
ary
scho
ol
664
teac
hers
TS
ES
(sho
rt)
SEM
TS
E→
Job
Satis
fact
ion:
β =
.33
Dom
énec
h-Be
tore
t (2
006)
Sp
ain
Stre
ss
Seco
ndar
y sc
hool
24
7 te
ache
rs
New
ly de
velo
ped
scale
M
AN
OV
A
HLM
H
igh
vs. L
ow T
SE→
cop
ing:
M =
74.
57 (
SD =
12.
77);
M
= 7
0.35
(SD
= 1
4.28
)
Dom
énec
h-Be
tore
t (2
009)
Sp
ain
Burn
out
Prim
ary
and
seco
ndar
y sc
hool
72
4 te
ache
rs
Teac
her p
erce
ived
te
achi
ng se
lf-ef
ficac
y sc
ale
SEM
Pr
imar
y sch
ool:
TS
E→
Stre
ssor
s→ E
mot
iona
l Exh
aust
ion:
β =
-.24
TS
E→
Stre
ssor
s→ D
eper
sona
lizat
ion:
β =
-.10
TS
E→
Stre
ssor
s→ R
educ
ed A
ccom
plish
men
t: ns
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
Secon
dary
schoo
l: TS
E→
Stre
ssor
s→ B
urno
ut: β
= -.
21
Duf
fy &
Len
t (20
09)
USA
Sa
tisf
Elem
enta
ry, m
iddl
e an
d hi
gh sc
hool
36
6 te
ache
rs
TSE
S (s
hort)
; PE
BS;
new
ly de
velo
ped
scale
SE
M
TSE→
Sat
isfac
tion:
β =
.17
TSE→
Goa
l Pro
gres
s: β
= .3
7 TS
E→
Wor
k Co
nditi
ons →
Sat
isfac
tion:
β =
.09
Ebm
eier (
2003
) U
SA
Com
mit
Elem
enta
ry, m
iddl
e, an
d hi
gh sc
hool
55
4 te
ache
rs
TES
SEM
TS
E→
Com
mitm
ent: β
= .3
8
Egy
ed &
Sho
rt (2
006)
U
SA
Burn
out
Elem
enta
ry sc
hool
10
6 te
ache
rs
TES
Corr
elatio
ns
TSE→
Bur
nout
(tot
al): r
= -.
23
TSE→
Em
otio
nal E
xhau
stio
n: r
= -.
12 (n
s) TS
E→
Dep
erso
naliz
atio
n: r
= -.
26
TSE→
Per
sona
l Acc
ompl
ishm
ent:
r = .2
8 E
vans
& T
ribbl
e (1
986)
U
SA
Com
mit
Pres
ervi
ce c
onte
xt
179
teac
hers
TE
S Co
rrela
tions
TS
E→
Ove
rall
Prob
lem S
core
s: r =
.07
(ns)
TSE→
Com
mitm
ent:
r = .2
3
Eve
rs e
t al.
(200
2)
Net
her-
lands
Bu
rnou
t Se
cond
ary
scho
ol
490
teac
hers
N
ewly
deve
lope
d m
easu
re
HLM
TS
E T
owar
d G
uidi
ng G
roup
s→ E
E, D
P, P
A: β
= -.
03
(ns);
-.16
; .32
, res
pect
ively
. TS
E T
owar
d U
sing
Task
s→ E
E, D
P, P
A: β
= .0
8 (n
s); -
.02
(ns);
.13,
resp
ectiv
ely.
TSE
Tow
ard
Usin
g In
nova
tions→
EE
, DP,
PA
: β =
-.60
; -.3
4; .3
3, re
spec
tively
.
Eve
rs e
t al.
(200
5)
Net
her-
lands
Bu
rnou
t Se
cond
ary
scho
ol
545
teac
hers
G
SES
(Dut
ch)
HLM
TS
E→
Em
otio
nal E
xhau
stio
n: β
= -.
27
TSE→
Dep
erso
naliz
atio
n: β
= -.
30
TSE→
Per
sona
l Acc
ompl
ishm
ent: β
= .4
4
Fern
et e
t al.
(201
2)
Cana
da
Burn
out
Elem
enta
ry, m
iddl
e an
d hi
gh sc
hool
(G
1–G
11)
806
teac
hers
CS
C (lo
ngitu
dina
l) SE
M
ΔTSE→
ΔE
mot
iona
l Exh
aust
ion:
β =
-.37
ΔT
SE→
ΔD
eper
sona
lizat
ion:
β =
-.38
ΔT
SE→
ΔPe
rson
al A
ccom
plish
men
t: β
= .6
3
Five
s et a
l. (2
007)
U
SA
Burn
out
Pres
ervi
ce c
onte
xt
49 te
ache
rs
TSE
S
Corr
elatio
ns
Resu
lts ac
ross
two t
ime p
oints:
TS
E-S
E→
EE
, DP,
PA
.: r =
-.13
(ns);
-.20
(ns);
.38
(tim
e 1)
and
r =
-.59
; -.5
9; .3
4 (ti
me
2), r
espe
ctiv
ely.
TSE
-IS→
EE
, DP,
PA
.: r =
-.21
(ns);
-.30
; .38
(tim
e 1)
an
d r =
-.54
; -.5
4; .3
4 (ti
me
2), r
espe
ctiv
ely.
TSE
-CM→
EE
, DP,
PA
.: r =
-.19
(ns);
-.32
; .36
(tim
e 1)
an
d r =
-.36
; -.3
3; .2
4 (n
s) (ti
me
2), r
espe
ctiv
ely.
Fried
man
(200
3)
Isra
el
Burn
out
Elem
enta
ry sc
hool
32
2 te
ache
rs
New
ly de
velo
ped
scale
Re
gres
sion
TSE
(Inf
luen
ce, C
onsid
erat
ion)→
Bur
nout
(tot
al): β
= -
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
.20;
β =
-.25
; TS
E (I
nflu
ence
, Con
sider
atio
n)→
Em
otio
nal
Exh
aust
ion:
β =
-.20
; β =
-14
; TS
E (I
nflu
ence
, Con
sider
atio
n)→
Una
ccom
plish
men
t: β
= -.
20; β
= -
.15;
TS
E (C
onsid
erat
ion)→
Dep
erso
naliz
atio
n: β
= -.
36
Helm
s-Lo
renz
et a
l. (2
012)
N
ethe
r-lan
ds
Stre
ss
Diss
atisf
Se
cond
ary
scho
ol
30 b
egin
ning
te
ache
rs
CSC
Corr
elatio
ns
Clas
sroo
m T
SE→
Ten
sion:
β =
-.28
(ns)
Clas
sroo
m T
SE→
Job
Disc
onte
nt: β
= -2
3 (n
s) Sc
hool
TSE→
Ten
sion:
β =
-.23
(ns)
Scho
ol T
SE→
Job
Disc
onte
nt: β
= -.
56
(indir
ect ef
fects
are n
ot dis
played
)
Høi
gaar
d et
al.
(201
2)
Nor
way
Bu
rnou
t Sa
tisf
Attr
ition
Unk
now
n 19
2 be
ginn
ing
teac
hers
PT
E
Regr
essio
n TS
E→
Job
Satis
fact
ion:
β =
-.04
(ns)
TSE→
Wor
k Bu
rnou
t: β
= -.
04 (n
s) TS
E→
Inte
ntio
n to
Qui
t: β
= -.
02 (n
s)
Hug
hes (
2012
) U
SA
Rete
ntio
n E
lemen
tary
, mid
dle,
and
high
scho
ol
(K–1
2)
782
teac
hers
N
ewly
deve
lope
d sc
ale
Logi
stic
Regr
essio
n TS
E-C
M→
Ret
entio
n: β
= -.
16 (n
s) TS
E-I
S→ R
eten
tion:
β =
.28
(ns)
TSE
-Stu
dent
Mot
ivat
ion
Ret
entio
n: β
= -.
20 (n
s) TS
E-T
echn
olog
y→ R
eten
tion:
β =
-.32
Hul
tell
et a
l. (2
013)
Sw
eden
Bu
rnou
t Pr
eser
vice
con
text
81
6 te
ache
rs
TSE
S (s
hort)
Cl
uste
r an
alysis
χ2
= 2
0.15
, p =
.003
Iman
ts &
Van
Zoe
len
(199
5)
Net
her-
lands
A
ttriti
on
Elem
enta
ry sc
hool
66
teac
hers
TP
SES
MA
NO
VA
N
o resu
lts ar
e give
n.
Klas
sen
& C
hui (
2010
) Ca
nada
Sa
tisf
Pres
ervi
ce c
onte
xt,
elem
enta
ry, m
iddl
e, an
d hi
gh sc
hool
1,43
0 te
ache
rs
TSE
S (s
hort)
SE
M
TSE
-IS→
Job
Satis
fact
ion:
β =
.29
TSE
-CM→
Job
Satis
fact
ion:
β =
.26
TSE
-SE→
Job
Satis
fact
ion:
ns
Klas
sen
& C
hiu
(201
1)
Cana
da
Qui
t Co
mm
it
Pres
ervi
ce c
onte
xt,
elem
enta
ry, m
iddl
e an
d hi
gh sc
hool
379
pres
ervi
ce
teac
hers
43
4 in
serv
ice
teac
hers
TSE
S (s
hort)
SE
M
Prac
tising
teac
hers:
TS
E-I
S→ C
omm
itmen
t: β
= .2
6 TS
E-I
S→ C
omm
itmen
t→ In
tent
ion
to Q
uit: β
= -.
19
Pres-
servic
e tea
chers
: TS
E-C
M→
Com
mitm
ent: β
= .3
2 TS
E-C
M→
Com
mitm
ent→
Inte
ntio
n to
Qui
t: β
= -.
58
Klas
sen
et a
l. (2
009)
Ca
nada
, Cy
prus
, Sa
tisf
Elem
enta
ry, m
iddl
e, an
d se
cond
ary
1,21
2 te
ache
rs
TSE
S (s
hort)
Co
rrela
tions
Re
sults
acro
ss fiv
e cou
ntrie
s: TS
E (o
vera
ll) →
Job
Satis
fact
ion:
β =
.33
– .4
8
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
Kor
ea,
Sing
apor
e, an
d U
SA
scho
ol
TSE
-IS→
Job
Satis
fact
ion:
β =
.17
– .4
5 TS
E-C
M →
Job
Satis
fact
ion:
β =
.19
– .4
4 TS
E-S
E →
Job
Satis
fact
ion:
β =
.34
– .4
4
Klas
sen
et a
l. (2
013)
Ca
nada
, H
ong
Kon
g,
UK
, and
Th
ailan
d
Com
mit
Pres
ervi
ce c
onte
xt
1,18
7 te
ache
rs
TSE
S (s
hort)
Bo
otst
rapp
ing
analy
sis
(regr
essio
n)
TSE→
Com
mitm
ent (
acro
ss 4
cou
ntrie
s): B
= .1
0 –
.32
Stud
ent B
ehav
ior S
tress→
TSE→
Com
mitm
ent (
acro
ss 4
co
untri
es):
B =
-.03
– -.
13
Wor
k St
ress→
TSE→
Com
mitm
ent (
acro
ss 4
cou
ntrie
s):
B =
-.04
(ns)–
.05
Lent
et a
l. (2
011)
It
aly
Satis
f M
iddl
e an
d hi
gh
scho
ol
235
teac
hers
TS
ES
(sho
rt)
SEM
TS
E→
Goa
l Pro
gres
s: β
= .3
8 TS
E→
Job
Satis
fact
ion:
β =
.09
(ns)
TSE→
Wor
k co
nditi
ons: β
= .1
6 TS
E→
Wor
k co
nditi
ons→
Job
Sat
isfac
tion:
β =
.06
TSE→
Goa
l Pro
gres
s→ L
ife S
atisf
actio
n: β
= .0
5
Malo
w-I
roff
et a
l. (2
007)
U
SA
Rete
ntio
n E
lemen
tary
scho
ol
(K–6
) 68
teac
hers
TE
S Re
gres
sion
TSE→
Dec
ision
to S
tay: β
= .0
6 (n
s)
Mar
tin e
t al.
(201
2)
USA
Bu
rnou
t
Elem
enta
ry, m
iddl
e an
d hi
gh sc
hool
63
1 te
ache
rs
TSE
S SE
M
TSE
-SE→
Inst
r. M
an.→
Stu
dent
Stre
ssor
s: β
= -.
43
TSE
-EN→
Inst
r. M
an.→
Per
s. A
ccom
p.: β
= -.
63
Moè
et a
l. (2
010)
It
aly
Satis
f E
lemen
tary
, mid
dle
and
high
scho
ol
399
teac
hers
TS
ES
SE
M
TSE→
Job
Satis
fact
ion:
β =
.32
Robe
rtson
&
Dun
smui
r (20
13)
UK
St
ress
Se
cond
ary
scho
ol
58 te
ache
rs
TSE
S (s
hort)
H
LM
TSE→
Tea
cher
Stre
ss: β
= -.
31
Rots
et a
l. (2
007)
Be
lgiu
m
Com
mit
Seco
ndar
y sc
hool
20
9 te
ache
rs
TSE
S (s
hort)
SE
M
TSE→
Tea
chin
g Co
mm
itmen
t: β
= .2
9
Salan
ova
et a
l. (2
011)
Sp
ain
Satis
f Se
cond
ary
scho
ol
483
teac
hers
G
TSE
S Lo
ngitu
dina
l SE
M
Resu
lts fo
r two
time
poin
ts:
TSE→
Ent
hous
iasm
: β =
.50;
β =
.34
TSE→
Sat
isfac
tion:
β =
.38;
β =
.36
TSE→
Com
fort:
β =
.41;
β =
.27
TSE→
Eng
agem
ent: β
= .2
2; β
= .2
3
Sass
et a
l. (2
011)
U
SA
Stre
ss
Diss
atisf
E
lemen
tary
, mid
dle,
and
high
scho
ol
479
teac
hers
TS
ES
SEM
TS
E-E
N→
Stu
dent
stre
ssor
s: β
= -.
50 .
TSE
-EN→
Stu
dent
stre
ssor
s→ Jo
b D
issat
isf.: β
= -.
04
Schw
arze
r & H
allum
(2
008)
Sy
ria a
nd
Ger
man
y Bu
rnou
t U
nkno
wn
1,20
3 te
ache
rs
458
teac
hers
G
TSE
S Co
rrela
tions
Re
sults
for S
yrian
and G
erman
teac
hers:
TS
E→
Em
otio
nal E
xhau
stio
n: r
= -.
17; r
= -.
48
TSE→
Dep
erso
naliz
atio
n: r
= -.
24; r
= -.
56
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
TSE→
Red
uced
Acc
ompl
ishm
ent:
r = -.
66; r
= -.
75
Schw
arze
r et a
l. (2
000)
H
ong
Kon
g,
Ger
man
y
Burn
out
Unk
now
n 54
2 te
ache
rs
GTS
ES
Corr
elatio
ns
Resu
lts fo
r Chin
ese an
d Germ
an su
bsam
ples:
TSE→
Em
otio
nal E
xhau
stio
n: r
= -.
36;
r = -.
50
TSE→
Dep
erso
naliz
atio
n: r
= -.
26; r
= -.
38
TSE→
Per
sona
l Acc
ompl
ishm
ent
r = .3
1; r
= .5
8
Shym
an (2
010)
U
SA
Burn
out
Unk
now
n
100
para
-ed
ucat
ors
TSE
S (s
hort)
H
LM
TSE→
Em
otio
nal E
xhau
stio
n: β
= .0
1
Skaa
lvik
& S
kaalv
ik
(200
7)
Nor
way
Bu
rnou
t E
lemen
tary
–Hig
h sc
hool
(G1–
G10
) 24
4 te
ache
rs
NTS
ES
SE
M
TSE→
Tea
cher
Bur
nout
: β =
-.76
Skaa
lvik
& S
kaalv
ik
(201
0)
Nor
way
Bu
rnou
t Sa
tisf
Elem
enta
ry–H
igh
scho
ol (G
1–G
10)
2,24
9 te
ache
rs
NTS
ES
SE
M
TSE→
Em
otio
nal E
xhau
stio
n: r
= -.
29
TSE→
Dep
erso
naliz
atio
n: r
= -.
41
TSE→
Job
Satis
fact
ion:
β =
.17
So-k
um T
ang
et a
l. (2
001)
H
ong
Kon
g Bu
rnou
t E
lemen
tary
, mid
dle,
and
high
scho
ol
269
teac
hers
G
TSE
S SE
M
TSE→
Bur
nout
(tot
al) β
= -.
53
TSE→
Neg
ativ
e M
enta
l hea
lth: β
= -.
19
TSE→
Bur
nout→
Neg
ativ
e M
enta
l hea
lth: β
= -.
24
Step
hano
u et
al.
(201
3)
Gre
ece
Sats
if E
lemen
tary
scho
ol
268
teac
hers
N
ewly
deve
lope
d sc
ale
HLM
TS
E→
Job
Satis
fact
ion:
r =
.77
Tsig
ilis e
t al.
(201
0)
Gre
ece
Satis
f Se
cond
ary
scho
ol
405
teac
hers
TS
ES
Co
rrela
tions
TS
E-I
S→ Jo
b Sa
tisfa
ctio
n: r
= .3
0 TS
E-C
M→
Job
Satis
fact
ion:
r =
.39
TSE
-EN→
Job
Satis
fact
ion:
r =
.40
Tsou
lopa
s et a
l. (2
010)
U
SA
Burn
out
Qui
t E
lemen
tary
, mid
dle
and
high
scho
ol
610
teac
hers
PS
ECM
SE
M
TSE→
Em
otio
nal E
xhau
stio
n: β
= -.
09
TSE→
Em
otio
nal E
xhau
stio
n→ A
ttriti
on: β
= -.
05
TSE→
Em
otio
nal E
xhau
stio
n→ M
igra
tion:
β =
-.05
Viel
-Rum
a et
al.
(201
0)
USA
Sa
tisf
Elem
enta
ry, m
iddl
e an
d hi
gh sc
hool
70
Spe
cial
educ
ator
s TE
S Co
rrela
tions
TS
E→
Job
Satis
fact
ion:
r =
.29
W
are
& K
itsan
tas
(200
7, 2
011)
U
SA
Com
mit
Publ
ic sc
hool
26
,257
teac
hers
SA
SS
Regr
essio
n H
LM
TSE
to E
nlist
Adm
in. D
irect
ion→
Com
mitm
ent: β
= .2
9 TS
E-C
M→
Com
mitm
ent: β
= -.
14
Note
. Bur
nout
= te
ache
r bur
nout
; Com
mit
= te
ache
r com
mitm
ent;
Qui
t = te
ache
r ret
entio
n an
d at
tritio
n; S
atisf
= te
ache
r job
satis
fact
ion;
Stre
ss =
teac
her s
tress
and
cop
ing;
TSE
-IS
= T
SE fo
r ins
truct
iona
l stra
tegi
es; T
SE-
CM =
TSE
for c
lassr
oom
man
agem
ent;
TSE
-SE
= T
SE fo
r stu
dent
eng
agem
ent.
Instr
umen
ts: C
SC =
Clas
sroo
m a
nd S
choo
l Con
text
Tea
cher
Self
-Eff
icacy
Sca
le; G
SES
= G
ener
al Se
lf-Ef
ficac
y Sc
ale; G
TSES
= G
ener
al Te
ache
r Se
lf-Ef
ficac
y Sc
ale; (
N)T
SES
= N
orw
egian
Tea
cher
Self
-Eff
icacy
Sca
le;
PEBS
= P
erso
nal E
ffica
cy B
elief
s Sc
ale; P
SECM
= P
erce
ived
Self
-Eff
icacy
in C
lassr
oom
Man
agem
ent
ques
tionn
aire;
PTE
= P
erso
nal T
each
er E
ffica
cy S
cale;
SA
SS =
SA
SS T
each
er; S
CMD
= S
elf-e
ffica
cy s
cale
for
Clas
sroo
m M
anag
emen
t and
Disc
iplin
e; SP
ES
= S
choo
l Par
ticip
ant E
mpo
wer
men
t Sca
le; T
ES
= T
each
er E
ffica
cy S
cale;
TSE
Q =
Tea
cher
Self
-Effi
cacy
Que
stio
nnair
e; TS
ES
= T
each
er S
ense
of S
elf-E
ffica
cy S
cale.
TIS
ES
= T
each
er In
terp
erso
nal S
elf-E
ffica
cy S
cale;
TO
Q =
Tea
cher
Opi
nion
Que
stio
nnair
e; TP
SES
= T
he T
each
ers’
and
Prin
cipals
’ Sen
se o
f Eff
icacy
Sca
le.
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
79
CHAPTER 3 INTER- AND INTRA-INDIVIDUAL DIFFERENCES IN TEACHERS’ SELF-EFFICACY:
A MULTILEVEL FACTOR EXPLORATION
_________________________________________________________________________
This study explored inter- and intra-individual differences in teachers’ self-efficacy (TSE) by
adapting Tschannen-Moran and Woolfolk Hoy’s (2001) Teachers’ Sense of Efficacy Scale
(TSES) to the domain- and student-specific level. Multilevel structural equation modeling was
used to evaluate the factor structure underlying this adapted instrument, and to test for
violations of measurement invariance over clusters. Results from 841 third- to sixth-grade
students and their 107 teachers supported the existence of one higher-order factor (Overall
TSE) and four lower-order factors (Instructional Strategies, Behavior Management, Student
Engagement, and Emotional Support) at both the between- and within-teacher level. In this
factor model, intra-individual differences in TSE were generally larger than inter-individual
differences. Additionally, the presence of cluster bias in 18 of 24 items suggested that the
unique domains of student-specific TSE at the between-teacher level cannot merely be
perceived as the within-teacher level factors’ aggregates. These findings underscore the
importance of further investigating TSE in relation to teacher, student, and classroom
characteristics.
_________________________________________________________________________ Zee, M., Koomen, H. M. Y., Jellesma, F. C., Geerlings, J., & de Jong, P. F. (2016). Inter- and intra-individual differences in teachers’ self-efficacy: A multilevel factor exploration. Journal of School Psychology, 55, 39–56. doi:10.1016/j.jsp.2015.12.003
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 3
80
INTRODUCTION
The last decades have witnessed the growth of teacher self-efficacy (TSE) studies from a small
side-branch of school effectiveness research to a major area of educational psychology
(Klassen, Tze, Betts, & Gordon, 2011). One of the triggers for this progress is the belief that
generalized TSE, or the self-confidence with which teachers approach and bring about their
daily teaching tasks, is a central determinant of teachers’ behaviors and actions in the
classroom (Bandura, 1997; Tschannen-Moran & Woolfolk Hoy, 2001). Both theoretical and
empirical sources have surfaced the tacit notion that teachers high in self-efficacy are more
likely than poorly efficacious educators to set high goals for themselves, to activate adequate
effort to perform specific teaching tasks, and to persist when the goings get tough (e.g.,
Bandura, 1997, 2000; Gibson & Dembo, 1984; Tschannen-Moran & Woolfolk Hoy, 2001).
Moreover, there is evidence to suggest that teachers with a resilient sense of self-efficacy are
generally effective in providing the instructional and affective supports that match their
students’ needs and lead to positive learning outcomes (e.g., Guo, McDonald Connor, Yang,
Roehring, & Morrison, 2012; Justice, Mashburn, Hamre, & Pianta, 2008; Leroy, Bressoux,
Sarrazin, & Trouilloud, 2007).
To date, empirical research has predominantly concentrated on measuring between-teacher
differences in TSE and its outcomes (cf. Ross, 1994). As such, most studies have implicitly
assumed TSE to be a relatively stable, almost trait-like teacher characteristic which, at best, may
fluctuate across various teaching tasks and domains (Raudenbusch, Rowan, & Cheong, 1992;
Tschannen-Moran & Woolfolk Hoy, 2001). Apart from its static aspects, however, TSE has
also been perceived as an inherently mutable state within teachers, which largely depends on
challenges presented by different types of students in class (Raudenbusch et al., 1992; Ross,
Cousins, & Gadalla, 1996; Tschannen-Moran, Woolfolk Hoy, & Hoy, 1998). Unfortunately,
though, the examination of intra-individual variability in teachers’ self-efficacy has largely gone
unheeded by educational research, as its measurement and analysis have generally been
presumed to be relatively complex. In the present study, therefore, we aimed to advance
understanding of the multifaceted nature of teachers’ sense of self-efficacy by exploring this
construct across various domains of teaching and learning and particular students.
Distinguishing inter- and intra-individual differences in TSE may be important for determining
how these capability beliefs are shaped and what their effects are on individual students’
academic adjustment in the classroom.
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
MEASUREMENT OF TEACHER SELF-EFFICACY
81
A SOCIAL-COGNITIVE PERSPECTIVE ON TEACHER SELF-EFFICACY
Empirical research on TSE has predominantly been grounded in Bandura’s (1977, 1986, 1997)
social-cognitive framework. Central to this framework is the idea that people are not merely
nudged by the whims of their environment or biological makeup, but rather operate within a
system of triadic reciprocal causation. This complex system indicates that environmental
constraints or resources are likely to operate through such important personal cognitions as
self-efficacy, which organize and produce actions for given purposes (Bandura, 1997, 2006;
Pajares, 1997). According to Bandura (1997), these capability beliefs provide the power to act
differently from what specific contextual forces dictate, by activating and sustaining the skills,
motivation, and effort required for desired achievements to be realized. Educational
researchers have, for instance, highlighted the importance of TSE for teachers’ ability to
manage and motivate difficult students, and their level of effort and persistence in getting these
students to study (e.g., Almog & Shechtman, 2007; Bandura, 1997; Lambert, McCarthy,
O'Donnell, & Wang, 2009; Tschannen-Moran & Woolfolk Hoy, 2001). Accordingly, teachers’
self-efficacy has generally been considered a vital predictor of behavior and action in the
domain of teaching and learning.
The basic tenets of the social-cognitive paradigm have offered some useful insights into how
self-efficacy could be best approached. Among those guiding principles is the recognition of
the “person-in-context” in capturing the construct of self-efficacy. For the domain of teaching
and learning, this emphasis on environment implies that the degree of specificity of teaching
tasks and domains has to be adequately identified (Bandura, 1997, 2006; Tschannen-Moran &
Woolfolk Hoy, 2001). Moreover, it underscores the importance of considering environmental
obstacles that embody gradations of challenge to which teachers can adjudge their sense of
self-efficacy.
DEGREE OF DOMAIN SPECIFICITY OF TSE
Teachers’ sense of self-efficacy has been generally conceptualized at various levels of
specificity. As such, this construct can be perceived to reside along a continuum from domain
generality at one end to increasingly advanced specificity levels at the other (Lent & Brown,
2006). At the most universal level, TSE has been regarded as a single-level, trait-like construct,
reflecting generalized capability beliefs that fluctuate between teachers (e.g., Schwarzer, 1992;
Schwarzer & Jerusalem, 1995). Investigators taking such a theoretical stance habitually
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 3
82
decontextualize TSE from a wider scope of tasks and domains in the classroom, resulting in
one-dimensional, all-purpose measures that are widely applicable to a range of outcomes (e.g.,
Bandura, 1997; Pajares, 1996). Moreover, they commonly treat within-teacher variations in
TSE as error variance, as these variations only represent deviations from teachers’ baseline
level of self-efficacy.
Generalized measures that capture between-teacher differences in TSE appear, by far, to be the
most frequently used in studies conducted from 1998 to 2009 (Klassen et al., 2011). Indicative
of such between-teacher tests are the oft-cited Teacher Efficacy Scale (TES; Gibson &
Dembo, 1984) and Schwarzer and Jerusalem’s (1995) General Efficacy Scale (GES). Despite
their popularity, however, these measures have been criticized for being invalid and lacking
predictive relevance (Bandura, 1997, 2006; Kagan, 1990; Pajares, 1996). For instance, domain-
general scales have been suggested to be problematically ambiguous in the sense that teachers
are forced to guess what the unspecified contextual details of individual items might be
(Bandura, 1997; Wheatley, 2005). Items such as “I know that I can motivate my students to
participate in innovative projects” (Schwarzer, Schmitz, & Daytner, 1999) may place a burden
on teachers to comprehend what is being asked of them, as it leaves unspecified what
“innovative projects” are. Moreover, it is likely that global measures fail to adequately match
with the particular outcomes in the classroom that are of interest to the researcher (Bandura,
1997, 2006). Those potential misalliances between predictor and outcome may come at the
expense of the explanatory and predictive merit of general TSE measures (Pajares, 1996).
Recognizing that further specification of TSE is required to elucidate the self-efficacy
regulation of teachers’ behaviors in the classroom, more recent scholars have shifted focus to
subject-, task-, or domain-specific conceptualizations of TSE (Brouwers & Tomic, 2000;
Dellinger, Bobbett, Olivier, & Ellett, 2008; Friedman & Kass, 2002; Riggs & Enochs, 1990;
Siwatu, 2007, 2011; Tschannen-Moran & Woolfolk Hoy, 2001; Tschannen-Moran & Johnson,
2011; Tsouloupas, Carson, Matthews, Grawitch, & Barber, 2010). One of the most celebrated
attempts at this domain-level of specificity comes from Tschannen-Moran and Woolfolk Hoy
(2001). In a seminar on efficacy in teaching and learning, these researchers pooled and
discussed both new and existing items to construct a TSE scale that assumedly considers the
full range of teaching tasks and responsibilities. This measure, which is generally known as the
Teachers’ Sense of Self-Efficacy Scale (TSES), holds promise as a flexible research tool that
can be used across grades (Tschannen-Moran & Woolfolk Hoy, 2001), subjects (Tschannen-
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
MEASUREMENT OF TEACHER SELF-EFFICACY
83
Moran & Woolfolk Hoy, 2007), and teaching contexts (Klassen et al., 2009; Woolfolk Hoy &
Burke Spero, 2005). Moreover, its factorial, convergent, and concurrent validity has been
demonstrated in several empirical studies (e.g., Heneman, Kimball, & Milanowski, 2006;
Klassen et al., 2009; Tschannen-Moran & Woolfolk Hoy, 2001; Wolters & Daugherty, 2007).
The TSES has taken account of three unique teaching domains that appear to be the most
germane to teachers’ daily activities and students’ academic adjustment. Tschannen-Moran and
Woolfolk Hoy (2001) labeled these domains as TSE for instructional strategies, student
engagement, and classroom management, with the first two domains usually being the most
highly correlated (e.g., Tsigilis, Koustelios, & Grammatikopoulos, 2010). Recently, attention
has also been drawn to another domain that may be relevant to teachers’ self-efficacy for
teaching and learning. This domain of emotional support involves tasks and responsibilities
related to how well teachers can establish caring relationships with students, acknowledge
students’ opinions and feelings, and create settings in which students feel secure to explore and
learn (e.g., Pianta, La Paro, & Hamre, 2008). A rich body of empirical research has indicated
that emotionally supportive teacher behaviors, next to instructional, motivational, and
organizational aspects of teaching and learning, are one of the strongest correlates of students’
achievement, engagement, and enjoyment during learning tasks (e.g., Crosnoe, Johnson, &
Elder, 2004; Hamre et al., 2014; Reyes, Brackett, Rivers, White, & Salovey, 2012; Rimm-
Kaufman & Chui, 2007; Rimm-Kaufman, La Paro, Downer, & Pianta, 2005; Roorda, Koomen,
Spilt, & Oort, 2011). Therefore, at the domain-level of specificity, adding the emotional
support domain may provide, above and beyond the domains of instructional strategies,
classroom management, and student engagement, relevant insights into the multifaceted nature
of TSE and its outcomes in the classroom.
Investigators have increasingly supported the need to use measures of TSE in specific domains
of teaching and learning (e.g., Bandura, 1997; Brouwers & Tomic, 2000; Dellinger et al., 2008;
Tschannen-Moran et al., 1998; Tsouloupas et al., 2010). Tschannen-Moran et al. (1998, p. 227-
228), for instance, are adamant of the idea that “teachers feel efficacious for teaching particular
subjects to certain students in specific settings, and [that] they can be expected to feel more or
less efficacious under different circumstances”. Consistent with this Bandurian notion, a
modest body of empirical research on within-teacher variations in TSE (Raudenbusch et al.,
1992; Ross et al., 1996) has furthermore indicated that teachers’ sense of self-efficacy may be
significantly affected by contextual factors, such as subject matter, student behavior, and the
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 3
84
type of students they teach. Apart from between-teacher differences, this within-person
variation in TSE is important to recognize, as it may advance understanding of the changing
states of teachers’ self-efficacy beliefs across domains and particular students. Unfortunately,
though, research on TSE toward particular children under different domains of functioning
seems to be more the exception than the rule. For this reason, we will not only consider the
degree of domain specificity of teachers’ self-efficacy, but the level of student specificity as
well.
DEGREE OF STUDENT SPECIFICITY OF TSE
Taking teachers’ self-efficacy to both the domain- and student-specific level without becoming
too specific is no easy matter. Similar to global capability beliefs, overly particularized self-
efficacy judgments have been criticized by prior research (e.g., Bandura, 1997; Pajares, 1996;
Tschannen-Moran et al., 1998) for their potential lack of external validity and practical
relevance to the field of education. Tschannen-Moran and Woolfolk Hoy (2001, p. 795), for
instance, strikingly illustrate how such microscopically operationalized self-efficacy items as “I
am confident I can teach simple subtraction to middle-income second graders in a rural setting
who do not have specific learning disabilities, as long as my class is smaller than 22 students
and good manipulatives are available” may lose both predictive power for other teaching
contexts and students, as well as practical utility. Potentially, such issues may be circumvented
by allowing the level of domain specificity to depend on obstacles against which teachers can
adjudge their self-efficacy (cf. Pajares, 1996). Assumedly, the behaviors and characteristics that
students bring to the classroom may function as such obstacles, determining the strength of
teachers’ self-efficacy across various domains of teaching and learning.
Past research on self-efficacy has since long acknowledged the importance of viewing teachers’
self-efficacy in light of various environmental obstacles (Bandura, 1997, 2006; Coladarci &
Breton, 1997; Pajares, 1996; Wheatley, 2005; Wyatt, 2014). Without such obstacles, the
interpretation of TSE may be ambiguous, as teachers are likely to base their responses on
imagined students or situations. For instance, teachers may respond confidently to such TSES-
items as “How much can you do to get children to follow classroom rules?” (Tschannen-
Moran & Woolfolk Hoy, 2001), but may reply far less self-confident when the question is
“How much can you do to get disruptive children in your classroom to follow classroom
rules?”. Hence, obstacles, such as disruptive children in this case, may avoid teachers to
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
MEASUREMENT OF TEACHER SELF-EFFICACY
85
become naïvely optimistic about their self-efficacy beliefs, and may ameliorate the predictive
validity of TSE (Bandura, 1997, 2006; Wheatley, 2005; Wyatt, 2014).
Defining obstacles may present, as Tschannen-Moran and Woolfolk Hoy (2001, p. 794) aptly
point out, “thorny issues”, as they may substantially increase the complexity of each individual
item. In existing measures of TSE, most of the items usually lack such clear obstacles. Yet,
some attempts have been made to include them in a handful of TSES-items. All these
embedded challenges extend to student characteristics or behaviors, including ‘very capable
students’, ‘problem students’, ‘students who show low interest in schoolwork’, and ‘students
who are failing’ (Tschannen-Moran & Woolfolk Hoy, 2001). Thus, the gradations of challenge
to teachers’ performance are likely to be mainly determined by individual students’ behaviors
and actions.
The idea that obstacles are predominantly reflected in student characteristics fits fairly well
with the assumption that TSE may vary across different students. Moreover, with this assertion
comes a way to resolve the persisting issue of how situational impediments should be defined.
By letting teachers report on their self-efficacy for individual students, it becomes possible to
specify the forms the impediments take, without unnecessarily complicating individual self-
efficacy items, or limiting the generalizability of the TSES or other domain-specific self-
efficacy instruments. In addition, through this particular manner of specifying obstacles,
teachers may be less likely to respond in a socially desirable direction, as they may rather
ascribe their low self-efficacy to characteristics of particular students, than to their incompetent
self.
PRESENT STUDY
From the social-cognitive paradigm, it follows that TSE is best approached by capturing the
teaching domains and students that generate inter- and intra-individual differences in teachers’
capability beliefs. Such domain-linked and student-specific self-efficacy beliefs may generally be
more predictive of specific teacher behaviors and actions, due to the variations in self-efficacy
percepts that occur across different task domains and specific students. Unfortunately,
however, conceptual and methodological issues have largely prevented researchers to take such
conditional self-efficacy states into consideration. This study, therefore, set out to explore
teachers’ sense of self-efficacy across various domains of teaching and learning and particular
students. To this end, we took Tschannen-Moran and Woolfolk Hoy’s (2001) original TSES to
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 3
86
the domain- and student-specific level by making its individual items student-specific and
including the domain of emotional support.
Initially, we examined the multilevel factor structure of the adapted instrument to explore
inter-individual (trait) differences in TSE at the between-teacher level and intra-individual
(state) differences at the within-teacher level. Largely consistent with the original TSES, we
expected to find empirical support for a four-factor multilevel model, representing the TSE
domains of Instructional Strategies, Behavior Management, Student Engagement, and
Emotional Support. To meaningfully compare domain- and student-specific TSE across
teachers, we subsequently tested for violations of measurement invariance over clusters, or
cluster bias (Jak, Oort, & Dolan, 2013, 2014). In the present study, the absence of cluster bias
would indicate that teachers’ self-efficacy reports are likely to measure the same constructs
across educators, and that its hypothesized domains at the between-teacher level can be
perceived as the aggregate of the within-teacher level dimensions (Jak et al., 2014). As such, it
can also be expected that our adapted instrument is likely to show moderate to strong
correspondence with the original TSES, providing evidence for the concurrent validity of this
measure.
METHOD
PARTICIPANTS
The participants in the present study included regular elementary school teachers and their
students drawn from third- to sixth-grade classrooms in the Netherlands. After ethical
approval from the Ethics Review Board of the Faculty of Social and Behavioral Sciences,
University of Amsterdam, was granted (project no. 2013-CDE-3188), approximately 700
schools across the Netherlands were drawn from the total pool of 6800 regular Dutch
elementary schools. To promote the sample’s representativeness with respect to the variables
measured in our study, we aimed at selecting a wide range of schools that were
demographically diverse in terms of geographical spread, denomination, school size, urbanicity,
and characteristics of the student population.
Of the schools that were initially invited, 42 ultimately agreed to take part in the study. This
sample of schools appeared to represent a relatively balanced cross-section of the larger
population of schools in the Netherlands (see Table 1). Non-participation was mainly due to
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
MEASUREMENT OF TEACHER SELF-EFFICACY
87
schools’ already full agendas, or their involvement in other research studies. After schools
agreed to participate, information letters about the nature and purposes of the study were sent
to all teachers who taught in the upper elementary grades, soliciting their voluntary
participation in the study. On average, three teachers per participating school (range = 1 – 8
teachers; participation rate = 70.8%) expressed their interest in participation, resulting in an
original sample of 113 teachers. Teachers who refrained from participation generally were
substitute teachers and educators with additional tasks and responsibilities, including
mentoring, coordinating, or remedial teaching tasks. Of the original sample of 113 teachers, six
(5.3%) additionally failed to complete all questionnaires due to long-term absence, sickness, or
strenuous workloads. Given that these data were not missing completely at random, we
decided to exclude those cases from analyses.
TABLE 1
Demographic Characteristics of Participating Schools
Total Sample Total Population N Percentage Percentage Geographical region
North East
South West
6
12 10 14
14.3% 28.6% 23.8% 33.3%
10.1% 22.5% 19.8% 47.6%
Denomination Public school
Protestant Christian school Catholic school
Other
19 10 10 3
45.3% 23.8% 23.8% 7.1%
33.0% 30.0% 29.0% 8.0%
School size < 101 students
101-201 students 201-501 students
> 501 students
5
16 17 4
11.9% 38.1% 40.5% 9.5%
18.9% 31.7% 44.9% 4.5%
Urbanicity Urban
Peri-Urban Rural
16 15 11
38.1% 35.7% 26.2%
– – –
Note. Demographic data for the total population of Dutch elementary schools are retrieved from CBS Statline (2015b).
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 3
88
TEACHER SAMPLE
Complete data were available for 107 teachers (73.5% females), ranging in age from 20 to 63
years (M = 42.02, SD = 12.36). On average, teachers had 16.58 years (SD = 11.58) of
professional teaching experience, with the least experienced teacher working only half a year in
primary education, and the most experienced teacher having a 44-year teaching career. These
demographic characteristics are comparable to those of the larger population of Dutch
teachers, who generally have a mean age of 43.25 years (range = 19 – 67 years), and are
typically female (84%; DUO, 2014).
Some past empirical research has suggested that teachers’ years of professional experience may
positively add to their sense of self-efficacy (e.g., Klassen & Chui, 2010; Morris-Rothschild &
Brassard, 2006). Other studies, however, have indicated that TSE is likely to decrease over time
(Cantrell et al., 2003) or may not at all be associated with teaching experience (e.g., Gaith &
Yaghi, 1997; Soodak & Podell, 1996). In the present sample, analyses of variance showed that
teachers with little experience (<5 years), average experience (5 – 10 years), or high experience
(>10 years) did not differ in their domain- and student-specific self-efficacy beliefs, p > .05.
STUDENT SAMPLE
For the student sample, both the first and fourth authors randomly selected four boys and four
girls from each teacher’s classroom. This sample contained children from grades 3 (n = 54), 4
(n = 262), 5 (n = 270) and 6 (n = 255), respectively. The students ranged from 7 to 13 years of
age (M = 10.83, SD = 1.04) and the gender composition was evenly distributed with 420 boys
(49.9%) and 421 girls (50.1%). Most students had a Dutch origin (73%), with the remaining
27% of students representing other ethnic backgrounds. Based on teacher reports of parents’
working status and educational level, most students were considered to have an average to high
socioeconomic status. Both parents were employed in 65.9% of the families, 27.5% had at least
one employed parent, and only 4.9% of the families included two unemployed parents. In
addition, teachers indicated the majority of the parents to have finished senior vocational
education (48.8%) or higher education (39.3%), leaving 9% of the parents to only have finished
primary education. For less than 3% of the students, teachers failed to provide information on
parents’ working status and educational background.
The student sample appeared to be relatively similar to the larger population of third- to sixth-
graders in the Netherlands in terms of gender (50.5% male students) and ethnicity (15% non-
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
MEASUREMENT OF TEACHER SELF-EFFICACY
89
Dutch origin; CBS Statline, 2015a, 2015b). Moreover, previous studies using nationally
representative elementary school samples (e.g., Hornstra, van der Veen, Peetsma, & Volman,
2013; Zee, Koomen, & van der Veen, 2013) reported demographic characteristics for third-
and sixth-graders that resemble those of the students included in the present study. Hence,
although the participating schools, teachers, and students cannot be considered to be fully
representative in this study, they seem to reasonably approximate the larger population.
INSTRUMENTS
OVERALL TEACHER SELF-EFFICACY
Teachers’ perceptions of their overall level of self-efficacy were measured using a short, 12-
item version of Teachers’ Sense of Efficacy Scale (TSES; Tschannen-Moran & Woolfolk Hoy,
2001). The TSES is specifically designed to evaluate teachers’ perceptions of their competence
across a variety of important teaching tasks. Analogous to the original 24-item instrument, the
short TSES has been evidenced to comprise three interrelated dimensions of teacher self-
efficacy, which are labeled Instructional Strategies (IS), Classroom Management (CM), and
Student Engagement (SE). The domain of IS (4 items) measures the extent to which teachers
feel able to use various instructional methods that enable and enhance student learning. The
CM domain (4 items) taps teachers’ perceptions of their ability to organize and guide students’
behavior. TSE for SE (4 items) captures teachers’ perceived ability to activate students’ interest
in their schoolwork. Example items for each of these domains of TSE include “To what extent
can you provide an alternative explanation or example when students are confused?”, “How
much can you do to get children to follow classroom rules?”, and “How much can you do to
help your students value learning?”, respectively. Although the TSES is usually measured on a
9-point rating scale, teachers in the present study responded on a 7-point rating scale, ranging
from 1 (nothing) to 7 (a great deal). Reason to deviate was that prior research (e.g., Diefenbach,
Weinstein, & O’Reilly, 1993) has indicated that 7-point scales generally outperform 2-, 5-, 9-,
11-, 12-, and 100-point scales on accuracy, perceived ease of use, and agreement of scale-
derived ranks with direct rankings.
The psychometric properties of the short form of the TSES have been shown to be adequate
and largely comparable to those of the long form (e.g., Tschannen-Moran & Woolfolk Hoy,
2001). In prior research, alpha coefficients ranged between .71 and .87 for IS, .83 and .94 for
CM, and .74 and .88 for SE, respectively (e.g., Klassen et al., 2009; Tschannen-Moran &
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 3
90
Woolfolk Hoy, 2001). In addition to these adequate alpha coefficients, Klassen and colleagues
(2009) found evidence of strong structural and measurement invariance in groups of teachers
who differed on language, cultural practices and beliefs, teaching environment, and school
level. Correlations between the TSES dimensions and adjacent constructs, including personal
efficacy, teaching efficacy and job satisfaction, also lend credence to the convergent and
concurrent validity of the short TSES (Klassen et al., 2009; Tschannen-Moran & Woolfolk
Hoy, 2001). Together, these reliability and validity assessments seem to support the
appropriateness of the short TSES for use in different contexts.
To evaluate the reliability and factorial validity of the short TSES in the present study, we
performed a confirmatory factor analysis for complex survey data, using robust maximum
likelihood estimation (MLR) in Mplus 7.11 (Muthén & Muthén, 1998-2012). This method takes
the non-independence of data due to clustering into account, and provides a mean-adjusted
chi-square test and standard errors that are robust for non-normality (Muthén & Muthén,
1998-2012; Yuan & Bentler, 2000). Both a three-factor solution (χ2(50) = 93.20, p < .001,
RMSEA = .033 (90% CI [.022, .043]), CFI = .89, SRMR = .076) and a one-factor solution
(χ2(43) = 77.12, p < .001, RMSEA = .031 (90% CI [.020, .043]), CFI = .89, SRMR = .066)
yielded a reasonable fit, after adding a theoretically plausible correlation residual to both
models. Although the CFI was below the conventional threshold of .90 for satisfactory fit, the
model showed quite sound goodness of fit according to established cutoff values of .08 for the
RMSEA and SRMR (Bentler, 1992; Browne & Cudeck, 1993; Hu & Bentler, 1999; Kline,
2011). Factor loadings ranged between .47 and .85 in the three-factor model, and between .37
and .79 in the one-factor solution. Alpha coefficients were .84 for Overall TSE, .71 for IS, .76
for CM and .77 for SE, respectively.
DOMAIN- AND STUDENT-SPECIFIC TEACHER SELF-EFFICACY
To measure teachers’ self-efficacy toward particular children in different domains of
functioning, we developed a new instrument, based on the original, 24-item TSES of
Tschannen-Moran & Woolfok Hoy (2001). The adaptation process began with the adjustment
of the original TSES items to the student-specific level (see Appendix 1). For instance, the item
“How much can you do to get children to follow classroom rules?” was changed into “How
much can you do to get this student to follow classroom rules?”. Classroom Management items
12 (“How well can you establish a classroom management system with each group of
students?”) and 16 (“How well can you establish routines to keep activities running
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
MEASUREMENT OF TEACHER SELF-EFFICACY
91
smoothly?”) of the original scale were omitted, as they could not be accurately made specific to
the level of individual students. Notably, whereas all other adapted items of this scale
concentrated on teachers’ perceived ability to manage the behavior of individual students,
items 12 and 16 mainly focused on aspects of classroom management. As such, these two
items also reflected a slightly different construct. In addition, several original TSES items
(items 8, 13, 19, and 21) included embedded obstacles that embody gradations of challenge to
teachers’ tasks in a given teaching domain. Examples of such obstacles are “very capable
students” (item 8), “problem students” (item 13), “students who show low interest in
schoolwork” (item 19), and “students who are failing” (item 21). By evaluating teachers’ self-
efficacy beliefs in relation to individual students, however, it becomes possible to specify the
forms the impediments take in all TSES items, without unnecessarily complicating these items.
In the process of making the original TSES items student-specific, we therefore consistently
removed all embedded obstacles from items 8, 13, 19, and 21.
After adjusting the original TSES items to be student-specific, we further shortened the
original TSES by removing four less relevant items. The first item (“To what extent can you
use a variety of assessment strategies?”) was discarded because this item was not representative
of the regular teaching tasks of Dutch elementary school teachers. Furthermore, this item
appeared to have one of the poorest factor loadings in samples of elementary school teachers
(e.g., Heneman et al., 2006; Klassen et al., 2009), suggesting that this item may be more
relevant for secondary school teachers. The main reason to remove the fifth item (“How well
can you respond to difficult questions from your students?”) involved the ambiguous nature of
this item. Specifically, this item might either relate to students’ difficulties regarding instruction
or learning tasks, or refer to issues of a more personal nature, such as family problems.
Probably, this ambiguity is also reflected in the relatively low factor loading of this item in
previous research (e.g., Wolters & Daugherty, 2007; Tschannen-Moran & Woolfolk Hoy,
2001). This may explain why this item is not part of the short form of the original TSES.
Additionally, after adjusting the level of specificity of item 14 (“How well can you respond to
defiant students?”), a substantial overlap between this question and other Classroom
Management items was recognized. Therefore, this item was removed as well. Given that the
reported factor loadings of Classroom Management items are usually quite substantial (> .70),
and of roughly equal magnitude (e.g., Heneman et al., 2006; Klassen et al., 2009; Wolters &
Daugherty, 2007), the removal of this item did probably not affect the consistency of this scale.
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 3
92
Lastly, item 20 (“How much can you assist families in helping their children do well in
school?”) was considered to have too little in common with the domain of Student
Engagement. Moreover, prior research reporting on the factor structure of the TSES
consistently showed that this item is least stable and has the poorest factor loading in general
(Heneman et al., 2006; Klassen et al., 2009; Wolters & Daugherty, 2007). The removal of six
items in total (in stage one and two) resulted in a total of 18 adapted items (6 IS, 5 CM, and 7
SE items) that were retained in the new instrument.
Subsequent to adapting the three original TSES domains, we used the CLASS framework (for
an overview, see Hamre et al., 2013 and Pianta et al., 2008) to construct seven new items that
aimed to cover the domain of Emotional Support. These items were based on the common
metric used to describe positive dimensions of the CLASS-domain of Emotional Support,
including Positive Climate, Teacher Sensitivity, and Regard for Student Perspectives (Pianta et
al., 2008). Three items concerned teachers’ perception of their ability to establish a warm
connection with individual students (Positive Climate). Two other pairs of items measured
teachers’ perceived ability to be aware of, and responsive to individual students’ academic and
emotional needs (Teacher Sensitivity) and to emphasize students’ viewpoints and interest
(Regard for Student Perspectives). The addition of these items resulted in a 25-item
instrument, reflecting the domains of Instructional Strategies (IS; 6 items), Behavior
Management (BM; 5 items), Student Engagement (SE; 7 items), and Emotional Support (ES; 7
items), respectively. Largely similar to the original TSES, responses to each of these items were
given on a seven-point rating scale, ranging from 1 (nothing) to 7 (a great deal).
The translation of the TSES, lastly, was performed using a standard forward-backward
procedure, involving two forward translators and one backward translator. In the first step of
the translation process, the first and second author, both native Dutch speakers, independently
translated the original English version of the TSES into the Dutch language. After the
translations were completed, they compared all items, and critically evaluated them on
parameters like difficulties in translation, doublets of items, and relevance for the Dutch school
context. Any discrepancies between the two translations were solved by consensus with the
other authors. This process resulted in a single conditional forward translation of the student-
specific TSES, which offered some alternative wordings for (parts of) items that appeared to
be difficult to translate, and included the seven new items on Emotional Support. This
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
MEASUREMENT OF TEACHER SELF-EFFICACY
93
provisional version was back-translated by a native English speaker from Dutch into English,
and checked by the first author.
In the second step of the translation process, the student-specific TSES was pilot tested with
six elementary school teachers, who reviewed the items for content validity, clarity of wording,
and relevance of the response scale. Based on their analysis, the first two authors slightly
reworded one adapted TSES-item (item 1) that was deemed too complex, without altering its
meaning.
PROCEDURE
Data for this study were collected between January and March 2014. Prior to data collection,
participating schools were asked to distribute a letter to students’ parents, explaining the nature
and purposes of the study and providing a form to refuse permission, which could be returned
to school. All parents voluntarily gave their consent to their child’s participation in this study.
Participating teachers signed a written informed consent form at the start of data collection.
To avoid common method variance, teacher survey data were collected in two parts. The first,
written part of the survey was administered during a planned school visit, and consisted of
demographic items and the short TSES, respectively. Teachers who were not present at the
time of data collection could return the survey by regular mail. The second part of the survey
was distributed directly after the school visit, by sending an e-mail invitation that contained an
anonymous survey link. This digital survey, which was completed for eight randomly selected
students from teachers’ classrooms, had a forced response format and involved the newly
developed student-specific TSES, and some general questions regarding parents’
socioeconomic status. Teachers were asked to return the digital survey within two weeks after
the invitation was sent. To improve the participation rate, reminders were sent to non-
responding teachers. Ultimately, six teachers failed to fill out the survey and another four
teachers completed the survey for less than eight students, due to time constraints. This
resulted in a total response rate of 94.6%.
DATA ANALYSIS
We used multilevel confirmatory factor analysis (MCFA) to test the factor structure of the
student-specific TSES. With this analytic technique, model fit and parameter estimate biases
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 3
94
can be avoided by decomposing the total sample covariance matrix into a pooled within-group
(ΣWITHIN) and a between-group (ΣBETWEEN) covariance matrix (Muthén, 1994). In addition, MCFA
is well suited to detect violations of measurement invariance across clusters, or cluster bias, in
multilevel data (Jak et al., 2013, 2014). This relatively new technique is particularly useful when
collecting the same measure from qualitatively different groups or individuals operating in
distinct contexts, as it aims to take differences in response processes into account (Muthén &
Asparouhov, 2013; Ryu, 2014). Generally, cluster bias indicates that teachers might answer
differently on the self-efficacy items, despite having similar beliefs in their capability. These
systematic differences in observed self-efficacy scores seem to occur when contextual factors
or personal teacher characteristics implicitly affect teachers’ interpretation of self-efficacy
items. Thus, in this study, the presence of cluster bias would indicate that the dimensions of
the student-specific TSES do not measure the same constructs over teachers, and that part of
the variance in teachers’ student-specific self-efficacy beliefs may be attributed to teacher
and/or classroom characteristics.
MODELING PROCEDURE
In line with Jak and colleagues’ (2014) strategies for the investigation of cluster bias, we
followed four analytical steps. First, to determine whether multilevel modeling was required,
we calculated the intraclass correlation coefficients (ICC) for each of the model’s indicators
and tested whether the between-teacher level variance and covariance deviated significantly
from zero. To this end, we fitted a Null Model (ΣBETWEEN = 0, ΣWITHIN = free) and an
Independence Model (ΣBETWEEN = diagonal, ΣWITHIN = free) to the data (Jak et al., 2013, 2014;
Muthén, 1994). Generally, poor fit of these models are indicative of meaningful between-
teacher level variance and covariance (Hox, 2002).
In step two, we first conducted a confirmatory factor analysis on the sample pooled-within
covariance matrix to determine the factor structure at the within-group level only (Dyer,
Hanges, & Hall, 2005; Hox, 2002; Muthén, 1994). Apart from the proposed four-factor model,
we also considered several alternative models, including one-factor and three-factor solutions,
and Tschannen-Moran and Woolfolk Hoy’s (2001) original three-factor and higher-order
factor models, to determine potential sources of model misspecification.
In the third step, we used the measurement model that was established in step two to
investigate cluster bias. We started with a fully constrained model, in which all factor loadings
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
MEASUREMENT OF TEACHER SELF-EFFICACY
95
were constrained to be equal across the within- and between-teacher level, and residual
variances at the between-teacher level were fixed at zero. To test whether strong factorial
invariance held across clusters, we sequentially allowed the between-teacher level residual
variances to be freely estimated. Generally, residual variances greater than zero are indicative of
cluster bias in their corresponding indicators (Jak et al., 2013, 2014). Subsequently, we
evaluated whether factor loadings could be considered equal across clusters. Unequal factor
loadings indicate that the unique domains of student-specific TSE at the between-teacher level
cannot merely be assumed to be the within-teacher level factor’s aggregates.
Finally, in the fourth step, we fitted a restricted factor analysis (RFA; Oort, 1992) to investigate
the concurrence between Tschannen-Moran and Woolfolk Hoy’s (2001) original TSES and the
student-specific TSES. To this end, we extended the multilevel measurement model to include
correlations between the generalized TSES and the student-specific TSES at the between-level
of measurement. Depending on the presence of cluster bias, we expected a moderate to strong
correspondence between the original and student-specific TSES.
MODEL GOODNESS-OF-FIT
Multilevel models were fitted in Mplus 7.11, using robust maximum likelihood estimation
(MLR; Muthén & Muthén, 1998-2012). This method of estimation offers a mean-adjusted χ2,
which is asymptotically equivalent to Yuan and Bentler’s (2000) T2-test statistic and generates
adjusted standard error estimates that are robust for non-normality (Muthén & Muthén, 1998-
2012). Generally, the adjusted χ2 test statistic indicates a good overall model fit when it does
not reach the significance threshold. However, as even trivial discrepancies between the
expected and the observed model may lead to the model’s rejection (Chen, 2007), other criteria
in evaluating fit were used as well. These included the root mean square of approximation
(RMSEA) and standardized root mean square residual (SRMR), with values ≤.05 reflecting a
close fit, and ≤.08 a satisfactory fit (Browne & Cudeck, 1993; Hu & Bentler, 1999; Kline,
2011), and the comparative fit index (CFI), with values ≥.95 indicating close fit, and values
≥.90 indicating acceptable fit (Bentler, 1992). To compare alternative models, we employed the
(Satorra–Bentler scaled) chi-square difference test (TRd; Satorra, 2000; Satorra & Bentler,
2010), with non-significant chi-squares indicating equivalent fit, and the CFI-difference, with
CFI changes ≥.02 being indicative of model nonequivalence (Cheung & Rensvold, 2002).
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 3
96
RESULTS
DATA SCREENING AND DESCRIPTIVE STATISTICS
Inspection of the distributional properties of both the total score and the three subscales of the
original, overall TSES-domains revealed no serious departures from normality and linearity.
Skewness levels were −0.20 for Overall TSE, −0.54 for IS, −0.78 for CM, and −0.30 for SE,
and kurtosis values −0.42 for Overall TSE, −0.01 for IS, 0.71 for CM, and 0.22 for SE,
respectively. Teachers’ mean responses on the original TSES, reported on a 7-point scale, were
lowest for SE (M = 5.46, SD = 0.69), followed by IS (M = 5.67, SD = 0.65), and CM (M =
5.90, SD = 0.67). The mean total score of teachers’ Generalized Self-Efficacy was 5.70 (SD =
0.52). These relatively high means and small standard deviations are consistent with previous
findings (e.g., Heneman et al., 2006; Tschannen-Moran & Woolfolk Hoy, 2001).
Teachers’ responses on the Student-Specific TSES domains of IS and SE were approximately
normally distributed. In these domains, most items did not reach the skewness threshold of ±
1.00 (range = −0.63 to −1.07 for IS and −0.64 to −1.19 for SE). Moreover, kurtosis values
ranged from −0.17 to 1.62 for items comprising the IS domain, and from 0.00 to −1.16 for
SE-items. Items appeared to be highly skewed, however, in the domains of BM (range = 1.16
to −1.84) and ES (range = −0.75 to −1.41), and were characterized by high kurtosis (range =
1.16 to 3.82 for BM and 0.35 to 2.32 for ES). To deal with these high skewness levels, we used
robust maximum likelihood estimation to obtain parameter estimates (Muthén & Muthén,
1998-2012), as this estimator is robust to non-normality and enables the adjustment of
standard errors.
Table 2 displays the means, within-teacher standard deviations, and between-teacher standard
deviations of the Student-Specific TSES items. The descriptive statistics indicate that all item
means were relatively high and largely comparable with the averages found for the original
TSES domains. Notably, the highest item means were found for items comprising the BM and
ES domains of Student-Specific Self-Efficacy. Inspection of the partitioned standard
deviations, which provide an indication of Self-Efficacy differences within and between
teachers, furthermore shows that there is more variability within teachers than between
teachers. This is in line with Bandura’s (1997) premise that self-efficacy is more likely to reflect
a dynamic state, than a relatively stable trait.
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
MEASUREMENT OF TEACHER SELF-EFFICACY
97
TABLE 2
Item Means and Standard Deviations of the Student-Specific TSES
Item M SDwithin SDbetween ICC TSE for Instructional Strategies
IS1 5.87 0.83 0.54 .30 IS2 5.46 1.11 0.61 .23 IS3 5.43 1.05 0.60 .25 IS4 5.71 0.87 0.55 .29 IS5 5.83 0.96 0.53 .24 IS6 5.43 1.01 0.59 .25
TSE for Behavior Management BM1 6.07 1.17 0.36 .09 BM2 6.13 1.11 0.39 .11 BM3 6.18 1.08 0.37 .11 BM4 6.15 1.08 0.41 .13 BM5 6.30 0.84 0.44 .21
TSE for Student Engagement SE1 5.87 0.93 0.50 .23 SE2 5.72 1.19 0.49 .15 SE3 5.72 1.21 0.52 .16 SE4 5.67 1.11 0.51 .18 SE5 5.46 1.11 0.63 .24 SE6 5.26 1.05 0.69 .31 SE7 5.81 1.10 0.45 .14
TSE for Emotional Support ES1 6.30 0.81 0.38 .19 ES2 6.20 0.77 0.44 .25 ES3 6.12 0.79 0.49 .28 ES4 5.81 0.92 0.63 .32 ES5 5.63 0.90 0.62 .32 ES6 5.65 0.92 0.68 .36 ES7 5.43 0.98 0.64 .30
Note. Item means are reported on a 7-point scale. TSE = teacher self-efficacy.
MULTILEVEL CONFIRMATORY FACTOR ANALYSIS OF THE STUDENT-SPECIFIC TSES
STEP 1: EVALUATING BETWEEN-TEACHER LEVEL VARIANCE AND COVARIANCE
The intraclass correlations (ICCs) for the Student-Specific TSES items (see Table 2) ranged
between .09 (item 7) and .36 (item 24), with a mean ICC of .23. Fit indices of the Null Model,
χ2(302) = 3244.27, RMSEA = .108, CFI = .77, SRMRWITHIN = .10, SRMRBETWEEN = .65, and the
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 3
98
Independence Model, χ2(278) = 1898.58, RMSEA = .083, CFI = .88, SRMRWITHIN = .09,
SRMRBETWEEN = .65, suggested that there is meaningful between-teacher level variance and
covariance. Hence, these clustering effects were substantial enough to warrant the use of
MCFA.
STEP 2: EVALUATING THE MEASUREMENT MODEL AT THE WITHIN-TEACHER LEVEL
Using the sample pooled-within covariance matrix, we examined the hypothesized four-factor
model. The overall fit of the model was reasonable, with RMSEA and SRMR values below .08
and a CFI greater than .90, χ2(269) = 1484.15, p < .001, RMSEA = .073 (90% CI [.070–.077]),
CFI = .91, SRMR = .05. To diagnose systematic patterns of misfit, we inspected the model’s
modification indices. These indices suggested model improvement by adding a correlation
between the residuals of SE-items 13 (“To what extent can you help this student to value
learning?”) and 14 (“To what extent can you motivate this student for his/her schoolwork?”).
These two items showed a considerable conceptual overlap, both focusing on teachers’
perceived capability to motivate individual students for their schoolwork. Following
Tabachnick and Fidell’s (2007) cut-off criteria, we additionally removed item 22 (“To what
extent can you timely recognize that this student does not feel well?”), which loaded poorly on
its corresponding factor (<.40). These alterations resulted in a satisfactory fit to the data:
χ2(245) = 1229.13, p < .001, RMSEA = .069 (90% CI [.065–.073]), CFI = .93, SRMR = .05.
Alternative models
Although the fit of the hypothesized model was acceptable, there might be alternative models
that generate roughly similar, or even better predicted covariances (Kline, 2011). To justify the
appropriateness of the hypothesized model, we therefore examined a series of theoretically
plausible competing models, including one-factor, three-factor, and higher-order factor
models.
The first two competing models tested were a one-factor model and a three-factor model, in
which the SE and ES dimensions were combined to create a single Engaging Strategies factor.
Comparison of the four-factor model with these one-factor, Δχ2(30) = 2407.90, p < .001, ΔCFI
= .17, and three-factor alternatives, Δχ2(27) = 345.72, p < .001, ΔCFI = .02, indicated that
both alternative models had a poorer fit to the data, and had slightly worse structural parameter
estimates. These results lend credence to the proposed four-factor structure of the student-
specific TSES.
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
MEASUREMENT OF TEACHER SELF-EFFICACY
99
Secondly, we evaluated whether the original factor structure proposed by Tschannen-Moran
and Woolfolk Hoy (2001) held in the present sample. To this end, we fitted a three-factor
model in which all Emotional Support items were omitted. This model obtained an acceptable
fit, χ2(132) = 708.87, p < .001, RMSEA = .072 (90% CI [.067–.077]), CFI = .95, SRMR = .04.
The results of this model suggest that the additional domain of Emotional Support can be
distinguished from the original TSES-domains and may provide information about teachers’
perceived capabilities that goes above and beyond their Self-Efficacy for Instructional
Strategies, Behavior Management, and Student Engagement.
Thirdly, and largely consonant with Tschannen-Moran and Woolfolk Hoy’s findings, we
considered a hierarchical factor model, in which one second-order factor of teachers’ General
Self-Efficacy beliefs toward particular students was hypothesized to underlie the four proposed
TSE domains of teaching and learning. Such higher-order models are particularly relevant
when hypothesizing general constructs that comprise several closely related domains (Chen,
West, & Sousa, 2006). Although this model fitted the data reasonably well, the χ2 difference test
statistic suggested that the hypothesized four-factor model is to be preferred over its higher-
order equivalent, Δχ2(2) = 107.54, p < .001, ΔCFI = .01. Based on these comparisons, we
gleaned that the proposed four-factor model is most likely the preferred solution.
STEP 3: DETECTING VIOLATIONS OF MEASUREMENT INVARIANCE ACROSS CLUSTERS
In the third step, we established a measurement model at both the within-teacher (state) and
between-teacher (trait) level, resulting in a poor overall fit, χ2(540) = 2817.20, p < .001,
RMSEA = .071, CFI = .83, SRMRWITHIN = .075, SRMRBETWEEN = .290. Similar to the within-
teacher level model, this baseline model appeared to poorly explain the observed correlation
between items 13 and 14, indicating that a correlation between the residuals of these items may
be required. In tests of cluster bias, however, residual variances on the between-teacher level
have to be fixed at zero, while constraining the factor loadings at the within- and between-
teacher level to be equal (Jak et al., 2014). To obtain an estimate of this residual covariance, we
therefore re-parameterized the measurement model by allowing items 13 and 14 to load on an
additional factor, which is uncorrelated to the four Student-Specific Self-Efficacy domains.
Moreover, we fixed the factor loading of these two items at one, such that the obtained factor
variance equals the estimate of the residual covariance (Jak, 2014).
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 3
100
Although the re-parameterized, fully constrained four-factor model significantly improved on
the baseline model, TRd(2) = 307.09, ΔCFI = .02, it did not converge to an admissible solution
and yielded an unacceptable fit, χ2(538) = 2559.23, p < .001, RMSEA = .069, CFI = .85,
SRMRWITHIN = .075, SRMRBETWEEN = .283. Generally, the pattern of discrepancies between the
model and the data indicated that strong factorial invariance across teachers does not hold.
Moreover, the substantial factor correlations (see Table 3) suggested that models with fewer
latent factors might provide a more plausible alternative. Based on the model’s parameters and
theory, we therefore successively fitted a one-factor model, a three-factor model in which the
IS and SE domains were combined, and a three-factor model in which the ES and SE domains
were combined. Neither the one-factor solution, TRd(12) = 2255.24, ΔCFI = .20, nor both
three-factor alternatives, TRd(6) = 74.66, ΔCFI = .01; TRd (6) = 94.72, ΔCFI = .01,
significantly improved the model’s fit.
TABLE 3
Estimated Correlations for the Latent Factors
1 2 3 4 5 1. TSE for Instructional Strategies 1.00 2. TSE for Behavior Management .59 1.00 3. TSE for Student Engagement .98 .60 1.00 4. TSE for Emotional Support .95 .57 .95 1.00 5. General TSE .99 .60 .99 .96 1.00
Note. All correlations are statistically significant (p < .001). TSE = teacher self-efficacy.
Given that TSE likely resides along a continuum from domain generality to domain- and
student specificity, we explored whether the four specified domains of teachers’ Self-Efficacy
toward particular students may be accounted for by one common underlying higher-order
construct of General Self-Efficacy. This model with four first-order factors and one second-
order factor showed no convergence problems and had a slightly better fit than the one-factor,
TRd(5) = 2740.44, ΔCFI = .00, and three-factor alternatives, TRd(1) = 12.44, ΔCFI = .00;
TRd(1) = 41.80, ΔCFI = .00.
Taking the model with four first-order factors and one second-order factor as a baseline, we
subsequently tested the significance of the between-teacher level residual variances. Based on
the modification indices, we successively freed 18 of 24 residual variances, resulting in a
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
MEASUREMENT OF TEACHER SELF-EFFICACY
101
statistically significant improvement of model fit, TRd(18) = 2818.49, ΔCFI = .05. Further
improvement of fit was established by allowing the factor loadings of nine items (4, 7, 11, 13,
14, 15, 17, 18, 19) to be freely estimated across teachers. These factor loadings were all more
indicative of higher Student-Specific TSE at the between-teacher level, suggesting that the
domains of TSE, and especially Student Engagement, do not have the same interpretation
across teachers. Hence, these violations of measurement invariance across clusters suggest that
the domains of Student-Specific TSE at the between-teacher level cannot merely be assumed
to be the within-teacher level factor’s aggregates. The final, partially constrained model, had an
acceptable fit to the data, χ2(518) = 1864.92, p < .001, RMSEA = .056, CFI = .90, SRMRWITHIN
= .068, SRMRBETWEEN = .152. The standardized factor loadings of the final model are depicted in
Figure 1.
STEP 4: EVALUATING THE CONCURRENCE BETWEEN THE GENERALIZED AND STUDENT-SPECIFIC TSES
To investigate the concurrence between the Generalized and Student-Specific TSES, we
allowed the total score of the Generalized TSES to correlate with the second-order common
Self-Efficacy factor at the between-teacher level of the final model from step 3 (see Figure 1).
Addition of this correlation resulted in a satisfactory model fit, χ2(541) = 1941.95, p < .001,
RMSEA = .055, CFI = .90; SRMRWITHIN = .068, SRMRBETWEEN = .151. Although the chi-square
value of this model indicated a statistically significant lack of fit, the CFI of .90 was reasonable,
and the RMSEA of .055 and SRMRWITHIN of .068 were smaller than Hu and Bentler’s (1999)
cutoff value of .08, suggesting acceptable fit. The SRMRBETWEEN value of .151 indicated that the
component fit of the between part was slightly worse than the within part of the model. This
poorer fit at the between-level has been noted by previous research as well (cf. Dyer et al.,
2005). Assessment of the correlation coefficient pointed to a statistically association between
generalized TSE and teachers’ student-specific TSE, r = .59, p < .001). This association
suggests a moderate correspondence between the original TSES and the adapted, student-
specific TSES.
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 3
102
FIG
URE
1
Fina
l Mod
el of
Teac
hers’
Sen
se of
Dom
ain- a
nd S
tude
nt-S
pecif
ic Se
lf-E
ffica
cy
Note
. Par
amet
er e
stim
ates
are
stan
dard
ized
and
stat
istica
lly si
gnifi
cant
(p <
.001
). Fo
r rea
sons
of p
arsim
ony,
the
resid
ual c
orre
latio
n be
twee
n ite
ms 1
3 an
d 14
is n
ot
disp
layed
in th
e m
odel
.
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
MEASUREMENT OF TEACHER SELF-EFFICACY
103
DISCUSSION
Since long, empirical studies have mainly equated teachers’ sense of self-efficacy with a
relatively stable omnibus trait that generates inter-individual differences between teachers.
Following the basic tenets of social-cognitive theory, however, TSE could also be considered
to embody domain-linked cognitive states that depend on challenges presented by particular
students (e.g., Bandura, 1997; Tschannen-Moran et al., 1998). As such, a premium has been
placed on the effort to disentangle within-teacher fluctuations in TSE across various teaching
tasks, domains, and students (Raudenbusch et al., 1992; Ross et al., 1996; Tschannen-Moran et
al., 1998). The present study is one of the first to come to grips with trait- and state-variability
in TSE, by evaluating these capability beliefs in relation to particular students, and across
various domains of teaching and learning. Recognizing the existence of both inter- and intra-
individual differences in TSE has important theoretical and practical implications for the
investigation of TSE.
DOMAIN SPECIFICATION OF TSE
In line with prior theory and research (e.g., Bandura, 1997; Lent & Brown, 2006; Tschannen-
Moran et al., 1998; Tschannen-Moran & Woolfolk Hoy, 2001), we hypothesized teachers’ self-
efficacy beliefs to reside along a continuum from domain generality to domain specificity. The
present study’s findings generally afforded credence to this idea. Initially, evidence was found
for the presence of a single, higher-order construct that potentially reflects teachers’
generalized sense of self-efficacy. This common factor of general TSE took the commonality
among the lower-order domains of self-efficacy into consideration, thereby providing a strong
rationale for the unidimensional total score of these capability beliefs. In their seminal study,
Tschannen-Moran and Woofolk Hoy (2001) have also found evidence for such a second-order
construct of teacher self-efficacy, which accounted for 75% of the variance and showed a high
internal consistency (α = .94).
In our study, the substantial factor loadings of the generalized teacher self-efficacy factor
indicated that between 21% and 98% of the variance is shared between the TSE domains at
the lower level of the structural hierarchy. Still, the markedly poorer fit of a first-order single
factor solution, as well as several other plausible alternatives, suggested that specific
dimensions of TSE can be distinguished. Markedly, the strongest support was found for the
unique domain of behavior management, which evaluates the extent to which teachers feel able
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 3
104
to promote positive behavior in a particular child. The interrelationships between this factor
and other domains of self-efficacy were moderate, suggesting that tasks and capabilities related
to behavior management may be relatively distinct from other core responsibilities, such as
providing the instructional, motivational, and emotional supports that generate gains in
learning. As such, these results substantiate previous findings from related studies, in which the
classroom management domain was also found to be the most distinctive (e.g., Fives,
Hamman, & Olivarez, 2007; Tschannen-Moran & Woolfolk Hoy, 2001). The potential
uniqueness of the behavior management factor may explain, in part, why this domain of TSE
has increasingly gained popularity among educational researchers as a separate field of study
(cf. Emmer & Hickman, 1991; O’Neill & Stephenson, 2011).
The final second-order model’s factor structure also provided evidence for the existence of the
unique TSE domains of instructional strategies and student engagement. These domains tap
into teachers’ perceived capability to use various instructional methods that enable and
enhance individual students’ learning, and activate their interest in their schoolwork. Notably,
the inter-factor correlations between TSE for instructional strategies and student engagement
appeared to be the highest, which is consistent with previous empirical findings (e.g.,
Tschannen-Moran & Woolfolk Hoy, 2001; Tsigilis et al., 2010). Following classroom-based
research (e.g., Hamre & Pianta, 2005), these strong links may be explained in terms of the
important role teachers’ instructional strategies play in making content relevant, meaningful,
and enjoyable to their students. Thereby, such skills and capabilities may set the stage for
students’ motivation and engagement in schoolwork, and may play a key role in enhancing
students’ knowledge and skills (Hamre & Pianta, 2005; Hamre et al., 2013; Hardré & Sullivan,
2009).
From a methodological viewpoint, the high correspondence among the instructional strategies
and student engagement domains can also be explicated by the less stable structure of the SE
factor in prior studies. The factor analytic results from Wolters and Daugherty (2007), for
instance, revealed a pattern of cross-loadings of items related to the student engagement
domain that was indicative of poor discriminant validity between the instructional strategies
and student engagement subscales of the original TSES. Moreover, Henson (2002) noted that
caution should be exercised when using scores from the student engagement subscale, as the
evidence for the existence of the third domain of the original TSES is far from conclusive.
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
MEASUREMENT OF TEACHER SELF-EFFICACY
105
Further large-scale research using the student-specific TSES is therefore needed to verify the
uniqueness of the self-efficacy domains of instructional strategies and student engagement.
Apart from the three domains proposed by Tschannen-Moran and Woolfolk Hoy (2001), the
student-specific TSES also appeared to be targeted to teachers’ emotional support.
Comparison of the four-factor solution with the original three-factor model suggested that
teachers’ self-efficacy for emotional support can be distinguished separately from other
domains of TSE. Yet, this dimension of self-efficacy also corresponded highly with domains of
instructional strategies and student engagement. It might well be that teachers with a strong
sense of self-efficacy for emotional support are generally better attuned and responsive to
individual students’ needs, ideas, and thoughts. Theoretical and empirical work from Hamre
and colleagues (2013, 2014) substantiates this notion, suggesting that the strategies teachers use
to foster students’ learning and engagement in the classroom are likely to be based on
individual students’ basic, affective needs for relatedness, autonomy, and competence. This
sensitivity to students’ perspectives might explain why these considerable associations were
found in the present study.
Adding the emotional support dimension to the extant domains of TSE may be of particular
importance for studies investigating outcomes related to teaching and learning. A sizeable
literature has provided evidence, both theoretically and empirically, that sensitive and
emotionally supportive teachers may provide students with experiences that foster their
motivation and learning outcomes in the classroom (Crosnoe et al., 2004; Hamre et al., 2014;
Pianta, La Paro, Payne, Cox, & Bradley, 2002; Roeser, Eccles, & Sameroff, 2000). Moreover,
teachers’ emotional support has frequently been shown to reduce the risk of low-quality
student–teacher relationships, especially for students who display uncontrollable or disruptive
behavior (Ahnert, Pinquart, & Lamb, 2006; Buyse, Verschueren, Doumen, Van Damme, &
Maes, 2008; Hamre & Pianta, 2005; La Paro, Pianta, & Stuhlman, 2004). Building self-efficacy
around the domain of emotional support may therefore advance further understanding of the
multifaceted ways in which teachers’ self-percepts of efficacy function.
Taken together, the overall, higher-order factor of TSE seems to account for substantial
amounts of variance shared by the four hypothesized domains of self-efficacy beliefs. As such,
it may be compelling to expand the original structure of the TSES by adding one higher-order
dimension, without losing sight of the relevance and potential independence of the four TSE
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 3
106
domains of teaching and learning. These domains remain essential, both theoretically and
practically, yet their commonality is not negligible. Thus, adapting the hierarchical structure of
the student-specific TSES, which suggests a continuum from domain generality to domain and
student specificity, may potentially advance our understanding of the nature of teachers’ sense
of efficacy.
INTER- AND INTRA-INDIVIDUAL DIFFERENCES IN TEACHERS’ SELF-EFFICACY
Results of this study indicated that the adapted, student-specific TSES may be suitable for
capturing both inter- and intra-individual differences in TSE. Generally, there was significant
state and trait variability for each of the model’s items. Intraclass correlations showed that the
variability at the state (within-teacher) level was larger than at the trait (between-teacher) level.
These larger within-teacher differences mirror the social-cognitive view that teachers’ self-
efficacy beliefs, despite reflecting some degree of trait variability, may vary across realms of
activity, situational demands, and characteristics of the students toward whom their behaviors
and actions are directed (Bandura, 1997; Tschannen-Moran et al., 1998).
Notably, the within-teacher variability seemed to be the largest in teachers’ student-specific
self-efficacy for behavior management. There might be several reasons for the smaller amount
of variation in TSE for behavior management at the between-teacher level. First, this lack of
variability might in part be attributable to the process of revising the original TSES. Three out
of eight items related to classroom management were removed from the adapted instrument,
as these could not be accurately made specific to the level of individual students, or overlapped
too substantially with other items. As a consequence, the domain of behavior management
seemed to reflect a greater focus on student behavior issues, thereby concentrating less on
classroom routines and organization of time and resources (e.g., Emmer & Stough, 2001;
O’Neill & Stephenson, 2009). Second, teachers’ beliefs about their capability to deal with
individual students’ classroom behaviors may depend more heavily than other TSE beliefs on
interpersonal aspects of teaching. Prior research suggests that teachers tend to appraise
individual students’ behavior on the basis of relationship beliefs, feelings, and expectations,
which usually stem from teachers’ previous affective experiences and day-to-day interactions
with the child (Bandura, 1997; Spilt & Koomen, 2009; Stuhlman & Pianta, 2002). Whereas
positive appraisals may lead teachers to believe in their capabilities to positively affect the
child’s behavior, negative appraisals may thwart teachers’ self-efficacy for behavior
management and subsequent behavior toward this child (ibid.). Arguably, teachers who doubt
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
MEASUREMENT OF TEACHER SELF-EFFICACY
107
their ability to effectively deal with individual students’ behaviors may unintentionally convey
poor expectations and ideals, thereby potentially further stimulating undesirable behavior and
attributes in the child and confirming their already poor efficacy beliefs. Hence, further
research on the reciprocal relationships between teachers’ appraisals of individual students’
behavior and relationship representations is needed to explain fluctuations in teachers’ self-
efficacy across individual students.
To meaningfully compare variations in domain- and student-specific TSE between teachers,
we tested for violations of measurement invariance over clusters. Recent research (e.g., Jak et
al., 2013, 2014; Muthén & Asparouhov, 2013; Ryu, 2014) underscored the necessity of using
this relatively new, but complex technique, as it attempts to take account of differences in
response processes that may result from personal and contextual characteristics, while still
allowing for comparisons of groups on similar latent variables.
In the present study, cluster bias was detected in 18 of 24 items, with the lowest amount of
bias found in the behavior management items and, after that, the emotional support items. The
partial absence of cluster bias in those items might be due to the smaller amount of variance in
these items across teachers. Furthermore, nine additional factor loadings could not be
considered equal across educators. These factor loadings were all indicative of higher TSE at
the between-teacher level, especially with respect to the domain of student engagement. Hence,
(the domains of) TSE at the between-teacher level cannot be merely perceived as the aggregate
of within-teacher level self-efficacy beliefs (Jak et al., 2013, 2014), which is also reflected in the
moderate correlation between the total score of the original TSES and the second-order
common self-efficacy factor at the between-teacher level.
Importantly, the presence of cluster bias in teachers’ self-efficacy underscores the complexity
of purely estimating these elusive capability beliefs. Both teachers’ and students’ idiosyncratic
characteristics and behaviors are likely to shape a unique classroom environment that
ultimately affects how teachers judge and interpret their own sense of efficacy. There is some
literature to suggest, for instance, that teachers’ knowledge and provision of instructional
strategies may be dependent on prior education, years of teaching experience, and satisfaction
with past performance (Tschannen-Moran & Woolfolk Hoy, 2001, 2007). Contextual factors,
including school and classroom climate, principal leadership, student behavior, available
teaching materials, and collective efficacy have also been proposed as sources of teachers’ self-
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 3
108
efficacy (Goddard & Goddard, 2001; Hipp & Bredeson, 1995; Moore & Esselman, 1992;
Tschannen-Moran & Woolfolk Hoy, 2001). Variability in such distinct features may potentially
lead to inconsistencies in self-efficacy reports across teachers, which are not accounted for by
the common factor structure. This indicates that two teachers with equal values on the latent
domain(s) of self-efficacy but from different classrooms are likely to vary in their expected
observed test score (Jak et al., 2013, 2014).
LIMITATIONS
The present study’s results should be interpreted in light of several limitations. First, the
generalizability of our findings remains to be established across teachers and classrooms.
Although our sample appeared to be largely comparable to the larger population of Dutch
schools, teachers, and students, it primarily consisted of female teachers with relatively high
levels of teaching experience. Moreover, a small amount of participating teachers (5.3%)
dropped out before data collection as a result of long-term sickness, strenuous workloads, or
burnout. This dropout might have given rise to both non-response bias and bias across
clusters, suggesting that teachers may report different self-efficacy scores, despite having
similar beliefs in their capability. Following Bandura’s (1997) notions of triadic reciprocal
causality, it may be reasonable to assume that teachers’ responses to individual items do not
only rely on their self-efficacy beliefs for a specific domain and/or student, but also on
personal characteristics and the context in which teachers operate. Yet, such biases across
teachers might have implications for the psychometric quality of self-efficacy measures, as well
as the interpretation of inter- and intra-individual differences in TSE. An important next step
for future research, therefore, is to explore the explaining factors underlying the cluster bias by
including features of both teachers and the classroom as potentially biasing attributes.
Moreover, additional tests for measurement invariance across (subgroups of) teachers may
warrant consideration in future research, to establish whether observed differences in teachers’
reports reflect systematic response biases across teachers, or substantive differences in TSE per
se.
Second, and in a related vein, participating students in this study were predominantly Dutch,
and had relatively high socioeconomic backgrounds. Probably, the nature of the student
sample might have affected teachers’ responses on the student-specific TSES. Indeed, previous
research has suggested that teachers may hold different self-efficacy beliefs in relation to
different students, depending on students’ demographic backgrounds and behaviors (e.g.,
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
MEASUREMENT OF TEACHER SELF-EFFICACY
109
Raudenbusch et al., 1992; Ross et al., 1996; Spilt & Koomen, 2009; Spilt, Koomen, & Thijs,
2011). In any attempt to replicate the results, it is therefore recommended that future
researchers consider individual student characteristics as covariates of teachers’ sense of
student-specific self-efficacy.
Third, it should be noted that the response rate among schools invited to participate was very
low. This low response rate may have biased the present study’s results, since schools with self-
efficacious teachers and an open mind to research were probably more likely to take part than
schools with already full agendas or strenuous workloads. Nonetheless, a sincere attempt was
made to increase the response rate among teachers within the participating schools, by
rewarding participation with school reports containing a conceptual overview of the study’s
results and gift vouchers. As a result, more than 70% of the teachers was willing to participate,
which may to some extent compensate for the low participation rate among schools.
Fourth, analytic techniques such as multilevel structural equation modeling are subject to
assumptions of multivariate normality of continuous data. In the present study, several
student-specific TSES items were found to be skewed, indicating potential non-normality of
the data. Essentially, violations of assumptions of multivariate normality may result in bias in
the model’s parameter estimates and fit indices. However, the size of our sample was
substantial, and robust maximum likelihood was used to deal with the non-normality of some
student-specific TSES items.
Fifth, the student-specific TSES was filled out by participating teachers for a limited number of
randomly selected students, thereby possibly raising questions of selective bias. It should be
noted, however, that Snijders and Bosker (1999) have demonstrated that inclusion of all
students from each classroom is insensible and needless when the cluster size of the sample is
sufficient, as is the case in the present study. Moreover, including the full amount of students
per class would have made the data gathering process excessively time-consuming and
burdensome for teachers.
IMPLICATIONS FOR RESEARCH AND PRACTICE
Despite these shortcomings, the present study may provide some promising avenues for
further research and practice in the field of teaching and learning. First, the present study
generally maintains the view that unique TSE domains of teachers’ functioning can be
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 3
110
distinguished, but may also converge to an overarching construct of general teacher self-
efficacy. Although more research is evidently needed to refine and further confirm the
instrument’s distinct dimensions, the adapted TSES may be already relevant for educational
researchers and practitioners alike. Specifically, our new instrument might provide meaningful
and relevant profiles of teachers’ self-efficacy judgments across various domains of
functioning, each of which require specific knowledge, skills, and competencies (cf. Bandura,
1997). Uncovering such distinctive patterns of TSE across teaching tasks and domains may
help school psychologists in their quest to develop intervention strategies for a myriad of
sources that may influence teachers’ sense of efficacy and associated performance in
instructional, affective, and behavioral teaching domains. For instance, helping teachers to
selectively focus on their performance attainments and to monitor their physiological reactions
to inefficacious control of difficult students or challenging teaching tasks may raise teachers’
self-efficacy for teaching domains in which they feel less confident (Bandura, 1997).
Second, the student-specific TSES is one of the first measures to empirically support the
social-cognitive view that TSE is a multifaceted phenomenon that fluctuates over teaching
domains and particular students (Bandura, 1997; Tschannen-Moran et al., 1998). Compared to
the original TSES, which exclusively focuses on inter-individual differences in TSE across
domains, our instrument seems well suited to grasp teachers’ unique sense of self-efficacy in
relation to different students as well. This interpersonal view on TSE may be relevant for
understanding teachers’ differential treatment of particular students in class, and fluctuations in
the affective quality of dyadic student–teacher relationships. Existing research has increasingly
encouraged such an interpersonal focus of analysis to comprehend the mechanisms behind
teachers’ and students’ behaviors and actions in the classroom (Pianta, Hamre, & Stuhlman,
2003; Spilt et al., 2011). In light of this recommendation, the unique domain of emotional
support seems a meaningful addition to the construct of teacher self-efficacy. Insights into
teachers’ sense of self-efficacy toward individual children in this particular domain may help
school psychologists to coach teachers in emotionally connecting with, and getting through to
particular students in class.
Third, and in a related vein, the present study’s results seem to underscore the importance of
investigating teachers’ self-efficacy in relation to the particular context in which they perform
their daily job. Although there are some relatively stable, trait aspects of TSE, these capability
beliefs cannot be merely perceived as context-free attributes of a teacher. Rather, features of
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
MEASUREMENT OF TEACHER SELF-EFFICACY
111
the classroom context, individual students, and teachers themselves may play an important role
in producing fluctuations in teachers’ capability beliefs. For future researchers, it may be a
challenge to uncover important variables that may further explain variations in self-efficacy
between and within teachers.
Lastly, self-efficacy measures that are tailored to various teaching domains and specific
students may increase the predictive power of the self-efficacy construct, and potentially afford
better explanation of teachers’ supportive behaviors and students’ school adjustment in the
classroom (Bandura, 1986, 1997). To date, evidence regarding the consequences of TSE for
students’ and teachers’ classroom performances seems far from conclusive (e.g., Klassen et al.,
2011). Probably, the lack of particularized instruments has, in large part, prevented
improvement in interpretations of these complex relationships. Measuring teachers’ self-
efficacy in relation to individual students may provide a rich context for understanding and
interpreting teachers’ differential treatment of, and day-to-day interactions with particular
students in the classroom. Educational researchers and practitioners such as school
psychologists may use these insights to develop training programs and interventions targeting
teacher’ student-specific efficacy beliefs as a means of improving students’ outcomes, and
especially those at risk of academic failure.
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 3
112
APPENDIX 1
Original and Student-Specific TSES Items Domain Item Original TSES Domain Item Student-Specific TSES IS 1 To what extent can you use a
variety of assessment strategies?
-
IS 2 To what extent can you provide an alternative explanation or example when students are confused?
IS 1 To what extent can you provide an alternative explanation or example when this student is confused?
IS 3 To what extent can you craft good questions for your students?
IS 2 To what extent can you craft stimulating questions for this student?
IS 4 How well can you implement alternative strategies in your classroom?
IS 3 How well can you let this student apply alternative problem solving strategies?
IS 5 How well can you respond to difficult questions from your students?
-
IS 6 How much can you do to adjust your lessons to the proper level for individual students?
IS 4 How well can you adjust your lessons to the proper level for this student?
IS 7 To what extent can you gauge student comprehension of what you have taught?
IS 5 To what extent can you gauge this student’s comprehension of what you have taught?
IS 8 How well can you provide appropriate challenges for very capable students?
IS 6 How well can you provide appropriate challenges for this student?
CM 9 How much can you do to control disruptive behavior in the classroom?
BM 7 How well can you control disruptive behavior in this student?
CM 10 How much can you do to get children to follow classroom rules?
BM 8 How well can you get this student to follow classroom rules?
CM 11 How much can you do to calm a student who is disruptive or noisy?
BM 9 How well can you calm this student when he/she is disruptive or noisy?
CM 12 How well can you establish a classroom management system with each group of students?
-
CM 13 How well can you keep a few problem students from ruining an entire lesson?
BM 10 How well can you prevent this student from negatively affecting the classroom atmosphere?
CM 14 How well can you respond to defiant students?
-
CM 15 To what extent can you make your expectation clear about student behavior?
BM 11 To what extent can you make your behavioral expectations clear to this student?
CM 16 How well can you establish routines to keep activities running smoothly?
-
SE 17 How much can you do to get students to believe they can do well in schoolwork?
SE 12 How well can you get this student to believe he/she can do well in schoolwork?
SE 18 How much can you do to help your students value learning?
SE 13 To what extent can you help this student to value learning?
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
MEASUREMENT OF TEACHER SELF-EFFICACY
113
SE 19 How much can you do to motivate students who show low interest in schoolwork?
SE 14 To what extent can you motivate this student for his/her schoolwork?
SE 20 How much can you assist families in helping their children do well in school?
-
SE 21 How much can you do to improve the understanding of a student who is failing?
SE 15 How well can you help this student to understand the learning content?
SE 22 How much can you do to help your students think critically?
SE 16 How well can you help this student to think critically?
SE 23 How much can you do to foster student creativity?
SE 17 To what extent can you help this student to explore new things?
SE 24 How much can you do to get through to the most difficult students?
SE 18 How well can you get through to this student?
ES 19 How well can you respond positively and sincerely to this student in the classroom?
ES 20 To what extent can you provide positive feedback to this student?
ES 21 How well can you provide a safe and secure environment for this student?
ES 22 To what extent can you timely recognize that this student does not feel well?
ES 23 How well can you timely provide support to this student?
ES 24 To what extent can you provide this student with the space to make his/her own choices?
ES 25 To what extent can you adjust learning tasks to this student’s needs and interests?
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
115
CHAPTER 4 TEACHERS’ SELF-EFFICACY IN RELATION TO INDIVIDUAL STUDENTS WITH A
VARIETY OF SOCIAL–EMOTIONAL BEHAVIORS: A MULTILEVEL INVESTIGATION
_________________________________________________________________________
The present study examined teachers’ domain-specific self-efficacy (TSE) in relation to
individual students with a variety of social–emotional behaviors in class. Using a sample of 526
third-to-sixth grade students and 69 teachers, multilevel modeling was conducted to examine
students’ externalizing, internalizing, and prosocial behaviors as predictors of TSE toward
individual students, and the potential moderating roles of teaching experience and teachers’
perceived amount of classroom misbehavior. Results showed that most of the variance in TSE
occurred within teachers. Students’ externalizing behavior was negatively associated with TSE
for instructional strategies, behavior management, student engagement, and emotional support.
In contrast, teachers reported higher levels of self-efficacy toward students with high levels of
prosocial behavior, irrespective of teaching domain. Students’ internalizing behavior predicted
lower levels of TSE for instructional strategies and emotional support, and higher levels of
TSE for behavior management. Lastly, teachers’ perceived levels of classroom misbehavior
exacerbated the negative association between externalizing student behavior and TSE for
behavior management. These findings illustrate the importance of viewing TSE from a dyadic
perspective.
_________________________________________________________________________ Zee, M., de Jong, P. F., & Koomen, H. M. Y. (2016). Teachers’ self-efficacy in relation to individual students with a variety of social–emotional behaviors: A multilevel investigation. Journal of Educational Psychology. Advance online publication. doi:10.1037/edu0000106
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 4
116
INTRODUCTION
Challenging students bring many behaviors and qualities to the classroom that may seriously
hamper teachers’ ability to execute their daily teaching tasks (Westling, 2010). Studies have
indicated that behaviorally or emotionally disturbed students unnecessarily take time away
from instruction, try teachers’ patience, fail to comply with classroom rules, and consequently,
may hinder teachers’ efforts to sustain a positive learning climate (Bru, 2009; Clunies-Ross,
Little, & Kienhuis, 2008; Putnam, Luiselli, Handler, & Jefferson, 2003). Undoubtedly, some
teachers may experience little trouble nipping such behaviors in the bud. For many others,
however, students’ challenging behavior frequently marks the beginning of a vicious cycle of
stress and burnout (e.g., Brouwers & Tomic, 2000; Fernet, Guay, Senécal, & Austin, 2012;
Friedman, 2006), which may eventually lead these teachers to leave the profession entirely
(Tsouloupas, Carson, Matthews, Grawitch, & Barber, 2010).
Scholars have laid claim to a number of factors that potentially discriminate teachers who cope
effectively from those who are commonly struggling to manage challenging behavior. Of these
factors, teachers’ self-efficacy (TSE) beliefs, or self-referent judgments of operative capability,
are probably one of the most pervasive (Bandura, 1997; Tschannen-Moran & Woolfolk Hoy,
2001). Past empirical evidence suggests that when educators have a resilient sense of self-
efficacy, they are more likely to successfully deal with challenging student behavior and to
persist longer than teachers who lack such beliefs (e.g., Almog & Shechtman, 2007; Lambert,
McCarthy, O'Donnell, & Wang, 2009). On a more theoretical note, self-efficacious teachers are
also presumed to be steadily capable of motivating challenging students, to believe in their
improvability, and to rely on intrinsic inducements to get these students to study (Bandura,
1997; Tschannen-Moran & Woolfolk Hoy, 2001).
To date, the significance of self-percepts of efficacy for teachers’ dealings with students at the
classroom level of analysis is fairly well-established in various teaching domains (Woolfolk Hoy,
Hoy, & Davis, 2009). There is, however, a dearth of studies considering TSE toward individual
students. This lack of research is disadvantageous, as efficacy judgments related to various
teaching domains and individual students may more reliably predict teachers’ behaviors toward
specific children, as well as the effort and persistence teachers put in teaching them (Bandura,
1997; Tschannen-Moran, Woolfolk Hoy, & Hoy, 1998). For a comprehensive understanding of
teachers’ ability to manage particular students, and targeting interventions for handling a
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
SOURCES OF TEACHER SELF-EFFICACY
117
variety of social–emotional student behaviors, knowledge of both domain- and student-specific
TSE may therefore be vital. To add to this knowledge, the present study aims to examine TSE
in relation to individual students with a variety of social–emotional behaviors (i.e.,
externalizing, internalizing, and prosocial behavior) in the classroom.
CONCEPTUALIZATION OF TEACHERS’ SELF-EFFICACY
Teachers’ self-percepts of efficacy have long been considered a vital cognitive resource for
teachers, with clear contributions to their performances and sense of well-being in the
classroom (Klassen & Tze, 2014; Tschannen-Moran & Woolfolk Hoy, 2001; Woolfolk Hoy et
al., 2009). When teachers generally perceive themselves as highly efficacious, they are more
likely to use differentiated instructional methods, employ emotionally supportive behaviors
that increase students’ confidence, and adopt proactive approaches to managing student–
teacher conflict (Andreou & Rapti, 2010; Hoy & Woolfolk, 1990; Martin & Sass, 2010; Morris-
Rothschild & Brassard, 2006; Thoonen, Sleegers, Oort, Peetsma, & Geijsel, 2011; Wertheim &
Leyser, 2002). Teachers with a robust sense of general, classroom-level self-efficacy have
furthermore been found to be more satisfied with their job and to suffer less from burnout
symptoms than less efficacious educators (Brouwers, Evers, & Tomic, 2001; Caprara,
Barbaranelli, Borgogni, & Steca, 2003; Friedman, 2003; Klassen & Chui, 2010; Skaalvik &
Skaalvik, 2010). These outcomes resonate well with the social-cognitive view that self-efficacy
is a potent force in affecting the motivational, affective, cognitive, and selective processes
needed for desired goals to be realized (Bandura, 1986, 1997).
Scholars have keenly been on the lookout for relevant dimensions in teachers’ sense of self-
efficacy (Tschannen-Moran & Woolfolk Hoy, 2001). Over the years, various
conceptualizations and measures of TSE have come onto the scene, from global TSE scales
based on locus of control theory (Gibson & Dembo, 1984; Guskey, 1981; Rose & Medway,
1981) to subject-, task-, or domain-specific measures that consider the contextualized,
multifaceted nature of TSE (e.g., Brouwers & Tomic, 2000; Friedman & Kass, 2002;
Tschannen-Moran & Johnson, 2011; Tsouloupas et al., 2010). Since the studies of Tschannen-
Moran and colleagues (Tschannen-Moran et al., 1998; Tschannen-Moran & Woolfolk Hoy,
2001), however, the well-validated three-factor model of TSE for instructional strategies,
classroom management, and student engagement has dominated the field. The domains of
TSE for instructional strategies and student engagement mainly focus on aspects of
instructional delivery. Generally, the instructional strategies domain attempts to capture
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 4
118
teachers’ perceived capability in using various instructional methods that enable and enhance
student learning. Teachers’ self-efficacy for student engagement is useful in measuring the
extent to which teachers feel able to activate students’ interest in their schoolwork. In addition
to the instructional aspects of teaching and learning, TSE for classroom management
encompasses teachers’ judgments of their ability to organize students’ time, behavior, and
attention (cf. Emmer & Stough, 1991). Although moderate to strong correlations among the
three domains of TSE exist, there is empirical evidence to suggest that each construct assesses
unique aspects of teachers’ sense of self-efficacy (e.g., Heneman, Kimball, & Milanowski, 2006;
Tschannen-Moran & Woolfolk Hoy, 2001). Thereby, Tschannen-Moran and Woolfolk Hoy’s
model substantiates the social-cognitive premise that TSE is specific to different tasks and
domains of teachers’ functioning (Bandura, 1997; Tschannen-Moran et al., 1998).
Despite general consensus on the highly context-specific nature of TSE, most research has
been conducted at the classroom-level of analysis, focusing on teachers’ general beliefs of
capability toward the class they currently teach. As such, these studies could be considered to
be subject to the ecological fallacy (Piantadosi, Byar, & Green, 1988) that teachers’ self-
percepts of efficacy also hold for individual students. Assumedly, students all bring idiosyncratic
behaviors and characteristics to the classroom that may more or less impact teachers’ self-
efficacy beliefs across different domains of teaching and learning. Whereas obliging and hard-
working students will most likely raise teachers’ self-efficacy, instances of misconduct may
seriously undermine teachers’ student-specific capability beliefs. Two multilevel studies
(Raudenbusch, Rowan, & Cheong, 1992; Ross, Cousins, & Gadalla, 1996), based on a single-
item measure to evaluate TSE at the classroom-level, indicated that between 13% and 44% of
the variance in TSE can be explained by such within-class variables as students’ grade, academic
level, and interest in their schoolwork. In addition, empirical research and theorizing from Spilt
and colleagues (Spilt & Koomen, 2009; Spilt, Koomen, & Thijs, 2011) suggested that individual
students who display behavioral problems are more likely to weaken teachers’ self-efficacy
beliefs and to evoke feelings of helplessness than students without such problems. These
findings suggest that teachers may significantly vary in their self-efficacy toward particular
students.
STUDENTS’ SOCIAL-EMOTIONAL BEHAVIORS AS PREDICTORS OF TSE
Social-cognitive theorists have generally asserted that self-percepts of efficacy are shaped, in
large part, by specific events and experiences linked to distinct realms of functioning (Bandura,
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
SOURCES OF TEACHER SELF-EFFICACY
119
1997). For teachers, such experiences typically derive from authentic educational endeavors
with students. Indeed, a sparse amount of existing research (Bandura, 1997; Tschannen-Moran
et al., 1998; Tschannen-Moran & Woolfolk Hoy, 2007) has theorized that successful
experiences with instructing, engaging, and managing students may significantly add to a
healthy sense of TSE. In contrast, unsuccessful dealings with individual students, and
particularly those who display challenging behavior, have been empirically evidenced to elicit
negative emotions that lead teachers to lose faith in their capabilities and collapse under the
burden of everyday stress (Emmer & Stough, 2001; Spilt & Koomen, 2009; Spilt, Koomen, &
Thijs, 2011; Tsouloupas et al., 2010). Accordingly, teachers’ classroom experiences and
subsequent feelings of self-efficacy may be heavily influenced by a variety of social–emotional
student behaviors in the classroom. In line with prior research on students’ social–emotional
adjustment (e.g., Roorda, Verschueren, Vancraeyveldt, van Craeyevelt, & Colpin, 2014), we
consider students’ externalizing, internalizing, and prosocial behaviors as sources of TSE
toward individual students.
EXTERNALIZING BEHAVIOR
Past empirical research has repeatedly pinpointed externalizing student behavior, including
aggression, hyperactivity, and antisocial behavior, to be at the core of the challenges most
teachers face on a daily basis (Brouwers & Tomic, 2000; Evers, Tomic, & Brouwers, 2004;
Hastings & Bham, 2003; Kokkinos, Panayiotou, & Davazoglou, 2004, 2005; Kyriacou, 2001;
Roehrig, Pressley, & Talotta, 2002). These disruptive behaviors may ripple through the entire
classroom and have been suggested to cause elevated levels of stress and emotional exhaustion
in teachers (Clunies-Ross et al., 2008; Kokkinos et al., 2004; Spilt & Koomen, 2009;
Tsouloupas et al., 2010). Evidently, individual students’ externalizing behavior patterns may
color teachers’ initial experiences and enduring beliefs of capability to effectively deal with
them. The correlational results of Lambert and colleagues (2009), for instance, put forward that
highly overactive and distractible students may generally hamper US teachers’ attitude toward
their teaching abilities, and their sense of self-efficacy in dealing with, and establishing positive
relationships with challenging students. Also focusing on US teachers’ self-efficacy for
classroom management, Tsouloupas et al. (2010) demonstrated that high levels of teacher-
perceived misbehavior in the classroom may negatively affect TSE in dealing with disruptive
behavior and stressful situations, which, in turn, may cause them to feel emotionally exhausted.
Other empirical research from Cyprus (e.g., Kokkinos et al., 2004, 2005) and the United States
(Roehrig et al., 2002) has indicated that behaviors of an externalizing nature, including conduct
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 4
120
problems, hyperactivity, anger, and disrespectfulness, generally yield the most negative
impressions on teachers and may lead them to feel helpless and inefficacious.
Additional to the literature linking students’ externalizing behavior to general or domain-
specific (classroom management) TSE at the classroom-level, a modest body of primarily
American research has also begun to explore within-person variability in teacher cognitions. For
instance, several scholars (e.g., Abidin & Robinson, 2002; Greene, Abidin, & Kmetz, 1997;
Greene, Beszterczey, Katzenstein, Park, & Goring, 2002) have highlighted teachers’ cognitions
and judgments of individual student behavior as crucial contributors to their differential
treatment of particular students in class. In line with this assertion, Spilt and Koomen (2009)
used Pianta’s (1999) Teacher Relationship Interview and associated coding system to assess
strengths and difficulties in teachers’ beliefs and feelings in relationships with specific,
disruptive students in the Netherlands. They revealed that teachers perceive themselves as
angrier and less self-efficacious in relation to individual students who display disruptive
behavior in the classroom. These outcomes are consistent with the idea that TSE may be
highly individualized in nature and might depend on how teachers appraise individual students’
disruptive, externalizing behaviors.
Notably, negative personal feelings, cognitions, and efficacy beliefs seem to be particularly
echoed in inexperienced teachers’ reports of their students’ behaviors (cf. Emmer & Stough,
2001). Using a grounded theory approach to study US teachers’ perceptions of student needs,
Feuerborn and Chinn (2012) revealed that novice teachers may express more emotionally-
laden reactions in relation to externalizing behavior than their experienced coworkers, and
seem more afflicted by the instructional disruptions these behaviors cause. These qualitative
findings stretch across empirical studies from Europe as well. Results from Kokkinos and
colleagues (Kokkinos et al., 2004, 2005) suggested that more experienced teachers generally
perceive disruptive student behavior as less challenging and more controllable in the
classroom. From this line of evidence, it can be hypothesized that increases in teachers’
experience may potentially buffer the negative association between teacher-perceived
externalizing student behavior and student-specific TSE.
INTERNALIZING BEHAVIOR
Counter to externalizing behavior, students with symptoms of internalizing behavior, including
shyness, verbal inhibition, anxiety, or social withdrawal (Coplan, 2000; Gazelle & Ladd, 2003;
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
SOURCES OF TEACHER SELF-EFFICACY
121
Merrell, 1999), have been suggested to evoke less challenging experiences or negative thoughts
in their teachers (Rubin & Coplan, 2004). These internalizing difficulties may be more subtle
than manifestations of externalizing conduct and usually tend to reflect more appropriate
classroom behavior and decorum (e.g., Coplan, 2000; Gresham & Kern, 2004; Kokkinos et al.,
2004; Rubin & Coplan, 2004). As such, internalizers are more likely to go undetected or
ignored by their teachers than students with externalizing conduct (Coplan & Prakash, 2003)
and may have little, if any, influence on teachers’ self-efficacy judgments toward them in
different teaching domains.
Yet, there might be some reason to believe that behaviors of a more internalizing nature may
still be bothersome to the teacher and contribute to their self-percepts of efficacy (e.g., Olson
& Cooper, 2001; Westling, 2010). Notably, the one empirical study to examine US teachers’
self-efficacy at the classroom-level in relation to internalizing student behavior indicated that
highly self-efficacious teachers may be more bothered by students’ internalizing behavior than
those who are less confident in their personal teaching effectiveness (Liljequist & Renk, 2007).
One of the scenarios that may account for this finding is that a healthy sense of TSE frequently
coincides with increases in teaching experience (e.g., Klassen & Chui, 2010). Empirical studies
of Kokkinos and colleagues (2004, 2005) pointed out that this growth in experience is essential
for gaining knowledge of, and becoming sensitized to internalizers’ more subtle behavioral and
affective cues. Without such vital knowledge and experience, teachers may feel less worried
about and less responsible for students’ internalizing behavior patterns, and thereby, less
hindered in their self-efficacy to deal with them (cf. Liljequist & Renk, 2007). In contrast, when
teachers consciously experience that their instructional initiatives are unsuccessful in
establishing reciprocal interchanges with a student who displays internalizing behavior, a
lowered sense of TSE toward this child is likely to arise. Hence, counter to the protective
effect of teaching experience on the negative association between externalizing behavior on
TSE, increases in teaching experience might serve as an additional risk factor for teachers’ self-
efficacy toward students with internalizing symptoms. Unless teachers believe they can gather
up the resources to successfully deal with individual students with internalizing symptoms, they
will probably dwell on their actions, exercise inadequate effort, and may consequently
experience failure.
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 4
122
PROSOCIAL BEHAVIOR
Most of the previous work on teacher self-efficacy has predominantly attempted to study
challenging student behavior as antecedents of these capability beliefs (e.g., Lambert et al.,
2009; Liljequist & Renk, 2007; Tsouloupas et al., 2010). It is likely, however, that students’
propensity to act prosocially may also contribute to teachers’ self-efficaciousness toward
individual children, but in a more favorable sense. Generally, prosocial behaviors are
implicated with various voluntary acts intended to benefit others, including helping, sharing,
comforting, and cooperating (Dunfield & Kulhmeier, 2013; Dunfield, Kuhlmeier, O’Connell,
& Kelley, 2011; Eisenberg, 1982). Such prosocial tendencies have frequently been linked to key
classroom outcomes such as academic achievement (e.g., Caprara, Barbaranelli, Pastorelli,
Bandura, & Zimbardo, 2000; Malecki & Elliott, 2002; Wentzel, 1993), engagement (Coolahan,
Fantuzzo, Mendez, & McDermott, 2000), and the quality of students’ relationships with
teachers and peers (Birch & Ladd, 1998; Henricsson & Rydell, 2004; Zimmer-Gembeck,
Geiger, & Crick, 2005). Assumedly, these agreeable behaviors and performances may provide
teachers with the classroom mastery experiences that reinforce a healthy sense of self-efficacy
(Goddard & Goddard, 2001; Goddard, Hoy, & Woolfolk Hoy, 2004). Therefore, teachers may
feel more self-efficacious when dealing with students who generally display prosocial behavior
in the classroom, irrespective of teachers’ domain of functioning.
TEACHERS’ PERCEIVED AMOUNT OF MISBEHAVIOR IN THE CLASSROOM
A number of empirical investigations from the United States have demonstrated that
classrooms with many aggressive students may have a negative impact on the behaviors of its
individual members. For instance, Werthamer-Larsson, Kellem, and Wheeler (1991) found that
regular students from poorly behaving classrooms were more often perceived as shy by their
teacher, which can be perceived as an aspect of internalizing behavior (e.g., Letcher, Smart,
Sanson, & Toumbourou, 2009). Several longitudinal studies have also indicated that students
who are enrolled in classrooms with many aggressive students are likely to gradually become
more aggressive themselves (e.g., Kellam, Ling, Merisca, Brown, & Ialongo, 1998; Thomas &
Bierman, 2006; Thornberry & Krohn, 1997). Evidently, such trends may place an additional
burden on teachers’ ability to control these students’ behaviors, and to maintain positive
relationships with them (Brophy, 1996; Doumen et al., 2008; Roorda et al., 2014). Hence, as
classmates may contribute to escalating trends in students’ challenging behaviors, teachers’
perceived negative classroom dynamics may be hypothesized to exacerbate the relationship
between individual students’ externalizing or internalizing behavior and TSE.
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
SOURCES OF TEACHER SELF-EFFICACY
123
PRESENT STUDY
The present study aimed to extend the current literature by exploring a variety of social–
emotional behaviors as predictors of teachers’ domain- and student-specific self-efficacy
beliefs. Although the consequences of classroom-level TSE for teachers’ dealings with student
behavior have been fairly well established (Woolfolk Hoy et al., 2009), empirical work on TSE
seems to have stopped short of considering how students’ social–emotional behaviors are
associated with TSE across various teaching domains (e.g., instructional strategies or classroom
management) and toward individual students (cf. Klassen, Tze, Betts, & Gordon, 2011).
Moreover, the handful of studies (e.g., Lambert et al., 2009; Tsouloupas et al., 2010; Spilt &
Koomen, 2009) that have specifically looked into these effects tend to focus solely on patterns
of externalizing behavior, thereby largely neglecting internalizing and prosocial behaviors as
correlates of TSE. Building an understanding of how teachers’ sense of self-efficacy is shaped
by individual students’ various behaviors in different domains of teaching and learning may
provide a vital foundation for interventions targeted to teachers’ dealings with challenging
students.
Based on the body of evidence on teachers’ classroom-level self-efficacy, several hypotheses
were formulated. First, we expected teachers to report lower levels of self-efficacy toward
individual students with externalizing and internalizing problems, and higher levels of self-
efficacy toward students who display prosocial behavior, irrespective of teachers’ domain of
functioning. Given the more subtle nature of students’ internalizing behavior, we expected the
link between this student behavior and student-specific TSE across domains of teaching and
learning to be weaker than the associations between students’ externalizing and prosocial
behavior and student-specific TSE. Secondly, we hypothesized that relatively high levels of
teachers’ perceived classroom misbehavior and a lack of teacher experience may further
worsen the negative association of individual students’ externalizing and internalizing behavior
with student-specific TSE.
METHOD
PARTICIPANTS
Data for the current study were collected from 69 regular Dutch elementary school teachers
and 526 third-to-sixth grade students. The schools from which the sample was drawn were
recruited via telephone and e-mail, after ethical approval was granted by the Ethics Review
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 4
124
Board of the Faculty of Social and Behavioral Sciences, University of Amsterdam (project no.
2013-CDE-3188). Of the 350 schools that were initially invited, 24 (6.9%) from both rural and
urban areas across the Netherlands ultimately agreed to take part in this study. Non-
participation was mainly due to the school’s already full agenda, or their involvement in other
research studies.
Participating teachers (72.6% females) had a mean age of 41.42 years (SD = 12.34, range = 23
to 63 years). The professional teaching experience of these educators in primary education
ranged from 1.5 to 44 years, with a mean of 16.67 years (SD = 11.87). Four teachers did not
provide complete demographic information. For the student sample, eight students (four boys
and four girls) were randomly selected from the pool of students from each teacher’s
classroom whose parents had initially provided informed consent. These students were
distributed across grades 3 (n = 54), 4 (n = 157), 5 (n = 165), and 6 (n = 150), respectively. At
recruitment, the sampled children ranged from 7.71 to 13.04 years of age (M = 10.57, SD =
1.11), and the gender composition was evenly distributed with 263 boys (50.0%) and 263 girls
(50.0%). Based on students’ self-reports, the study sample appeared to be 85.2% Dutch, and
12.3% non-Dutch. In 2.5% of the cases, students failed to provide information regarding their
ethnicity. Based on employment statistics and parents’ education, most students could be
considered to have an average to high socioeconomic status. Teachers reported both parents
of participating students to be employed in 76.8% of the families. In 20.4% of the cases, at
least one parent appeared to be employed, and only 2.5% of the families included two
unemployed parents. In addition, teachers indicated the majority of the parents to have
finished senior vocational education (49.0%) or higher education (46.2%), leaving less than 5%
of the parents to only have finished primary education.
INSTRUMENTS
STUDENTS’ SOCIAL–EMOTIONAL BEHAVIORS
Teachers were asked to complete the Dutch version of the Strengths and Difficulties
Questionnaire (SDQ; van Widenfelt, Goedhart, Treffers, & Goodman, 2003) to evaluate a
variety of students’ social-emotional behaviors. The SDQ is a brief 25-item behavioral
screening questionnaire that measures students’ adjustment and psychopathology in the
classroom. The scale originally consists of positive and negative student attributes that together
represent five factors reflecting strengths (Prosocial Behavior) and difficulties (Emotional
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
SOURCES OF TEACHER SELF-EFFICACY
125
Symptoms, Conduct Problems, Hyperactivity-Inattention, and Peer Problems). In the present
study, however, use was made of the more general Internalizing, Externalizing, and Prosocial
Behavior subscales, which generally are preferred over the original SDQ factors in low-risk
samples (Goodman, Lamping, & Ploubidis, 2010). The Externalizing Behavior dimension (10
items) combines the subscales of Hyperactivity-Inattention and Conduct Problems, with items
such as “Restless, hyperactive, cannot sit still for long” and “Often has temper tantrums or hot
tempers”. Additionally, the Internalizing Behavior subscale (8 items) comprises all items from
the Emotional Symptoms factor, and three items from the Peer Problems factor (i.e., “Rather
solitary, tends to play alone”, “Gets on better with adults than with other children” and
“Picked on or bullied by other children”). The 7-item Prosocial Behavior scale, lastly, reflects
all five items from the Prosocial scale, and two items from the Peer Problems scale (i.e.,
“Generally liked by other children” and “Has at least one good friend”). Teachers responded
on the 25 items on a 5-point Likert scale, ranging from 1 (not true) to 5 (certainly true).
The psychometric properties of the three-factor SDQ model have been demonstrated to be
especially suited for use in non-risk samples (Dickey & Blumberg, 2004; Goodman et al., 2010;
van Leeuwen, Meerschaert, Bosmans, de Medts, & Braet, 2006). To evaluate whether the
SDQ’s three-factor solution also held in the present study, we performed a confirmatory factor
analysis (CFA), using maximum likelihood estimation with robust standard errors and a mean-
adjusted chi-square test statistic (MLR; Muthén &Muthén, 1998-2012). Guided by the residual
covariance matrix and modification indices, we added four theoretically plausible correlated
residuals to the baseline model. Two of those correlated residuals were indicative of aspects of
students’ externalizing behavior. Specifically, the residuals of items 2 and 10 both reflected
students’ hyperactivity, and the residuals of items 15 and 25 primarily evaluated students’
attention span. Also correlated were the residuals of prosocial items 9 and 20, which indicated
students’ willingness to help others. Lastly, the residuals of internalizing items 16 and 24 were
allowed to correlate, as they were both symptomatic of students’ nervousness and anxiety.
Despite a relatively low comparative fit index (CFI), this revised model yielded an acceptable fit
according to established cutoff values of .08 for the root-mean-square error of approximation
(RMSEA) and standardized root-mean-square residual (SRMR; Browne & Cudeck, 1993; Hu &
Bentler, 1999; Kline, 2011), χ2(268) = 890.04, p < .001, RMSEA = .067 (90% CI [.062, .072]),
CFI = .84, SRMR = .074. These fit indices are consistent with previous research (Goodman et
al., 2010; van Leeuwen et al., 2006), reporting acceptable RMSEA and SRMR values for the
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 4
126
three-factor solution, but CFIs below the conventional threshold of .90 for satisfactory fit (e.g.,
Bentler, 1990, 1992; Little, 2013). Recommendations for cutoff values for various fit indices
have previously been called into question, however, given that the mean value and the
distribution of most fit indices are likely to change with sample size, the distribution of the
data, and the chosen test statistic (e.g., Yuan, 2005). The factor loadings of the SDQ subscales
in the present study were adequate, ranging from .42 to .73 for Externalizing Behavior, from
.41 to .80 for Internalizing Behavior, and from .50 to .82 for Prosocial Behavior, respectively.
Cronbach’s alphas were .81 for Internalizing Behavior, .87 for Externalizing Behavior, and .86
for Prosocial Behavior, respectively.
CLASSROOM MISBEHAVIOR
A short, three-item scale developed by Tsouloupas et al. (2010) was used to measure teachers’
perceived amount of student behavior problems in their classroom. Items that made up this
instrument included “How frequently do you experience negative interactions with students?”,
“How often do you deal with student discipline problems?” and “On average, how emotionally
intense are your dealings with student discipline problems?”. All items were scored on a 5-
point Likert-type scale, ranging from 1 (almost never occurs) to 5 (occurs very frequently). In the
present sample, Cronbach's alpha for this measure was .83.
DOMAIN- AND STUDENT-SPECIFIC TEACHER SELF-EFFICACY
Teachers’ perceptions of their self-efficacy toward individual students across various teaching
domains were estimated using the Student-Specific Teacher Self-Efficacy Scale (Zee &
Koomen, 2015). This instrument, which is adapted from the Teachers’ Sense of Efficacy Scale
(TSES; Tschannen-Moran & Woolfolk Hoy, 2001), is specifically designed to evaluate teachers’
student-specific capability beliefs across various domains of teaching and learning. Largely
similar to the original TSES, this instrument represents the three domains of Instructional
Strategies (IS; 6 items), Behavior Management (BM; 5 items), and Student Engagement (SE; 6
items). The domain of IS measures the extent to which teachers feel able to use various
instructional methods that enable and enhance individual students’ learning, with items such as
“How well can you respond to difficult questions from this student?”. Slightly different from
the original Classroom Management dimension is the BM domain, which no longer taps
aspects of classroom organization, but rather concentrates on teachers’ perceptions of their
ability to organize and guide the behaviors of a particular student. A sample item of this
subscale includes “How much can you do to get this child to follow classroom rules?”.
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
SOURCES OF TEACHER SELF-EFFICACY
127
Teachers’ self-efficacy for SE captures teachers’ perceived ability to activate the interest of a
particular student in his or her schoolwork. This domain of TSE includes items such as “How
much can you do to get this student to believe he/she can do well in schoolwork?”.
Next to the three broad domains proposed by Tschannen-Moran and Woolfolk Hoy (2001),
the student-specific TSES is also targeted to the domain of Emotional Support (ES; 7 items).
This additional domain involves tasks and responsibilities related to how well teachers can
establish caring relationships with students, acknowledge students’ opinions and feelings, and
create settings in which students feel free to explore and learn. One example item of this
subscale includes “How well can you establish a safe and secure environment for this
student?”.
All items that made up this measure were rated by teachers on a seven-point Likert-type scale,
ranging from 1 (nothing) to 7 (a great deal). A CFA using MLR (Muthén & Muthén, 1998-2012)
provided sufficient fit to the present study’s data, after adding correlations between the
residuals of items 13 and 14, and 19 and 20, χ2(244) = 810.36, p < .001, RMSEA = .067 (90%
CI [.062, .072]), CFI = .91, SRMR = .073. Both correlated residuals seemed theoretically
plausible. Specifically, SE-items 13 and 14 focused on teachers’ perceived capability to
motivate individual students for their schoolwork. Items 19 and 20, in addition, concentrated
on the extent to which the teacher felt capable of responding positively and sincerely to a
particular student. All standardized factor loadings were considered high in this model ( >.55),
thereby supporting the factorial validity of the student-specific TSES. Internal consistency
scores of the student-specific TSES domains were .89 for IS, .94 for BM, .90 for SE, and .85
for ES, respectively.
PROCEDURE
During recruitment, either school principals or participating teachers distributed information
letters and consent forms to parents of all students from teachers’ classrooms. On average,
parental consent rates per classroom ranged between 46% and 100%. From all consents
received, we randomly selected eight students from participating teachers’ classrooms and
subsequently let these teachers know which eight students to report on. Students were asked to
fill out several questions about their background characteristics, including students’ age,
gender, and ethnicity, during a planned school visit. Teacher-reported questionnaires assessing
students’ social–emotional behavior at school and teachers’ self-efficacy in relation to
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 4
128
individual students were collected via an individually-addressed digital survey link that was
distributed by e-mail. Teachers filled out these questionnaires for each of the eight selected
students from their classroom. Participating educators additionally reported on some general
questions regarding their background characteristics. The total survey took approximately one
hour to complete. Teachers were asked to return the digital survey within two weeks after the
survey link was sent. To improve the participation rate, reminders were sent to nonresponding
teachers, resulting in a total response rate of 93.9%. Nonparticipation was due to long-term
sickness absence or teachers’ busy schedule. After participation, all teachers received a gift
voucher of €20,00.
DATA ANALYSIS
To examine the contribution of teachers’ and students’ background characteristics and a variety
of student behaviors in predicting teachers’ sense of self-efficacy toward individual students,
we fitted a series of multivariate hierarchical linear models using Mplus 7.11 (Muthén &
Muthén, 1998-2012). This analytical technique is quite flexible in that it corrects for nested data
structures, and avoids aggregation bias and underestimation of standard errors that sometimes
compromise the outcomes of Ordinary Least Squares-analyses of multilevel data (Snijders &
Bosker, 1999). All fixed and random effects parameters in these models were based on
maximum likelihood estimation with robust standard errors and a mean-adjusted chi-square
test statistic (MLR). Predictors were centered around the grand mean to ease their
interpretation.
Scale scores, represented by teachers’ mean response to relevant items, were used to reflect the
main constructs of interest. Several empirical sources (e.g., Allen & Seaman, 2007; Kislenko &
Grevholm, 2008; Leung, 2011; Parker, McDaniel, & Crumpton-Young, 2002) have indicated
that scale scores may be treated as interval-level measures, as long as the psychometric
properties of the scale are sufficient. Generally, such scale scores have been shown to be
largely insensitive to the violation of the interval assumption at the item-level (e.g., Leung,
2011; Parker et al., 2002).
In accordance with the methods proposed by Raudenbusch and Bryk (2002), we adopted a
stepwise sequential modeling strategy, reflecting an increasing complexity with each successive
model. In the first step, we estimated an unconditional means model without predictors to
partition the variance of teachers’ student-specific self-efficacy at the within- and between-
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
SOURCES OF TEACHER SELF-EFFICACY
129
teacher level. This preliminary model was used as a baseline for subsequent model
comparisons. In the second step, we added students’ background characteristics, and their
externalizing, internalizing, and prosocial behaviors as within-level (fixed) effects of teachers’
student-specific self-efficacy. After these individual student characteristics were accounted for,
we added between-teacher covariates to the equation to explain variance at the between-
teacher level. Lastly, to examine the existence of cross-level interactions of students’ behaviors
and teaching experience with teachers’ perceived classroom misbehavior, we allowed potential
random slopes to vary across teachers. If a particular association between students’ behaviors
and teachers’ student-specific self-efficacy significantly varied across teachers, cross-level
interactions were added.
RESULTS
DESCRIPTIVE STATISTICS
Table 1 presents descriptive statistics, including zero-order correlations, means, and standard
deviations of the variables. Consistent with expectations, moderate to strong negative
correlations were found between students’ Externalizing Behavior and dimensions of teachers’
Student-Specific Self-Efficacy. Notably, the association between Externalizing Behavior and
TSE for BM appeared to be the strongest, suggesting that teachers felt the least confident in
dealing with disruptive students in the domain of Behavior Management. Somewhat smaller
negative correlations were found between students’ Internalizing Behavior and teachers’
Student-Specific self-percepts of Efficacy. These behaviors seemed to have a slightly higher
association with teachers’ belief in their capability to provide individual students with adequate
emotional support and security. The positive correlations between students’ Prosocial Behavior
and TSE in relation to individual students were also in line with hypotheses. Teachers who
generally perceived their students to act prosocially in the classroom seemed to experience
higher levels of Self-Efficacy toward these students in all domains of teaching and learning.
Teachers’ perceptions of the amount of misbehavior in the classroom were not associated with
any of the domains of Student-Specific TSE. Interestingly, though, teachers who reported a
large amount of Student Misbehavior in the classroom did not appear to judge the
externalizing behaviors of individual students to be higher than those who reported a smaller
amount of Classroom Misbehavior. In contrast, a negative association was noted between
teachers’ perceived Classroom Misbehavior and individual students’ Internalizing Behavior.
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
TA
BLE
1
Desc
riptiv
e Sta
tistic
s and
Corr
elatio
ns
Var
iable
1
2 3
4 5
6 7
8 9
10
11
12
1. T
each
er G
ende
r 1.
00
2. T
each
er E
xper
ienc
e –.
28**
1.
00
3. S
tude
nt G
ende
r .0
3 –.
03
1.00
4. S
tude
nt A
ge
.05
–.08
–.
11**
1.
00
5. E
xter
naliz
ing
Beha
vior
–.
08
–.03
–.
26**
.1
0*
1.00
6. In
tern
aliz
ing
Beha
vior
–.
18**
.1
3**
–.03
.0
8 .4
2**
1.00
7. P
roso
cial
Beha
vior
.0
2 .0
3 .3
1**
–.12
**
–.55
**
–.41
**
1.00
8. C
lassr
oom
Beh
avio
r Pro
blem
s .0
8 –.
02
.03
–.02
–.
07
–.12
**
.06
1.00
9. S
tude
nt–S
peci
fic T
SE fo
r IS
–.04
.1
5**
.15*
* –.
13**
–.
46**
–.
27**
.4
5**
.07
1.00
10. S
tude
nt–S
peci
fic T
SE fo
r BM
.0
0 .1
1*
.27*
* –.
08
–.73
**
–.28
**
.59*
* .0
2 .5
0**
1.00
11. S
tude
nt–S
peci
fic T
SE fo
r SE
–.
04
.18*
* .1
8**
–.17
**
–.57
**
–.31
**
.54*
* .0
8 .8
8**
.59*
* 1.
00
12. S
tude
nt–S
peci
fic T
SE fo
r ES
.02
.16*
* .2
4**
–.17
**
–.56
**
–.35
**
.56*
* .0
8 .8
0**
.65*
* .8
4**
1.00
M
– 16
.67
– 10
.57
1.96
2.
03
4.07
2.
48
5.53
6.
14
5.60
5.
82
SD
– 11
.87
– 1.
11
0.81
0.
78
0.74
0.
78
0.91
0.
99
0.96
0.
77
* p
< .0
5; *
* p
< .0
1. G
ende
r: 0
= b
oys/
male
teac
hers
, 1 =
girl
s/fe
male
teac
hers
. TSE
= T
each
ers’
self–
effic
acy;
IS =
Inst
ruct
iona
l stra
tegi
es; B
M =
Beh
avio
r man
agem
ent;
SE =
St
uden
t eng
agem
ent;
ES
= E
mot
iona
l sup
port.
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
SOURCES OF TEACHER SELF-EFFICACY
131
Last, the correlations among students’ and teachers’ background characteristics, students’
behaviors, and Student-Specific TSE revealed, first, that male teachers and more experienced
educators generally reported their students to display higher levels of Internalizing Behavior.
Teaching Experience also seemed to be positively linked to all domains of Student-Specific
TSE, indicating that more experienced teachers perceive themselves as more efficacious than
their less experienced counterparts. In addition, teachers were likely to report higher levels of
Externalizing Behavior and lower levels of Prosocial Behavior for boys and older students, and
felt the least efficacious when dealing with these particular students. Lastly, it is interesting to
note that students’ Internalizing and Externalizing Behavior were moderately correlated with
each other, potentially suggesting comorbidity between behaviors in the externalizing and
internalizing spectrum (cf. Keiley, Lofthouse, Bates, Dodge, & Pettit, 2003). In the present
study, the focus was on the unique associations between students’ social–emotional behaviors
and teachers’ self-efficacy beliefs across domains and individual students.
UNCONDITIONAL MEANS MODEL
In the first step of the analyses, we fitted an unconditional means model, only containing the
four outcome variables (teachers’ Student-Specific Self-Efficacy for IS, BM, SE, and ES), and
no predictors other than the intercept. Intraclass correlations in this model indicated that
14.8% to 30.7% of the variance in teachers’ self-efficacy toward individual students occurred
between teachers. Generally, less than 5% of the variance in the domains of Student-Specific
TSE, however, was found to be associated with the school-level of hierarchy, implying that
teachers’ Student-Specific capability beliefs did not vary much across schools. Given the
substantial variance accounted for at the within- and between-teacher level, it can be concluded
that the data require a model that addresses the nesting of students within teachers.
STUDENT PREDICTORS OF TEACHERS’ STUDENT-SPECIFIC SELF-EFFICACY
Fixed effects of students’ background characteristics (Age and Gender) and behaviors
(Internalizing, Externalizing, and Prosocial Behavior) were modeled to allow the identification
of variables that were uniquely related to variation among dimensions of Student-Specific TSE.
This first model (see Table 2) significantly improved the prediction of teachers’ Student-
Specific Self-Efficacy beliefs, TRd(6) = 826.82, p < .001. Assessment of unstandardized
coefficients pointed to statistically significant negative associations between students’
Externalizing Behavior and teachers’ Student-Specific Self-Efficacy for IS (B = –.38, p < .001),
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 4
132
BM (B = –.73, p < .01), SE (B = –.55, p < .001), and ES (B = –.27, p < .001). This indicates
that with each scale point higher on students’ Externalizing Behavior, teachers’ Student-
Specific Self-Efficacy across domains is expected to decrease between –.27 and –.73 scale
points (Hox, 2002). In addition, students’ Internalizing Behavior was only uniquely and
positively associated with Student-Specific TSE for BM (B = .13, p < .001), and negatively
associated with Student-Specific TSE for ES (B = –.08, p < .05). After accounting for
Externalizing and Internalizing Behaviors, students’ Prosocial Behavior yielded statistically
significant positive results for all dimensions of Student-Specific TSE (IS: B = .28, p < .001;
BM: B = .40, p < .001, SE: B = .34, p < .001; ES: B = .41, p < .001). Regarding students’
background characteristics, only students’ Age appeared to be negatively associated with
Student-Specific TSE for SE (B = –.11, p < .01) and ES (B = –.06, p < .05), indicating that
teachers generally feel less self-efficacious in providing emotional support and promoting
students’ engagement when dealing with older students.
TEACHER PREDICTORS OF TEACHERS’ STUDENT-SPECIFIC SELF-EFFICACY
After the effects of students’ background characteristics and behaviors were accounted for at
the within-teacher level, we subsequently added teachers’ Gender, Teaching Experience, and
perceived Classroom Misbehavior to the model to explain variance at the between-teacher
level. Table 2 presents the results of these fixed and random effects of the analysis (Model 2).
Compared to Model 1, we generally found no significant changes in the variables at the within-
teacher level. After addition of the teacher variables, however, the association between
students’ Internalizing Problems and Student-Specific TSE for IS became statistically
significant in Model 2 (B = –.13, p < .01), suggesting that teachers’ appraisals of students’
Internalizing Behavior may be affected by features inherent to the teacher. Yet, the significant
link between students’ Age and TSE for ES failed to reach the significance threshold in this
second model.
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
TABL
E 2
Fixe
d an
d Ran
dom
Esti
mates
for P
redict
ors of
Tea
chers
’ Dom
ain- a
nd S
tude
nt–S
pecif
ic Se
lf–E
ffica
cy
* p
< .0
5; *
* p <
.01.
Gen
der:
0 =
boy
s/m
ale te
ache
rs, 1
= g
irls/
fem
ale te
ache
rs; T
SE =
Tea
cher
s’ se
lf–ef
ficac
y; IS
= In
stru
ctio
nal s
trate
gies
; BM
= B
ehav
ior m
anag
emen
t; SE
=
Stud
ent e
ngag
emen
t; E
S =
Em
otio
nal s
uppo
rt.
St
uden
t–Sp
ecifi
c TS
E fo
r IS
St
uden
t–Sp
ecifi
c TS
E fo
r BM
Stud
ent–
Spec
ific
TSE
for S
E
St
uden
t–Sp
ecifi
c TS
E fo
r ES
M
1 M
2
M1
M2
M
1 M
2
M1
M2
Pred
icto
r B
(S.E
.) B
(S.E
.)
B (S
.E.)
B (S
.E.)
B
(S.E
.) B
(S.E
.)
B (S
.E.)
B (S
.E.)
Fixe
d pa
rame
ters
In
terc
ept
5.59
(.09
)**
5.72
(.13
)**
6.
13 (.
05)**
6.
15 (.
11)**
5.67
(.07
)**
5.78
(.11
)**
5.
80 (.
06)**
5.
83 (.
08)**
St
uden
t–lev
el va
riable
s
Stud
ent G
ende
r –.
06 (.
07)
–.03
(.07
)
.05
(.06)
.0
6 (.0
6)
–.
11(.0
7)
–.08
(.07
)
.07
(.06)
.1
0 (.0
5)
Stud
ent A
ge
–.09
(.05
) –.
07 (.
06)
–.
03 (.
03)
–.01
(.03
)
–.11
(.04
)**
–.11
(.04
)**
–.
06 (.
03)*
–.05
(.03
) E
xter
naliz
ing
Beha
vior
–.
38 (.
07)**
–.
41 (.
07)**
–.73
(.06
)**
–.74
(.06
)**
–.
55 (.
07)**
–.
60 (.
06)**
–.27
(.05
)**
–.28
(.04
)**
Inte
rnali
zing
Beh
avio
r –.
06 (.
05)
–.13
(.05
)**
.1
3 (.0
4)**
.1
0 (.0
4)*
.0
1 (.0
6)
–.08
(.05
)
–.08
(.04
)* –.
14 (.
04)**
Pr
osoc
ial B
ehav
ior
.28
(.07)
**
.20
(.06)
**
.4
0 (.0
6)**
.3
5 (.0
7)**
.34
(.08)
**
.21
(.07)
**
.4
1 (.0
6)**
.3
7 (.0
6)**
Te
ache
r–lev
el va
riable
s
Teac
her G
ende
r
–.22
(.16
)
–.
04 (.
12)
–.20
(.12
)
–.
06 (.
10)
Teac
her E
xper
ienc
e
.01
(.01)
.0
0 (.0
0)
.01
(.01)
**
.01
(.00)
* Cl
assr
oom
Misb
ehav
ior
.1
1 (.0
9)
–.06
(.05
)
.0
8 (.0
8)
.01
(.06)
Ra
ndom
par
amete
rs
Betw
een–
Teac
her V
arian
ce
With
in–T
each
er V
arian
ce
.4
7 (.0
5)**
.2
1 (.0
4)**
.3
7 (.0
4)**
.3
0 (.0
3)**
.0
8 (.0
2)**
.3
0 (.0
3)**
.5
8 (.0
7)**
.1
2 (.0
3)**
.4
5 (.0
6)**
.2
6 (.0
3)**
.1
0 (.0
2)**
.2
1 (.0
2)**
R2 st
atist
ics
R2w
ithin
.3
3 .4
0
.65
.65
.3
9 .4
6
.49
.56
R2be
twee
n
.14
.09
.25
.1
6
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 4
134
Regarding the teacher-level variables, only statistically significant associations were noted
between Teacher Experience and teachers’ sense of Student-Specific Self-Efficacy for SE (B =
.01, p < .01) and ES (B = .01, p < .05). The relationships of teachers’ Gender and perceived
Classroom Misbehavior with the dimensions of Self-Efficacy toward particular students were
not statistically significant. Overall, student variables accounted for 40% of the within-teacher
variance in Student-Specific TSE for IS, 65% in TSE for BM, 46% in TSE for SE, and 56% in
TSE for ES, respectively. At the between-teacher level, 14%, 9%, 25%, and 16% of the
variance in the respective Student-Specific TSE domains for IS, BM, SE, and ES was explained
by the student- and teacher-level predictors.
CROSS-LEVEL INTERACTIONS
To evaluate whether Teacher Experience and perceived Classroom Misbehavior interacted in
the prediction of Student-Specific TSE, the slopes of the student predictors were first allowed
to vary across teachers. The random slope coefficients of the association between students’
Externalizing Behavior and Student-Specific TSE for BM (σ2 = .08, p < .01), and between
Prosocial Behavior and Student-Specific TSE for BM (σ2 = .09, p < .01) and ES (σ2 = .02 p <
.05) were significantly different from zero, indicating that these parameters varied across
teachers. Consequently, cross-level interactions between the teacher variables (i.e., Teacher
Experience and perceived Classroom Misbehavior) and these student predictors were added
stepwise to the model. Adding these cross-level interactions did not affect the significance of
the parameter estimates of Model 2. None of these cross-level interactions reached the
significance threshold, except for the negative effect of teachers’ perceptions of Classroom
Behavior Problems on the association between students’ Externalizing Behavior and Student-
Specific TSE for BM (B = –.19, p < .01). This finding indicates that teachers feel less
efficacious in managing individual students’ externalizing behavior when they perceive high
amounts of misbehavior in the classroom.
DISCUSSION
This study investigated the associations between a variety of social–emotional student
behaviors and teachers’ self-efficacy beliefs toward individual students in various teaching
domains. In addition, the moderating role of teachers’ professional experience and perceived
classroom misbehavior was examined. Results from this study offer new insights into the ways
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
SOURCES OF TEACHER SELF-EFFICACY
135
in which students’ externalizing, internalizing, and prosocial behaviors may hamper or support
teachers’ self-efficacy beliefs across teaching domains at a dyadic level.
TEACHERS’ SELF-EFFICACY IN RELATION TO EXTERNALIZING BEHAVIOR
Consistent with expectations, teachers perceived themselves as less self-efficacious in relation
to students who exhibited externalizing behavior in class, after controlling for students’ and
teachers’ background characteristics. This is in support of previous research on teachers’
classroom-level self-efficacy (e.g., Lambert et al., 2009; Tsouloupas et al., 2010), indicating that
disruptive children may hamper teachers’ self-efficacy in dealing with challenging behavior and
stressful situations in the classroom. However, whereas past studies have almost solely
concentrated on total efficacy scores or domain-specific TSE for behavior management, our
results additionally show that these under-controlled behaviors are consistently linked to various
domains of self-efficacy for teaching and learning. Accordingly, unsuccessful encounters with
students who display externalizing conduct are likely to undermine teachers’ perceived
capability to effectively instruct, motivate, manage, and emotionally support individual students.
Such poorer self-efficacy beliefs, in turn, may also bring about more disruptive student
behavior in new situations (e.g., Bandura, 1997).
Not surprisingly, the association between externalizing student behavior and teachers’
perceived capability in deploying effective methods to prevent and redirect instances of student
misbehavior appeared to be the largest. Possibly, these patterns of externalizing misconduct
reflect a poorer fit with teachers’ expectations for appropriate behavior in the classroom than
other challenging student behaviors (Gresham & Kern, 2004). Such behavioral mismatches
may trigger a pattern of disturbed student–teacher interactions, which potentially undermine
teachers’ feelings of efficacy and satisfaction in teaching (cf. Koomen & Spilt, 2011). This is
alarming, given that an unhealthy sense of self-efficacy for behavior management may
encourage teachers’ use of ineffective conflict management styles, which may exacerbate
students’ disruptive behavior and potentially advance the erosion of teachers’ already feeble
capability beliefs (e.g., Goddard et al., 2004; Jennings & Greenberg, 2008; Morris-Rothschild &
Brassard, 2006).
Perhaps of a more interesting note is the finding that symptoms of externalizing student
behavior may also come at the expense of teachers’ student-specific self-efficacy beliefs in the
instructional domain. There are some studies to support this finding, indicating that teachers
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 4
136
generally feel less confident and effective in proactively involving disruptive students in high-
quality instructional interactions and activities, and consequently resort to controlling and
punitive behaviors toward these students (e.g., Arbeau & Coplan, 2007; Sutherland & Oswald,
2005; Wehby, Symons, Canale, & Go, 1998). Probably, such a lack of efficacy in instructing
and motivating challenging students may further reinforce these children’s expressions of anger
and frustration toward the teacher, as well as increase their off-task behavior and
maladjustment in class (Arnold, 1997; Stipek & Miles, 2008). Thereby, a vicious cycle may be
set into motion in which teachers’ student-specific self-efficacy percepts and instructional
actions, and students’ subsequent social–emotional and task behaviors in class may influence
each other in a reciprocal manner (cf. Bandura, 1997; Stipek & Miles, 2008). Hence, given that
externalizing student behaviors may hamper student-specific TSE in both instructional and
social–emotional domains, it seems essential to provide educators with the knowledge and
skills necessary for teaching disruptive students self-regulation strategies that improve their
classroom adjustment (cf. Koomen & Spilt, 2011).
TEACHERS’ SELF-EFFICACY IN RELATION TO INTERNALIZING BEHAVIOR
Consistent with expectations, internalizing behaviors seemed to be less of a factor than
externalizing student behavior in explaining variations in teachers’ self-percepts of student-
specific self-efficacy. This finding resonates well with those of past research (e.g., Coplan &
Prakash, 2003; Gresham & Kern, 2004; Kokkinos et al., 2004), suggesting that students’
internalizing symptoms might go undetected by their teachers, or are merely perceived as less
serious. Accordingly, it is possible that teachers may display a greater zeal and persistence in
educating internalizing children than externalizing children.
As yet, our results give reason to believe that behaviors in the internalizing spectrum may
contribute to some aspects of teachers’ sense of student-specific self-efficacy. Specifically,
teachers’ student-specific self-efficacy for emotional support seemed to be predicted best by
students’ internalizing behaviors, after accounting for students’ and teachers’ background
features. One possibility that may explain this negative association is that internalizers feel
more wary and anxious in the face of social stimuli and consequently tend to refrain from daily
interactions with their teacher (e.g., Arbeau, Coplan, & Weeks, 2010; Coplan & Prakash, 2003;
Rudasill, 2011). Such socially withdrawn behaviors may result in a student–teacher relationship
pattern characterized by lower levels of closeness and higher levels of dependency (e.g., Arbeau
et al., 2010; Henricsson & Rydell, 2004; Roorda et al., 2014). When teachers recurrently fail to
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
SOURCES OF TEACHER SELF-EFFICACY
137
connect and get through to these internalizing children, poorer self-efficacy beliefs toward
these particular children may be prompted (e.g., Bandura, 1997). This may explain why
teachers usually fall back into regulatory and dominant behaviors toward students with
internalizing behavior (Roorda, Koomen, Spilt, Thijs, & Oort, 2013).
Somewhat surprisingly, teachers also reported slightly elevated levels of self-efficacy in the
domain of behavior management toward students with internalizing behavior. One mainly
methodological explanation for this finding may be that internalizing student behavior merely
functioned as a suppressor for predicting the fairly stronger, unique association among
students’ externalizing behavior and TSE for behavior management. According to Maassen
and Bakker (2001), this phenomenon may occur when a predictor is positively correlated with
another independent variable, but not with the criterion. In the present study, suppression may
indicate that internalizing student behavior has more in common with externalizing conduct
than with teachers’ student-specific self-efficacy for behavior management, and thereby
improved externalizing behavior as a predictor of TSE for behavior management. This
potential suppressor effect mirrors previous empirical research, suggesting that comorbid
externalizing and internalizing symptoms may occur more frequently than single-form
behaviors, and should therefore be interpreted in combination with each other, rather than
separately (e.g., Keiley et al., 2003). Another, more theoretical justification for this effect is that
students with anxious and withdrawn patterns of behavior (without potentially co-occurring
externalizing symptoms) usually do not disturb their peers or challenge their teachers’
authority. Thereby, these students seem to meet teachers’ behavioral values and expectations in
the classroom (Gresham & Kern, 2004). As such, it is possible that teachers might actually feel
quite self-efficacious in managing these students’ behaviors.
Lastly, internalizing student behavior did not seem to seriously upset their teachers’ self-
efficacy for tasks related to motivation and instructional delivery. The lack of association
between students’ internalization and student-specific TSE for student engagement was, for
instance, at odds with our expectation that teachers may feel less efficacious in activating their
students’ interest in schoolwork when dealing with emotionally disturbed students. Moreover,
the negative association between students’ internalizing behavior and TSE for instructional
strategies only reached the significance threshold after accounting for teachers’ gender,
experience, and perceived classroom misbehavior. It may be that educators’ recognition of, and
responsiveness to internalizers’ subtle cues are more likely to be affected by factors inherent or
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 4
138
contextual to the teacher than their preoccupation with externalizers’ more blatant signs.
Research of Kokkinos and colleagues (Kokkinos et al., 2005; Kokkinos & Kargiotidis, 2014),
for instance, put forth that teachers’ ability to recognize the needs and behaviors of students
with internalizing problems increases as they have more teaching experience, and may depend
on their own interpersonal sensitivity and gender. Correlational patterns between students’
social–emotional behaviors and teacher-level variables in the present study, including teaching
experience and gender, largely substantiate this assumption. Also, there is a strong possibility
that students with internalizing symptoms, due to their subdued behaviors, generally provoke
less negative thoughts about instruction or feelings of inefficacy in their teachers, as it is more
difficult for teachers to gauge these students’ comprehension of what they have taught (e.g.,
Rubin & Coplan, 2004).
TEACHERS’ SELF-EFFICACY IN RELATION TO PROSOCIAL BEHAVIOR
In line with expectations, teachers consistently reported higher levels of self-efficacy in relation
to students who exhibit high levels of prosocial behavior. Again, stronger associations were
noted for teachers’ self-efficacy toward emotional and behavioral domains of teaching and
learning, than for instruction-related tasks. This is perhaps not surprising, as the domains of
behavior management and emotional support are, in large part, concerned with how well
teachers relate to, and interact with their students. Several empirical sources have shown that
patterns of prosocial student behavior may pave the way for higher quality relationships with
their teachers (Birch & Ladd, 1998; Henricsson & Rydell, 2004; Roorda et al., 2014). Such
enactive mastery experiences may raise teachers’ beliefs in their self-efficacy (Bandura, 1997;
Goddard et al., 2004), potentially further stimulating individual students’ prosocial behaviors in
the classroom.
Despite teachers’ higher self-efficacy beliefs in relation to students who display relatively high
levels of prosocial behavior, teachers have repeatedly been shown to spend less time with
prosocial students, and regularly fail to give them credit for their positive behavior, especially
when they get older (e.g., Arbeau & Coplan, 2007; Nesdale & Pickering, 2006). To maintain
and further encourage prosocial behavior in their students, teachers should recognize the need
to praise and respond to students’ appropriate behaviors in class. In doing so, teachers may
further enhance their feelings of self-efficacy toward these individual children.
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
SOURCES OF TEACHER SELF-EFFICACY
139
TEACHERS’ SELF-EFFICACY IN RELATION TO STUDENT AND TEACHER CHARACTERISTICS
In investigating students’ background characteristics, we only found students’ age to be
negatively associated with teachers’ student-specific self-efficacy for student engagement. This
finding is supported by prior research (Wolters & Daugherty, 2007), noting that teachers, when
dealing with older children, tend to report less confidence in their ability to keep students
engaged. This intriguing finding seems to complement those of studies on student motivation
(e.g., Fredricks & Eccles, 2009), which demonstrated a downward spiral in students’
competence-related behaviors and motivation during their transition to middle school. Future
research should take the complex interplay between teachers’ self-efficacy, students’ age, and
motivation into account.
Although bivariate correlations suggested a potential association between professional teaching
experience and dimensions of TSE toward individual students, multilevel analyses indicated
that teaching experience only added to the prediction of student-specific TSE for student
engagement and emotional support. This finding suggests that educators’ teaching experience
particularly ameliorates their self-efficacy in the affective domain of teaching, including such
tasks as providing emotional support and increasing individual students’ interest in
schoolwork. Previous studies have supported this slight increase in more experienced teachers’
self-efficacy, both for affective domains as well as other areas of teaching and learning (Klassen
& Chui, 2010; Ross et al., 1996; Wolters & Daugherty, 2007). The potential value of teachers’
experience for their self-efficacy might explain, in part, why experienced teachers seem to be
more effective in managing students’ behaviors and addressing their needs than inexperienced
teachers (Kokkinos et al., 2004).
THE MODERATING ROLE OF TEACHING EXPERIENCE AND PERCEIVED CLASSROOM MISBEHAVIOR
In seeking to discern the moderating role of teachers’ experience and perceived classroom
misbehavior, we noted that years of experience did not buffer or exacerbate the association
between students’ social-emotional behaviors and teachers’ self-efficacy toward individual
students. This is unlike the findings of Kokkinos et al. (2004), which seemed to suggest that
teachers’ experience-induced behavioral knowledge, skills and awareness may buffer or
exacerbate the potential negative relationship between challenging student behavior and TSE.
However, results did point to a moderation effect of teachers’ perceptions of classroom
misbehavior. Specifically, teachers in poorly behaving classrooms experienced lower levels of
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 4
140
self-efficacy in managing the behavior of individual students with externalizing conduct than in
classrooms with fewer instances of misbehavior. This finding substantiates prior research (e.g.,
Bynes, 1994), indicating that teachers may develop increasingly negative attitudes toward their
students in classrooms with many challenging students. Importantly, however, the moderating
role of teacher-perceived amounts of classroom misbehavior could not be ascribed to teachers’
appraisals of individual students’ externalizing behavior. In the present study, the zero-order
correlation between misbehavior in class and ratings of externalizing student behavior was not
significant. Hence, these findings underline the relevance of considering characteristics of the
classroom when investigating teachers’ beliefs of self-efficacy.
LIMITATIONS
The present study’s findings need to be interpreted in the context of several limitations. First,
the correlational and cross-sectional nature of the study precludes any speculation on causal
relations. Although our results provide preliminary support of the potential relationships
between students’ behavior and TSE, it may well be that the nature of these associations are
reciprocal. Indeed, Bandura’s (1997) model of triadic reciprocal causation asserts that teachers’
personal factors, their behaviors, and aspects of the classroom context may function as
interacting factors that influence one another bi-directionally. Longitudinal, cross-lagged
designs could advance our understanding of how individual students’ behaviors and teachers’
self-efficacy toward these students in various domains of teaching and learning influence one
another across time.
In relation to this issue, some caution is warranted when generalizing the results of this study
to other populations and settings. Specifically, this study relied on a sample of primarily
experienced, female teachers who generally taught students with mid- to high socioeconomic
backgrounds. These teachers, by virtue of their experience and more advantaged student
population, may have felt more efficacious and better prepared to deal with their students
across teaching domains. Including teachers from a wider range of backgrounds may result in a
more reliable and generalizable picture of teachers’ self-efficacy in relation to particular
students in different spheres of functioning.
Third, teachers not only reported about their sense of self-efficacy toward individual students,
but also about these students’ behaviors. As such, this study might have been threatened by
shared source variance, resulting in an overestimation of the strength of associations. However,
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
SOURCES OF TEACHER SELF-EFFICACY
141
teachers’ self-efficacy is most likely constructed from information conveyed by experienced
events in the classroom (Bandura, 1997). Given that teachers’ own experiences and self-
knowledge are crucial sources of their self-efficacy, teacher reports may seem an adequate
method of measuring students’ classroom behaviors. Still, it would be useful for future
research to employ multiple methods, including interviews and observations, to further
elucidate the present study’s findings.
Fourth, although we made use of multilevel analysis to handle the clustering of students within
teachers, we did not address the nesting of classrooms within schools. One reason for
choosing to ignore a third level of nesting is that we generally found less than 5% of the
variance in TSE to be associated with the school-level of hierarchy, suggesting that teachers’
capability beliefs did not vary much across schools. Probably, this lack of variation might be
explained by the fact that the 69 teachers who participated in this study were relatively evenly
distributed across the 24 schools. Indeed, only two to three teachers per school decided to take
part. Nevertheless, a number of studies on the sources of TSE has indicated that teachers’ self-
efficacy may depend, in part, on aspects such as school atmosphere, principal leadership, and
social support provided by parents and colleagues (e.g., Cheung, 2008; Lee, Dedrick, & Smith,
1991; Moore & Esselman, 1992; Tschannen-Moran & Woolfolk Hoy, 2007). With this in mind,
it may be important to include such school contextual influences at the school-level of analysis
when investigating teachers’ self-efficacy beliefs.
Fifth, it is possible that the relations discovered in this study emanate from a common relation
with contextual or structural features of the classroom context. Although we were able to
account for differences between teachers in their gender, years of experience, and perceived
classroom misbehavior, there might have been other important between-teacher factors that
we did not include in this study. For instance, teachers’ collegial support (Brownell & Pajares,
1999; Ciani, Summers, & Easter, 2008), instructional quality and classroom management
(Holzberger, Philipp, & Kunter, 2013), and perceived work pressure (Leroy, Bressoux,
Sarrazin, & Trouilloud, 2007) have been shown to be associated with teachers’ sense of self-
efficacy. Thus, in any attempt to replicate the results, it is recommended that future researchers
should take account of classroom and teacher characteristics to explain between-teacher
differences in TSE.
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 4
142
Lastly, teachers’ perceptions of self-efficacy were characterized by relatively high means and
small standard deviations, suggesting the existence of social desirability bias. Generally, social
desirability has been presumed to generate more flattering reports about the self and a limited
range of answers (Goffin & Gellatly, 2001). This potential bias in teachers’ responses have also
been noted in prior research on teachers’ domain-specific self-efficacy at the classroom-level
(e.g., Heneman, Kimball, & Milanowski, 2006), and might have weakened the associations with
students’ behaviors in this study.
CONCLUSION
Despite its limitations, the present study has demonstrated the theoretical and practical
relevance of studying TSE in relation to individual students’ social–emotional behaviors across
various domains of teachers’ functioning. Teachers’ self-efficacy has long been conceptualized
as a relatively stable teacher characteristic which, at best, may be dependent upon particular
teaching tasks and domains (Raudenbusch et al., 1992; Tschannen-Moran & Woolfolk Hoy,
2001). Our results show, however, that most of the variance in TSE occurred within teachers,
suggesting that these capability beliefs may also vary over the particular students they teach.
Central contributors to such self-efficacy fluctuations seem to be both prosocial and
challenging student behaviors, and externalizing behavior in particular. Notably, these
behaviors not only appear to relate to teachers’ perceived effectiveness in providing behavioral
and affective support during reciprocal student–teacher interchanges, but their TSE in
delivering instruction as well. This is an important finding, given that teachers’ dealings with
individual students’ misbehavior are likely to come at the expense of high-quality instructional
activities and student–teacher interactions (e.g., Arbeau & Coplan, 2007; Sutherland & Oswald,
2005).
The results of the present study, if they are replicated in future studies, may have several
implications for educational researchers and practitioners alike. First, the ways teachers
appraise and integrate individual students’ behavior into student-specific self-efficacy
judgments may play an important role in teachers’ preparedness and motivation to deal with a
particular child (Bandura, 1997). Assumedly, educators who perceive themselves as unable to
teach and affectively support a child have a tendency to shy away from these children or
slacken their efforts when the goings get tough. Teachers must be made aware that such
behaviors and actions may have serious implications for the academic and social–emotional
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
SOURCES OF TEACHER SELF-EFFICACY
143
adjustment of challenging students, and externalizing children in particular. Specifically,
children with externalizing problems may become easily frustrated or unhappy about their
teachers’ lack of instructional or emotional support, and may express these feelings by acting
more aggressively toward the teacher in future situations (cf. Stipek & Miles, 2008). As such,
teachers’ self-efficacy beliefs toward disruptive students and associated behavior and actions
may serve as an additional risk factor for poor quality student–teacher relationships and
students’ social-emotional and academic maladjustment in school. Yet, the importance of
teachers’ confidence in their ability to provide internalizing students with adequate emotional
support should also not be underestimated. These capability beliefs may serve as important
tools for helping students with internalizing symptoms to come out of their shell and to
navigate the social world. Thus, helping teachers to reflect on the effects of their cognitions
about externalizing and internalizing children may be vital to improving the quality of students’
and teachers’ shared interactions and experiences.
Second, the dynamic interplay between students’ disruptive behaviors and TSE may not only
hamper students’ academic adjustment, but may also result in increased levels of emotional
labor, daily stress, and burnout in teachers (e.g., Chang, 2013; Hargreaves, 1998; Spilt et al.,
2011). This suggests that teacher training and development programs must incorporate
strategies that teachers might use to bolster their self-efficacy in relation to individual
(disruptive) students, including goal setting, behavior management, and providing emotional
support. These activities may allow teachers to gain more pleasant emotional experiences with,
and social feedback from their students, resulting in less stress and higher TSE (Spilt et al.,
2011).
In conclusion, it behooves educational researchers and practitioners alike to further investigate
the complex ways in which teachers’ self-efficacy in relation to individual students with
externalizing and internalizing symptoms and their subsequent behaviors and actions toward
them affect students’ motivation, conduct, and achievement in the classroom. Viewing
teachers’ self-efficacy from a dyadic perspective may be a first step forward.
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
145
CHAPTER 5 STUDENTS’ DISRUPTIVE BEHAVIOR AND THE DEVELOPMENT OF TEACHERS’ SELF-
EFFICACY: THE ROLE OF TEACHER-PERCEIVED CLOSENESS AND CONFLICT IN THE
STUDENT–TEACHER RELATIONSHIP
_________________________________________________________________________
Data gathered from a short-term longitudinal study within regular upper elementary schools
were used to evaluate a theoretical model within which teachers’ perceptions of conflict and
closeness in the student–teacher relationship were considered as the intermediary mechanisms
by which individual students’ disruptive behavior may generate changes in teachers’ student-
specific self-efficacy beliefs (TSE) across teaching domains (instructional strategies, behavior
management, student engagement, and emotional support). Surveys were administered among
a Dutch sample of 524 third-to-sixth graders and their 69 teachers. Longitudinal mediation
models indicated that individual students’ disruptive behavior generally predicted higher levels
of teacher-perceived conflict which, in turn, resulted in lower student-specific TSE across
teaching domains. Teacher-perceived closeness, however, was not found to mediate the link
between disruptive student behavior and student-specific TSE. Instead, support was found for
an alternative model representing the hypothesis that TSE, irrespective of teaching domain,
mediated behavior-related changes in teachers’ perceptions of closeness in the student–teacher
relationship.
________________________________________________________________________________________ Zee, M., de Jong, P. F., & Koomen, H. M. Y. (2015). Students’ disruptive behavior and the development of teachers’ self-efficacy: The role of teacher-perceived closeness and conflict in the student–teacher relationship. Manuscript submitted for publication.
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 5
146
INTRODUCTION
Teachers’ self-efficacy beliefs (TSE) have been widely acknowledged to be one of the most
basic, yet potent psychological resources of teachers’ functioning in the classroom (Bandura,
1997; Klassen, Tze, Betts, & Gordon, 2011; Tschannen-Moran & Woolfolk Hoy, 2001).
Accumulating evidence has indicated that teachers with a firm belief in their capabilities may
translate their knowledge and abilities into proficient action, whereas those who lack such
beliefs will probably not attempt to make things happen in class (e.g., Bandura, 1997; Klassen
& Tze, 2014). When teachers live up to their generalized sense of self-efficacy, they are more
likely to provide high-quality instruction, adopt proactive approaches to managing disruptive
behavior, and convey supports that activate students’ motivation and engagement in class (e.g.,
Dunn & Rakes, 2011; Martin & Sass, 2010; Morris-Rothschild & Brassard, 2006; Reyes et al.,
2012; Wertheim & Leyser, 2002). Given the important role TSE might play in students’
socioemotional and academic development, it is critical to explore the factors and processes
that may account for these beliefs.
One potentially compelling contribution to the corpus of evidence on the sources of TSE has
recently been provided by cross-sectional investigations focusing on teachers’ sense of self-
efficacy in relation to individual students (e.g., Zee & Koomen, 2015; Zee, Koomen, Jellesma,
Geerlings, & de Jong, 2016). These studies have suggested that teachers are likely to develop
differentiated sets of self-beliefs about their ability to deal with individual children in distinct
teaching domains, depending on these students’ disruptive, or externalizing behaviors in the
classroom (ibid.). Relatively little information has been generated, however, about the
mechanisms by which individual students’ disruptive behavior may generate changes in these
student-specific TSE beliefs. Following Bandura (1997), there is a need for research to move
away from cross-sectional examinations of TSE and its underlying sources, and explore the
role of potential mediating processes through which sources of self-efficacy may become
instructive to teachers’ self-efficacy beliefs across time. In the present study, therefore, we seek
to expand the available information on the sources of student-specific TSE, by evaluating an
interpersonal social-cognitive model within which teachers’ perceptions of closeness and
conflict in the relationships with individual students are hypothesized to form the intermediary
mechanisms by which individual students’ disruptive behavior may affect teachers’ student-
specific self-efficacy over time. Theoretical and empirical knowledge in this direction may help
educational researchers and practitioners identify levers to increase teachers’ self-efficacy
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
PROCESSES UNDERLYING TEACHER SELF-EFFICACY
147
toward disruptive students, and thereby improve these students’ classroom experiences and
academic adjustment.
AN INTERPERSONAL SOCIAL-COGNITIVE MODEL OF TEACHERS’ SELF-EFFICACY
In this study, we extended Bandura’s (1997) social-cognitive assumptions about self-efficacy by
embedding them within an interpersonal framework of student–teacher relationships (Pianta,
1999; Pianta, Hamre, & Stuhlman, 2003). With this integrated model, we aimed to subscribe to
the longstanding notion that TSE, rather than being a single-level, trait-like construct, is
intrinsically related to the specific students with whom they interact in distinct realms of
activity (Tschannen-Moran, Woolfolk Hoy, & Hoy, 1998; Tschannen-Moran & Woolfolk Hoy,
2001; Zee et al., 2016). This conception of TSE as being both student- and domain-specific
maintains, generally, that features of individual students, such as their background
characteristics and behaviors, may serve as key sources of information about whether teachers
can muster whatever it takes to adequately instruct, manage, motivate, and emotionally support
a particular student (Bandura, 1997; Pianta et al., 2003; Zee et al., 2016). Consistent with this
view, a modest body of work on within-teacher predictors of TSE has spawned some evidence
that teachers’ general efficaciousness can rise or fall according to their students’ level of
engagement and achievement in class (e.g. Raudenbusch, Rowan, & Cheong, 1992; Ross,
Cousins, & Gadalla, 1996). Other studies have marked students’ disruptive behaviors as the
type of information teachers attend to and use as direct sources of their self-efficacy in
domains of behavior management, relationship building, instructional strategies, and student
motivation (e.g., Lambert, McCarthy, O'Donnell, & Wang, 2009; Spilt & Koomen, 2009;
Tsouloupas, Carson, Matthews, Grawitch, & Barber, 2010; Zee, de Jong, & Koomen, 2016).
Relative to other sources of self-efficacy, these disruptive student behaviors have thus far been
demonstrated to achieve the highest explanatory and predictive power for both classroom-level
and student-specific TSE (ibid.).
Further broadening beyond the original social-cognitive paradigm, our framework adheres to
the notion that disruptive student behaviors, as sources of student-specific TSE, may not per
se be enlightening to the formation of these beliefs (cf. Bandura, 1982, 1997). Rather,
individual students’ conduct can be presumed to become instructive to TSE only through
teachers’ subjective evaluations of these behaviors in the context of their daily interactions with
individual students. Theory and research on student–teacher interactions (e.g., Pianta et al.,
2003; Spilt & Koomen, 2009; Spilt, Koomen, & Thijs, 2011; Stuhlman & Pianta, 2002) has
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 5
148
indicated that teachers’ evaluations of student behavior may derive, in part, from previous
experiences with individual students, stored in underlying representational models of
relationships with these students. This idea is premised on the attachment-based assumption
that relationship representations may yield internalized and relatively stable patterns of beliefs,
feelings, and expectations about the self as a teacher and the student in the relationship (Pianta
et al., 2003; Spilt & Koomen, 2009). Such belief systems can be primarily positive, reflecting
experiences of close student–relationships, or predominantly negative, incorporating a history of
conflict in the relationship with a particular child (e.g., Verschueren & Koomen, 2012).
Accordingly, teachers’ representations, or perceptions, of closeness and conflict in the student–
teacher relationship can be considered powerful cognitive tools, as they largely guide their
interpretations of individual students’ underlying intentions, behaviors, and actions in the
relationship, and provide teachers with vital information about their capability to deal with the
child (Howes, Hamilton, & Matheson, 1994; Pianta, 1999; Pianta et al., 2003; Spilt et al., 2011).
Guided by the interpersonal social-cognitive principles proposed above, we aim to explore a
model (see Figure 1a) in which teachers’ perceptions of closeness and conflict in the student–
teacher relationship are considered to be the intermediary mechanisms that could explain why
teachers may develop a positive or negative sense of self-efficacy toward individual disruptive
children. Theoretical and empirical justification for the sequence of linkages delineated by our
hypotheses are provided in the next sections.
DISRUPTIVE STUDENT BEHAVIOR AND TEACHERS’ RELATIONSHIP PERCEPTIONS
Multiple sources of evidence have increasingly indicated that disruptive student behavior
matters for teachers’ perceptions of conflict and closeness in the student–teacher relationship
(e.g., Mejia & Hoglund, 2016; Roorda, Verschueren, Vancraeyveldt, van Craeyevelt, & Colpin,
2014). Drawing on both attachment and developmental systems frameworks, these studies
have postulated that teachers generally have more difficulty forming relationships with
disruptive students that are marked by warmth, trust, and affection (i.e., closeness), and instead
develop relationships that reflect high levels of negativity, discordance, and distrust (i.e., conflict;
Pianta, 1999). In line with this assumption, both cross-sectional and longitudinal studies have
convincingly disclosed the negative effect of disruptive, aggressive, or antisocial student
behavior on teachers’ experiences of student–teacher conflict (Birch & Ladd, 1998; Henricsson
& Rydell, 2004; Jerome et al., 2009; Ladd & Burgess, 1999; Murray & Murray, 2004; Murray &
Zvoch, 2011; O’Connor, 2010).
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
PROCESSES UNDERLYING TEACHER SELF-EFFICACY
149
FIGURE 1A Hypothesized Mediational Model
Note. IS = Instructional Strategies, BM = Behavior Management, SE = Student Engagement, ES = Emotional Support. Coefficients a and b reflect associations between predictor and mediators, and mediators and outcome measures, respectively. The product ab reflects the hypothesized indirect effect of individual students’ disruptive behavior on domains of student-specific self-efficacy, through teachers’ perceptions of conflict and closeness. Coefficient c reflects the direct association between predictor (disruptive student behavior) and outcome variables (domains of student-specific self-efficacy).
FIGURE 1B Alternative Mediational Model
Note. IS = Instructional Strategies, BM = Behavior Management, SE = Student Engagement, ES = Emotional Support. Coefficients a and b reflect associations between predictor and mediators, and mediators and outcome measures, respectively. The product ab reflects the hypothesized indirect effect of individual students’ disruptive behavior on teachers’ perceptions of conflict and closeness, through domains of student-specific self-efficacy. Coefficient c reflects the direct association between predictor (disruptive student behavior) and outcome variables (teachers’ perceptions of conflict and closeness).
Individual Students’ Disruptive Behavior
Teachers’ Perceptions of Conflict and Closeness in the Student–
Teacher Relationship
Student-Specific Self Efficacy for IS, BM, SE and ES
c
Individual Students’ Disruptive Behavior
Student-Specific Self-Efficacy for IS, BM, SE, and ES
Teachers’ Perceptions of Conflict and Closeness in the Student–
Teacher Relationship
c
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 5
150
Furthermore, some studies (Doumen et al., 2008; Roorda et al., 2014) have even acknowledged
that students’ displays of externalizing behavior may be sufficient to commence a vicious cycle
of disharmonious relationships and escalating problem behaviors. These outcomes are
consistent with the idea that student behavior and teachers’ perceptions of the student–teacher
relationship are reciprocally related to one another. Hence, it can be suggested that disruptive
student behavior may generate negative changes in teachers’ perceptions of conflict in the
student–teacher relationship.
Far less consistent are the findings regarding the linkage between disruptive student behavior
and teachers’ perceptions of closeness in the student–teacher relationship. Specifically, several
primarily cross-sectional studies have identified disruptive student behavior as a negative predictor
of teachers’ perceptions of relational closeness (Birch & Ladd, 1998; Buyse et al., 2008; Mejia
& Hoglund, 2016; Thijs, Westhof, & Koomen, 2012). Following these investigations, teachers
may thus experience lower, concurrent levels of closeness in the relationship with students
who display disruptive behavior in the classroom. The handful of prior longitudinal studies, in
addition, has generally indicated that the modest association between individual students’
disruptive behaviors and teacher-reported degrees of closeness may remain relatively stable
over time (Baker, Grant, & Morlock, 2008; Henricsson & Rydell, 2004; Jerome, Hamre, &
Pianta, 2009; Mejia & Hoglund, 2016; Roorda et al., 2014; Zhang & Sun, 2011). For example,
some cross-lagged panel studies (Mejia & Hoglund, 2016; Roorda et al., 2014) have revealed
significant within-time correlations between individual students’ disruptive behavior and
teacher-reported closeness, but no additional effects of these behaviors on prospective levels
of relational closeness, after accounting for the stability in both constructs. Whether the link
between students’ disruptive behavior and teachers’ subsequent student-specific self-efficacy
beliefs can be explained by changes in teachers’ perceptions of closeness thus remains to be
explored.
TEACHERS’ RELATIONSHIP PERCEPTIONS AND SELF-EFFICACY BELIEFS
To date, only a scant amount of literature has provided empirical illustrations of our hypothesis
that teachers’ experiences in relationships with individual students may generate changes in
their student-specific self-efficacy beliefs. In part, this lack of research may stem from the fact
that TSE, in contrast to the dyadic constructs of closeness and conflict, is usually defined at the
classroom-level of analysis, thereby reflecting the collective valence of teachers’ sense of self-
efficacy toward their students in the classroom. Yet, the results of these studies seem to yield a
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
PROCESSES UNDERLYING TEACHER SELF-EFFICACY
151
fairly consistent picture across dimensions of the student–teacher relationship, pointing to
student–teacher conflict as the strongest predictor of general, classroom-level TSE (e.g.,
O’Connor 2008; Spilt et al., 2011). In a sample of secondary school teachers, for instance, Yeo,
Ang, Chong, Huan, and Quek (2008) indicated that teachers who experience high levels of
conflict in the relationships with their students are likely to develop unhealthy, classroom-level
self-efficacy beliefs in the teaching domains of classroom management and instructional
strategies. Other research explicates that poor relationships with students may lead to increases
in emotional vulnerability in teachers, and result in feelings of professional and personal failure
(Hargreaves 1998, 2000; Newberry & Davis, 2008; O’Connor, 2008; Spilt et al., 2011).
Together, these findings lend credence to the idea that teachers, through their perceptions of
conflict in the student–teacher relationship, come to see the task of teaching disruptive
students as more difficult and consequently adjust their self-percepts of self-efficacy toward
these students downward.
Counter to student–teacher conflict, high levels of relational closeness can be assumed to
provide teachers with the affective cues, performance successes, and persuasive boosts that
convince them they have whatever it takes to succeed with a child. In the study of Yeo et al.
(2008), however, this hypothesized association could not be confirmed. Their findings revealed
that positive aspects of student–teacher relationships, including teachers’ instrumental help and
satisfaction, were not associated with teachers’ general sense of self-efficacy for instructional
strategies, classroom management, and student engagement. Patterns of bivariate correlations
from a study of Spilt, Koomen, Thijs, and van der Leij (2012) largely mirror these findings.
Their results indicated that the linkage between teachers’ reports of closeness in the
relationships with disruptive kindergartners and general TSE was not significant.
Several empirical studies have also spawned some evidence for the alternative hypothesis that
teachers’ self-efficacy beliefs may affect their perceptions of student–teacher relationships,
although the results are a bit mixed (e.g., Chung, Marvin, & Churchill, 2005; Spilt et al., 2011;
Yoon, 2002). Specifically, Mashburn, Hamre, Downer, and Pianta (2006) indicated that
generally self-efficacious teachers were likely to experience more close, but not less
conflictuous relationships with individual, regular preschool students. When explicitly focusing
on problematic students, Hamre, Pianta, Downer, and Mashburn (2008) even found that
preschool teachers with generally low self-efficacy judgments at the classroom-level tended to
experience higher degrees of conflict with individual students than would be expected based
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 5
152
on their judgments of these students’ problem behaviors. For this reason, we also aimed to
explore an alternative model, in which teachers’ sense of self-efficacy in relation to individual
students’ disruptive behavior may feedback on their perceptions of the student–teacher
relationship in confirming or disconfirming ways.
PRESENT STUDY
The present study aims to broaden the purview of primarily cross-sectional research on
teachers’ general sense of self-efficacy at the classroom-level by testing a theoretical model
describing teachers’ student–teacher relationship perceptions (i.e., closeness and conflict) as the
processes through which individual students’ disruptive behavior may contribute to teachers’
subsequent student-specific self-efficacy beliefs across domains of teaching and learning (i.e.,
instructional strategies, behavior management, student engagement, and emotional support).
Guided by our interpersonal social-cognitive model, we first examined whether teachers’
perceived levels of closeness and conflict in the student–teacher relationship mediates the
longitudinal association between individual students’ disruptive behavior and student-specific
TSE in various domains of teaching and learning (see Figure 1a). Based on the idea that
disruptive behavior is more likely to be perceived as a threat to TSE when teachers have
internalized negative feelings about the student–teacher relationship, we expected teacher-
perceived conflict to mediate the negative association between disruptive student behavior and
student-specific TSE. In addition, due to mixed results in previous studies, we did not have
clear expectations about the mediating role of closeness in the association between disruptive
student behavior and student-specific TSE.
As an additional test of validity for the hypothesized model, we secondly tested an alternative
model in which student-specific TSE mediates the association between disruptive behavior and
teachers’ perceptions of closeness and conflict in the student–teacher relationship (see Figure
1b). Support for these alternative models would consist of evidence indicating that individual
students’ disruptive behavior leads to changes in teachers’ sense of self-efficacy in relation to
individual students across teaching domains, which, in turn, leads to changes in their
perceptions of conflict and closeness.
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
PROCESSES UNDERLYING TEACHER SELF-EFFICACY
153
METHOD
PARTICIPANTS
The present study contained Dutch elementary school teachers and third- to sixth-grade
students who participated in a short-term, two-wave longitudinal study on teachers’ dealings
with diversity. Sample selection proceeded in three phases. First, 350 randomly selected
schools across the Netherlands were contacted by telephone and e-mail, after obtaining ethical
approval from the Ethics Review Board of the Faculty of Social and Behavioral Sciences,
University of Amsterdam (project no. 2013-CDE-3188). Of these schools, 24 were inclined to
participate in the study. Second, all upper elementary teachers from participating schools
received a letter about the study’s purposes and an informed consent form, which was
ultimately signed by 70 teachers. Information letters describing the nature and purposes of the
research project were also sent to students’ homes. After parental consent was obtained, we
randomly selected four boys and four girls from participating teachers’ classrooms in the last
phase, resulting in an initial sample of 550 students.
Within the dataset, however, data were both missing cross-sectionally and longitudinally due to
teacher and student non-response, long-term absence or sickness during data collection, or
students moving to another school. Of all teachers assessed, 4.4% had missing data during the
first wave, and 10.6% during the second wave. Whereas cases with incomplete data for the
main study variables at both waves were excluded, we decided to retain participants with
incomplete data at only one time point. These missing data were treated using full information
maximum likelihood estimation. This resulted in a final sample of 69 teachers in relation to 524
students.
Participating teachers were predominantly female (72.6%), having a mean age of 41.42 years
(SD = 12.34, range = 23 – 63 years). Most teachers could be considered veteran teachers, with
an average professional teaching experience of 16.67 years (SD = 11.87, range = 1.5 – 44
years). The average tenure in teachers’ current job ranged from only half a year to 36 years (M
= 10.64, SD = 9.09). For four teachers, demographic data were not available.
At the time of data collection, students attended third (n = 53), fourth (n = 157), fifth (n =
165), and sixth grade (n = 149), respectively. Children ranged from 7.71 to 13.04 years of age
(M = 10.57, SD = 1.11) and the gender composition was evenly distributed with 262 boys
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 5
154
(50.0%) and 262 girls (50.0%). Based on their parents’ working status and educational level, the
vast majority of students were considered to have an average to high socioeconomic status:
both parents were employed in 76.8% of the families, 20.4% had at least one employed parent,
and 2.5% of the families included two unemployed parents. Additionally, teachers indicated the
majority of the parents to have finished senior vocational education (49.0%) or higher
education (46.2%), leaving less than 5% of the parents to have finished only primary education.
INSTRUMENTS
TEACHERS’ PERCEPTIONS OF THE STUDENT–TEACHER RELATIONSHIP
Teachers' perceptions of the quality of their relationships with individual students were
measured using a short form of the authorized translated Dutch version of the Student–
Teacher Relationship Scale (STRS; Koomen, Verschueren, van Schooten, Jak, & Pianta, 2012;
Koomen, Verschueren, & Pianta, 2007; Pianta, 2001). Similar to the original STRS, the short
form estimates specific, teacher-perceived student–teacher relationship patterns of Closeness,
Conflict, and Dependency, using a 5-point Likert-type scale (1 = definitely does not apply; 5 =
definitely applies). In the present study, we made use of the Closeness and Conflict dimensions of
the STRS. The Closeness dimension (5 items) evaluates the extent to which teachers perceive
the student–teacher relationship to be warm, open, and secure, with items such as “I share an
affectionate and warm relationship with this child”. The Conflict dimension (5 items) generally
concentrates on negative aspects of the student–teacher relationship, including tension, anger,
and mistrust in the relationship. An example item is “This child and I always seem to be
struggling”. In a previous study, the psychometric properties of the short form of the STRS
have been demonstrated to be adequate (Zee, Koomen, & van der Veen, 2013). In the present
investigation, alpha coefficients at the first and second wave of measurement were satisfactory,
.85 and .86 for Closeness, and .89 and .88 for Conflict, respectively.
DISRUPTIVE STUDENT BEHAVIOR
Teachers completed the Dutch version of the Strengths and Difficulties Questionnaire (SDQ;
van Widenfelt, Goedhart, Treffers, & Goodman, 2003) to judge selected students’ disruptive
behaviors in the classroom. This behavioral screening questionnaire originally yields positive
and negative student attributes that together represent five factors reflecting strengths
(Prosocial Behavior) and difficulties (Emotional Symptoms, Conduct Problems, Hyperactivity-
Inattention, and Peer Problems). For purposes of the present study, however, we only used the
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
PROCESSES UNDERLYING TEACHER SELF-EFFICACY
155
broader Externalizing Behavior domain proposed by Goodman, Lamping, and Ploubidis
(2010), which combines the Conduct Problems (5 items) and Hyperactivity-Inattention (5
items) subscales. This more comprehensive domain has been shown to have more adequate
psychometric properties than the original SDQ factors in low-risk samples (Dickey &
Blumberg, 2004; Goodman et al., 2010; van Leeuwen, Meerschaert, Bosmans, de Medts, &
Braet, 2006). Teachers responded on the SDQ-items on a 5-point Likert scale, ranging from 1
(not true) to 5 (certainly true). Example items are “Restless, hyperactive, cannot sit still for long”
and “Often has temper tantrums or hot tempers”. The internal consistency of the
Externalizing Subscale of the SDQ was satisfactory, α = .87. Moreover, several researchers
(Goodman et al., 2010; van Leeuwen et al., 2006) have provided sufficient evidence for the
construct validity of the scale.
DOMAIN- AND STUDENT-SPECIFIC TEACHER SELF-EFFICACY
Teachers rated their self-efficacy beliefs toward each of the selected students using the
Student-Specific Teacher Self-Efficacy Scale (Zee & Koomen, 2015; Zee et al., 2016). This 24-
item self-report instrument, adapted from Tschannen-Moran and Woolfolk Hoy’s (2001)
original measure, has been shown to represent teachers’ capability beliefs in relation to
individual students across four comprehensive domains of teaching and learning, including
Instructional Strategies (IS), Student Engagement (SE), Behavior Management (BM), and
Emotional Support (ES). Of these domains, the former two mainly focus on aspects of
instructional delivery. The IS subscale (6 items) captures the extent to which teachers feel
capable of using various instructional methods that enable and enhance individual students’
learning, including items such as “How well can you respond to difficult questions from this
student?”. The SE domain (6 items), in addition, reflects items that tap into teachers’ perceived
ability to activate the interest of a particular student in his or her schoolwork. A sample item of
this domain is “How much can you do to get this student to believe he/she can do well in
schoolwork?”. Next to these instruction-oriented subscales, the BM domain (5 items)
encompasses teachers’ judgments of their ability to organize and guide the behaviors of a
particular student, with items such as “How much can you do to get this child to follow
classroom rules?”. Lastly, inspired by the CLASS-framework (for an overview, see Hamre et
al., 2013 and Pianta, La Paro, & Hamre, 2008), the domain of ES (7 items) is related to how
well teachers can establish caring relationships with students, acknowledge students’ opinions
and feelings, and create settings in which students feel free to explore and learn (e.g., “How
well can you establish a safe and secure environment for this student?”).
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 5
156
All items were rated by teachers on a seven-point Likert-type scale, ranging from 1 (nothing) to
7 (a great deal). Support for the construct validity of the student-specific TSES has been
provided by Zee and colleagues (Zee et al., 2016; Zee & Koomen, 2015). Internal consistency
scores of the student-specific TSES domains across waves were .89 and .92 for IS, .94 and .94
for BM, .90 and .92 for SE, and .85 and .86 for ES, respectively.
PROCEDURE
Data were collected from teachers in two waves (January-March and May-July) with a three-
month time interval. During each wave, teachers completed a two-part survey on demographic
background factors, the quality of the student–teacher relationship, and their sense of student-
specific self-efficacy for the eight selected students from their classroom. Teachers were asked
to fill out the first, written part of the survey during two planned school visits. This part
contained items regarding teachers’ perceived quality of their relationships with the eight
selected students and students’ and teachers’ background characteristics, which served as
covariates in this study. Directly after the school visits, teachers received an e-mail invitation
with a personal link to the second part of the survey that contained, among others, items
regarding disruptive student behavior and the student-specific self-efficacy questionnaire about
the eight selected students. Teachers were requested to return this digital survey within two
weeks after the invitation was sent. To improve the participation rate, regular reminders were
sent to non-responding teachers.
Once all surveys were collated, the cover sheet of the written part of the survey (containing the
name of the participants) was discarded and all completed surveys were assigned a unique
identification number that could be used to identify responses for matching T1 and T2 data in
longitudinal analyses. This unique identifier was also used to assure anonymity and
confidentiality for all participants.
DATA ANALYSIS
Given that mediation, by its very definition, infers change over time, we specified a series of
two-wave longitudinal mediation models (see Figure 2) in Mplus 7.11, adjusting for the nested
nature of the data (Muthén & Muthén, 1998-2012). Although models with at least three time-
points would essentially be required to establish a true indirect pathway across time, two-wave
models have previously been recognized as a relatively valid method to test for mediation (Cole
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
PROCESSES UNDERLYING TEACHER SELF-EFFICACY
157
& Maxwell, 2003; Little, 2013). Specifically, similar to full-longitudinal models with three
waves, two-wave mediation models rely on the assumptions that the causal parameters are
constant over time (i.e., stationarity), and that the relationships among the predictors (X),
mediators (M), and outcome variables (Y) are unchanging in terms of their variances and
covariances (i.e., equilibrium; Cole & Maxwell, 2003; Little, 2013). Under these two assumptions,
the hypothesized associations between the mediators at Wave 1 and outcome variables at Wave
2 (mediation parameters a and b, and the direct effect c in Figure 2) can be expected to be equal
to the same associations measured at later time-points, and estimates of the effects of X on Y
through M in the two-wave model can be expected to be the same as in the three-wave model.
Additionally, two-wave models allow the modeling of prior levels of M and Y to isolate the
amount of change variance in these variables (Little, 2013). As such, these models can generally
be considered as superior to cross-sectional research on mediation, in which this change
information is not an explicit part of the design (ibid.).
FIGURE 2A
The Hypothesized Longitudinal Mediation Model
Note. X1 = Predictor at Wave 1; M1, M2 = Mediators at Waves 1 and 2; Y1, Y2 = Outcome variables at Wave 1 and 2. Coefficients a and b reflect associations between predictor and mediators, and mediators and outcome measures, respectively. The product ab reflects the indirect effect of individual students’ disruptive behavior on domains of student-specific self-efficacy, through teachers’ perceptions of conflict and closeness. Coefficient c reflects the direct association between predictor (students’ disruptive behavior) and outcome variables (domains of student-specific self-efficacy). Dashed lines represent autoregressive paths.
Students’ Disruptive Behavior (X1)
Conflict or Closeness (M1)
Domain- and Student-Specific TSE (Y1)
Conflict or Closeness (M2)
Domain- and Student-Specific TSE (Y2)
E
E
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 5
158
FIGURE 2B
The Alternative Longitudinal Mediation Model
Note. X1 = Predictor at Wave 1; M1, M2 = Mediators at Waves 1 and 2; Y1, Y2 = Outcome variables at Wave 1 and 2. Coefficients a and b reflect associations between predictor and mediators, and mediators and outcome measures, respectively. The product ab reflects the indirect effect of individual students’ disruptive behavior on teachers’ perceptions of conflict and closenesss, through domains of student-specific self-efficacy. Coefficient c reflects the direct association between predictor (students’ disruptive behavior) and outcome variables (teacher-perceived conflict and closeness). Dashed lines represent autoregressive paths.
MODELING PROCEDURE
To ensure adequate statistical power, separate two-wave longitudinal mediation models were
fitted for each of the relationship dimensions (i.e., closeness and conflict) in relation to each of
the domains of student-specific TSE (i.e., instructional strategies, behavior management,
student engagement, and emotional support), resulting in eight different models. Based on
Cole and Maxwell’s (2003) recommendations, all eight models were specified in three steps (see
Figure 2). First, we estimated path a in the regression of M2 (student–teacher conflict or
closeness at Wave 2) onto X1 (individual students’ disruptive behavior at Wave 1), controlling
for the effects of M1 and path b in the regression of Y2 (student-specific TSE domain at Wave
2), after taking prior levels of Y1 into account (Cole & Maxwell, 2003; Little, 2013). The
product of paths a and b then offered an estimate of the mediation effect of X on Y via M.
This first model pertained to the hypothesized full-mediation model depicted in Figure 1a.
E
Students’ Disruptive Behavior (X1)
Domain- and Student-Specific TSE (M1)
Conflict or Closeness (Y1)
Domain- and Student-Specific TSE (M2)
Conflict or Closeness (Y2)
E
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
PROCESSES UNDERLYING TEACHER SELF-EFFICACY
159
Second, we performed follow-up tests to explore the possible existence of direct effects of
individual students’ disruptive behavior on domains of student-specific TSE over time. To this
end, we estimated path c in the regression of Y2 onto X1. The statistical significance of this
direct path would indicate that teachers’ perceptions of closeness and conflict in the student–
teacher relationship only partially mediate the longitudinal association between disruptive
student behavior and domains of student-specific TSE. Third, as an additional test of validity
for the hypothesized model, we tested the alternative proposition that student-specific TSE has
a mediational effect on the association between disruptive student behavior and teachers’
student–teacher relationship perceptions (see Figure 1b).
After estimating all models, we employed the Monte Carlo simulation approach developed by
MacKinnon, Lockwood, and Williams (2004; see also Preacher & Selig, 2012) to formally test
the statistical significance of the mediation effects. This method involves directly spawning
sample statistics based on the joint asymptotic distribution of the component statistics to
obtain multiple estimates of the mediating pathway (Little, 2013). As such, the Monte Carlo
method largely resembles other recommended approaches for testing the significance of the
indirect effects, including bootstrap estimation (Preacher & Hayes, 2008). In line with our
hypotheses, we reported 90% confidence intervals for all Conflict models, and 95% confidence
intervals for the Closeness models, based on 5000 simulated draws for the indirect effects. If
the confidence interval around the point estimate of the indirect effect covers zero, this effect
is considered to be non-significant.
MODEL GOODNESS-OF-FIT
Overall fit of each of the specified models was gauged by using a number of absolute and
relative fit indices. Absolute fit was evaluated with the (mean-adjusted) model χ2. Generally,
non-significant χ2 tests are considered indicative of good model fit, implying that the
reproduced variance-covariance matrices are statistically equal to the observed matrices (Kline,
2011; Little, 2013). However, as even trivial discrepancies between the expected and the
observed model may lead to the model’s rejection (Chen, 2007), other fit indices were
consulted as well. Among those were the root mean square error of approximation (RMSEA),
the standardized root mean square residual (SRMR), and the comparative fit index (CFI). The
RMSEA and SRMR are absolute fit indices of the degree of misfit in the model, with values
≤.05 reflecting a close fit, and ≤.08 a satisfactory fit (Browne & Cudeck, 1993; Hu & Bentler,
1999; Kline, 2011). The CFI essentially reflects the ratio of misfit of the specified model, with
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 5
160
values ≥.95 indicating close fit, and values ≥.90 indicating acceptable fit (Bentler, 1992; Little,
2013). Based on these model fit criteria, modification indices, and theoretical considerations,
the most parsimonious and best fitting models were chosen as final models.
RESULTS
DATA SCREENING AND DESCRIPTIVE STATISTICS
Prior to main analysis, all variables used in this study were examined for conformity to
multivariate regression assumptions. Means, standard deviations, and bivariate correlations (see
Table 1) were inspected to determine whether the main constructs correlated in the expected
directions. The correlation coefficients supported a priori expectations. Specifically, both
teachers’ Student-Specific Self-Efficacy percepts and Student–Teacher Relationship judgments
appeared to be relatively stable over time, with correlations between time-adjacent variables
ranging from .65 (Student-Specific TSE for IS) to .81 (Conflict). Individual students’
Disruptive Behavior was negatively associated with Closeness, and positively associated with
Conflict, both concurrently and predictively. Moreover, statistically significant negative
correlations were documented between students’ Disruptive Behavior and all domains of
Student-Specific TSE, and the domain of BM in particular.
Associations among Closeness and Conflict and Student-Specific TSE were also in the
expected direction. Whereas Closeness was associated with stronger Self-Efficacy toward
individual students in all teaching domains, Conflict was found to be negatively correlated with
these capability beliefs. Notably, the highest correlations were noted between Student-Specific
TSE for Behavior Management and Conflict. Lastly, the correlations among domains of
Student-Specific TSE were all moderate to high, suggesting potential multicollinearity among
dimensions of teachers’ Student-Specific Self-Efficacy. To circumvent issues related to
multicollinearity, we estimated separate models for each of the Student-Specific TSE domains.
Students’ Age and Gender, and teachers’ years of Teaching Experience and Gender served as
the study’s covariates. Correlations showed that teachers were likely to report higher levels of
Closeness and Student-Specific TSE domains in relation to girls and younger students, and
higher levels of Conflict and Disruptive Behavior in relation to boys. Teaching Experience,
lastly, was positively associated both with teacher-perceived Closeness, and teachers’ sense of
Student-Specific TSE, irrespective of teaching domain.
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
TABL
E 1
Desc
riptiv
e Sta
tistic
s and
Corr
elatio
ns
1.
2.
3.
4.
5.
6.
7.
8.
9.
10
. 11
. 12
. 13
. 14
. 15
. 16
. 17
. St
uden
t Beh
avior
1. D
isrup
tive
Beha
vior
1.
00
Stud
ent–
Teac
her R
elatio
nship
2. C
lose
ness
T1
-.25*
* 1.
00
3.
Con
flict
T1
.69*
* -.3
8**
1.00
4.
Clo
sene
ss T
2 -.2
3**
.70*
* -.3
5**
1.00
5. C
onfli
ct T
2 .6
4**
-.25*
* .8
1**
-.36*
* 1.
00
Stud
ent-S
pecif
ic TS
E
6.
TSE
for I
S T1
-.4
6**
.31*
* -.4
1**
.34*
* -.3
5**
1.00
7. T
SE fo
r BM
T1
-.74*
* .3
2**
-.73*
* .3
3**
-.66*
* .5
0**
1.00
8.
TSE
for S
E T
1 -.5
7**
.34*
* -.4
8**
.36*
* -.4
2**
.87*
* .5
6**
1.00
9. T
SE fo
r ES
T1
-.55*
* .4
6**
-.51*
* .4
8**
-.47*
* .8
0**
.66*
* .8
1**
1.00
10
. TSE
for I
S T2
-.3
9**
.27*
* -.3
3**
.38*
* -.3
2**
.65*
* .3
4**
.65*
* .5
7**
1.00
11. T
SE fo
r BM
T2
-.60*
* .2
2**
-.58*
* .2
8**
-.59*
* .3
7**
.66*
* .4
2**
.50*
* .5
2**
1.00
12
. TSE
for S
E T
2 -.4
9**
.27*
* -.3
9**
.35*
* -.3
9**
.62*
* .4
0**
.70*
* .5
9**
.90*
* .6
0**
1.00
13. T
SE fo
r ES
T2
-.46*
* .3
8**
-.39*
* .4
8**
-.40*
* .5
6**
.47*
* .5
9**
.66*
* .8
2**
.69*
* .8
3**
1.00
Cova
riates
14. T
each
er G
ende
r -.0
8 .0
8 -.0
4 .1
3**
-.09*
-.0
4 .0
0 -.0
4 .0
1 .0
2 .0
6 -.0
0 .0
42
1.00
15. T
each
ing
Exp
erie
nce
-.04
.09*
-.0
5 .1
3**
-.02
.15*
* .1
1*
.18*
* .1
7**
.10
.14*
* .1
3**
.13*
* -.2
8**
1.00
16
. Stu
dent
Gen
der
-.26*
* .3
0**
-.17*
* .3
0**
-.19*
* .1
5**
.27*
* .1
6**
.26*
* .1
1*
.22*
* .1
3**
.20*
* .0
3 -.0
2 1.
00
17
. Stu
dent
Age
-.0
3 -.1
5**
.02
-.14*
* .0
5 -.1
8**
-.05
-.15*
* -.1
7**
-.13*
* -.0
2 -.1
3**
-.11*
-.2
8**
.05
-.07
1.00
Desc
riptiv
e Sta
tistic
s
Mea
n 1.
96
3.91
1.
55
4.00
1.
58
5.53
6.
14
5.57
5.
81
5.56
6.
16
5.56
5.
85
- 16
.67
- 10
.57
Stan
dard
Dev
iatio
n 0.
82
0.81
0.
88
0.78
0.
87
0.91
0.
99
1.00
0.
78
0.94
0.
95
1.00
0.
79
- 11
.87
- 1.
11
Note
. * p
< .0
5; **
p <
.01.
Gen
der:
0 =
boy
s/m
ale te
ache
rs, 1
= g
irls/
fem
ale te
ache
rs. T
SE =
Tea
cher
s’ st
uden
t-spe
cific
self–
effic
acy;
IS =
Inst
ruct
iona
l stra
tegi
es; B
M =
Be
havi
or m
anag
emen
t; SE
= S
tude
nt e
ngag
emen
t; E
S =
Em
otio
nal s
uppo
rt.
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 5
162
LONGITUDINAL MEDIATION MODELS
Model fit indices and parameter estimates of the Hypothesized and/or Alternative Mediation
Models per domain of Student-Specific TSE and dimension of the Student–Teacher
Relationship are provided in the following sections. In all models, teachers’ Gender and
Teaching Experience, and students’ Gender and Age were entered first into the regression
equation to accurately gauge the unique effect of the model’s predictors and mediators on the
outcome variables. Notably, though, none of these covariates appeared to be statistically
significant, nor did these variables alter the direction and magnitude of the coefficients in our
models. For reasons of parsimony, all models were therefore reported without covariates.
INDIRECT EFFECTS OF DISRUPTIVE BEHAVIOR ON STUDENT-SPECIFIC TSE THROUGH CONFLICT
Student-specific TSE for instructional strategies
The Hypothesized Mediation Model showed quite sound goodness of fit, χ2(2) = 3.34, p = .19,
RMSEA = .036 (90% CI [.000–.101]), CFI = .996, SRMR = .014. Freely estimating the direct
path from Disruptive Student Behavior to Student-Specific TSE for IS in the second step
could not further improve this already well-fitting model. Moreover, the Alternative Model,
placing Student-Specific TSE for IS in a mediational role between Disruptive Student Behavior
and teachers’ perceptions of Conflict, appeared to reflect a poorer fit of the data than the
Hypothesized Mediation Model, χ2(2) = 20.62, p < .001, RMSEA = .133 (90% CI [.085–.188]),
CFI = .941, SRMR = .021. Accordingly, we retained the Hypothesized Full-Mediation Model.
Table 2 presents the standardized path estimates for the final model. After accounting for prior
levels of teacher-perceived Conflict, individual students’ Disruptive Behavior predicted more
subsequent Conflict (β = .18, p < .001). Perceptions of Conflict, in turn, predicted lower levels
of Student-Specific TSE for IS over time (β = –.11, p < .05). The estimate of the indirect effect
of individual students’ Disruptive Behavior on their teachers’ Student-Specific TSE in the
domain of IS was –.025 (Monte Carlo 90% CI [–.045 –.004]), suggesting a statistically
significant mediation effect.
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
TABL
E 2
Fina
l Lon
gitud
inal M
odels
with
Med
iatin
g Effe
cts A
mong
Disr
uptiv
e Stu
dent
Beh
avior
, Con
flict,
and
Stu
dent
-Spe
cific
Self-
Effi
cacy
N
ote. *
p <
.05;
** p
< .0
1. S
tand
ardi
zed
regr
essio
n co
effic
ient
s (β)
are
repo
rted.
Poi
nt e
stim
ates
of t
he in
dire
ct e
ffec
ts a
re u
nsta
ndar
dize
d.
M
odel
1
Stud
ent-S
peci
fic T
SE fo
r IS
M
odel
2
Stud
ent-S
peci
fic T
SE fo
r BM
Mod
el 3
St
uden
t-Spe
cific
TSE
for S
E
M
odel
4
Stud
ent-S
peci
fic T
SE fo
r ES
Co
nflic
t T2
TS
E fo
r IS
T2
Co
nflic
t T2
TS
E fo
r BM
T2
Conf
lict
T2
TSE
for S
E
T2
Co
nflic
t T2
TS
E fo
r ES
T2
Dire
ct E
ffects
Disr
uptiv
e St
uden
t Beh
avio
r T1
.18*
* –
.1
2*
–.25
**
.1
8**
–
.18*
* –
Conf
lict T
1 .6
9**
–.11
*
.61*
* –
.7
0**
–.10
*
.70*
* –.
11*
Stud
ent-S
peci
fic T
SE fo
r IS
T1
– .6
1**
–
–
– –
–
– St
uden
t-Spe
cific
TSE
for B
M T
1 –
–
–.16
**
.49*
*
– –
–
– St
uden
t-Spe
cific
TSE
for S
E T
1 –
–
– –
–
.67*
*
– –
Stud
ent-S
peci
fic T
SE fo
r ES
T1
– –
–
–
– –
–
.61*
*
In
direct
Effe
cts
Th
roug
h Co
nflic
t –
–.03
*
– –
–
–.02
*
– .0
2*
Thro
ugh
TSE
for B
M
– –
.0
4*
–
– –
–
–
R2
statis
tics
.68*
* .4
4**
.6
9**
.48*
*
.68*
* .5
2**
.6
9**
.47*
*
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 5
164
Student-specific TSE for behavior management
The Hypothesized Mediation Model for Conflict did not reach a satisfactory fit to the data,
χ2(2) = 11.50, p < .01, RMSEA = .095 (90% CI [.047–.152]), CFI = .959, SRMR = .026.
Although slight improvement in model fit was achieved by adding a direct path from students’
Disruptive Behavior to Student-Specific TSE for BM, follow-up analyses indicated that the
Alternative Model with Student-Specific TSE for BM as the mediator produced better
parameter estimates and yielded a slightly better fit than the Hypothesized Model, χ2(2) = 8.70,
p < .01, RMSEA= .080 (90% CI [.031–.138]), CFI = .971, SRMR = .024. Modification indices
suggested some further improvement by adding the direct path from Disruptive Student
Behavior to Conflict. This resulted in a well-fitting final model, χ2(1) = 3.05, p = .08, RMSEA
= .063 (90% CI [.000–.148]), CFI = .991, SRMR = .019.
Table 2 displays the standardized coefficients for the final Alternative Partial-Mediation Model.
Teachers were found to experience lower TSE for BM (β = –.25, p < .01) and more Conflict (β
= .12, p < .05) in relation to individual students with Disruptive Behavior, when controlling for
initial levels of Student-Specific TSE. In addition, teachers’ Student-Specific capability beliefs
for BM predicted less subsequent Conflict in the student–teacher relationship (β = –.16, p <
.01). The Monte Carlo confidence limits suggested that the indirect effect of Disruptive
Student Behavior on Conflict through Student-Specific TSE for BM is statistically significant,
(point estimate = .044, Monte Carlo 90% CI [.012–.076]). Hence, contrary to expectations,
Student-Specific TSE for BM partially mediated the association between Disruptive Student
Behavior and teachers’ perceptions of Conflict in the relationship.
Student-specific TSE for student engagement
The model fit of the Hypothesized Mediation Model was satisfactory, χ2(2) = 4.14, p = .13,
RMSEA = .045 (90% CI [.000–.108]), CFI = .994, SRMR = .014. In the second step, we added
the direct path between individual students’ Disruptive Behavior and Student-Specific TSE for
SE. This path did not reach the significance threshold and could not further improve the fit of
this model. Moreover, the Alternative Model clearly reflected an inferior summary of the data,
χ2(2) = 16.89, p < .001, RMSEA = .119 (90% CI [.071–.175]), CFI = .955, SRMR = .019. For
this reason, the Hypothesized Full-Mediation Model was chosen as the most parsimonious and
best fitting model.
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
PROCESSES UNDERLYING TEACHER SELF-EFFICACY
165
The standardized coefficients for the final model for Student-Specific TSE for SE are depicted
in Table 2. Assessment of path coefficients pointed to a small, though statistically significant
association between individual students’ Disruptive Behavior and teachers’ succeeding
perceptions of Conflict in the relationship (β = .18, p < .001), after initial levels of Conflict
were taken into account. In turn, teachers who perceived the relationship with the child to be
marked by Conflict were likely to feel less efficacious in relation to the child in the domain of
SE (β = –.10, p < .05). Using the Monte Carlo simulation approach, the estimate of the indirect
effect was –.023 (Monte Carlo 90% CI [–.044 – –.003]). As the confidence interval did not
cover zero, the indirect effect of Disruptive Student Behavior on Student-Specific TSE for SE
through Conflict can be assumed to be statistically significant.
Student-specific TSE for emotional support
Fit indices suggested that the Hypothesized Mediation Model fitted the sample reasonably well,
χ2(2) = 8.27, p < .05, RMSEA = .077 (90% CI [.028–.135]), CFI = .980, SRMR = .023.
Examination of the modification indices, as well as the parameters estimates and their standard
errors, indicated that no further estimates would improve the model’s fit. Hence, the
Hypothesized Full-Mediation Model appeared to be a reasonable approximation of the data,
and fitted slightly better than the Alternative Model, χ2(2) = 12.43, p < .01, RMSEA = .100
(90% CI [.052–.156]), CFI = .967, SRMR = .018.
The final model (see Table 2) generally reflected the hypothesized indirect effects of students'
Disruptive Behavior on the Student-Specific TSE for ES, through Conflict. Specifically,
Disruptive Student Behavior led to significant changes in teachers–perceived Conflict (β = .18,
p < .001), after controlling for preceding levels of Conflict. Additionally, teachers’ perceptions
of Conflict resulted in lower levels of Student-Specific TSE for ES (β = –.11, p < .05). The
point estimate of the indirect effect (.021, Monte Carlo 90% CI [.002–.041]) deviated
significantly from zero, suggesting that Conflict mediates the association between Disruptive
Student Behavior and Student-Specific TSE for ES.
INDIRECT EFFECTS OF DISRUPTIVE BEHAVIOR ON STUDENT-SPECIFIC TSE THROUGH CLOSENESS
Student-specific TSE for instructional strategies
Contrary to the Conflict Model, the Hypothesized Model that placed Closeness in a mediating
role between Disruptive Student Behavior and Student-Specific TSE for IS had a poor fit to
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 5
166
the data, χ2(2) = 19.80, p < .001, RMSEA = .130 (90% CI [.082–.185]), CFI = .942, SRMR =
.052. To identify possible sources of misfit, we examined the model’s modification indices,
parameter estimates, and standard errors. These provided clear evidence in favor of the
Alternative Model, specifying an indirect effect of Disruptive Student Behavior on Closeness
through Student-Specific TSE for IS. This Alternative Model indeed approximated the data
well, χ2(2) = 3.91, p < .001, RMSEA = .043 (90% CI [.000–.106]), CFI = .994, SRMR = .021,
and could not be further improved by adding a direct path between Disruptive Student
Behavior and Closeness.
As displayed in Table 3, students’ Disruptive Behavior, while controlling for initial levels of
Student-Specific TSE for IS, was significantly related to subsequently lower levels of these
capability beliefs (β = –.13, p < .05). In turn, teachers’ Student-Specific Self-Efficacy beliefs for
IS predicted higher levels of Closeness (β = .19, p < .001), after accounting for the stability in
these positive relationship perceptions. Using the Monte Carlo simulation approach, the
estimate of the mediating pathway proved to be statistically significant, –.024 (Monte Carlo
95% CI [–.047– –.000]).
Student-specific TSE for behavior management
The Hypothesized Mediation Model reflected a far poorer fit to the data (χ2(2) = 19.04, p <
.001, RMSEA = .127 (90% CI [.079–.183]), CFI = .931, SRMR = .037) than the Alternative
Mediation Model (χ2(2) = 0.64, p = .72, RMSEA = .000 (90% CI [.000–.062]), CFI = 1.00,
SRMR = .010). Moreover, follow-up tests provided no evidence for the existence of direct
effects of students’ Disruptive Behavior on teachers’ perceptions of Closeness in the Student–
Teacher Relationship. Therefore, the Alternative Model was chosen as the final model.
The standardized path estimates for this model are presented in Table 3. Even after controlling
for cross-wave stability, the negative association between Disruptive Student Behavior and
teachers’ subsequent levels of Student-Specific Self-Efficacy for BM was statistically significant
(β = –.25, p < .001). In turn, Student-Specific TSE for BM predicted higher levels of teacher-
perceived Closeness in the relationship (β = .16, p < .001). The indirect effect of Disruptive
Student Behavior on Closeness via Student-Specific TSE was also statistically significant, point
estimate = –.040, Monte Carlo 95% CI [–.067– –.013].
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
TABL
E 3
Fina
l Lon
gitud
inal M
odels
with
Med
iatin
g Effe
cts A
mong
Disr
uptiv
e Stu
dent
Beh
avior
, Clos
eness
, and
Stu
dent
-Spe
cific
Self-
Effi
cacy
Note
. * p
< .0
5; **
p <
.01.
Sta
ndar
dize
d re
gres
sion
coef
ficie
nts (β)
are
repo
rted.
Poi
nt e
stim
ates
of t
he in
dire
ct e
ffec
ts a
re u
nsta
ndar
dize
d.
M
odel
1
Stud
ent-S
peci
fic T
SE fo
r IS
M
odel
2
Stud
ent-S
peci
fic T
SE fo
r BM
Mod
el 3
St
uden
t-Spe
cific
TSE
for S
E
M
odel
4
Stud
ent-S
peci
fic T
SE fo
r ES
Cl
osen
ess T
2 TS
E fo
r IS
T2
Cl
osen
ess
T2
TSE
for B
M T
2
Clos
enes
s T2
TS
E fo
r SE
T2
Cl
osen
ess T
2 TS
E fo
r ES
T2
Dire
ct E
ffects
Disr
uptiv
e St
uden
t Beh
avio
r T1
– –.
13*
–
–.25
**
–
–.14
*
– –.
15**
Cl
osen
ess T
1 .6
5**
–
.65*
* –
.6
4**
–
.58*
* –
Stud
ent-S
peci
fic T
SE fo
r IS
T1
.19*
* .6
0**
–
–
– –
–
– St
uden
t-Spe
cific
TSE
for B
M T
1 –
–
.16*
* .4
9**
–
–
– –
Stud
ent-S
peci
fic T
SE fo
r SE
T1
– –
–
–
.18*
* .6
4**
–
– St
uden
t-Spe
cific
TSE
for E
S T1
–
–
– –
–
–
.26*
* .5
9**
Indir
ect E
ffects
Thro
ugh
TSE
for I
S –.
02*
–
– –
–
–
– –
Thro
ugh
TSE
for B
M
– –
–.
04*
–
– –
–
– Th
roug
h TS
E fo
r SE
–
–
– –
–.
03*
–
– –
Thro
ugh
TSE
for E
S –
–
– –
–
–
–.04
* –
R2 sta
tistic
s .5
2**
.45*
*
.52*
* .4
8**
.5
2**
.53*
*
.54*
* .4
7**
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 5
168
Student-specific TSE for student engagement
The fit of the Hypothesized Mediation Model for Student-Specific TSE for ES was poor, χ2(2)
= 18.44, p < .05, RMSEA = .125 (90% CI [.077–.180]), CFI = .950, SRMR = .043. Inspection
of parameter estimates and modification indices suggested that Student-Specific TSE for SE,
rather than teachers’ perceptions of Closeness, is more likely to serve as a mediator. This
assumption was indeed substantiated by the Alternative Model’s fit criteria, χ2(2) = 0.66, p =
.72, RMSEA = .000 (90% CI [.000–.062]), CFI = 1.00, SRMR = .009. Because the direct path
in the regression of teacher-perceived Closeness at Wave 2 onto Disruptive Student Behavior
at Wave 1 was not statistically significant, we retained the Alternative Full-Mediation Model as
the final model.
Table 3 presents the standardized path estimates for the Alternative Model testing the indirect
effect of Disruptive Student Behavior on teacher-perceived Closeness via Student-Specific
TSE for SE. Inspection of these estimates suggest that, after accounting for the stability in
Student-Specific TSE for SE, teachers are likely to experience lower subsequent levels of self-
efficacy for SE in relation to individual students who display Disruptive Behavior (β = –.14, p
< .05). In turn, Student-Specific TSE for SE appeared to be a statistically significant positive
predictor of teachers’ perceived levels of Closeness over time (β = .18, p < .001). The product
of these two pathways was also significant, –.026 (Monte Carlo 95% CI [–.050 – –.001]),
thereby providing support for the indirect effect of Disruptive Student Behavior on Closeness
through Student-Specific TSE for SE.
Student-specific TSE for emotional support
The Hypothesized Mediation Model appeared to be a far worse representation of the data
(χ2(2) = 35.89, p < .001, RMSEA = .180 (90% CI [.131–.234]), CFI = .901, SRMR = .070) than
the Alternative Mediation Model (χ2(2) = 4.33, p = .11, RMSEA = .047 (90% CI [.000 –.109]),
CFI = .993, SRMR = .024). Moreover, the direct path from Disruptive Student behavior to
teachers’ perceptions of Closeness in the relationship did not significantly add to prediction
and consequently deteriorated the model's initial fit. Therefore, the Alternative Full-Mediation
Model was chosen as the final model (see Table 3).
After accounting for initial levels of Student–Specific TSE for ES, teachers’ reported lower
subsequent levels of self-efficacy for Emotional Support in relation to children with Disruptive
Behavior (β = –.15, p < .01). Also, teachers’ sense of Student-Specific Self-Efficacy for ES,
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
PROCESSES UNDERLYING TEACHER SELF-EFFICACY
169
when controlling for initial levels of Closeness, was significantly and positively associated with
their subsequent experiences of relational Closeness (β = .26, p < .001). Using the Monte Carlo
simulation approach, the coefficient of the indirect effect is estimated as –.039 (Monte Carlo
95% CI [–.073 – –.006]). Hence, these results suggest that Student-Specific TSE for ES
significantly mediates the association between Disruptive Student Behavior and teachers’
perceptions of Closeness in the student–teacher relationship.
DISCUSSION
Following Bandura’s (1986, 1997) social-cognitive principles, we aimed to explore a model
within which teachers’ perceptions of closeness and conflict in the student–teacher relationship
acted as the intermediary mechanisms by which individual students’ disruptive behavior may
affect teachers’ student-specific self-efficacy over time. Our approach departed from previous
work on the sources of TSE in three essential ways. First, we adhered to and extended
Bandura’s original conceptualization of self-efficacy by embedding TSE in an interpersonal
social-cognitive framework and measuring this complex construct both at the student- and
domain-specific level. Second, rather than focusing on direct sources of TSE, we were explicitly
interested in specifying mediating processes through which disruptive student behavior, as a
source of self-efficacy information, may become instructive to teachers’ student-specific
capability beliefs. Lastly, given that mediation essentially is a statement of change (Little, 2013),
we used a longitudinal design to evaluate hypothesized and alternative models, controlling for
prior levels of teachers’ perceptions of student–teacher closeness and conflict, and judgments
of student-specific TSE.
LINKAGES BETWEEN DISRUPTIVE BEHAVIOR, CONFLICT, AND STUDENT-SPECIFIC TSE
Generally, the results of our study provide a first indication that teachers’ perceptions of
relational conflict may function as the mediating or explaining mechanism whereby individual
students’ disruptive behavior leads to changes in student- and domain-specific TSE. To be
specific, teachers seemed to experience slightly higher subsequent levels of conflict in
relationships with individual students who initially displayed disruptive behavior in class which,
in turn, translated into lower levels of self-efficacy toward these students in various teaching
domains. These associations held even after taking relatively stable prior levels of student–
teacher conflict and student-specific teacher self-efficacy into account. Previous longitudinal
studies with younger elementary school children (e.g., Mejia & Hoglund, 2016; Roorda et al.,
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 5
170
2014; Zhang & Sun, 2011) are largely in line with our findings, reporting cross-lagged paths
between externalizing student behavior and teachers’ perceptions of conflict that were similar
in magnitude to the coefficients reported in the present study. However, no empirical studies
have yet uncovered that deleterious judgments of the student–teacher relationship quality may
also serve as a go-between, passing on efficacy-relevant sources of information from individual
students to the teacher. By unveiling these complex processes, our study gently corroborates
and extends Bandura’s (1997) longstanding belief that teachers not only have to manage
various sources of self-efficacy during their interactions with students, but also weigh and
integrate this information via such common judgmental processes as their representations of
relational conflict.
Interestingly, what our models seem to emphasize is that the role of teachers’ percepts of
conflict may vary across different domains of teaching and learning. More precisely, it appears
that the associations between individual students’ behavior and the more instructional and
affective domains of TSE are primarily mediated by teacher-perceived conflict. Through their
perceptions of conflict, teachers may thus come to see the task of teaching, engaging, and
emotionally supporting disruptive students as more problematic and may subsequently adjust
their self-efficacy toward these students downward. This finding accords well with prior
notions that, for most teachers, it is probably a major and time-consuming challenge simply to
get disruptive students with whom they entertain conflictuous relationships to learn and pay
attention in class (e.g., Arbeau & Coplan, 2007; Sutherland & Oswald, 2005; Yeo et al., 2008).
The sequels of such challenges evidently are that teachers, despite their sustained efforts, feel
less effective in teaching and motivating disruptive students, thereby stimulating student–
teacher interactions marked by even more anger, conflict, and disruptive student behavior over
time (e.g., Emmer & Stough, 2001; Pianta, 2001; Spilt et al., 2011; Yeo et al., 2008). This is
alarming, given that challenging students, and especially those with conflictuous student–
teacher relationships, have repeatedly been shown to be at risk for social and academic
adjustment problems (e.g., Hamre & Pianta, 2001; Roorda, Koomen, Spilt, & Oort, 2011).
Markedly, relational conflict did not appear to act as a mediator in the association between
disruptive student behavior and student-specific TSE for behavior management. Rather,
individual students who displayed disruptive behavior first seemed to hamper teachers’ efforts
to adequately manage these students’ behavior in class which, in turn, resulted in higher levels
of conflict in the student–teacher relationship. This relatively unexpected finding corroborates
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
PROCESSES UNDERLYING TEACHER SELF-EFFICACY
171
the idea that teaching tasks related to behavior management may be relatively distinct from
other core responsibilities, such as providing the instructional, motivational, and emotional
supports that generate gains in learning (cf. Tschannen-Moran & Woolfolk Hoy, 2001). An
explanation for this contrasting result that aligns with prior empirical work (e.g., Tsouloupas et
al., 2010; Zee et al., 2016) is that student-specific TSE for behavior management may serve as a
strong and direct proxy for teachers’ inability to deal with disruptive students’ behavior.
Thereby, these student-specific capability beliefs for behavior management may, more than any
other domain of TSE, be more contiguous with students’ disruptive behavior than perceptions
of conflict. This is a notable outcome, given that students’ disruptive behavior, among other
child-level correlates, have previously been found to be most predictive of teachers’
experiences of relational conflict, and may even promote vicious cycles of disharmonious
relationships and escalating problem behaviors (e.g., Doumen et al., 2008; Hamre et al., 2008;
Murray & Murray, 2004; Roorda et al., 2014).
Although the sequence of linkages described in the present study are only preliminary in nature
and not fully consistent, they generally seem to suggest that teachers’ student-specific capability
beliefs are inextricably intertwined with their experiences of conflict in relationships with
disruptive students. Helping teachers to reflect on their actions and behaviors toward
disruptive students, and associated emotions and cognitions during daily interactions with
these children, may be a step forward to break negative relationship patterns between teachers
and behaviorally at-risk elementary students (e.g., Spilt et al., 2012).
LINKAGES BETWEEN DISRUPTIVE BEHAVIOR, CLOSENESS, AND STUDENT-SPECIFIC TSE
Initial evidence from this study corroborates the alternative premise that the association
between individual students’ disruptive behavior and teachers’ perceptions of closeness in the
relationship is mediated, or explained, by student-specific TSE. Counter to the mixed findings
in prior cross-sectional and longitudinal work (e.g., Roorda et al., 2014; Thijs et al., 2012),
teachers were consistently found to develop less healthy self-efficacy beliefs toward disruptive
students in all teaching domains, and consequently, to experience less closeness in the dyadic
relationship with these students. The theoretical significance of these findings is substantial,
given that there is a general shortage of evidence on how features of teachers may impact on
the formation of their relationships with individual students (Pianta et al., 2003). Moreover, the
observed differences between closeness and conflict in the sequence of associations appear to
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 5
172
underscore that these constructs reflect two distinct qualities of the relationship, as opposed to
falling along an underlying continuum.
We can only make a well-educated guess about why closeness and conflict play different roles
in the development of teachers’ sense of efficacy toward individual disruptive students. For
instance, sources of self-efficacy, including students’ behaviors and characteristics, can be
suggested to significantly vary in the degree of information they provide to teachers (cf.
Bandura, 1997). Probably, disruptive student behaviors are stronger and more reliable
indicators of student–teacher conflict than closeness, and may thereby contribute less
information to teachers’ representations of relational closeness and subsequent self-efficacy
beliefs. Indeed, prior research (Hamre et al., 2008; Jerome et al., 2009) has indicated that
conflict may depend more on stable student attributes (e.g., disruptive behavior), whereas
closeness seems to be more proximal to dynamic teacher characteristics (e.g., student-specific
TSE). This may explain why teachers’ sense of student-specific self-efficacy may better account
for the association between disruptive student behavior and closeness in the student–teacher
relationship than closeness for the association between those challenging behaviors and
student-specific TSE.
One other compelling proposition of Bandura (1997) is that the route to low-quality student–
teacher relationships may go through teachers’ perceived (social) inefficacy to develop affective
relationships with students who bring stress to teachers’ job. Presumably, when teachers
believe they cannot muster whatever it takes to support and deal with a disruptive child, they
are apt to slacken their teaching efforts, avoid warm and open communications with the child,
and settle for mediocre results or controlling actions (ibid.). This presumption fits reasonably
well with our findings that individual disruptive students may particularly hamper teachers’
perceptions of relational closeness through their student-specific self-efficacy for emotional
support and behavior management. Thus, teachers’ lack of self-efficacy may ultimately come at
the expense of trust, warmth and affect between teachers and disruptive children.
Overall, the model evaluations in the current study seem to be in line with the social-cognitive
and dynamic systems models advanced by Bandura (1997) and Pianta et al. (2003), suggesting
that teachers’ and students’ personal characteristics and behaviors, as well as their daily
interactions, may influence one another in a complex, reciprocal way. Future, longitudinal
research in which multiple methods and data sources are integrated is needed to spur further
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
PROCESSES UNDERLYING TEACHER SELF-EFFICACY
173
understanding of the complex relationships between disruptive student behavior, student–
teacher conflict and closeness, and student- and domain-specific TSE.
LIMITATIONS AND FUTURE DIRECTIONS
The methodology and design of the present investigation brought several limitations that
require further attention in future studies. First, analytic techniques such as longitudinal
(multilevel) structural equation modeling are also bound by several specific assumptions,
including multicollinearity, stationarity, and equilibrium (Cole & Maxwell, 2003; Sobel, 1990).
Although we circumvented the issue of multicollinearity by evaluating separate models for the
two student–teacher relationship qualities and the four domains of student-specific TSE, we
cannot be sure whether the stationarity and equilibrium assumptions held. To be specific, with
only two waves of data, it was virtually impossible to test whether the measured variables are
invariant over time (i.e., stationarity), and whether the relationships among those variables are
unchanging in terms of their variances and covariances (i.e., equilibrium; Cole & Maxwell, 2003;
Little, 2013). Fortunately, however, several authors have argued that violating those two
assumptions of mediation testing does not necessarily invalidate evidence of statistically
significant mediation effects (ibid.). Nevertheless, future studies that incorporate analyses of
stationarity and equilibrium over at least three time intervals could provide a stronger basis
from which to discuss the complex, mediating processes proposed in the present study.
Related to this, the lags for the measurement occasions might not have been optimal for
detecting changes in teachers’ judgments of student-specific self-efficacy and experiences of
closeness and conflict. Empirical research from Roorda and colleagues (2014) has indicated,
for instance, that students’ disruptive behavior and teachers’ relationship perceptions mainly
affect one another during the first couple of months of the school year, when relationships
between teachers and students have yet to be crystalized. Possibly, teachers’ relationships with
individual students and their student-specific self-efficacy beliefs in the present study were
already stabilized at the time of data collection (middle and end of the school year), making it
more difficult to detect changes in teacher-perceived closeness and conflict, and student-
specific TSE. Therefore, longitudinal data on changes in teachers’ perceptions of the student–
teacher relationships and student-specific self-efficacy beliefs from the beginning to the end of
the school year would probably provide a more fine-grained picture of the processes by which
individual students’ disruptive behavior may exert pressure to change teachers’ self-efficacy
beliefs toward these children in different domains of teaching and learning.
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 5
174
Last, this study concentrated only on teachers’ perceptions of relational closeness and conflict
as mediators of the association between disruptive student behavior and student-specific TSE.
It is likely, however, that the mediation processes presented in the current study may be far
more complex, and that other cognitive or motivational factors or processes are responsible
for changes in teachers’ sense of self-efficacy in relation to particular students with disruptive
behavior. Examples of such factors may be teachers’ beliefs about student control, their
motivation to engage in high-quality interactions with the child, their (perceived) skill level, and
their classroom goal orientations (cf. Bandura, 1997; Cho & Shim, 2013; Deemer, 2004; Pianta
et al., 2003; Tschannen-Moran et al., 1998; Woolfolk & Hoy, 1990). These and other
potentially relevant factors and processes, measured either at a more general level or the dyadic
level, may warrant consideration in future longitudinal studies.
CONCLUSION
In summary, we sought to expand the available evidence on the sources of student-specific
TSE by evaluating an interpersonal social-cognitive model in which teachers’ perceptions of
the student–teacher relationship quality were assumed to account for the association between
disruptive student behavior and student-specific TSE. Generally, data from this investigation
provided initial support for the idea that teacher-perceived conflict may function as one
explaining mechanism through which individual students’ disruptive behavior results in
changes in student- specific TSE across domains. Interestingly, though, teachers’ experiences
of closeness in their relationship with individual students did not mediate the association
between students’ disruptive behavior and student-specific TSE. Instead, convincing evidence
was found for the alternative premise that teachers, through their poorer self-percepts of
domain- and student-specific efficacy, are less capable of teaching and helping behaviorally
disruptive students in ways that lead to closeness in the student–teacher relationship. These
findings clearly suggest that student–teacher conflict and closeness each may play a different
role in the development of teachers’ sense of self-efficacy toward disruptive students in various
teaching domains. For the development of empirically-based intervention programs for
teachers, it is therefore essential to spur further understanding of the complex
interrelationships among individual students’ disruptive behavior, the student–teacher
relationship quality, and teachers’ student-specific self-efficacy across domains of teaching and
learning.
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
175
CHAPTER 6
GENERAL DISCUSSION
_________________________________________________________________________
This dissertation aimed to take stock of where the field of teacher self-efficacy has been and
the steps needed to be taken to move current theory and research on TSE forward. First off,
Chapter 1 outlined several critical challenges in the field of TSE that must be accounted for to
advance understanding of the self-efficacy construct and identify useful research-based insights
about TSE that may help teachers better deal with diversity in class. Motivated by Bandura’s
(1977, 1986, 1997) social-cognitive principles, these theoretical and methodological issues were
subsequently explored in Chapters 2 to 5, within which the focus gradually shifted from
teachers’ general, classroom-level self-efficacy beliefs to TSE at the student-specific level. The
General Discussion now seeks a return to the challenges raised in Chapter 1. In this last
chapter, thought is given to the extent to which these issues have been addressed by the four
studies in this dissertation, to noteworthy or contradictory empirical findings across these
studies, and to new challenges that seem to have emerged out of this work. In addition,
implications of the results of the studies for educational research and practice are discussed in
the closing section of this chapter.
ADDRESSING CHALLENGES REGARDING THE NATURE AND CONSEQUENCES OF TSE
The last forty-odd years have seen a rise in research focusing on the effects of teacher self-
efficacy on teachers’ behaviors, feelings, and actions, and their students’ learning outcomes in
class. What initially started out as a modest side-branch of school effectiveness research now
seems to have blossomed into a massive body of work reflecting different traditions and
theories, various definitions, and innumerable outcomes of TSE related to the quality of
classroom processes, students’ academic adjustment, and teachers’ well-being (e.g., Klassen &
Chiu, 2010, 2011; Klassen, Tze, Betts, & Gordon, 2011; Skaalvik & Skaalvik, 2007, 2010;
Tschannen-Moran & Woolfolk Hoy, 2001). To a considerable extent, these different and
sometimes isolated traditions have each provided important pockets of insight into teachers’
sense of self-efficacy. However, one remaining challenge identified in Chapter 1 is to bring
these existing fields of study together and afford a clear, integrative perspective on the
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 6
176
construct of TSE and its consequences (cf. Henson, 2001; Wheatley, 2005; Wyatt, 2016). In
Chapter 2, we took up this challenge by proposing a process-oriented framework that allowed
us to integrate and synthesize the current body of work on TSE and its consequences. Of note,
this model seems to complement and extend other seminal frameworks of teacher self-efficacy
(e.g., Tschannen-Moran, Woolfolk Hoy, & Hoy, 1998; Woolfolk Hoy, Hoy, & Davis, 2009) in
two essential ways.
THE NATURE OF TSE
Unlike prior conceptual models (Tschannen-Moran et al., 1998), the process-oriented
framework attempted, first, to distinguish Bandura’s ideas from other potentially relevant
theories and constructs, including locus of control, self-concept, competence, and self-esteem.
According to Bandura (1997), these theories and concepts fail to do justice to the highly
particularized and multifaceted nature of teacher self-efficacy, and usually tend to measure a
different construct. For that reason, we took a first theoretical step in elucidating and
reinstating the social-cognitive foundation of TSE, which has every so often been obscured by
Rotter’s (1966) conceptual scheme (cf. Henson, 2001; Tschannen-Moran & Woolfolk Hoy,
2001; Wheatley, 2005; Wyatt, 2014). In view of the results of Chapter 2, this step did not seem
an unnecessary exercise. Largely consistent with the review of Klassen et al. (2011), we noted
that only a quarter of the 165 reviewed studies tended to treat TSE as a multifaceted construct
involving various domains of activity. Of this quarter of studies, some defined the self-efficacy
construct at a highly specific level (e.g., TSE for data-driven decision making or TSE in
student-centered teaching styles), whereas others operationalized teachers’ self-efficacy as a
construct that may also be generalizable to other contexts (e.g., computer self-efficacy).
Additionally, a considerable number of studies, despite employing Tschannen-Moran and
Woolfolk Hoy’s (2001) multifaceted measure, tended to calculate total scores of TSE, instead
of gauging the three intended domains of instructional strategies, classroom management, and
student engagement. Hence, the current field of work on TSE seems to have fallen short in
defining the construct of TSE in the way Bandura initially intended, thereby hindering the
process of seeking consensus on TSE and its consequences.
It is an interesting question why the large majority of studies may still resort to the “one
measure fits all” (Bandura, 2006, p. 307) approach in studying teachers’ sense of self-efficacy.
Indeed, in Chapter 3, it was argued that domain-general operationalizations of TSE, though
conveniently serving diverse purposes, may be problematic in the sense that they leave much
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
GENERAL DISCUSSION
177
ambiguity about what is being measured and which specific tasks teachers have to accomplish
(cf. Bandura, 1997, 2006). Also, teachers’ general capability beliefs have previously been
claimed to inadequately match with the particular outcomes that are being measured, thereby
potentially losing their explanatory and predictive merit (ibid.). Of note, these issues were, in
part, also evident in Chapter 2, where teachers’ general self-efficacy beliefs appeared to be
somewhat poorer predictors of a range of adjustment outcomes in class than domain-specific
capability beliefs.
Beyond the “thorny issues” that Tschannen-Moran and Woolfolk Hoy (2001, p. 794) describe
in their paper on the meaning and measurement of TSE, there may be some other reasonable
explanations for the current lack of domain-specific self-efficacy research. One justification
that can be extrapolated from Chapter 2 is that many empirical studies, probably due to
pragmatic reasons, tend to investigate samples involving teachers from multiple grades and
across different subjects. In such cases, it may be particularly difficult to tailor measures of
TSE to relevant realms of activity. First-grade teachers who usually have one class and teach
several subjects may have to deal, for instance, with far different task and situational demands
than high-school teachers, who typically specialize in one subject area and may see various
classes a day. General measures of TSE, then, may have more practical relevance in that they
are designed to serve various samples and teaching contexts. The potential benefits of these
general measures may, however, be bought at the price of the predictive power of TSE. Efforts
to discover how teachers’ self-efficacy beliefs may contribute to their functioning in different
domains of activity should therefore ideally focus on particular grades or school periods (e.g.,
preschool, the elementary years, or the high-school period). This narrower focus may help
future researchers disclose areas of teaching and learning within which teachers feel less
capable and need additional training or intervention. Results from the studies described in
Chapters 3 to 5, for instance, underscore the need to provide upper elementary teachers with
some additional help in domains of behavior management and emotional support.
Another reason for falling back on teachers’ more general self-efficacy beliefs is that the
outcome domains of interest may, somewhat paradoxically, be too specific as well. To be more
precise, some of the outcome variables of the reviewed studies in Chapter 2, including
teachers’ attitudes toward web-based instruction or their self-survival concerns, require self-
efficacy measures that are also cast in a very specific form (Bandura, 1997, 2006). Yet, there
may be a danger of developing items that are so detailed that they prevent researchers from
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 6
178
drawing conclusions about TSE that, to some extent, are generalizable across outcome
variables and teaching domains (e.g., Pajares, 1996; Tschannen-Moran & Woolfolk Hoy, 2001).
To avoid such issues, relying on teachers’ general sense of efficacy may be a somewhat safer
option. Studies concentrating on very specific outcomes thus serve as a useful reminder that, to
understand TSE and its consequences, the selection of and generalizability across outcome
variables of interest are just as important as the adequate measurement of TSE itself.
THE CONSEQUENCES OF TSE
A second distinction from prior conceptual models of TSE (Tschannen-Moran et al., 1998) is
that the process-oriented framework yielded a set of specific parameters for meaningfully
differentiating between the various consequences of teacher self-efficacy. By carefully sorting
through the latest theory and evidence on TSE, and using both the frameworks of Pianta et al.
(2008) and Woolfolk Hoy et al. (2009), it was possible to identify three major outcome
domains of TSE, including the quality of classroom processes, students’ academic adjustment,
and teachers’ sense of well-being. These domains, as well as their underlying dimensions,
helped in the quest to interpret the findings emerging from each separate field of study and
provided directions for future research.
The quality of classroom processes
Findings related to instructional, organizational, and emotional aspects of classroom processes
generally appeared to lack a clear focus. Overall, studies in this field covered a wide range of
teaching strategies, behaviors, attitudes, and decisions, each of which tended to be explored
only once or twice in isolated, cross-sectional studies focusing on different student groups
and/or grades. As a result, it appeared to be difficult to seek consensus on the direct links
among TSE and the quality of classroom processes. Across these studies, two other interesting
outcome patterns did emerge, though. First, a clear lack of research was noticed that
specifically concentrated on teachers’ ability to establish a warm connection with their students
and to be responsive to their social, emotional, and developmental needs (cf. Hamre, Hatfield,
Pianta, & Jamil, 2014; Pianta et al., 2008). Importantly, subsequent chapters show that this
domain of emotional support can be distinguished separately from other important domains of
TSE (Chapter 3), and may provide new insights into teachers’ ability to deal with individual
students with a variety of social-emotional behaviors (Chapters 4 and 5). As such, teachers’
sense of self-efficacy in the domain of emotional support may advance further understanding
of the multifaceted ways in which teachers’ self-percepts of efficacy function.
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
GENERAL DISCUSSION
179
Second, in a number of the reviewed studies, teachers’ years of experience appeared to play a
role in the association between TSE and teacher practices that define the classroom quality.
For instance, tenured educators with high levels of general self-efficacy were found to be likely
to use more diverse instructional strategies, to differentiate more frequently, to change their
goals according to students’ needs, and to be more positive about the implementation of such
instructional strategies than less experienced teachers (Allinder, 1995; Martin, Sass, & Schmitt,
2012; Wertheim & Leyser, 2002; Weshah, 2012). Other empirical research (e.g., Klassen &
Chui, 2010; Morris-Rothschild & Brassard, 2006) indicated that TSE may increase with
experience as teachers become better able to effectively instruct, manage, and motivate the
children in their classrooms. The conclusion that teaching experience may have a potentially
beneficial (by)effect on TSE did not hold across the empirical studies on student-specific TSE,
though. Chapter 3 demonstrated, for instance, that teachers with little (<5 years), average (5–
10 years), or high experience (>10 years) did not significantly differ in their sense of self-
efficacy across domains and individual students. Using a smaller, longitudinal sample in
Chapter 5, we additionally failed to establish the presumed associations between teaching
experience and domain- and student-specific TSE over time. Only in Chapter 4 did teachers’
years of experience concurrently add to the prediction of student-specific TSE, but only in
domains of student engagement and emotional support.
There may be several reasons for these contradictory findings. On the theoretical front, it can
be suggested that teachers’ years of experience are most likely to help them become sensitized
to students’ signals, emotional needs, and expectations in class (e.g., Kokkinos, Panayiotou, &
Davazoglou, 2005). Compared to instructional and classroom organizational skills, such more
soft competencies do usually not form a major part of their training and may therefore be best
learned and developed on the job (Hargreaves, 1998). This might explain why the studies
repeatedly failed to establish significant associations between teaching experience and student-
specific TSE for instructional strategies and behavior management. Another possibility is that
(some) student-specific capability beliefs, more than generalized, classroom-level TSE, tend to
depend on characteristics of individual students, rather than such teacher features as
experience. Indeed, results from both Chapter 3 and 4 demonstrated that most of the
variability in TSE occurred within teachers, mirroring the social-cognitive view that TSE,
despite reflecting some degree of trait variability, is most likely to vary across teaching domains
and the students toward whom their behaviors are directed. Lastly, it should be noted that the
samples used in Chapters 3 to 5 included teachers who worked, on average, more than 16 years
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 6
180
in primary education. This is perhaps not surprising, given that more than one third of all
elementary school teachers in the Netherlands are over 50 (DUO, 2014). Yet, this relative lack
of beginning teachers, whose beliefs in their capability have yet to be established, might have
biased our results. To avoid such potential biases, teacher self-efficacy research incorporating a
larger number of beginning teachers is definitely warranted. Such research may also help
explain the complex interrelationships between TSE, teachers’ years of experience, and their
decision to leave or stay in the profession.
Students’ academic adjustment
The studies reviewed in Chapter 2 generally imply that teachers’ sense of self-efficacy is a
positive predictor of students’ academic performance in various subjects, including math,
history, biology, and, to a lesser extent, reading and writing. In addition, aspects of students’
motivation, including student engagement, intrinsic and extrinsic motivation, academic
expectations, self-efficacy, and goal orientations, appeared to be predicted by their teachers’
general self-efficacy beliefs. It should be kept in mind, however, that small samples, generalized
instruments, poor methodologies, and cross-sectional designs were fairly common in this
literature. Particularly those studies focusing on student achievement appeared to lack
methodological rigor and to reveal small associations between TSE and students’ academic
performance. Following Bandura (1997, 2006), upcoming research on links between TSE and
students’ academic adjustment would profit from self-efficacy measures that are tailored to
tasks and domains that best reflect teachers’ capability to increase individual students’
motivation and academic achievement. Compared to generalized instruments, such highly
particularized scales may have more predictive power, as they measure the type of TSE beliefs
that determine which activities teachers embark on and how well they perform those activities
in relation to a particular child (Bandura, 1997). In this sense, the student-specific TSES, as
described in Chapter 3, may be a useful tool to better understand the role of teachers’ self-
efficacy in students’ academic adjustment.
Also noteworthy is that potential indirect associations between TSE and students’ academic
adjustment have been investigated in only three of the 165 reviewed studies. This is despite the
nowadays common belief that TSE, as a personal characteristic, mainly affects student and
teacher outcomes through patterns of teacher behavior and practices that define the quality of
the classroom environment (Guo, McDonald Connor, Yang, Roehring, & Morrison, 2012;
Midgley, Feldlaufer, & Eccles, 1989; Woolfolk Hoy & Davis, 2005). Perhaps, the general lack
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
GENERAL DISCUSSION
181
of rigorous longitudinal (structural equation modeling) techniques and small samples used in
the reviewed studies may explain why researchers have fallen short in investigating the
hypothesized indirect links between TSE and students’ academic adjustment. The field of TSE,
therefore, would probably profit from research using models in which mediational
relationships can be established and differing pathways of influences can be compared.
Teachers’ well-being
In contrast to the quality of classroom processes and students’ adjustment, the literature on
TSE and its consequences for teachers’ well-being yielded far more reliable and consistent
results. In total, 71 relatively well-executed studies provided support for the contention that
generally self-efficacious teachers may suffer less from stress and burnout symptoms, and
experience higher levels of personal commitment and job satisfaction. Perhaps even more
compelling are the handful of studies positing that classroom processes and experiences related
to student misbehavior and positive affect may function as a go-between in the relationship
between teachers’ self-efficacy and their subsequent sense of well-being (e.g., Briones,
Tabernero, & Arenas 2010; Doménech-Betoret, 2009; Duffy & Lent, 2009; Lent et al., 2011;
Sass, Seal, & Martin, 2011). These investigations seem to accord relatively well with evidence
from Chapter 5, in which teachers’ student-specific capability beliefs were found to be
inextricably intertwined with their experiences of conflict and closeness in relationships with
disruptive students. Therefore, it seems to be relevant for future researchers to combine
unique elements of self-efficacy research on classroom processes and teacher well-being in a single,
longitudinal model. This may yield a fuller portrait of teachers’ sense of self-efficacy and its
consequences over time than presented by any one existing strand of research, and provide
much needed guidelines for educational researchers and practitioners alike.
ADDRESSING CHALLENGES REGARDING THE MEASUREMENT OF TSE
In Chapter 1, several important issues regarding the measurement of TSE were highlighted
that may warrant further attention in future educational research. Most of those, including
adequate domain specification and the inclusion of environmental obstacles against which
teachers can judge their self-efficacy, are as old as the work of Tschannen-Moran and
colleagues (1998, 2001). Yet, despite giving full attention to these issues (e.g., Henson, 2002;
Klassen et al., 2011; Wheatley, 2005; Wyatt, 2014) none of the current studies have, to our
knowledge, come up with a single measure that may adequately address them. Using
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 6
182
Tschannen-Moran and Woolfolk Hoy’s (2001) Teacher Sense of Efficacy Scale (TSES) as a
baseline, a new measure was therefore developed and evaluated in Chapter 3 that may gauge
the teacher self-efficacy construct in ways that it more reliably reflects the social-cognitive
foundation of self-efficacy.
DOMAIN SPECIFICATION OF TSE
The search for methods to empirically capture the meaning of TSE began by clarifying what it
takes for elementary school teachers to succeed in given domains of teaching and learning. To
this end, we largely drew on the review results from Chapter 2 and the CLASS-framework of
Pianta et al. (2008) and discovered that the TSES, next to its original domains of instructional
strategies, classroom/behavior management, and student engagement, might require an
additional domain of emotional support. Similar to the CLASS-dimensions of positive climate,
student sensitivity, and regard for student perspectives, this additional domain was specified to
involve tasks and responsibilities related to how well teachers can establish caring relationships
with students, acknowledge students’ opinions and feelings, and create settings in which
students feel secure to explore and learn. Of note, some other domains that have previously
been suggested by Bandura (undated) were not included in the student-specific TSES. These
domains, comprising tasks and responsibilities related to teachers’ ability to influence decision
making, school resources, and community involvement, have already been discarded by
Tschannen-Moran and Woolfolk Hoy (2001) as being not representative of the kinds of
teaching tasks that typically make up elementary teachers’ daily activities. Moreover, such
domains, albeit part of teachers’ job, did not seem relevant to study teachers’ sense of self-
efficacy for teaching and learning in relation to individual students in the classroom.
The final confirmatory factor model in Chapter 3 suggested that the four domains of the
student-specific TSES can be distinguished separately from one another. At the same time,
though, the results pointed to a high degree of correspondence among domains of
instructional strategies, student engagement, and emotional support. Evidently, these results
may raise issues with respect to construct and discriminant validity. Yet, there may be some
conditions that may explain this unexpectedly high level of covariance. First, large associations
among teachers’ self-efficacy beliefs in various domains of teaching and learning may occur
when these domains influence one another in a reciprocal way. For instance, it is likely that
teachers’ emotional connection and positive communications with individual students (emotional
support) may help these students become motivated for their schoolwork (student engagement), and
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
GENERAL DISCUSSION
183
see the relevance and meaningfulness of their teachers’ instruction (instructional support; cf.
Hamre & Pianta, 2005; Pianta et al., 2008). In a related vein, it is possible that teachers
experience roughly the same student-specific self-efficacy beliefs across teaching domains
because they may develop different instructional, affective, and classroom organizational skills
simultaneously around the behaviors and needs of individual children (Bandura, 1997, 2006;
Hamre et al., 2013, 2014). It is likely that this process of concurrent skill development in
dissimilar teaching areas, in which individual children serve as the common denominator, may
result in student-specific abilities that are somewhat different from teachers’ more general teaching
skills. Accordingly, it might explain why the domains of student-specific TSE show a higher
level of correspondence with one another than self-efficacy domains measured at the
classroom-level (e.g., Tschannen-Moran & Woolfolk Hoy, 2001; Wolters & Daugherty, 2007).
Lastly, Bandura (1997, 2006) has pointed to powerful experiences of mastery or failure with
individual children that may yield a transformational reorganization of TSE, manifested across
various domains of teaching and learning. To some extent, results from Chapter 5 seem to
substantiate this notion, suggesting that teachers unfortunately experience higher levels of
conflict in the relationship with disruptive children, which may subsequently transform into
lower levels of self-efficacy toward these individual students across teaching domains.
Taken together, the substantial correlations among the domains of the student-specific TSES
give some reason to believe that teachers, next to more generic teaching skills, tend to
concurrently develop and orchestrate highly overlapping cognitive, social, emotional, and
behavior skills to deal with a particular child. The presence of such student-specific skills may
explain why only moderate correlations between the original, classroom-level TSES and the
student-specific TSES at the (aggregated) between-teacher level were established in Chapter 3.
Evidently, replication of the results in Chapter 3 is needed to firmly establish the validity of the
student-specific TSES, both in comparable, but larger samples, as well as across grades,
countries, types of education, and different children. Yet, it may be safe to argue that teachers’
functioning in distinct areas of teaching may be better observed in relation to the particular
children toward whom their actions are directed than the classroom as a whole.
DEFINING OBSTACLES
In response to Bandura’s (1997, 2006) claim that self-efficacy beliefs must be measured in light
of environmental obstacles, the original TSES was further adapted by making its individual
items student-specific. By letting teachers report on their self-efficacy for randomly selected
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 6
184
students, it became possible to specify gradations of challenge to which teachers could adjudge
their capability beliefs, without further complicating the items of the original TSES. The
decision to take the TSES to the student-specific level was based on the idea that obstacles to
TSE tend to be mainly reflected in the behaviors, needs, and characteristics of individual
students in class. In the conceptual model of Tschannen-Moran and colleagues (1998), for
instance, teachers’ assessment of what will be required of them in the anticipated teaching
situation has been hypothesized to be affected by micro-contextual factors, including students’
abilities and motivation. To some extent, this idea is also mirrored in their Teacher Sense of
Efficacy Scale, where some items include such gradations of challenge as ‘very capable
students’, ‘problem students’, and ‘students who are failing’ (Tschannen-Moran & Woolfolk
Hoy, 2001). The results from Chapter 3 provided supporting evidence for the ideas of
Tschannen-Moran et al. (1998), indicating that the variability at the state (within-teacher) level
was larger than at the trait (between-teacher) level. This indicates that teachers’ sense of self-
efficacy may not only fluctuate across teaching tasks and domains, but may also differ in
relation to individual students.
Teachers’ personal self-efficacy beliefs in relation to individual students appeared to vary most
in the domain of behavior management, suggesting that this domain may be most dependent
on such obstacles as individual students’ behaviors and characteristics. This finding accords
well with evidence from Chapter 4, in which individual students’ prosocial, internalizing, and
particularly externalizing behavior predicted more variance in teachers’ self-efficacy for
behavior management than any other teaching domain. Other research (e.g., Emmer &
Hickman, 1991; Kyriacou, 2001; Roehrig, Pressley, & Talotta, 2002) has also suggested that
issues such as spending too much time on discipline, not knowing when and how to punish a
student, and dealing with students who are behaviorally challenging, are usually the most
problematic for teachers. This may explicate, in part, why teachers’ sense of self-efficacy for
classroom management and/or discipline appeared to be most frequently investigated in the
literature (see Chapter 2), either as a single teaching area (e.g., Brouwers & Tomic, 2000;
Emmer & Hickman, 1991; Yoon, 2004) or as a sub-domain of TSE (e.g., Skaalvik & Skaalvik,
2007; Tschannen-Moran & Woolfolk Hoy, 2001; Woolfson & Brady, 2009). Overall, both
prior research and current findings seem to suggest that teachers may gain the most from
tailored advice on managing individual students’ behaviors, and especially those of an
externalizing nature.
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
GENERAL DISCUSSION
185
TEACHERS’ INDIVIDUAL INTERPRETATIONS OF THE STUDENT-SPECIFIC TSES
It should be noted that the examination of teachers’ student-specific self-efficacy beliefs may
present difficulties with respect to measurement non-invariance across teachers, or cluster bias.
Specifically, Chapter 3 demonstrated that teachers are likely to differ in their individual
interpretation of the student-specific TSES-items, and particularly those that tapped into their
sense of student-specific self-efficacy for instructional strategies and student engagement.
Given that these self-efficacy domains primarily make an appeal to teachers’ cognitive and
affective skills, our findings may raise the question of which other internal personal factors may
cause differences in response processes. Following Bandura’s (1997) model of triadic reciprocal
causation, such internal personal factors, including cognitive and affective events, may act as
interacting determinants that influence teachers’ perceptions of environmental events, their
self-efficacy, and their behaviors. Accordingly, it is possible that personal factors, such as
teachers’ affective state, their knowledge and skill level, or their personality, may affect the way
teachers ultimately interpret the individual items of the student-specific TSES, thereby causing
potential differences in response processes. Exploration of personal internal factors, as well as
other potential contextual features that may explain measurement non-invariance across
teachers therefore evidently merits attention in future research. One potential way of doing so
is to investigate cluster bias in the student-specific TSES with respect to violators at the within-
and between-teacher level separately for each teaching domain. Such analyses may be more
feasible in that they would allow for smaller sample sizes to achieve adequate statistical power,
and may overcome the issue of multicollinearity.
ADDRESSING CHALLENGES REGARDING THE FORMATION AND DEVELOPMENT OF TSE
Although ample research has attested to the predictive power of TSE for a range of student
and teacher outcomes (Chapter 2), there has been a noticeable lack of efforts to investigate the
various sources of TSE and the processes through which these beliefs are formed. Chapters 4
and 5 of this dissertation, therefore, were specifically targeted at exploring these unresolved
challenges, both fully concentrating on TSE at the student-specific level. Generally, results
from Chapter 4 seem to verify the assumption made in Chapter 1 that personal characteristics
of individual students, including their externalizing, internalizing, and prosocial behavior, may
serve as potent sources of teachers’ self-efficacy. Other background characteristics, such as
students’ gender and age, did not appear to be predictive of teachers’ student-specific self-
efficacy beliefs.
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 6
186
Perhaps the most important finding of Chapter 4 is that elementary school teachers were likely
to experience the lowest levels of self-efficacy in relation to individual students who exhibited
externalizing behavior in class. These externalizing student behaviors explained most of the
variance in teachers’ student-specific self-efficacy for behavior management, followed by
domains of student engagement, instructional strategies, and emotional support. Remarkably,
those outcomes seem to suggest that teachers’ beliefs in their capability to establish warm
connections with individual students and to be sensitive and responsive to their needs are least
affected by externalizing student behavior. It could be that teachers may use their emotional
supports as a way to increase externalizing students’ academic and social behaviors. Prior
research from Henricsson and Rydell (2004), for instance, has shown that disruptive children
generally get more attention, praise, and encouragement from their teachers than
unproblematic students, whose behaviors are more likely to be maintained by intrinsic interest
in their schoolwork (e.g., Beaman & Wheldall, 2000). Such positive communications may
provide teachers with the type of enactive mastery experiences that, in part, help them
overcome unsuccessful dealings with disruptive children and, in turn, increase their sense of
efficacy toward these students in the emotional support domain (Bandura, 1997). Possibly, this
might also explain why individual students’ prosocial behavior appeared to be the most
important source of TSE for emotional support, followed hot on the heels by domains of
behavior management, instructional strategies, and student engagement.
Spurred by finding that disruptive student behaviors are likely to be one of the more vital
sources of teachers’ self-efficacy, these behaviors were investigated further in Chapter 5. Here,
the quality of the student–teacher relationship was explored as an intermediary mechanism by
which the association between students’ disruptive behaviors and student-specific TSE could
be explained. Results from this short-term longitudinal study only partly supported the idea
that disruptive student behaviors, as sources of student-specific TSE, become instructive to
TSE only through teachers’ subjective evaluations of these behaviors in the context of their
daily interactions with individual students. More specifically, tentative evidence was found for
the hypothesis that teacher-perceived conflict in the student–teacher relationship mediates the
longitudinal association between students’ disruptive behavior and teachers’ student-specific
self-efficacy beliefs. Contrary to expectations, however, teacher-perceived closeness was not
found to mediate the link between disruptive student behavior and student-specific TSE.
Rather, support was provided for an alternative model, representing the hypothesis that
student-specific TSE, irrespective of teaching domain, mediated the association between
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
GENERAL DISCUSSION
187
disruptive student behavior and teachers’ perceptions of closeness in the student–teacher
relationship over time.
The complex outcomes reported in Chapter 5 seem to complement and extend the cross-
sectional results from Chapter 4, both empirically and theoretically. On the empirical front, the
differences in the magnitude of coefficients between Chapter 4 and Chapter 5 suggest that the
much larger cross-sectional associations between disruptive student behavior and student-
specific TSE in Chapter 4 were probably inflated by the stable characteristics of the association
between these two constructs (Little, 2013). As such, the longitudinal, cross-lagged approach of
Chapter 5 probably yielded more reliable coefficients.
Theoretically, the differential patterns of association for closeness and conflict seem to
underscore the importance of viewing teachers’ self-efficacy beliefs as both products and
constructors of daily interactions and relationships with individual children. Prior studies echo
this standpoint (e.g., Bandura, 1997; Fives & Alexander, 2004; Raudenbusch, Rowan, &
Cheong, 1992; Wyatt, 2016). Those studies suggest that teachers’ self-efficacy beliefs in relation
to individual students in specific contexts and their knowledge and belief structures (i.e., mental
representational models) should be considered in relation to one another, rather than
independently, to reveal a fuller portrait of the forms of knowledge and beliefs that teachers
draw upon when interacting with a particular child. Unfortunately, however, very little research
to date has explored such reciprocal associations, probably because TSE, in contrast to dyadic
student–teacher relationships, is usually defined at the classroom-level of analysis and has not
previously been integrated with attachment, dynamic systems, or bio-ecological theories.
PUTTING SOCIAL-COGNITIVE THEORY INTO PRACTICE
The current dissertation may yield some important insights into how teachers can be assisted in
their quest to deal with a variety of students with different behaviors, needs, and (dis)abilities.
First, teachers ought to be made aware of the potential influence their capability beliefs may
have on their students. Findings from forty years of research on TSE suggest that teachers’
sense of self-efficacy, at least at the classroom-level, may essentially be intertwined with their
teaching behaviors and practices in class and, as such, affect their students’ academic
adjustment. Moreover, at the student-specific level, it has been suggested that TSE may change the
quality of teachers’ relationships with individual (disruptive) students in ways that either
confirm or disconfirm their capability beliefs toward these children. To be more precise, a
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 6
188
healthy sense of self-efficacy tends to allow teachers to get into “yes mode”. Such optimistic
feelings may help them create supportive learning environments that are more in tune with
students' social, affective, and academic needs. Negative self-efficacy beliefs, however, may
stymie teachers’ effective practices in class, block their flow of positive thoughts, and
ultimately, hamper students’ motivation for their schoolwork and academic performance. This
may be especially true for disruptive students, toward whom teachers generally feel the least
efficacious. Helping teachers to be aware of, and reflect on their behaviors and feelings toward
particular students may therefore be a first step forward in the process of increasing their self-
efficacy. To facilitate such a reflective process, the relationship-focused reflection program of
Spilt, Koomen, Thijs, and van der Leij (2012) may be a particularly helpful tool.
Relatedly, the domain- and student-specific nature of teachers’ self-efficacy judgments
underscores the importance of ascertaining in which cases such capability beliefs may be
particularly problematic and therefore require intervention. Thus far, teachers seem to
experience the lowest levels of self-efficacy for behavior management in relation to students
who display disruptive, externalizing behavior, and the lowest levels of self-efficacy for
emotional support toward internalizing students. These findings, though preliminary, align well
with a recent Dutch report on appropriate education (Smeets, Ledoux, Regtvoort, Felix, & Mol
Lous, 2015), in which two third of Dutch elementary school teachers expresses a need for
further skill development in areas of interpersonal teaching and dealing with challenging
student behavior. Accordingly, training and development programs for preservice and inservice
teachers should incorporate more strategies teachers might use to bolster their skills in these
domains, such as anticipating problems, setting consistent expectations for student behavior,
providing clear routines, and engaging more in social conversations with individual students
(cf. Hamre et al., 2014). At the same time, it is worthwhile to replicate and extend the current
findings focusing on TSE in relation to students who differ in ability, motivation, and
educational needs. Such research may shed further light on the processes by which a diverse
student population may shape their teachers’ sense of student-specific self-efficacy in different
teaching domains. This information can be used to identify further training needs among
elementary school teachers.
Lastly, the high correlations among the domains of student-specific TSE give some reason to
believe that teachers, next to more generic teaching skills and capabilities, may develop various
instructional, behavioral, and affective subskills specific to individual students. If this is the case,
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
GENERAL DISCUSSION
189
then educational researchers and school psychologists should work to identify both the content
of, and possible deficiencies in such student-specific skills and capabilities to develop coaching
programs that are specifically tailored to teachers’ problems and needs with regard to individual
students. Such programs may help them better deal with difficult students in class and, in turn,
develop a healthier sense of efficacy toward these students and potentially, toward the class as
a whole.
CONCLUDING REMARKS
Research on teacher self-efficacy continues to be a vibrant and productive field of study. As
has been noted throughout this dissertation, there has been a steady increase of studies
examining the nature, measurement, sources, and consequences of TSE over the past forty
years. These studies have produced new insights demonstrating that teachers’ sense of self-
efficacy, at least at the classroom-level of analysis, may set the tone for a high-quality classroom
environment, may play a role in their students’ academic performance and motivation for their
schoolwork, and might affect their own sense of well-being in class. At the same time,
however, this body of work has raised many new challenges in the study of TSE that need to
be addressed if the field is to evolve over the next couple of years in ways benefiting both
theory and practice.
In this dissertation, therefore, we have taken stock of the current state of theory and research
on TSE and aimed to address several challenges the field is currently facing by taking teachers’
self-efficacy beliefs to the student-specific level. This refinement and extension of Bandura’s
original ideas enabled us, first, to provide a new benchmark for how the teacher self-efficacy
construct could be operationalized – as a personal belief of capability tailored to various
domains of teaching and learning, as well as individual students. Directly associated with this
benchmark is our newly developed, multifactorial instrument, which may capture variation in
teachers’ sense of self-efficacy across teaching domains and individual students. Hopefully, this
measure of self-efficacy may contribute to more adequate analyses of TSE in future years and
generate more comparable results that are less difficult to interpret.
Taking teacher’ sense of self-efficacy to the student-specific level may, in part, also address
challenges regarding the sources and underlying processes of TSE. Contrary to the modest or
non-significant results of prior research on the sources of TSE (e.g., Ruble, Usher, & McGrew,
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
CHAPTER 6
190
2011; Tschannen-Moran & Woolfolk Hoy, 2007), this dissertation provided evidence that
individual students’ social-emotional behaviors in class serve as relatively strong predictors of
student-specific TSE, and disruptive behavior in particular. Moreover, teachers’ perceptions of
conflict and closeness in the student–teacher relationship each appear to play a different role in
the development of teachers’ sense of self-efficacy toward disruptive students in various
teaching domains. Although these findings are preliminary, they seem to underscore a need to
integrate social-cognitive insights with theoretical perspectives on student–teacher
relationships, including attachment, dynamic systems, and self-determination theories. On a
more practical note, this evidence also calls for a need to set up (intervention) studies that
examine how teachers’ sense of self-efficacy toward and their affective relationships with
difficult children affect one another in a reciprocal fashion.
Overall, the current studies on student-specific teacher self-efficacy may hold some promise
for the future. Yet, much more has to be done to further define and move the field forward on
this front. In our view, this will be an important challenge in the study of teacher self-efficacy
for the years to come.
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
191
SUMMARY
FROM GENERAL TO STUDENT-SPECIFIC TEACHER SELF-EFFICACY
_________________________________________________________________________
Diversity in elementary classrooms nowadays seems to be the rule rather than the exception.
Since the inception of inclusive and appropriate education in the Netherlands, the number of
students with behavioral, learning, and other educational disabilities in regular elementary
classes has been shown to increase slowly but surely. Although some teachers handle this new
reality with notable ease, many others do not seem to feel up to the task of dealing with a
diverse student population and providing all students with an equal opportunity to learn.
Thereby, these teachers not only run the risk of physical stress and burnout, but may, in the
long run, also hamper their students’ academic performance and school engagement.
One psychological factor that has potential to explain why teachers may be more or less
successful in dealing with classroom diversity, is teachers’ sense of self-efficacy (TSE). Over
the past 40 years, these capability beliefs have been explored in multiple ways, such that
currently much information is available on teachers’ general ability to give shape to their
common actions in class and to motivate and regulate their execution. Yet, the absence of a
clear understanding of the nature, sources, and consequences of TSE, and psychometrically
sound instruments that adequately measure the construct seem to have hampered our efforts
to identify useful research-based insights about TSE that help teachers better deal with
individual students who may differ in behavior, needs, and abilities. For this reason, the present
dissertation aimed to take stock of the current state of research on TSE and address the
challenges the field is currently facing by gradually taking teachers’ general self-efficacy beliefs
to the student-specific level.
Starting out at the most general, classroom-level of analysis, Chapter 2 aimed to address
current challenges regarding the nature of TSE and its consequences. Using a process-oriented
framework, 40 years of social-cognitive research on TSE were reviewed and synthesized to
elucidate the nature of this construct and explore its direct and indirect associations with the
quality of classroom processes, students’ academic adjustment, and teachers’ psychological
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
SUMMARY
192
well-being. Based on 165 studies conducted among samples of both pre- and in-service
teachers and their students in pre-, elementary, middle, and high school, it could generally be
inferred that general, classroom-level TSE is a positive predictor of various teacher practices
related to the classroom quality. Among others, these include teachers’ dealings with problem
behavior, their instructional strategies and goals, and their student-centered teaching styles.
Modest positive associations were also noted between TSE and students’ motivation and
academic performance in a range of subjects. Teacher practices defining the classroom quality,
and particularly those related to emotional support, have been found to mediate these
associations. Lastly, aspects of teachers’ well-being, such as job satisfaction, stress and burnout
symptoms, commitment, and attrition and retention, were found to be predicted by general
TSE, either directly or, in some cases, indirectly, through instructional and socioemotional
teaching practices. Yet, several issues, including the cross-sectional nature of most reviewed
research, the sometimes poor research designs and methodologies of these studies, and the
limited breadth of the global scales used to measure TSE, prevented definitive conclusions
from this review.
Largely in an attempt to address such methodological issues, Chapter 3 therefore focused on
the development and evaluation of a new instrument that may capture fluctuations in TSE
across various teaching domains and individual students. Results from 841 third- to sixth-grade
students and their 107 regular elementary school teachers supported the existence of one
higher-order factor (General TSE) and four lower-order factors (TSE for Instructional
Strategies, Behavior Management, Student Engagement, and Emotional Support), both at the
between- and within-teacher level. These domains of TSE appeared to vary more within
teachers than between teachers, signifying that TSE, despite reflecting some degree of trait
variability, is likely to vary across characteristics of students toward whom teachers’ behaviors
are directed. It should be noted, however, that the presence of cluster bias in the majority of
teacher self-efficacy items suggested that student-specific TSE at the between-teacher level
may have a different interpretation than this construct at the within-teacher level. The
moderate association between general TSE and student-specific TSE at the between-teacher
level supports this claim. Accordingly, these findings underscore the importance of shifting
focus from general to student-specific teacher self-efficacy.
Fully refraining from general TSE at the classroom-level of analysis, then, Chapters 4 and 5
aimed to address current challenges regarding the sources of student-specific TSE and the
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
SUMMARY
193
underlying processes by which such sources become instructive to those capability beliefs. To
begin with the sources of TSE, Chapter 4 explored the predictive value of teachers’
perceptions of individual students’ internalizing, externalizing, and prosocial behaviors for
student-specific TSE, and the moderating role of teacher-perceived common classroom
misbehavior and years of experience. Using a cross-sectional sample of 69 teachers and 526
third-to-sixth graders, teachers were generally found to experience lower self-efficacy beliefs in
relation to individual students with externalizing behavior and higher self-efficacy beliefs
toward prosocial students, irrespective of teaching domain. Moreover, students’ internalizing
behavior appeared to be a negative source of student-specific TSE for instructional strategies
and emotional support, and a positive source of TSE for behavior management. Lastly,
teachers’ perceived levels of classroom misbehavior exacerbated the negative association
between externalizing student behavior and TSE for behavior management. Together, these
outcomes suggest that individual students’ externalizing behavior, more than other socio-
emotional behaviors, may be the most potent source of teachers’ student-specific self-efficacy.
Further insight into the association between externalizing, or disruptive student behavior and
student-specific TSE was, for that reason, gained in Chapter 5. In this chapter, teachers’
perceptions of closeness and conflict in the student–teacher relationship were explored as
intermediary mechanisms by which individual students’ disruptive behavior may affect student-
specific TSE over time. Short-term longitudinal results from a sample of 524 third-to-sixth
graders and their 69 teachers indicated that the association between disruptive student behavior
and student-specific TSE may be far more complex than expected on the basis of the results of
Chapter 4. Specifically, teachers generally experienced more conflict in dyadic relationships
with disruptive students, which, in turn, translated into lower student-specific TSE across
teaching domains. For closeness, a different pattern of associations was found, suggesting that
student-specific TSE, irrespective of teaching domain, mediated the link between externalizing
student behavior and teachers' perceptions of closeness in the student–teacher relationship.
These findings indicate that student–teacher conflict and closeness each may play a different
role in the development of teachers’ sense of self-efficacy toward disruptive students in various
teaching domains. For the development of empirically-based intervention programs for
teachers, it is essential to spur further understanding of the complex interrelationships among
individual students’ behaviors and needs, student–teacher relationships, and student-specific
TSE across domains of teaching and learning.
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
SUMMARY
194
In sum, this dissertation aimed to address several conceptual and methodological challenges
the field of TSE is currently facing by gradually taking teachers’ self-efficacy beliefs to the
student-specific level. In so doing, further research-based insights were gained about the highly
specific and multifaceted nature of TSE, possible ways to adequately measure this construct,
the student characteristics and relationship processes through which teachers’ self-efficacy
beliefs may develop, and lastly, the various consequences of TSE at a more general level. How
this information may help teachers better deal with a diverse student population and inform
future studies and interventions about TSE is discussed in Chapter 6.
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
195
SAMENVATTING (SUMMARY IN DUTCH)
VAN ALGEMENE NAAR LEERLING-SPECIFIEKE LEERKRACHT SELF-EFFICACY
_________________________________________________________________________
Diversiteit in de klas lijkt tegenwoordig eerder regel dan uitzondering. Sinds de invoering van
inclusief en passend onderwijs in Nederland is het aantal leerlingen met gedrags–, leer– en
andere onderwijsproblemen in het regulier basisonderwijs langzaam maar zeker aan het stijgen.
Hoewel sommige leerkrachten deze nieuwe realiteit met gemak tegemoet lijken te treden,
voelen andere zich niet opgewassen tegen de taak met een diverse leerlingpopulatie om te gaan
en al hun leerlingen gelijke leerkansen te bieden. Hierdoor lopen deze leerkrachten niet enkel
het risico op fysieke stress- en burn-outklachten, maar belemmeren zij mogelijk ook de
leerprestaties en schoolse betrokkenheid van hun leerlingen op langere termijn.
Eén psychologische factor die mogelijk kan verklaren waarom leerkrachten in meer of mindere
mate succesvol zijn in het omgaan met diversiteit in de klas, is leerkracht self-efficacy (LSE). In de
afgelopen 40 jaar zijn deze self-efficacy-, of doelmatigheidspercepties van leerkrachten op diverse
manieren verkend. Hierdoor is momenteel veel informatie voorhanden over de algemene
doelmatigheid van leerkrachten om hun eigen handelen in de klas te kunnen motiveren en
reguleren. Door een gebrek aan inzicht in de aard, bronnen en consequenties van LSE, en
instrumenten die dit construct betrouwbaar en valide kunnen meten, blijkt het niettemin lastig
om tot relevante inzichten over LSE te komen die leerkrachten helpen beter om te gaan met
individuele leerlingen die verschillen in gedrag, behoeften, en leervermogen. Om deze reden
werd in dit proefschrift de balans opgemaakt van de huidige staat van onderzoek over LSE en
werden bovenstaande kwesties aangepakt door de focus te verleggen van algemene naar
leerling-specifieke self-efficacy.
In Hoofdstuk 2 werden problemen met betrekking tot de aard en consequenties van LSE
nader onder de loep genomen. De algemene mate van doeltreffendheid van leerkrachten ten
aanzien van de hele klas vormde hierbij het vertrekpunt. Om de aard en directe en indirecte
gevolgen van LSE helder te krijgen, werd gebruik gemaakt van een proces-georiënteerd,
heuristisch raamwerk waarmee 40 jaar aan sociaal-cognitief onderzoek over LSE geëvalueerd
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
SAMENVATTING (SUMMARY IN DUTCH)
196
en samengevat kon worden. Op basis van 165 studies, uitgevoerd onder startende en ervaren
leerkrachten en hun leerlingen in peuterklassen, basisscholen en het middelbaar onderwijs,
werd geconcludeerd dat algemene LSE op klasniveau een positieve predictor is van diverse
leerkrachthandelingen en –opvattingen die gerelateerd zijn aan de kwaliteit van het klasklimaat.
Voorbeelden daarvan zijn het adequaat omgaan van leerkrachten met probleemgedrag, hun
instructiestrategieën en –doelen, en hun leerling-gecentreerde doceerstijlen. Daarnaast werden
bescheiden positieve associaties van LSE met de motivatie en leerprestaties van leerlingen in
diverse vakken gevonden. Leerkrachthandelingen die met name gerelateerd zijn aan emotionele
ondersteuning van leerlingen bleken deze associaties bovendien te mediëren. Tot slot werd
gevonden dat aspecten van het welzijn van leraren, zoals stress en burnout, werktevredenheid,
betrokkenheid en werkuitval, voorspeld worden door algemene LSE, zowel direct als indirect,
door leerkrachthandelingen gericht op instructie en sociaal-emotionele ondersteuning. Echter
dient opgemerkt te worden dat de cross-sectionele aard van veel van de geëvalueerde
onderzoeken, de matige onderzoeksdesigns en de beperkte reikwijdte van de instrumenten die
zijn gebruikt om LSE te meten meer definitieve conclusies uitsluiten.
In een poging om vat te krijgen op bovengenoemde methodologische problemen lag de focus
in Hoofdstuk 3 daarom op het ontwikkelen en evalueren van een nieuw instrument dat variatie
in LSE over diverse leerdomeinen en individuele leerlingen bloot kan leggen. Resultaten uit een
steekproef van 107 reguliere basisschoolleerkrachten in relatie tot 841 leerlingen uit groep 5 tot
en met 8 ondersteunden één hogere orde factor (Algemene LSE) en vier lagere orde factoren,
of domeinen, van leerkracht self-efficacy (LSE voor Instructiestrategieën, Gedragsmanagement,
Leerlingmotivatie en Emotionele Ondersteuning). Deze domeinen van LSE werden zowel
tussen als binnen leerkrachten gevonden. Daarnaast bleken de verschillen in self-efficacy binnen
leerkrachten aanzienlijk groter te zijn dan dan verschillen in LSE tussen leerkrachten. Dit
suggereert dat leerkracht self-efficacy waarschijnlijk niet volledig stabiel is, maar kan veranderen,
afhankelijk van de kenmerken van individuele leerlingen uit de klas van de leerkracht. Echter
dient opgemerkt te worden dat het merendeel van de items niet geheel zuiver gemeten kon
worden tussen leerkrachten. Deze vraagonzuiverheid (cluster bias) geeft aan dat potentiële
verschillen in leerkrachtkenmerken kunnen leiden tot verschillen in leerling-specifieke LSE
tussen leerkrachten die niet verklaard kunnen worden door het construct zelf. De
middelmatige correlatie tussen de algemene en leerling-specifieke self-efficacy-percepties van
leerkrachten lijkt dit idee te ondersteunen. Deze bevindingen onderstrepen dus het belang van
een focusverschuiving van algemene naar leerling-specifieke LSE.
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
SAMENVATTING (SUMMARY IN DUTCH)
197
In Hoofdstukken 4 en 5 lag de nadruk daarom volledig op LSE in relatie tot individuele
leerlingen. Centraal in deze hoofdstukken stonden uitdagingen met betrekking tot de bronnen
van deze doelmatigheidspercepties en de onderliggende processen waardoor deze bronnen
mogelijk betekenis krijgen voor de self-efficacy-gevoelens van leerkrachten. Om te beginnen met
de bronnen van LSE, werd in Hoofdstuk 4 de voorspellende waarde van leerkrachtpercepties
over het internaliserend, externaliserend en pro-sociaal gedrag van individuele leerlingen voor
LSE onderzocht. Tevens werd de modererende rol van leerkrachtpercepties over storend
leerlinggedrag in de klas en onderwijservaring geëxploreerd. Uit een cross-sectionele steekproef
van 69 leraren en 526 leerlingen (groep 5 t/m 8) bleek dat leerkrachten zichzelf over het
algemeen minder doelmatig voelden in relatie tot individuele leerlingen met externaliserend
gedrag en meer doelmatig voelden in relatie tot pro-sociale kinderen. Bovendien bleken
symptomen van internaliserend gedrag te fungeren als een negatieve bron van leerling-
specifieke LSE in het domein van instructiestrategieën en emotionele ondersteuning. Tot slot
kwam naar voren dat de associatie tussen externaliserend leerlinggedrag en LSE voor gedrags-
management sterker werd als leerkrachten tevens veel storend gedrag in de klas ondervonden.
Samen suggereren deze uitkomsten dat, van alle sociaal-emotionele leerlinggedragingen,
externaliserend gedrag mogelijk de belangrijkste bron van leerling-specifieke LSE is.
Verder inzicht in de associatie tussen externaliserend, of storend leerlinggedrag en leerling-
specifieke LSE werd om deze reden verkregen in Hoofdstuk 5. In dit hoofdstuk werd gekeken
of leerkrachtpercepties over nabijheid en conflict in de leerkracht–leerlingrelatie mogelijk
fungeren als mediërend mechanisme waarlangs storend gedrag van individuele leerlingen
invloed uitoefent op leerling-specifieke LSE over tijd. Korte-termijn longitudinale resultaten uit
een steekproef van 524 bovenbouwleerlingen in relatie tot hun 69 leraren suggereerden dat de
relatie tussen storend leerlinggedrag en leerling-specifieke LSE veel complexer is dan werd
verwacht op basis van de resultaten uit Hoofdstuk 4. Gevonden werd dat leraren over het
algemeen meer conflict in dyadische relaties met storende leerlingen beleefden, wat zich
vervolgens vertaalde in negatievere leerling-specifieke doelmatigheidspercepties over
leerdomeinen heen. Wat nabijheid betreft werd een ander patroon aangetoond. Dit patroon
was indicatief voor een mediërend effect van leerling-specifieke LSE, ongeacht leerdomein, op
de associatie tussen storend leerlinggedrag en leerkrachtpercepties van nabijheid in de
leerkracht–leerlingrelatie. Deze bevindingen roepen de gedachte op dat conflict en nabijheid in
de leerkracht–leerlingrelatie elk een andere rol spelen in de ontwikkeling van LSE in diverse
leerdomeinen en in relatie tot individuele leerlingen met storend gedrag. Voor het ontwikkelen
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
SAMENVATTING (SUMMARY IN DUTCH)
198
van evidence-based interventieprogramma’s voor leerkrachten is het daarom essentieel om meer
inzicht te krijgen in de complexe inter-relaties tussen de behoeften en gedragingen van
individuele leerlingen, leerkracht–leerlingrelaties en leerling-specifieke LSE in diverse
leerdomeinen.
Samenvattend werd in dit proefschrift gepoogd om diverse conceptuele en methodologische
kwesties in het veld van leerkracht self-efficacy aan te pakken door de focus te verleggen van
algemene naar leerling-specifieke self-efficacy van leerkrachten. Hierdoor werden verdere, op
onderzoek gebaseerde, inzichten verkregen over de zeer specifieke en veelzijdige aard van LSE,
over mogelijke manieren om dit construct op een adequate manier te meten, over
leerlingkenmerken en relatieprocessen waardoor leerling-specifieke LSE mogelijk tot
ontwikkeling kan komen en, tot slot, over diverse gevolgen van LSE op een meer algemeen
niveau. Hoe deze informatie leerkrachten kan helpen bij het omgaan met een diverse
leerlingpopulatie en bij kan dragen aan toekomstige studies en interventies is besproken in
Hoofdstuk 6.
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
199
REFERENCES _________________________________________________________________________
Abidin, R. R., & Robinson, L. L. (2002). Stress, biases, or professionalism: What drives teachers’ referral
judgments of students with challenging behaviors? Journal of Emotional and Behavioral Disorders, 10, 204–212. doi:10.1177/10634266020100040201
Abuh-Tineh, A. M., Khasawneh, S. A., & Khalaileh, H. A. (2011). Teacher self-efficacy and classroom manage-ment styles in Jordanian schools. Management in Education, 25, 175–181. doi:10.1177/0892020611420597
Ahmad, T. B. T., Basha, K. B., Marzuki, A. M., Hisham, N. A. H. I., & Sahari, M. S. (2010). Faculty's acceptance of computer based technology: Cross-validation of an extended model. Australasian Journal of Educational Technology, 26, 268–279.
Ahnert, L., Pinquart, M., & Lamb, M. E. (2006). Security of children's relationships with nonparental care providers: A meta-analysis. Child Development, 77, 664–679. doi:10.1111/j.1467-8624.2006.00896.x
Ahsan, M. T., Sharma, U., & Deppeler, J. M. (2012). Exploring pre-service teachers’ perceived teaching-efficacy, attitudes and concerns about inclusive education in Bangladesh. International Journal of Whole Schooling, 8, 1–20.
Allen, I. E., & Seaman, C. A. (2007). Likert scales and data analyses. Quality Progress, 40, 64–65. Allinder, R. M. (1995). An examination of the relationship between teacher efficacy and curriculum-based
measurement and student achievement. Remedial & Special Education, 16, 247–255. Almog, O., & Shechtman, Z. (2007). Teachers' democratic and efficacy beliefs and styles of coping with be-
havioural problems of pupils with special needs. European Journal of Special Needs Education, 22, 115–129. doi:10.1080/08856250701267774
Aloe, A. M., Amo, L. C., & Shanahan, M. E. (2014). Classroom management self-efficacy and burnout: A multi-variate meta-analysis. Educational Psychology Review, 26, 101–126. doi:10.1007/s10648-013-9244-0
Andreou, E., & Rapti, A. (2010). Teachers' causal attributions for behaviour problems and perceived efficacy for class management in relation to selected interventions. Behaviour Change, 27, 53–67. doi:10.1375/bech.27. 1.53
Angle, J., & Moseley, C. (2010). Science teacher efficacy and outcome expectancy as predictors of students' end-of-instruction (EOI) biology I test scores. School Science and Mathematics, 109, 473–483. doi:10.1111/ j.1949-8594.2009.tb18294.x
Arbeau, K. A., & Coplan, R. J. (2007). Kindergarten teachers' beliefs and responses to hypothetical prosocial, asocial, and antisocial children. Merrill-Palmer Quarterly, 53, 291–318. doi:10.1353/mpq.2007.0007
Arbeau, K. A., Coplan, R. J., & Weeks, M. (2010). Shyness, teacher–child relationships, and socio-emotional adjustment in grade 1. International Journal of Behavioral Development, 34, 259–269. doi:10.1177/0165025 409350959.
Armor, D., Conroy-Oseguera, P., Cox, M., King, N., McDonnell, L., Pascal, A., … Zellman, G. (1976). Analysis of the school preferred reading programs in selected Los Angeles minority schools (Rep. No. R-2007-LAUSD). Santa Monica, CA: Rand Corporation.
Arnold, D. H. (1997). Co-occurrence of externalizing behavior problems and emergent academic difficulties in young high-risk boys: A preliminary evaluation of patterns and mechanisms. Journal of Applied Developmental Psychology, 18, 317–330. doi:10.1016/S0193-3973(97)80003-2
Ashton, P. T., Olejnik, S., Crocker, L., & McAuli e, M. (1982). Measurement problems in the study of teachers’ sense of efficacy. Paper presented at the annual meeting of the American Educational Research Association, New York, NY.
Avanzi, L., Miglioretti,, M., Velasco, V., Balducci, C., Vecchio, L., Fraccaroli, F., & Skaalvik, E. M. (2012). Cross-validation of the Norwegian Teacher’s Self-Efficacy Scale (NTSES). Teaching and Teacher Education, 31, 69–78. doi:10.1016/j.tate.2013.01.002
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
REFERENCES
200
Baker, J. A., Grant, S., & Morlock, L. (2008). The teacher-student relationship as a developmental context for children with internalizing or externalizing behavior problems. School Psychology Quarterly, 23, 3–15. doi:10.1037/1045-3830.23.1.3
Bakker, A. B., Hakanen, J. J., Demerouti, E., & Xanthopoulou, D. (2007). Job resources boost work engagement, particularly when job demands are high. Journal of Educational Psychology, 99, 274–284. doi: 10.1037/0022-0663.99.2.274
Bandura, A. (undated). Teacher Self-Efficacy Scale. Retrieved from http://www.coe.ohio-state.edu/ahoy/ researchinstruments. htm#Ban
Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84, 191–215. doi:10.1037/0033-295X.84.2.191
Bandura, A. (1982). Self-efficacy mechanism in human agency. American Psychologist, 37, 122–147. doi:10.1037/ 0003-066X.37.2.122
Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice Hall. Bandura, A. (1997). Self-efficacy: The exercise of control. New York, NY: W.H. Freeman. Bandura, A. (2000). Exercise of human agency through collective efficacy. Current Directions in Psychological Science, 9,
75–78. doi:10.1111/1467-8721.00064 Bandura, A. (2001). Social cognitive theory: An agentic perspective. Annual Review of Psychology, 52, 1–26.
doi:10.1146/annurev.psych.52.1.1 Bandura, A. (2006). Guide for constructing self-efficacy scales. In F. Pajares & T. Urdan (Eds.), Adolescence and
education: Vol. 5. Self-efficacy and adolescence (pp. 307–337). Greenwich, CT: Information Age. Barouch Gilbert, R., Adesopea, O. O., & Schroeder, N. L. (2013). Efficacy beliefs, job satisfaction, stress and their
influence on the occupational commitment of English-medium content teachers in the Dominican Republic. Educational Psychology, 34, 1–23. doi:10.1080/01443410.2013.814193
Beaman, R., & Wheldall, K. (2000). Teachers' use of approval and disapproval in the classroom. Educational Psychology, 20, 431–446. doi: 10.1080/713663753
Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107, 238–246. doi:10.1037/0033-2909.107.2.238
Bentler, P. M. (1992). On the fit of models to covariances and methodology to the Bulletin. Psychological Bulletin, 112, 400–404. doi:10.1037/0033-2909.112.3.400.
Berman, P., & McLaughlin, M. (1977). Factors affecting implementation and continuation (Report No R-1589/7-HEW). In Federal programs supporting educational change, Vol. II. Santa Monica, CA: Rand Corporation.
Birch, S. H., & Ladd, G. W. (1998). Children's interpersonal behaviors and the teacher–child relationship. Developmental Psychology, 34, 934–946. doi:10.1037/0012-1649.34.5.934
Blackburn, J. J., & Robinson, J. S. (2008). Assessing teacher self-efficacy and job satisfaction of early career agriculture teachers in Kentucky. Journal of Agricultural Education, 49, 1–11.
Bogler, R., & Somech, A. (2004). Influence of teacher empowerment on teachers’ organizational commitment, professional commitment and organizational citizenship behavior in schools. Teaching and Teacher Education, 20, 277–289. doi:10.1016/ j.tate.2004.02.003
Brady, K., & Woolfson, L. (2008). What teacher factors influence their attributions for children’s difficulties in learning? British Journal of Educational Psychology,78, 527–544. doi:10.1348/000709907X268570
Briones, E., Tabernero, C., & Arenas, A. (2010). Job satisfaction of secondary school teachers: Effect of demographic and psycho-social factors. Revista de Psicología del Trabajo y de las Organizaciones, 26, 115–122. doi:10.5093/tr2010v26n2a3
Brissie, J. S., Hoover-Dempsey, K. V., & Bassler, O. C. (1988). Individual, situational contributors to teacher burnout. The Journal of Educational Research, 82, 106–112.
Bronfenbrenner, U., & Morris, P. A. (1998). The ecology of developmental processes. In W. Damon (Series Ed.) & R. M. Lerner (Vol. Ed.), Handbook of child psychology: Vol. 1. Theory (5th ed.). New York, NY: Wiley.
Brophy, J. E.(1996). Teaching problem students. New York, NY: Guilford Press. Brouwers, A., Evers, W. J. G., & Tomic, W. ( 2001). Self-efficacy in eliciting social support and burnout among
secondary-school teachers. Journal of Applied Social Psychology, 31, 1474–1491. doi:10.1111/j.15591816. 2001.tb02683.x
Brouwers, A., & Tomic, W. (2000). A longitudinal study of teacher burnout and perceived self-efficacy in classroom management. Teaching and Teacher Education, 16, 239–253. doi:10.1016/S0742-051X(99)00057-8
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
REFERENCES
201
Brown, E. T. (2005). The influence of teachers’ efficacy and beliefs regarding mathematics instruction in the early childhood classroom. Journal of Early Childhood Teacher Education, 26, 239–257. doi:10.1080/109010205003 69811
Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen, & J. S. Long (Eds.), Testing structural equation models (pp. 136–162). Newbury Park, CA: Sage.
Brownell, M. T., & Pajares, F. (1999). Teacher efficacy and perceived success in mainstreaming students with learning and behavior problems. Teacher Education and Special Education, 22, 154–164. doi:10.1177/088840 649902200303
Bru, E. (2009). Academic outcomes in school classes with markedly disruptive pupils. Social Psychology Review, 12, 461–479. doi:10.1007/s11218-009-9095-1
Brudnik, M., (2009). Perception of self-efficacy and professional burnout in general education teachers. Human Movement, 10, 170–175. doi:10.2478/v10038-009-0013-3
Bruinsma, M., & Jansen, E W. P. A. (2010). Is the motivation to become a teacher related to pre service teachers’ intentions to remain in the profession? European Journal of Teacher Education, 33,185–200. doi:10.1080/ 02619760903512927
Buyse, E., Verschueren, K., Doumen, S., Van Damme, J., & Maes, F. (2008). Classroom problem behavior and teacher-child relationships in kindergarten: The moderating role of the classroom climate. Journal of School Psychology, 46, 367–391. doi:10.1016/ j.jsp.2007.06.009
Canrinus, E. T., Helms-Lorenz, M., Beijaard, D., Buitink, J.,& Hofman, A. (2010). Self-efficacy, job satisfaction, motivation and commitment: Exploring the relationships between indicators of teachers’ professional identity. European Journal of Psychological Education, 27, 115–132. doi:10.1007/s10212-011-0069-2
Cantrell, S. C., Almasi, J. F., Carter, J. C., & Rintamaa, M. (2013). Reading intervention in middle and high schools: Implementation fidelity, teacher efficacy, and student achievement. Reading Psychology, 34, 26–58. doi:10.1080/02702711.2011.577695
Cantrell, S. C., & Hughes, H. K. (2008). Teacher efficacy and content literacy implementation: An exploration of the effects of extended professional development with coaching. Journal of Literacy Research, 40, 95–127. doi:10.1080/10862960802070442
Cantrell, P., Young, S., & Moore, A. (2003). Factors affection science teaching efficacy of preservice elementary teachers. Journal of Science Teacher Education, 14, 177–192. doi:10.1023/A:1025974417256
Capa-Aydin, Y., Sungur, S., & Uzuntiryaki, E. (2009). Teacher self regulation: Examining a multidimensional construct. Educational Psychology, 29, 345–356. doi:10.1080/01443410902927825
Caprara, G. V., Barbaranelli, C., Borgogni, L., & Steca, P. (2003). Efficacy beliefs as determinants of teachers’ job satisfaction. Journal of Educational Psychology, 95, 821–832. doi:10.1016/j.jsp.2006.09.001
Caprara, G. V., Barbaranelli, C., Steca, P., & Malone, P. S. (2006). Teachers’ self-efficacy beliefs as determinants of job satisfaction and students’ academic achievement: A study at the school level. Journal of School Psychology, 44, 473–490. doi:10.1016/j.jsp.2006.09.001
CBS Statline (2015a). (Speciaal) basisonderwijs; culturele minderheden, (achterstands)leerlingen. [(Special) primary education; cultural minorities, (at-risk) students]. Retrieved from http://statline.cbs.nl
CBS Statline (2015b). (Speciaal) basisonderwijs en speciale scholen; leerlingen, schoolregio. [(Special) primary education and special schools; students, school region]. Retrieved from http://statline.cbs.nl
Chacon, C. T. (2005). Teachers’ perceived efficacy among English as a foreign language teachers in middle schools in Venezuela. Teaching and Teacher Education, 21, 257–272. doi:10.1016/j.tate.2005.01.001
Chan, D. W. (2008). Self-efficacy, job satisfaction, motivation and commitment: Exploring the relationships between indicators of teachers’ professional identity. Educational Psychology, 28, 397–408. doi:10.1080/014 43410701668372
Chan, W. T., Lau, S., Nie, Y., Lim, S., & Hogan, D. (2008). Organizational and personal predictors of teacher commitment: The mediating role of teacher efficacy and identification with school. American Educational Research Journal, 45, 597–630.
Chang, I. H. (2011). A study of the relationships between distributed leadership, teacher academic optimism and student achievement in Taiwanese elementary schools. School Leadership and Management, 31, 491–515. doi:10.1080/13632434.2011.614945
Chen, F. F. (2007). Sensitivity of goodness of fit indexes to lack of measurement invariance. Structural Equation Modeling, 14, 464–504. doi:10.1080/10705510701301834
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
REFERENCES
202
Chen, R. J. (2010). Investigating models for preservice teachers’ use of technology to support student-centered learning. Computers & Education, 55, 32–42. doi:10.1016/j.compedu.2009.11.015
Cheung, H. Y. (2008). Teacher efficacy: A comparative study of Hong Kong and Shanghai primary in-service teachers. The Australian Educational Researcher, 35, 103–123. doi:10.1007/BF03216877
Cheung, G. W., & Rensvold, R. B. (2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling, 9, 233–255. doi:10.1207/ S15328007SEM0902_5.
Cho, Y., & Shim, S. S. (2013). Predicting teachers’ achievement goals for teaching: The role of perceived school goal structure and teachers’ sense of efficacy. Teaching and Teacher Education, 32, 12–21. doi:10.1016/ j.tate.2012.12.003
Chong, W. H., Klassen, R. M., Huan, V. S.,Wong, I., Kates, A. D. (2010). The relationships among school types, teacher efficacy beliefs, and academic climate: Perspective from Asian middle schools. The Journal of Educational Research, 103, 183–190. doi:10.1080/00220670903382954
Chung, L. C., Marvin, C. A., & Churchill, S. L. (2005). Teacher factors associated with preschool teacher-child relationships: Teaching efficacy and parent-teacher relationships. Journal of Early Childhood Teacher Edu-cation, 25, 131–142. doi:10.1080/1090102050250206
Ciani, K. D., Summers, J. J., & Easter, M. A. (2008). A “top-down” analysis of high school teacher motivation. Contemporary Educational Psychology, 33, 533–560. doi:10.1016/j.cedpsych.2007.04.002
Clunies-Ross, P., Little, E., & Kienhuis, M. (2008). Self-reported and actual use of proactive and reactive classroom management strategies and their relationship with teacher stress and student behaviour. Educational Psychology, 28, 693–710. doi:10.1080/01443410802206700
Coladarci, T. (1992). Teachers' Sense of Efficacy and Commitment to Teaching. The Journal of Experimental Edu-cation, 60, 323–337. doi:10.1080/00220973.1992.9943869
Coladarci, T., & Breton, W. A. (1997). Teacher efficacy, supervision, and the special education resource-room teacher. The Journal of Educational Research, 90, 230–239. doi:10.1080/00220671.1997.10544577
Cole, D. A., & Maxwell, S. E. (2003). Testing mediational models with longitudinal data: questions and tips in the use of structural equation modeling. Journal of Abnormal Psychology, 112, 558–577. doi:10.1037/0021843X. 112.4.558
Collie, R. J., Shapka, J. D., & Perry, N. E. (2012). School climate and social–emotional learning: Predicting teacher stress, job satisfaction, and teaching efficacy. Journal of Educational Psychology, 104, 1189–1204. doi:10.1037 /a0029356
Coolahan, K.C., Fantuzzo, J., Mendez, J.L. & McDermott, P.A. (2000). Preschool peer interactions and readiness to learn: Relationships between classroom peer play and learning behaviors and conduct. Journal of Educational Psychology, 92, 458–465. doi:10.1037/0022-0663.92.3.458
Coplan, R. J. (2000). Assessing nonsocial play in early childhood: Conceptual and methodological approaches. In K. Gitlin-Weiner & A. Sandgrund (Eds.), Play diagnosis and assessment (2nd ed., pp. 563-598). New York, NY: Wiley.
Coplan, R. J., & Prakash, K. (2003). Spending time with teacher: Characteristics of preschoolers who frequently elicit versus initiate interactions with teachers. Early Childhood Research Quarterly, 18, 143–158. doi:10. 1016/S0885-2006(03)00009-7
Crosnoe, R., Johnson, M. K., & Elder, G. H. (2004). Intergenerational bonding in school: The behavioral and contextual correlates of student-teacher relationships. Sociology of Education, 77, 60–81. doi:10.1177/ 003804070407700103
Davis, H. A. (2003). Conceptualizing the role and influence of student-teacher relationships on children's social and cognitive development. Educational Psychologist, 38, 207–234. doi:10.1207/S15326985EP3804_2
Deemer, S. (2004). Classroom goal orientation in high school classrooms: Revealing links between teacher beliefs and classroom environments. Educational Research, 46, 73–90. doi:10.1080/0013188042000178836
DeForest, P. A., & Hughes, J. N. (1992). Effect of teacher involvement and teacher self-efficacy on ratings of consultant effectiveness and intervention acceptability. Journal of Educational and Psychological Consultation, 3, 301–316.
De Jong, R., Mainhard, T., Tartwijk, J., Veldman, I., Verloop, N., & Wubbels, T. (2014). How pre service teachers' personality traits, self efficacy, and discipline strategies contribute to the teacher–student relationship. British Journal of Educational Psychology, 84, 294–310. doi:10.1111/bjep.12025
Dellinger, A. B. (2005).Validity and the review of literature. Research in the Schools, 12, 41–54.
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
REFERENCES
203
Dellinger, A.B., Bobbett, J. J., Olivier, D. F., & Ellett, C. D. (2008). Measuring teachers’ self-efcacy beliefs: Development and use of the TEBS-Self. Teaching and Teacher Education, 24, 751–766. doi:10.1016/j.tate. 2007.02.010
Derriks, M., Ledoux, G., Overmaat, M. & Van Eck, E. (2002). Omgaan met verschillen. Competenties van leerkrachten en schoolleiders. Amsterdam: SCO-Kohnstamm Instituut.
Dickey, W. C., & Blumberg, S. J. (2004). Revisiting the factor structure of the strengths and difficulties questionnaire: United States, 2001. Journal of the American Academy of Child & Adolescent Psychiatry, 43, 1159–1167. doi:10.1097/01.chi.0000132808.36708.a9
Diefenbach, M. A., Weinstein, N. D., & O'Reilly, J. (1993). Scales for assessing perceptions of health hazard susceptibility. Health Education Research, 8, 181–192.
Doménech-Betoret, F. (2006). Stressors, self-efficacy, coping resources and burnout among secondary school teachers in Spain. Educational Psychology, 26, 519–539. doi:10.1080/01443410500342492
Doménech-Betoret, F. (2009). Self efficacy, school resources, job stressors and burnout among Spanish primary and secondary school teachers: A structural equation approach. Educational Psychology, 29, 45–68. doi:10.1 080/01443410802459234
Doumen, S., Verschueren, K., Buyse, E., Germeijs, V., Luyckx, K., & Soenens, B. (2008). Reciprocal relations between teacher–child conflict and aggressive behavior in kindergarten: A three-wave longitudinal study. Journal of Clinical Child and Adolescent Psychology, 37, 588–599. doi:10.1080/15374410802148079
Downer, J., Sabol, T. J., & Hamre, B. (2010). Teacher–child interactions in the classroom: Toward a theory of within-and cross-domain links to children's developmental outcomes. Early Education and Development, 21, 699–723. doi:10.1080/10409289.2010.497453
Duffy, R. D., & Lent, R. W. (2009). Test of a social cognitive model of work satisfaction in teachers. Journal of Vocational Behavior, 75, 212–223. doi:10.1016/j.jvb.2009.06.001
Dunfield, K. A., & Kulhmeier, V. A. (2013). Classifying prosocial behavior: Children's responses to instrumental need, emotional distress, and material desire. Child Development, 84, 1766–1776. doi:10.1111/cdev.12075
Dunfield, K. A., Kuhlmeier, V. A., O’Connell, L., & Kelley, E. (2011). Examining the diversity of prosocial Behavior: helping, sharing, and comforting in infancy. Infancy, 16, 227–247. doi:10.1111/j.1532-7078. 2010.00041.x
Dunn, K. E., Airola, D. T., Lo, W. J., & Garrison, M. (2013). Becoming data driven: The influence of teachers’ sense of efficacy on concerns related to data-driven decision making. The Journal of Experimental Education, 81, 222–241. doi:10.1080/00220973.2012.699899
Dunn, K. E., & Rakes, G. C. (2011). Teaching teachers: An investigation of beliefs in teacher education students. Learning Environments Research, 14, 39–58. doi:10.1007/s10984-1-9083-1
DUO (2014). Leeftijd van personeel in het primair onderwijs. [Age of employees in primary education]. Retrieved from http://www.onderwijsincijfers.nl/kengetallen/primair-onderwijs/personeelpo/leeftijd-personeel
Dussault, M. (2006). Teachers’ self-efficacy and organizational citizenship behaviors. Psychological Reports, 98, 427–432. doi:10.2466/pr0.98.2.427-432
Dyer, N. G., Hanges, P. J., & Hall, R. J. (2005). Applying multilevel confirmatory factor analysis techniques to the study of leadership. The Leadership Quarterly, 16, 149–167. doi:10.1016/j.leaqua.2004.09.009
Ebmeier, H. (2003). How supervision influences teacher efficacy and commitment: An investigation of a path model. Journal of Curriculum & Supervision, 18, 110–142.
Egyed C. J., & Short, R. J. (2006). Teacher self-efficacy, burnout, experience and decision to refer a disruptive student. School Psychology International, 27, 462–474. doi:10.1177/ 0143034306070432
Eisenberg, N. (1982). The development of reasoning about prosocial behavior. In N. Eisenberg (Ed.), The development of prosocial behavior (pp. 219–249). New York, NY: Academic Press.
Eisenberg, N., Cumberland, A., Spinrad, T. L., Fabes, R. A., Shepard, S. A., Reiser, M., ... & Guthrie, I. K. (2001). The relations of regulation and emotionality to children's externalizing and internalizing problem behavior. Child development, 72, 1112–1134. doi:10.1111/1467-8624.00337
Emmer, E., & Hickman, J. (1991). Teacher efficacy in classroom management and discipline. Educational and Psychological Measurement, 51, 755–765. doi:10.1177/0013164491513027
Emmer, E. T., & Stough, L. M. (2001). Classroom management: A critical part of educational psychology, with implications for teacher education. Educational Psychologist, 36, 103–112. doi:10.1207/S15326985EP3602_5
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
REFERENCES
204
Eren, A. (2009). Examining the teacher efficacy and achievement goals as predictors of Turkish student teachers’ conceptions about teaching and learning. Australian Journal of Teacher Education, 34, 69–87. doi:10.14221/ ajte.2009v34n1.6
Eren, A. (2012). Prospective teachers’ future time perspective and professional plans about teaching: The mediating role of academic optimism. Teaching and Teacher Education, 28, 111–123. doi:10.1016/j.tate. 2011.09.006
Eslami, Z. R., & Fatahi, A. (2008). Teachers' sense of self-efficacy, English proficiency, and instructional strategies: A study of nonnative EFL teachers in Iran. TESL-EJ, 11, 1–19.
Eun, B., & Heining-Boynton, A. L. (2007). Impact of an English-as-a-second-language professional development program. The Journal of Educational Research, 101, 36–49. doi:10.3200/JOER.101.1.36-49
Evans, E. D., & Tribble, M. (1986). Perceived teaching problems, self-efficacy, and commitment to teaching among preservice teachers. The Journal of Educational Research, 80, 81–85.
Evers, W., Brouwers, A., & Tomic, W. (2002). Burnout and self-efficacy: A study on teachers’ beliefs when implementing an innovative educational system in the Netherlands. British Journal of Educational Psychology, 72, 227–243. doi:10.1348/000709902158865
Evers, W. J. G., Tomic, W., & Brouwers, A. (2004). Burnout among teachers: Students’ and teachers’ perceptions compared. School Psychology International, 25, 131–148. doi:10.1177/0143034304043670
Evers, W. Tomic, W. & Brouwers, A. (2005). Does equity sensitivity moderate the relationship between self-efficacy beliefs and teacher burnout? Representative Research in Social Psychology, 28, 35–46.
Fernet, C., Guay, F., Senecal, C., & Austin, S. (2012). Predicting intraindividual changes in teacher burnout: The role of perceived school environment and motivational factors. Teaching and Teacher Education, 28, 514–525. doi:10.1016/j.tate.2011.11.013
Feuerborn, L., & Chinn, D. (2012). Teacher perceptions of student needs and implications for positive behavior supports. Behavioral Disorders, 37, 219–231.
Fives, H., & Alexander, P. A. (2004). How schools shape teacher efficacy and commitment: another piece in the achievement puzzle. Big Theories Revisited, 4, 329–359.
Fives, H., Hamman, D., & Olivarez, A. (2007). Does burnout begin with student-teaching? Analyzing efficacy, burnout, and support during the student-teaching semester. Teaching and Teacher Education, 23, 916–934. doi:10.1016/j.tate.2006.03.013
Fredricks, J. A., & Eccles, J. S. (2002). Children's competence and value beliefs from childhood through adolescence: Growth trajectories in two male sex-typed domains. Developmental Psychology, 38, 519–533. doi:10.1037//0012-1649.38.4.519.
Friedman, I. A. (2003). Self-efficacy and burnout in teaching: The importance of interpersonal-relations efficacy. Social Psychology of Education, 6, 191–215.
Friedman, I.A. (2006). Classroom management and teacher stress and burnout. In C.M. Evertson & C.S. Weinstein (Eds.), Handbook of classroom management: Research, practice, and contemporary issues (pp. 925–944). Mahwah, NJ: Lawrence Erlbaum.
Friedman, I. A., & Kass, E. (2002). Teacher self-efcacy: A classroom-organization conceptualization. Teaching and Teacher Education, 18, 675–686. doi:10.1016/S0742-051X(02)00027-6
Gao,W., & Mager, G. (2011). Enhancing preservice teachers’ sense of efficacy and attitudes toward school diversity through preparation: A case of one U.S. inclusive teacher education program. International Journal of Special Education, 26, 92–107.
Gazelle, H., & Ladd, G. W. (2003). Anxious solitude and peer exclusion: A diathesis–stress model of internalizing trajectories in childhood. Child development, 74, 257–278. doi:10.1111/1467-8624.00534
Geijsel, F. P., Sleegers, P. J. C., Stoel, R. D., & Kruger, M. L. (2009). The effect of teacher psychological and school organizational and leadership factors on teachers’ professional learning in Dutch schools. The Elementary School Journal, 109, 406–427.
Ghaith, G., & Shaaban, K. (1999). The relationship between perceptions of teaching concerns, teacher efficacy, and selected teacher characteristics. Teaching and Teacher Education, 15, 487–496. doi:10.1016/S0742-051X (99)00009-8
Ghaith, G., & Yaghi, M. (1997). Relationships among experience, teacher efficacy and attitudes toward the implementation of instructional innovation. Teaching and Teacher Education, 13, 451–458. doi:10.1016/ S0742-051X(96)00045-5
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
REFERENCES
205
Gibbs, S., & Powell, B. (2011). Teacher efcacy and pupil behaviour: The structure of teachers’ individual and collective beliefs and their relationship with numbers of pupils excluded from school. British Journal of Educational Psychology, 82, 564–584. doi:10.1111/j.2044-8279.2011.02046.x
Gibson, S., & Dembo, M. (1984). Teacher efficacy: a construct validation. Journal of Educational Psychology, 76, 569–582. doi:10.1037/0022-0663.76.4.569
Goddard, R. D., & Goddard, Y. L. (2001). A multilevel analysis of teacher and collective efficacy. Teaching and Teacher Education, 17, 807–818. doi:10.1016/S0742-051X(01)00032-4
Goddard, R. D., Hoy, W. K., & Woolfolk Hoy, A. E. (2004). Collective efficacy beliefs: Theoretical developments, empirical evidence, and future directions. Educational Researcher, 33, 3–13.
Godfrey, C. (2001). Computers in school: Changing technologies. Australian Educational Computing, 16, 14–17. Goffin, R. D., & Gellatly, I.R. (2001). A multi-rater assessment of organizational commitment: Are self-report
measures biased? Journal of Organizational Behavior, 22, 437–451. doi:10.1002/job.94 Goodman, A., Lamping, D., & Ploubidis, G.B. (2010). When to use broader internalising and externalising
subscales instead of the hypothesised five subscales on the Strengths and Difficulties Questionnaire (SDQ): Data from British parents, teachers and children. Journal of Abnormal Child Psychology, 38, 1179–1199. doi:10.1007/s10802-010-9434-x
Gorozidis, G., & Papaioannou, A. (2011). Teachers’ self-efficacy, achievement goals, attitudes and intentions to implement the new Greek physical education curriculum. European Physical Education Review, 17, 231–253. doi:10.1177/1356336X11413654
Greene, R. W., Abidin, R. R., & Kmetz, C. (1997). The Index of Teaching Stress: A measure of student-teacher compatibility. Journal of School Psychology, 35, 239–259. doi:10.1016/S0022-4405(97)00006-X
Greene, R. W., Beszterczey, S. K., Katzenstein, T., Park, K., & Goring, J. (2002). Are students with ADHD more stressful to teach?: Patterns of teacher stress in an elementary school sample. Journal of Emotional and Behavioral Disorders, 10, 79–89. doi:10.1177/10634266020100020201
Gresham, F. M., & Kern, L. (2004). Internalizing behavior problems in children and adolescents. In R. Rutherford, M. Quinn, & S. Mathur (Eds.), Handbook of research in behavior disorders (pp. 262–281). New York, NY: The Guilford Press.
Guo, Y., McDonald Connor, C., Yang, Y., Roehring, A. D., & Morrison, F. J. (2012). The effects of teacher qualification, teacher self-efficacy, and classroom practices on fifth graders’ literacy outcomes. The Elementary School Journal, 113, 3–24. doi:10.1086/665816
Guo, Y., Piasta, S. B., Justice, L. M., & Kaderavek, J. N. (2010). Relations among preschool teachers' self-efficacy, classroom quality, and children's language and literacy gains. Teaching and Teacher Education,26, 1094–1103. doi:10.1016/j.tate.2009.11.005
Guo, Y., Sawyer, B. E., Justice, L. M., & Kaderavek, J. N. (2013). Quality of the literacy environment in inclusive early childhood special education classrooms. Journal of Early Intervention, 35, 40–60. doi:10.1177/ 1053815113500343
Guskey, T. R. (1981). Measurement of responsibility teachers assume for academic successes and failures in the classroom. Journal of Teacher Education, 32, 44–51. doi:10.1177/002248718103200310
Hamre, B., Hatfield, B., Pianta, R., & Jamil, F. (2014). Evidence for general and domain-specific elements of teacher–child interactions: Associations with preschool children's development. Child Development, 85, 1257–1284. doi:10.1111/cdev.12184
Hamre, B. K., & Pianta, R. C. (2001). Early teacher-child relationships and the trajectory of children's school outcomes through eighth grade. Child Development, 72, 625–638.
Hamre, B. K., & Pianta, R. C. (2004). Self-reported depression in nonfamilial caregivers: Prevalence and associations with caregiver behavior in child-care settings. Early Childhood Research Quarterly, 19, 297–318. doi:10.1016/j.ecresq.2004.04.006
Hamre, B. K., & Pianta, R. L. (2005). Can instructional and emotional support in the first grade classroom make a difference for children at risk of school failure? Child Development, 76, 949–967. doi:10.1111/j.1467-8624.2005.00889.x
Hamre, B. K., & Pianta, R. L. (2010). Classroom environments and developmental processes. In Meece, J. L., & J. S. Eccles. Handbook of research on schools, schooling, and human development (pp. 25–41). New York, NY: Routledge.
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
REFERENCES
206
Hamre, B. K., Pianta, R. C., Downer, J. T., Decoster, J., Jones, S., Brown, J., Cappella, E, … Kaefer, T. (2013). Teaching through interactions: testing a developmental framework of effective teaching in over 4,000 classrooms. The Elementary School Journal, 113, 461–487. doi:10.1086/669616
Hamre, B. K., Pianta, R. C., Downer, J. T., & Mashburn, A. J. (2008). Teachers’ perceptions of conflict with young students: Looking beyond problem behaviors. Social Development, 17, 115–136. doi:10.1111/j.1467-9507.2007.00418.x
Hardré, P.L., Huang, S.H., Chen, C.H., Chiang, C.T., Jen, F.L., & Warden, L. (2006). High school teachers’ motivational perceptions and strategies in an East Asian nation. Asia-Pacific Journal of Teacher Education 34, 199–221. doi:10.1080/13598660600720587
Hardré, P. L., & Sullivan, D. W. (2008). Teacher perceptions and individual differences: How they influence rural teachers’ motivating strategies. Teaching and Teacher Education , 24, 2059– 2075. doi:10.1016/j.tate.2008.04. 007
Hardré, P. L., & Sullivan, D. W. (2009). Motivating adolescents: high school teachers’ perceptions and classroom practices. Teacher Development, 13, 1–16. doi:10.1080/13664530902858469
Hargreaves, A. (1998). The emotional practice or teaching, Teaching and Teacher Education, 14, 835-854. Hargreaves, A. (2000). Mixed emotions: Teachers’ perceptions of their interactions with students. Teaching and
Teacher Education, 16, 811–826. doi:10.1016/S0742-051X(00)00028-7 Hastings, R. P., & Bham, M. S. (2003).The relationship between student behaviour patterns and teacher burnout.
School Psychology International, 24, 115–127. doi:10.1177/0143034303024001905 Haverback, H. R. (2009). Situating pre service reading teachers as tutors: implications of teacher self efficacy on
tutoring elementary students. Mentoring & Tutoring: Partnership in Learning, 17, 251–261. doi:10.1080/1361 1260903050171
Helms-Lorenz, M., Slof, B., Vermue, C. E., & Canrinus, E. T. (2012). Beginning teachers’ self-efficacy and stress and the supposed effects of induction arrangements. Educational Studies, 38, 189–207. doi:10.1080/ 03055698.2011.598679
Heneman, H.G., Kimball, S., & Milanowski, A. (2006). The Teacher Sense of Efficacy Scale: Validation evidence and behavioral prediction (WCER Working Paper No. 2006-7). Madison, WI: Wisconsin Center for Education Research.
Henricsson, L., & Rydell, A. M. (2004). Elementary school children with behavior problems: Teacher–child relations and self-perception. A prospective study. Merrill-Palmer Quarterly, 50, 111–138. doi:10.1353/ mpq.2004.0012
Henson, R. (2001). Teacher self-efficacy: Substantive implications and measurement dilemmas. (ERIC Document Reproductive Service No. ED 452208).
Henson, R. K. (2002). From adolescent angst to adulthood: Substantive implications and measurement dilemmas in the development of teacher efficacy research. Educational Psychologist, 37, 137–150. doi:10.1207/ S15326985EP3703_1
Hines, M. T. (2008). The interactive effects of race and teacher self-efficacy on the achievement gap in school. National Forum of Multicultural Issues Journal, 7, 1–11.
Hipp, K. A., & Bredeson, P. V. (1995). Exploring connections between teacher efficacy and principals’ leadership behavior. Journal of School Leadership, 5, 136–150.
Høigaard, R., Giske, R., & Sundsli, K. (2012). Newly qualified teachers’ work engagement and teacher efficacy influences on job satisfaction, burnout, and the intention to quit. European Journal of Teacher Education, 35, 347–357. doi:10.1080/02619768.2011.633993
Holzberger, D., Philipp, A., & Kunter, M. (2013). How teachers’ self-efficacy is related to instructional quality: A longitudinal analysis. Journal of Educational Psychology, 105, 774–786. doi:10.1037/a0032198
Hornstra, L., van der Veen, I., Peetsma, T., & Volman, M. (2013). Developments in motivation and achievement during primary school: A longitudinal study on group-specific differences. Learning and Individual Differences, 23, 195–204. doi:10.1016/j.lindif.2012.09.004
Howes, C., Hamilton, C. E., & Matheson, C. C. (1994). Children's Relationships with Peers: Differential Associations with Aspects of the Teacher Child Relationship. Child Development, 65, 253–263. doi:10.11 11/j.1467-8624.1994.tb00748.x
Hox, J. (2002). Multilevel analysis: Techniques and applications. Mahwah, NJ: Erlbaum.
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
REFERENCES
207
Hoy, W. K., & Woolfolk, A. E. (1990). Socialization of student teachers. American Educational Research Journal, 27, 279–300.
Hoy, W. K. & Woolfolk, A. E. (1993). Teachers’ sense of efficacy and the organizational health of schools. The Elementary School Journal, 93, 356–372.
Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indices in covariance structure analysis: conventional versus new alternatives. Structural Equation Modeling, 6,1–55. doi:10.1080/10705519909540118
Hughes, G. D. (2012). Teacher retention: Teacher characteristics, school characteristics, organizational characteristics, and teacher efficacy. The Journal of Educational Research, 105, 245–255. doi:10.1080/ 00220671.2011.584922
Hughes, J. N., Barker, D., Kemenoff, S., & Hart, M. (1993). Problem ownership, causal attributions, and self-efficacy as predictors of teachers' referral decisions. Journal of Educational & Psychological Consultation, 4, 369–384.
Hultell, D., Melin, B., & Gustavsson, J. P. (2013). Getting personal with teacher burnout: A longitudinal study on the development of burnout using a person-based approach. Teaching and Teacher Education, 32, 75–86. doi:10.1016/j.tate.2013.01.007
Imants, J., & Van Zoelen, A. (1995). Teachers’ sickness absence in primary schools, school climate and teachers’ sense of efficacy. School Organisation, 15, 77–86.
Jak, S. (2014). Testing strong factorial invariance using three-level structural equation modeling. Frontiers in psychology, 5, 1–7. doi:10.3389/fpsyg.2014.00745
Jak, S., Oort, F. J., & Dolan, C. V. (2013). A test for cluster bias: detecting violations of measurement invariance across clusters in multilevel data. Structural Equation Modeling, 20, 265–282. doi:10.1080/10705511. 2013.769392
Jak, S., Oort, F. J., & Dolan, C. V. (2014). Measurement bias in multilevel data. Structural Equation Modeling,21, 31–39. doi:10.1080/10705511.2014.856694
Jennings, P. A., & Greenberg, M. T. (2008). The prosocial classroom: Teacher social and emotional competence in relation to student and classroom outcomes. Review of Educational Research, 79, 491–525. doi:10.3102/ 0034654308325
Jerome, E. M., Hamre, B. K., & Pianta, R. C. (2009). Teacher–Child Relationships from Kindergarten to Sixth Grade: Early Childhood Predictors of Teacher perceived Conflict and Closeness. Social Development, 18, 915–945. doi:10.1111/j.1467-9507.2008.00508.x
Jimmieson, N. L., Hannam, R. L.. & Yeo, G. B. (2010). Teacher organizational citizenship behaviours and job efficacy: Implications for student quality of school life. British Journal of Psychology, 101, 453–479. doi:10.1348/000712609X470572
Johnson, S., Cooper, C., Cartwright, S., Donald, I., Taylor, P., & Millet, C. (2005). The experience of work-related stress across occupations. Journal of managerial psychology, 20, 178-187. doi:10.1108/02683940510579803
Justice, L. M., Mashburn, A. J., Hamre, B. K., & Pianta, R. C. (2008). Quality of language and literacy instruction in preschool classrooms serving at-risk pupils. Early Childhood Research Quarterly, 23, 51–68. doi:10.1016/ j.ecresq.2007.09.004
Kagan, D. M. (1990). Ways of evaluating teacher cognition: Inferences concerning the goldilocks principle. Review of Educational Research, 60, 419–469. doi:10.3102/ 00346543060003419
Kao, C. P., Wu, Y. T., & Tsai, C. C. (2011). Elementary school teachers’ motivation toward web-based professional development, and the relationship with Internet self-efficacy and belief about web-based learning. Teaching and Teacher Education, 27, 406–415. doi:10.1016/j.tate.2010.09.010
Keiley, M. K., Lofthouse, N., Bates, J. E., Dodge, K. A., & Pettit, G. S. (2003). Differential risks of covarying and pure components in mother and teacher reports of externalizing and internalizing behavior across ages 5 to 14. Journal of Abnormal Child Psychology, 31, 267–283. doi:10.1023/A:1023277413027
Kellam, S.G., Ling, X., Merisca, R., Brown, C.H., & Ialongo, N. (1998). The effect of the level of aggression in the first grade classroom on the course and malleability of aggressive behavior in middle school. Development and Psychopathology, 10, 165–185.
Kirsch, I. (1985). Self-efficacy and expectancy: Old wine with new labels. Journal of Personality and Social Psychology, 49, 824–830.
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
REFERENCES
208
Kislenko, K., & Grevholm, B. (2008, July). The Likert scale used in research on affect: A short discussion of terminology and appropriate analyzing methods. Paper presented at the 11th International Congress on Mathematical Education, Monterrey, Mexico.
Klassen, R. M., & Chiu, M. M. (2010). Effects on teachers’ self-efficacy and job satisfaction: teacher gender, years of experience, and job stress. Journal of Educational Psychology, 102, 741–756. doi:10.1037/a0019237
Klassen, R. M., & Chiu, M. M. (2011). The occupational commitment and intention to quit of practicing and pre-service teachers: Influence of self-efficacy, job stress, and teaching context. Contemporary Educational Psychology, 36, 114–129. doi:10.1016/j.cedpsych.2011.01.002
Klassen, R. M., Bong, M., Usher, E. L., Chong, W. H., Huan, V. S., Wong, I. Y., & Georgiou, T. (2009). Exploring the validity of a teachers’ self-efficacy scale in five countries. Contemporary Educational Psychology, 34, 67–76. doi:10.1016/j.cedpsych.2008.08.001
Klassen, R. M., & Tze, V. M. (2014). Teachers’ self-efficacy, personality, and teaching effectiveness: A meta-analysis. Educational Research Review, 12, 59–76. doi:10.1016/j.edurev.2014.06.001
Klassen, R. M., Tze, V. M., Betts, S. M., & Gordon, K. A. (2011). Teacher efficacy research 1998 –2009: Signs of progress or unfulfilled promise? Educational Psychological Review, 23, 21–43. doi:10.1007/s10648-010-91418
Klassen, R., Wilson, E., Siu, A. F. Y., Hannok, W., Wong, M. W., Wongsri, N., … Jansem, A. (2013). Preservice teachers’ work stress, self-efficacy, and occupational commitment in four countries. European Journal of Psychological Education, 28, 1289–1309. doi:10.1007/s10212-012-0166-x
Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed.).New York, NY: Guilford. Kokkinos, C. M., & Kargiotidis, A. (2014). Rating students’ problem behaviour: the role of teachers’ individual
characteristics. Educational Psychology, (ahead-of-print), 1–17. doi:10.1080/01443410.2014.993929 Kokkinos, C. M., Panayiotou, G., & Davazoglou, A. M. (2004). Perceived seriousness of pupils’ undesirable
behaviours: The student teachers’ perspective. Educational Psychology, 24, 109–120. doi:10/1080.01443410 32000146458
Kokkinos, C. M., Panayiotou, G., & Davazoglou, A. M. (2005). Teacher appraisals of student behaviors. Psychology in the Schools, 42, 79–89. doi:10.1002/pits.20031
Koomen, H. M. Y., & Spilt, J. L. (2011). Emotionele en gedragsproblemen [Emotional and behavioral problems]. In P. F. de Jong & H. M. Y. Koomen (Eds.), Interventie bij Onderwijsleerproblemen. Antwerp: Garant.
Koomen, H. M. Y. Verschueren, K., & Pianta, R. C. (2007). Leerling–Leerkracht Relatie Vragenlijst (LLRV): Handleiding. [Student–Teacher Relationship Scale: Manual]. Houten: Bohn Stafleu van Loghum
.Koomen, H. M. Y., Verschueren, K., Van Schooten, E., Jak, S., & Pianta, R. C. (2012). Validating the Student-Teacher Relationship Scale: Testing factor structure and measurement invariance across child gender and age in a Dutch sample. Journal of School Psychology, 50, 215–234. doi:10.1016/j.jsp.2011.09.001
Kuncel, N. R., Credé, M., & Thomas, L. L. (2005). The validity of self-reported grade point averages, class ranks, and test scores: A meta-analysis and review of the literature. Review of Educational Research, 75, 63–82.
Kyriacou, C. (2001). Teacher stress: Directions for future research. Educational Review, 53, 27–35. doi:10.1080/ 00131910120033628
Labone, E. (2004). Teacher efficacy: Maturing the construct through research in alternative paradigms. Teaching and Teacher Education, 20, 341–359. doi:10.1016/j.tate.2004.02.013
Lachman, M. E. (2006). Perceived control over aging-related declines: Adaptive beliefs and behaviors. Current Directions in Psychological Science, 15, 282–286.
Ladd, G. W., & Burgess, K. B. (1999). Charting the relationship trajectories of aggressive, withdrawn, and aggressive/withdrawn children during early grade school. Child Development, 70, 910–929.
Lakshmanan, A., Heath, B. P., Perlmutter,A., & Elder, M. (2011). The impact of science content and professional learning communities on science teaching efcacy and standards-based instruction. Journal of Research in Science Teaching, 48, 534–551. doi:10.1002/tea.20404
Lambert, R. G., McCarthy, C., O'Donnell, M., & Wang, C. (2009). Measuring elementary teacher stress and coping in the classroom: Validity evidence for the Classroom Appraisal of Resources and Demands. Psychology in the Schools, 10, 973–988. doi:10.1002/pits.20438
La Paro, K. M., Pianta, R. C., & Stuhlman, M. (2004). The Classroom Assessment Scoring System: Findings from the prekindergarten year. The Elementary School Journal, 104, 409–426.
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
REFERENCES
209
Lee, B., Cawthon, S., & Dawson, K. (2013). Elementary and secondary teacher self-efficacy for teaching and pedagogical conceptual change in a drama-based professional development program. Teaching and Teacher Education, 30, 84–98. doi:10.1016/j.tate.2012.10.010
Lee, V. E., Dedrick, R. F., & Smith, J. B. (1991). The effect of the social organization of schools on teachers' efficacy and satisfaction. Sociology of Education, 64, 190–208. doi:10.2307/2112851
Lee, M. H., & Tsai, C. C. (2010). Exploring teachers’ perceived self-efficacy and technological pedagogical content knowledge with respect to educational use of the World Wide Web. Instructional Science, 38, 1–21. doi:10.1007/s11251-008-9075-4
Lent, R. W., & Brown, S. D. (2006). On conceptualizing and assessing social cognitive constructs in career research: A Measurement Guide. Journal of Career Assessment, 14, 12–35. doi:10.1177/1069072705281364
Lent, R. W., Nota, L., Soresi, S., Ginevra, M. C., Duffy, R. D., & Brown, S. D. (2011). Predicting the work and life satisfaction of Italian teachers: test of a social cognitive model. Journal of Vocational Behavior, 79, 91–97. doi:10.1016/j.jvb.2010.12.006
Leroy, N., Bressoux, P., Sarrazin,P., & Trouilloud, D. (2007). Impact of teachers’ implicit theories and perceived pressures on the establishment of an autonomy supportive climate. European Journal of Psychology of Education, 22, 529–545. doi:10.1007/BF03173470
Letcher, P., Smart, D., Sanson, A., & Toumbourou, J. W. (2009). Psychosocial precursors and correlates of differing internalizing trajectories from 3 to 15 years. Social Development, 18, 618–646. doi:10.1111/j.1467-9507.2008.00500.x
Leung, S. O. (2011). A comparison of psychometric properties and normality in 4-, 5-, 6-, and 11-point Likert scales. Journal of Social Service Research, 37, 412–421. doi:10.1080/01488376.2011.580697
Liljequist, L., & Renk, K. (2007). The relationships among teachers’ perceptions of student behaviour, teachers’ characteristics, and ratings of students’ emotional and behavioural problems. Educational Psychology, 27, 557–571. doi:10.1080/01443410601159944
Little, T. D. (2013). Longitudinal structural equation modeling. New York, NY: The Guildford Press. Lumpe, A., Czerniak, C., Haney, J. & Beltyukova, S. (2012). Beliefs about Teaching Science: The relationship
between elementary teachers’ participation in professional development and student achievement. International Journal of Science Education, 34, 153–166. doi:10.1080/09500693.2010.551222
Maassen, G. H., & Bakker, A. B. (2001). Suppressor variables in path models: Definitions and interpretations. Sociological Methods & Research, 30, 241–270. doi:10.1177/0049124101030002004
MacKinnon, D. P., Lockwood, C. M., & Williams, J. (2004). Confidence limits for the indirect effect: Distribution of the product and resampling methods. Multivariate Behavioral Research, 39, 99–128. doi:10.1207/ s15327906mbr3901_4
Malecki, C. K., & Elliott, S. N. (2002). Children’s social behaviors as predictors of academic achievement: A longitudinal analysis. School Psychology Quarterly, 17, 1–23. doi:10.1521/scpq.17.1.1.19902
Malinen, O. P., Savolainen, H., Engelbrecht, P., Xu, J., Nel, M., Nel, N., & Tlale, D. (2013). Exploring teacher self-efficacy for inclusive practices in three diverse countries. Teaching and Teacher Education, 33, 34–44. doi:10.1016/j.tate.2013.02.004
Malinen, O. P., Savolainen, H., & Xu, J. (2012). Beijing in-service teachers’ self-efficacy and attitudes towards in-clusive education. Teaching and Teacher Education, 28, 526–534. doi:10.1016/j.tate.2011.12.004
Malow-Iroff, M. S., O'Connor, E. A., & Bisland, B. M. (2007). Intention to return: Alternatively certified teachers’ support, ideology and efficacy beliefs. Teacher Development, 11, 263–275. doi:10.1080/13664530701644573
Martin, N. K., & Sass, D. A. (2010). Construct validation of the Behavior and Instructional Management Scale. Teaching and Teacher Education, 26, 1124–1135. doi:10.1016/j.tate.2009.12.001
Martin, N. K., Sass, D. A., & Schmitt, T. A. (2012). Teacher efficacy in student engagement, instructional management, student stressors, and burnout: A theoretical model using in-class variables to predict teachers' intent-to-leave. Teaching and Teacher Education, 28, 546–559. doi:10.1016/j.tate.2011.12.003
Mashburn, A. J., Hamre, B. K., Downer, J. T., Pianta, R. C. (2006). Teacher and classroom characteristics associated with teachers’ ratings of prekindergartners’ relationships and behaviors. Journal of Psychoeducational Assessment, 24, 367–380. doi:10.1177/0734 282906290594
Meece, J. L., Anderman, E. M., & Anderman, L. H. (2006). Classroom goal structure, student motivation, and academic achievement. Annual Review of Psychology, 57, 487–503. doi:10.1146/annurev.psych.56.091103. 070258
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
REFERENCES
210
Meijer, C., & Foster, S. (1988). The effect of teacher self-efficacy on referral chance. Journal of Special Education, 22, 378–385.
Mejia, T. M., & Hoglund, W. L. (2016). Do children's adjustment problems contribute to teacher–child relationship quality? Support for a child-driven model. Early Childhood Research Quarterly, 34, 13–26. doi:10.1016/j.ecresq.2015.08.003
Merrell, K.W. (1999). Behavioral, social, and emotional assessment of children and adolescents. Mahwah, NJ: Erlbaum. Midgley, C., Anderman, E., & Hicks, L. (1995). Differences between elementary and middle school teachers and
students: A goal theory approach. Journal of Early Adolescence, 15, 90–113. Midgley, C., Feldlaufer, H., & Eccles, J. (1989). Change in teacher e cacy and student self- and task-related
beliefs in mathematics during the transition to junior high school. Journal of Educational Psychology, 81, 247–258. doi:10.1037/0022-0663.81.2.247
Ministry of Education, Culture and Science (2014). Wet Passend Onderwijs [Appropriate Education Act]. Retrieved from https://www.passendonderwijs.nl/wp-content/uploads/2014/11/Wet-passend-onderwijs.pdf
Moè, A., Pazzaglia, F., & Ronconi, L. (2010). When being able is not enough. The combined value of positive affect and self-efficacy for job satisfaction in teaching. Teaching and Teacher Education, 26, 1145–1153. doi:10.1016/j.tate.2010.02.010
Mohamadi, F. S., & Asadzadeh, H. (2012). Testing the mediating role of teachers’ self-efficacy beliefs in the relationship between sources of efficacy information and students achievement. Asia Pacific Educational Review, 13, 427–433. doi:10.1007/s12564-011-9203-8
Mojavezi, A., & Poodineh Tamiz, M. (2012). The impact of teacher self-efficacy on the students’ motivation and achievement. Theory and Practice in Language Studies, 2, 483–491. doi:10.4304/tpls.2.3.483-491
Moore, V. & Esselman, M. (1992, April). Teacher efficacy, empowerment, and focused instructional climate: Does student achievement benefit? Paper presented at the annual meeting of the American Educational Research Association, San Francisco.
Morris-Rothschild, B. K., & Brassard, M. R. (2006). Teachers' conflict management styles: The role of attachment styles and classroom management efficacy. Journal of School Psychology, 44, 105–121. doi:10.1016/j.jsp. 2006.01.004
Mueller, J., Wood, E., Willoughby, T., Ross, C., & Specht, J. (2008). Identifying discriminating variables between teachers who fully integrate computers and teachers with limited integration. Computers & Education, 51, 1523–1537. doi:10.1016/j.compedu.2008.02.003
Murray, C., & Murray, K. M. (2004). Child level correlates of teacher–student relationships: An examination of demographic characteristics, academic orientations, and behavioral orientations. Psychology in the Schools, 41, 751–762. doi:10.1002/pits.20015
Murray, C., & Zvoch, K. (2011). Teacher–Student Relationships Among Behaviorally At-Risk African American Youth From Low-Income Backgrounds: Student Perceptions, Teacher Perceptions, and Socioemotional Adjustment Correlates. Journal of Emotional and Behavioral Disorders, 19, 41–54. doi:10.1177/106342660 9353607
Muthén, B. O. (1994). Multilevel covariance structure analysis. Sociological Methods and Research, 22, 376–398. doi:10.1177/0049124194022003006
Muthén, B., & Asparouhov, T. (2013). New Methods for the Study of Measurement Invariance with Many Groups. Technical report. Retrieved from http://www.statmodel.com.
Muthén, L. K., & Muthén, B. O. (1998-2012). Mplus user’s guide (7th ed.). Los Angeles, CA: Muthén & Muthén. Nesdale, D., & Pickering, K. (2006). Teachers’ reactions to children's aggression. Social Development, 15, 109–127.
doi: 10.1111/j.1467-9507.2006.00332.x Newberry, M., & Davis, H. A. (2008). The role of elementary teachers’ conceptions of closeness to students on
their differential behaviour in the classroom. Teaching and Teacher Education, 24, 1965–1985. doi:10.1016/ j.tate.2008.02.015
Nie, Y., Tan, G. H., Liau, A. K., Lau, S., & Chua, B. L. (2013). The roles of teacher efficacy in instructional innovation: Its predictive relations to constructivist and didactic instruction. Educational Research for Policy and Practice,12, 67–77. doi:10.1007/s10671-012-9128-y
Ngidi, D. P. (2012). Academic optimism: An individual teacher belief. Educational Studies, 38, 139–150. doi:10.1080/03055698.2011.567830
Olson, J., & Cooper, P. (2001). Dealing with disruptive children in the classroom. London: Routledge.
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
REFERENCES
211
O’Connor, K. E. (2008). “You choose to care”: Teachers, emotions and professional identity. Teaching and Teacher Education, 24, 117–126. doi:10.1016/j.tate.2006.11.008
O'Connor, E. (2010). Teacher–child relationships as dynamic systems. Journal of School Psychology, 48, 187–218. doi:10.1016/j.jsp.2010.01.001
O’Neill, S. C., & Stephenson, J. (2011). The measurement of classroom management self-efficacy: A review of measurement instrument development and influences. Educational Psychology, 31, 261–299. doi:10.1080/ 01443410.2010.545344
Oort, F. J. (1992). Using restricted factor analysis to detect item bias. Methodika, 6, 150–166. Pajares, F. (1996, April). Assessing self-efficacy beliefs and academic outcomes: The case for specificity and correspondence. Paper
presented at the annual meeting of the American Educational Research Association, New York, NY. Pajares, F. (1997). Current directions in self-efficacy research. In H. W. Marsh, R. G. Craven, & D. M. McInerney
(Eds.), International advances in self research (pp. 1–49). Greenwich, CT: Information Age Publishing. Pakarinen, E. Lerkkanen, M. K., Poikkeus, A. M., Kiuru, N., Siekkinen, M., Rasku-Puttonen, H., & Nurmi, J. E.
(2010). A validation of the Classroom Assessment Scoring System in Finnish kindergartens. Early Education and Development, 21, 95–124. doi:10.1080/10409280902858764
Parker, P. L., McDaniel, H. S., & Crumpton-Young, L. L. (2002). Do research participants give interval or ordinal answers in response to Likert scales? Retrieved from http://citeseerx.ist..psu.edu/viewdoc/download?doi=10.1.1.19. 6352&rep=rep1&type=pdf
Pas, E. T., Bradshaw, C. P., Hershfeldt, P. A., & Leaf, P. J. (2010). A multilevel exploration of the influence of teacher efficacy and burnout on response to student problem behaviour and school-based service use. School Psychology Quarterly, 25, 13–27. doi:10.1037/a0018576
Pianta, R. C. (1999). Assessing child-teacher relationships. In R. C. Pianta, Enhancing relationships between children and teachers (pp. 85–104). Washington, DC: American Psychological Association.
Pianta, R. C. (2001). STRS: Student-teacher Relationship Scale: Professional manual. Lutz, FL: Psychological Assessment Resources.
Pianta, R. L., & Hamre, B. K., (2009). Conceptualization, measurement, and improvement of classroom pro-cesses: Standardized observation can leverage capacity. Educational Researcher, 38, 109–119. doi:10.3102/ 0013189X09332374
Pianta, R. C., Hamre, B., & Stuhlman, M. (2003). Relationships between teachers and children. In W. M. Reynolds & G. E. Miller (Eds.), Handbook of psychology: Educational psychology (Vol. 7, pp. 199–234). Hoboken, NJ: Wiley.
Pianta, R. C., La Paro, K. M., & Hamre, B. K. (2008). Classroom Assessment Scoring System. Baltimore, MD: Brookes. Pianta, R. C., La Paro, K. M., Payne, C., Cox, M. J., & Bradley, R. (2002). The relation of kindergarten classroom
environment to teacher, family, and school characteristics and child outcomes. The Elementary School Journal, 102, 225–238.
Piantadosi, S., Byar, D. P., & Green, S. B. (1988). The ecological fallacy. American Journal of Epidemiology, 127, 893–904.
Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40, 879–891. doi:10.3758/BRM.40.3.879
Preacher, K. J., & Selig, J. P. (2012). Advantages of Monte Carlo confidence intervals for indirect effects. Communication Methods and Measures, 6, 77–98. doi:10.1080/19312458. 2012.679848
Putnam, R.F., Luiselli, J.K., Handler, M.W., & Jefferson, G.L. (2003). Evaluating student discipline practices in a public school through behavioral assessment of office referrals. Behavior Modification, 27, 505–523. doi:10.1177/0145445503255569
Ransford, C. R., Greenberg, M. T., Domitrovich, C. E., Small, M., Jacobson, L. (2009). The role of teachers’ psychological experiences and perceptions of curriculum supports on the implementation of a social and emotional learning curriculum. School Psychology Review, 38, 510–532.
Raudenbusch, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods. Newbury Park: Sage.
Raudenbusch, S. W., Rowan, B., & Cheong, Y. F. (1992). Contextual effects on the self-perceived efficacy of high school teachers. Sociology of Education, 65, 150–167.
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
REFERENCES
212
Reyes, M. R., Brackett, M. A., Rivers, S. E., White, M., & Salovey, P. (2012). Classroom emotional climate, student engagement, and academic achievement. Journal of Educational Psychology, 104, 700–712. doi:10.1037/ a0027268
Riggs, I. M., & Enochs, L. G. (1990). Toward the development of an elementary teacher’s science teaching efficacy belief instrument. Science Education, 74, 625–637. doi:10.1002/sce.3730740605
Rimm-Kaufman , S. E., & Chui, Y. J. I. (2007). Promoting social and academic competence in the classroom: An intervention study examining the contribution of the Responsive Classroom approach. Psychology in the Schools, 44, 397–413. doi:10.1002/pits.20231
Rimm-Kaufman, S. E., La Paro, K. M., Downer, J. T., & Pianta, R. C. (2005). The contribution of classroom setting and quality of instruction to children’s behavior in kindergarten classrooms. The Elementary School Journal, 105, 377–394. doi:10.1086/429948
Robertson, C., & Dunsmuir, S. (2013). Teacher stress and pupil behavior explored through a rational-emotive behaviour therapy framework. Educational Psychology, 33, 215–232. doi:10.1080/01443410.2012.730323
Roehrig, A. D., Pressley, M., & Talotta, D. (2002). Stories of beginning teachers: First year challenges and beyond. Notre Dame, IN: University of Notre Dame Press.
Roeser, R W, Eccles, J. S., & Sameroff, A J. (2000). School as a context of early adolescents' academic and social-emotional development: A summary of research findings. The Elementary School Journal, 100, 443–447.
Rohaan, E. J., Taconis, R., & Jochems, W. M. G. (2012). Analysing teacher knowledge of technology education and its effects on pupils' concept and attitude. Retrieved from https://www.iteaconnect.org/Conference/PATT/PATT22/ Rohaan.pdf
Roorda, D. L., Koomen, H. M., Spilt, J. L., & Oort, F. J. (2011). The influence of affective teacher–student relationships on students’ school engagement and achievement a meta-analytic approach. Review of Educational Research, 81, 493–529. doi:10.3102/0034654311421793
Roorda, D. L., Koomen, H. M., Spilt, J. L., Thijs, J. T., & Oort, F. J. (2013). Interpersonal behaviors and complementarity in interactions between teachers and kindergartners with a variety of externalizing and internalizing behaviors. Journal of School Psychology, 51, 143–158. doi:10.1016/j.jsp.2012.12.001
Roorda, D. L., Verschueren, K., Vancraeyveldt, C., Van Craeyevelt, S., & Colpin, H. (2014). Teacher–child relationships and behavioral adjustment: Transactional links for preschool boys at risk. Journal of School Psychology, 52, 495–510. doi:10.1016/j.jsp.2014.06.004
Rose, J. S., & Medway, F. J. (1981). Measurement of teachers' beliefs in their control over student outcome. Journal of Educational Research, 74, 185–190.
Ross, J. A. (1992). Teacher efficacy and the effects of coaching on student achievement. Canadian Journal of Education, 17, 51–65. doi:10.2307/1495395
Ross, J. A. (1994). The impact of an in-service to promote cooperative learning on the stability of teacher efficacy. Teaching and Teacher Education, 10, 381–394.
Ross, J. A. (1998). The antecedents and consequences of teacher efficacy. In J. Brophy (Ed.), Advances in Research on Teaching (Vol.7, pp. 49–73). Greenwitch, CT: JAI.
Ross, J. A., Cousins, J. B., & Gadalla, T. (1996). Within-teacher predictors of teacher efficacy. Teaching and Teacher Education, 12, 385–400.
Ross, J. A., Hogaboam-Gray, A., & Hannay, L. (2001). Effects of teacher efficacy on computer skills and computer cognitions of Canadian students in grades K-3. The Elementary School Journal, 102, 141–156.
Rots, I., Aelterman, A., Vlerick, P., & Vermeulen, K. (2007). Teacher education, graduates’ teaching commitment and entrance into the teaching profession. Teaching and Teacher Education, 23, 543–556. doi:10.1016/ j.tate.2007.01.012
Rotter, J. B. (1966). Generalized expectancies for internal versus external control of reinforcement. Psychological Monographs, 80, 1–28.
Rubie-Davies, C. M., Flint, A., & McDonald, L. G. (2011). Teacher beliefs, teacher characteristics, and school contextual factors: What are the relationships? British Journal of Educational Psychology, 82, 270–288. doi:10.1111/j.2044-8279.2011.02025.x
Rubin, K. H., & Copian, R. J. (2004). Paying attention to and not neglecting social withdrawal and social isolation. Merrill-Palmer Quarterly, 50, 506–534.
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
REFERENCES
213
Ruble, L. A., Usher, E. L., & McGrew, J. H. (2011). Preliminary investigation of the sources of self-efficacy among teachers of students with autism. Focus on Aautism and Other Developmental Disabilities, 26, 67–74. doi:10.1177/1088357610397345
Rudasill, K. M. (2011). Child temperament, teacher–child interactions, and teacher–child relationships: A longitudinal investigation from first to third grade. Early Childhood Research Quarterly, 26, 147–156. doi:10.1016/j.ecresq.2010.07.002
Ryan, R.M., & Deci, E.L. (2002). Overview of self-determination theory: An organismic dialectical perspective. In E.L. Deci & R.M. Ryan (Eds.), Handbook on Self-Determination Research (pp. 3–33). Rochester, NY: University of Rochester Press.
Ryu, E. (2014). Factorial invariance in multilevel confirmatory factor analysis. British Journal of Mathematical and Statistical Psychology, 67, 172–194. doi:10.1111/bmsp.12014
Saklofske, D. H., Michayluk, J. O., & Randhawa, B. S. (1988). Teachers’ efficacy and teaching behaviors. Psychological Reports, 63, 407–414.
Salanova, M., Llorens, S., & Schaufeli, W. B. (2011). “Yes, I can, I feel good, and I just do it!” On gain cycles and spirals of efficacy beliefs, affect, and engagement. Applied Psychology: An International Review, 60, 255–285. doi:10.1111/j.1464-0597.2010.00435.x
Sameroff, A.J., & Fiese, B.H. (2000). Transactional regulation: The developmental ecology of early intervention. In J.P. Shonkoff, & S.J. Meisels (Eds.), Handbook of early childhood intervention, (2nd Edition, pp. 135-159), New York. NY: Cambridge University Press.
Sang, G., Valcke, M., van Braak, J., & Tondeur, J. (2010). Student teachers’ thinking processes and ICT integration: Predictors of prospective teaching behaviors with educational technology. Computers & Education, 54, 103–112. doi:10.1016/j.compedu.2009.07.010
Sass, D. A., Seal, A. K., & Martin, N. K. (2011). Predicting teacher retention using stress and support variables. Journal of Educational Administration, 49, 200–215. doi:10.1108/09578231111116734
Satorra, A. (2000). Scaled and adjusted restricted tests in multi-sample analysis of moment structures. In R. D. H. Heijmans, D. S. G. Pollock, & A. Satorra (Eds.), Innovations in multivariate statistical analysis. A Festschrift for Heinz Neudecker (pp. 233–247). London, UK: Kluwer Academic Publishers.
Satorra, A., & Bentler, P. M. (2010). Ensuring positiveness of the scaled difference chi-square test statistic. Psychometrika, 75, 243–248. doi:10.1007/s11336-009-9135-y
Schram, E., Van der Meer, F., & Van Os, S. (2012). Omgaan met verschillen: (g)een kwestie van maatwerk. Naar een doorgaande lijn in de toerusting van leraren voor passend onderwijs. Enschede: SLO.
Schwarzer, R. (Ed.) (1992). Self-efficacy: Thought control of action. Washington, DC: Hemisphere. Schwarzer, R., & Hallum, S. (2008). Perceived teacher self-efficacy as a predictor of job stress and burnout:
Mediation analyses. Applied Psychology: An International Review, 57, 152–171. doi:10.1111/j.1464-0597. 2008.00359.x
Schwarzer, R., & Jerusalem, M. (1995). Generalized self-efficacy scale. In J. Weinman, S. Wright, & M. Johnston (Eds.), Measures in health psychology: A user’s portfolio (pp. 35–38). Windsor: NFER-Nelson.
Schwarzer, R., Schmitz, G.S., & Daytner, G.T. (1999). The Teacher Self-Efficacy scale. Retrieved from http://www.fu-berlin.de/gesund/skalen/t_se.htm
Schwarzer, R., Schmitz, G. S., & Tang, C. (2000). Teacher burnout in Hong Kong and Germany: A cross-cultural validation of the Maslach Burnout Inventory. Anxiety, Stress, & Coping, 13, 309–326. doi:10.1080/1061 5800008549268
Shyman, E. (2010). Identifying predictors of emotional exhaustion among special education paraeducators: A preliminary investigation. Psychology in the Schools, 47, 828–841. doi:10.1002/pits.20507
Siwatu, K.O. (2007). Pre-service teachers’ culturally responsive teaching self-efficacy and outcome expectancy beliefs. Teaching and Teacher Education, 23, 1086–1101. doi:10.1016/j.tate.2006.07.011
Siwatu, K. O. (2011). Preservice teachers’ culturally responsive teaching self-efficacy forming experiences: A mixed methods study. Journal of Educational Research, 104, 360–369.doi:10.1080/00220671.2010.487081
Skaalvik, E.M., & Skaalvik, S. (2007). Dimensions of teacher self-efficacy and relations with strain factors, perceived collective teacher efficacy, and teacher burnout. Journal of Educational Psychology, 99, 611–625. doi:10.1037/0022-0663.99.3.611
Skaalvik, E. M., & Skaalvik, S. (2010). Teacher self-efficacy and teacher burnout: A study of relations. Teaching and Teacher Education, 26, 1059–1069. doi:10.1016/j.tate.2009.11.001
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
REFERENCES
214
Smeets, E., Ledoux, G., Blok, H., Felix, C., Heurter, A., Van Kuijk, J., & Vergeer, M. (2013). Op de drempel van passend onderwijs: Beleid en aanbod rond specifieke onderwijsbehoeften in zes samenwerkingsverbanden. Nijmegen: ITS.
Smeets, E., Ledoux, G., Regtvoort, A., Felix, C. & Mol Lous, A. (2015). Passende competenties voor passend onderwijs: Onderzoek naar competenties in het basisonderwijs. Nijmegen: ITS.
Smeets, E. & Rispens, J. (2008). Op zoek naar passend onderwijs: Overzichtsstudie van de samenhang tussen regulier en speciaal (basis)onderwijs. Nijmegen: ITS.
Snijders, T. A. B., & Bosker, R. J. (1999). Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling. London, UK: Sage Publishers.
Sobel, M. E. (1990). Effect analysis and causation in linear structural equation models. Psychometrika, 55, 495–515. doi:10.1007/BF02294763
So-kum Tang, C., Au, W. T., Schwarzer, R., & Schmitz, G. (2001). Mental health outcomes of job stress among Chinese teachers: Role of stress resource factors and burnout. Journal of Organizational Behavior, 22, 887–901. doi:10.1002/job.120
Soodak, L. C., & Podell, D. M. (1993). Teacher efficacy and student problem as factors in special education referral. Journal of Special Education, 27, 66–81.
Soodak, L. C., & Podell, D. M. (1996). Teacher efficacy: Toward the understanding of a multi-faceted construct. Teaching and Teacher Education, 12, 401–411.
Soodak, L. C., Podell, D. M., & Lehman, L. R. (1998). Teacher, student, and school attributes as predictors of teachers’ responses to inclusion. The Journal of Special Education, 31, 480–497.
Spilt, J. L., & Koomen, H. M. Y. (2009). Widening the view on teacher–child relationships: Teachers’ narratives concerning disruptive versus nondisruptive children. School Psychology Review, 38, 86–101.
Spilt, J. L., Koomen, H. M. Y., & Thijs, J. T. (2011). Teacher wellbeing: The importance of teacher–student relationships. Educational Psychology Review, 23, 457–477. doi:10.1007/ s10648-011-9170-y
Spilt, J. L., Koomen, H. M., Thijs, J. T., & van der Leij, A. (2012). Supporting teachers’ relationships with disruptive children: The potential of relationship-focused reflection. Attachment & Human Development, 14, 305–318. doi:10.1080/14616734.2012.672286
Stephanou, G., Gkavras, G., & Doulkeridou, M. (2013). The role of teachers’ self-and collective-efficacy beliefs on their job satisfaction and experienced emotions in school. Psychology, 4, 268–278. doi:10.4236/ psych.2013.43A040
Stipek, D., & Miles, S. (2008). Effects of aggression on achievement: Does conflict with the teacher make it worse?. Child Development, 79, 1721–1735. doi:10.1111/j.1467-8624.2008.01221.x
Stuhlman, M. W., & Pianta, R. C. (2002). Teachers' narratives about their relationships with children: Associations with behavior in classrooms. School Psychology Review, 31, 148–163.
Sutherland, K. S., & Oswald, D. P. (2005). The relationship between teacher and student behavior in classrooms for students with emotional and behavioral disorders: Transactional processes. Journal of Child and Family Studies, 14, 1–14. doi:10.1007/s10826-005-1106-z
Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (5th ed.).Boston, MA: Allyn and Bacon. Tejeda-Delgado, M. D. C. (2009). Teacher efficacy, tolerance, gender, and years of experience and special
education referrals. International Journal of Special Education, 24, 112–119. Temiz, T., & Topcu, M. S. (2013). Preservice teachers’ teacher efficacy beliefs and constructivist-based teaching
practice. European Journal of Psychology of Education, 28, 1435–1452, doi:10.1007/s10212-013-0174-5 Teo, T. (2009). Examining the relationship between student teachers' self-efficacy beliefs and their intended uses
of technology for teaching: A structural equation modelling approach. The Turkish Online Journal of Educational Technology, 8, 1–16.
Thijs, J., Westhof, S., & Koomen, H. (2012). Ethnic incongruence and the student–teacher relationship: The perspective of ethnic majority teachers. Journal of School Psychology, 50, 257–273. doi:10.1016/j.jsp.2011. 09.004
Thomas, D. E., Bierman, K. L., & The Conduct Problems Prevention Research Group (2006). The impact of classroom aggression on the development of aggressive behavior problems in children. Developmental Psychopathology, 18, 471–487. doi:10.1017/S0954579406060251
Thoonen, E. E. J., Sleegers, P. J. C., Oort, F. J., Peetsma, T. T. D., & Geijsel, F. P. (2011a). How to improve teaching practices: The role of teacher motivation, organizational factors, and leadership practices. Educational Administration Quarterly, 47, 496–536. doi:10.1177/0013161X11400185
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
REFERENCES
215
Thoonen, E. E. J., Sleegers, P. J. C., Peetsma, T. T. D., & Oort, F. J. (2011b). Can teachers motivate students to learn? Educational Studies, 37, 345–360. doi:10.1080/03055698.2010.507008
Thornberry, T. P., & Krohn, M. D. (1997). Peers, drug use, and delinquency. In D. M. Stoff, J. Breiling, & J. D. Maser (Eds.), Handbook of antisocial behavior (pp. 218–233). New York, NY: Wiley.
Throndsen, I., & Turmo, A. (2013). Primary mathematics teachers’ goal orientations and student achievement. Instructional Science,41,307–322. doi:10.1007/s11251-012-9229-2
Tournaki, N., & Podell, D. M. (2005). The impact of student characteristics and teacher efficacy on teachers’ predictions of student success. Teaching and Teacher Education, 21, 299–314. doi:10.1016/j.tate.2005.01.003
Tschannen-Moran, M., & Johnson, D. (2011). Exploring literacy teachers’ self-efficacy beliefs: Potential sources at play. Teaching and Teacher Education, 27, 751–761. doi:10.1016/ j.tate.2010.12.005
Tschannen Moran, M., & McMaster, P. (2009). Sources of self efficacy: Four professional development formats and their relationship to self efficacy and implementation of a new teaching strategy. The Elementary School Journal, 110, 228–245. doi:10.1086/605771
Tschannen-Moran, M., Woolfolk Hoy, A., & Hoy, W.K. (1998). Teacher efficacy: Its meaning and measure. Review of Educational Research, 68, 202–248. doi:10.3102/00346543068002202
Tschannen-Moran, M., & Woolfolk Hoy, A. (2001). Teacher efficacy: capturing an elusive construct. Teaching and Teacher Education, 17, 783–805. doi:10.1016/S0742-051X(01)00036-1
Tschannen-Moran, M., & Woolfolk Hoy, A. (2007). The differential antecedents of self-efficacy beliefs of novice and experienced teachers. Teaching and Teacher Education, 23, 944–956. doi:10.1016/j.tate.2006.05.003
Tsigilis, N., Koustelios, A., & Grammatikopoulos, V. (2010). Psychometric properties of the Teachers' Sense of Efficacy Scale within the Greek educational context. Journal of Psychoeducational Assessment, 28, 153–162. doi:10.1177/0734282909342532
Tsouloupas, C. N., Carson, R. L., Matthews, R., Grawitch, M. J., & Barber, L. K. (2010). Exploring the association between teachers’ perceived student misbehaviour and emotional exhaustion: the importance of teacher efficacy beliefs and emotion regulation. Educational Psychology, 30, 173–189. doi:10.1080/014434109 03494460
Van Gennip, H., Marx, T., & Smeets, E. (2007). Gedragsproblemen in de basisschool en competenties van leraren. Nijmegen: ITS.
Van Leeuwen, K., Meerschaert, T., Bosmans, G., De Medts, L., & Braet, C. (2006). The strengths and difficulties questionnaire in a community sample of young children in Flanders. European Journal of Psychological Assessment, 22, 189–197.
Vannatta, R. A., & Fordham, N. (2004). Teacher dispositions as predictors of classroom technology use. Journal of Research on Technology in Education, 36, 253–271.
Van Uden, J. M., Ritzen,H., & Pieters, J. M. (2013). I think I can engage my students: Teachers’ perceptions of student engagement and their beliefs about being a teacher. Teaching and Teacher Education, 32, 43–54. doi:10.1016/j.tate.2013.01.004
Van Widenfelt, B. M., Goedhart, A. W., Treffers, P. D. A., & Goodman, R. (2003). Dutch version of the strengths and difficulties questionnaire (SDQ). European Child & Adolescent Psychiatry, 12, 281–289.
Verschueren, K., & Koomen, H. M. (2012). Teacher–child relationships from an attachment perspective. Attachment & Human Development, 14, 205–211. doi:10.1080/14616734.2012.672260
Viel-Ruma, K., Houchins, D., Jolivette, K., & Benson, G. (2010). Efficacy beliefs of special educators: The relationships among collective efficacy, teacher self-efficacy, and job satisfaction. Teacher Education and Special Education, 33, 225–233. doi:10.1177/ 0888406409360129
Ware, H. W., & Kitsantas, A. (2007). Teacher and collective efficacy beliefs as predictors of professional commitment. The Journal of Educational Research,100, 303–310. doi:10.3200/JOER.100.5.303-310
Ware, H. W., & Kitsantas, A. (2011). Predicting teacher commitment using principal and teacher efficacy variables: An HLM approach. The Journal of Educational Research, 104, 183–193. doi:10.1080/0022067 1003638543
Wehby, J. H., Symons, F. J., Canale, J. A., & Go, F. J. (1998). Teaching practices in classrooms for students with emotional and behavioral disorders: Discrepancies between recommendations and observations. Behavioral Disorders, 24, 51–56.
Wentzel, K. R. (1993). Does being good make the grade? Social behavior and academic competence in middle school. Journal of Educational Psychology, 85, 357–364.
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
REFERENCES
216
Werthamer-Larsson, L., Kellam, S. G., & Wheeler, L. (1991). Effect of first-grade classroom environment on shy behavior, aggressive behavior, and concentration problems. American Journal of Community Psychology, 19, 585–602. doi:10.1007/BF00937993
Wertheim, C., & Leyser, Y. (2002). Efficacy beliefs, background variables, and differentiated instruction of Israeli prospective teachers. Journal of Educational Research,96,54–63.
Weshah, H. A. (2012). Teaching efficacy and teaching performance among student teachers in a Jordanian childhood education program. Journal of Early Childhood Teacher Education, 33,163-177. doi:10.1080/ 10901027.2012.675941
Westling, D. L. (2010). Teachers and challenging behavior: Knowledge, views, and practices. Remedial and Special Education, 31, 48–63. doi:10.1177/0741932508327466
Wheatley, K. F. (2005). The case for reconceptualizing teacher efficacy research. Teaching and Teacher Education, 21, 747–766. doi:10.1016/j.tate.2005.05.009
Wolters, C. A., & Daugherty, S. G. (2007). Goal structures and teachers’ sense of efficacy: their relation and association to teaching experience and academic level. Journal of Educational Psychology, 99, 181–193.
Wong, K. T., Teo, T., & Russo, S. (2010). Influence of gender and computer teaching efficacy on computer acceptance among Malaysian student teachers: An extended technology acceptance model. Australasian Journal of Educational Technology, 28, 1190–1207.
Woolfolk, A. E., & Hoy, W. K. (1990). Prospective teachers’ sense of e cacy and beliefs about control. Journal of Educational Psychology, 82, 81–91.
Woolfolk, A. E., Rosoff, B., & Hoy, W. K. (1990). Teachers’ sense of efficacy and their beliefs about managing students. Teaching and Teacher Education, 6, 137–148. doi:10.1016/0742-051X(90)90031-Y
Woolfolk Hoy, A., & Burke-Spero, R. (2005). Changes in teacher efcacy during the early years of teaching: A comparison of four measures. Teaching and Teacher Education, 21, 343–356. doi:10.1016/j.tate.2005.01.007
Woolfolk Hoy, A., & Davis, H. A. (2005). Teachers’ sense of efficacy and its influence on the achievement of adolescents. In T. Urdan & F. Pajares (Eds.), Adolescence and education: Volume V: Self-efficacy beliefs during adolescence (pp. 117–137). Greenwich, CT: Information Age.
Woolfolk Hoy, A., Hoy, W. K., & Davis, H. A. (2009). Teachers’ self-efficacy beliefs. In K. Wentzel & A. Wigfield (Eds.), Handbook of Motivation at School (pp. 627–653). New York, NY: Routledge.
Woolfolk Hoy, A., Hoy, W. K., & Kurz, N. M. (2008). Teacher's academic optimism: The development and test of a new construct. Teaching and Teacher Education, 24, 821–835. doi:10.1016/j.tate.2007.08.004
Woolfson, L. M., & Brady, K. (2009). An investigation of factors impacting on mainstream teachers’ beliefs about teaching students with learning difficulties. Educational Psychology, 29, 221–238. doi:10.1080/0144341080 2708895
Wyatt, M. (2014). Towards a re-conceptualization of teachers' self-efficacy beliefs: Tackling enduring problems with the quantitative research and moving on. International Journal of Research & Method in Education, 37, 166–189. doi:10.1080/1743727X.2012.742050
Wyatt, M. (2016). “Are they becoming more reflective and/or efficacious?” A conceptual model mapping how teachers’ self-efficacy beliefs might grow. Educational Review, 68, 114–137. doi:10.1080/00131911.2015. 1058754
Yeo, L. S., Ang, R. P., Chong, W. H., Huan, V. S., & Quek, C. L. (2008). Teacher efficacy in the context of teaching low achieving students. Current Psychology, 27, 192–204. doi:10.1007/s12144-008-9034-x
Yildirim, K., & Ates, S. (2012). Turkish pre-service teachers` perceived self-efficacy beliefs and knowledge about using expository text as an instructional tool in their future classroom settings. Australian Journal of Teacher Education, 37, 12–31. doi:10.14221/ajte.2012v37n8.4
Yilmaz, C. (2011). Teachers' perceptions of self-efficacy, English proficiency, and instructional strategies. Social Behavior and Personality, 39, 91–100. doi:10.2224/sbp.2011.39.1.91
Yoon, J. S. (2002). Teacher Characteristics as predictors of teacher-student relationships: Stress, negative affect, and self-efficacy. Social Behavior and Personality, 30, 485–494. doi:10.2224/sbp.2002.30.5.485
Yoon, J. S. (2004). Predicting teacher interventions in bullying situations. Education & Treatment of Children, 27, 37–45.
Yuan, K.H. (2005). Fit indices versus test statistics. Multivariate Behavioral Research, 40, 115–148. doi:10.1207/ s15327906mbr4001_5
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
REFERENCES
217
Yuan, K. H., & Bentler, P. M. (2000). Three likelihood-based methods for mean and covariance structure analysis with nonnormal missing data. Sociological Methodology, 30, 165–200. doi:10.1111/0081-1750.00078
Zee, M., De Jong, P. F., & Koomen, H. M. Y. (2016). Teachers' self-efficacy in relation to individual students with a variety of social-emotional behaviors: A multilevel investigation. Journal of Educational Psychology. Advance online publication. doi:10.1037/edu0000106
Zee, M. & Koomen, H. M. Y. (2015, March). Student-specific teacher self-efficacy: Investigating the factorial, convergent, and concurrent validity of a new instrument. Poster presented at the biennial meeting of SRCD, Philadelphia, PA.
Zee, M., Koomen, H. M. Y., Jellesma, F. C., Geerlings, J., & de Jong, P. F. (2016). Inter- and intra-individual differences in teachers' self-efficacy: A multilevel factor exploration. Journal of School Psychology, 55, 39–56. doi:10.1016/j.jsp.2015.12.003
Zee, M., Koomen, H. M., & Van der Veen, I. (2013). Student–teacher relationship quality and academic adjustment in upper elementary school: The role of student personality. Journal of school psychology, 51, 517–533. doi:10.1016/j.jsp.2013.05.003
Zhang, X., & Sun, J. (2011). The reciprocal relations between teachers’ perceptions of children's behavior problems and teacher–child relationships in the first preschool year. The Journal of Genetic Psychology, 172, 176–198. doi:10.1080/00221325.2010.528077
Zimmer-Gembeck, M. J., Geiger, T. C., & Crick, N. R. (2005). Relational and physical aggression, prosocial behavior, and peer relations: Gender moderation and bidirectional associations. Journal of Early Adolescence, 25, 421–452. doi:10.1177/0272431605279841
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
219
DANKWOORD
(ACKNOWLEDGMENTS IN DUTCH)
_________________________________________________________________________
“No man is an island, entire of itself”
– John Donne
Toen ik begon met studeren, vroeg mijn vader zich bezorgd af of ik, met elke stap voorwaarts
die ik binnen de wetenschap zette, niet teveel op een eilandje terecht zou komen. Nu ik tien
jaar verder ben en een proefschrift rijker, kan ik niets anders zeggen dan dat hiervan geenszins
sprake is. Wat heb ik een fantastische groep mensen om me heen met wie ik kan lachen,
huilen, discussiëren, nadenken, praten over (n)iets en bij wie ik simpelweg mezelf kan zijn! Een
aantal van hen wil ik op deze plek in het bijzonder bedanken.
Lieve Helma en Peter, jullie wil ik allereerst enorm bedanken voor de kans die jullie mij
geboden hebben om bij jullie te komen promoveren. Peter, het zal me niet verbazen als ik de
meest eigenwijze, irritante en veeleisende promovenda ben die jij ooit hebt begeleid. Des te
bijzonderder vind ik het dat je mij de ruimte en het vertrouwen gegeven hebt mijn aio-plek veel
eerder dan gepland in te ruilen voor een postdoc. Ik kijk enorm uit naar onze toekomstige
gedachtewisselingen, gezamenlijke papers en gezellige praatjes op de gang. Jouw mening
waardeer ik altijd enorm. Helma, met jou als begeleider viel ik echt met mijn neus in de boter.
Wij lijken het per definitie wel met elkaar eens te zijn, vullen elkaar naadloos aan en hebben
aan een half woord genoeg. Zowel in het laatste jaar van mijn research master als tijdens mijn
promotie kreeg je het altijd weer voor elkaar mijn werk naar een hoger niveau te tillen en bood
je me daarnaast een luisterend oor wanneer ik dat nodig had. Ik ken maar weinig mensen die
zo intelligent, betrokken en bevlogen zijn als jij. Je bent me enorm dierbaar en ik sta te
springen om alle ideeën die we samen hebben ten uitvoer te gaan brengen!
Ook wil ik de leden van mijn promotiecommissie, prof. dr. Karine Verschueren, prof. dr.
Alexander Minnaert, prof. dr. Thea Peetsma, prof. dr. Geert Jan Stams en prof. dr. Frans Oort,
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
DANKWOORD (ACKNOWLEDGMENTS IN DUTCH)
220
hartelijk danken. Ik waardeer het enorm dat jullie de tijd en moeite hebben willen nemen om
mijn proefschrift te lezen en te beoordelen.
Jochem en Jolien, jullie wil ik bedanken voor de constructieve samenwerking tijdens de
afgelopen tweeënhalf jaar. Met name het gezamenlijk werven van scholen, het plannen en
uitvoeren van het interventie-onderzoek, en het uitwisselen van ervaringen in deze laatste fase
van het project heb ik als prettig ervaren. Bedankt ook Francine, voor je feedback op mijn
eerste paper en de mogelijkheid om mee te schrijven aan één van je onderzoeken.
Ik zou lang niet zoveel plezier en inspiratie uit mijn werk op de UvA halen als ik niet omringd
zou worden door de geweldige collegae en mede-promovendi van POWL. Lieve OLPeople, bij
jullie voel ik me echt thuis! Marlie, als iemand voor een vrolijk en warm welkom kan zorgen,
dan ben jij het wel! Bedankt dat je me zo geweldig hebt opgevangen op mijn eerste werkdag,
bedankt voor je grenzeloze enthousiasme en gezelligheid, de vele thee-haal-momenten en je
immer doeltreffende regelacties. Op jou kunnen we bouwen! Sietske, als ik twijfel over mijn
statistiek, taal, of APA, iets dringends kwijt moet, of een heldere blik nodig heb, dan ben jij de
eerste tot wie ik me wend. Ik vind het ontzettend fijn om terecht te kunnen bij iemand die
dezelfde ambities en drijfveren heeft als ik. Hopelijk kunnen we nog lang van en met elkaar
leren en mooie dingen beleven! Madelon, jij bent mijn grote voorbeeld wat betreft prachtige
papers, hoge hakken en kleine wagens met grote knuffelfactor, en Bettina, stille kracht met
geweldige humor, wat was het fijn om samen met jou een “eilandje” te bewonen in D8.12.
Haytske, we hebben hetzelfde meegemaakt en doen allebei onderzoek naar leerkracht–
leerlingrelaties. Dat schept een band. Ik waardeer jouw wilskracht en niet aflatende interesse in
het wel en wee van anderen enorm en praat graag weer eens met je bij! Loes, Janneke en
Alexander, wat zijn jullie ook weer een geweldige aanwinst voor het team. Met jullie is het
nooit saai! Jans en Klepel, jullie O&O Bowlingcompetitie verdient nu al met recht het stempel
legendarisch! Mariska, Merlijn en Annette, leuk dat we af en toe onder het genot van een kop
koffie of thee even bij kunnen kletsen. Dat houden we erin! Heel veel dank ook funky-
geweldige-briljante-megalieve Elise. Als jij er bent, gaat de zon spontaan schijnen. Wat leer ik
veel van jou! Ik beschouw het als een groot voorrecht dat ik met jou samen mag werken.
Dan mijn paranimfen, Britt en Debora. Lieve B, wij vonden elkaar toen we het erover eens
waren dat de veel te lange vlecht van een spreker op het Graduate School Colloquium toch
echt afgeknipt moest worden. Ook onze voorliefde voor katten en poezen bleek een gemene
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
DANKWOORD (ACKNOWLEDGMENTS IN DUTCH)
221
deler te zijn. Inmiddels wisselen we veel meer uit dan enkel plaatjes van en verhalen over onze
harige viervoeters. Vooral tijdens de laatste periode van onze promotie zijn we echt naar elkaar
toe gegroeid. Hoe bijzonder was het om in deze fase samen op te kunnen trekken, kleine en
grote successen te vieren en samen de eindstreep te halen. Ik heb enorm veel zin in ons cross-
over paper met Elise en Helma en ik hoop dat ik nog vaak bij jou en Charlotte de pannen mag
komen leeglikken (zonder gloet ish goed!). Lieve Debster, wat ben ik blij dat jij in het eerste jaar
van mijn promotie weer terugkwam naar de UvA. Niet enkel liggen we helemaal op één lijn
wat onderzoeksinteresses betreft, maar ook buiten de wetenschap blijken we een gouden team
te zijn! Met Haytske bij jou in Leuven logeren, samen een outfit scoren voor het SRCD, New
York onveilig maken, de vele borrels bij de Roeter… ik had het allemaal voor geen goud willen
missen! Ik ben benieuwd wat we deze zomer allemaal gaan beleven in Vilnius en Riga!
Graag wil ik nog een aantal student-assistenten bij naam noemen die hebben bijgedragen aan
dit proefschrift. Kelly, Emma, Marjolein, Lisa, Annouschka, Jaleesah, Jantine, Mailis, Carlijne
en Malou, jullie hulp bij het werven van scholen, invoeren van data, en het afnemen van
vragenlijsten en interviews was onmisbaar! Uiteraard ben ik ook de ruim 140 leerkrachten en
3000 leerlingen die bereid waren deel te nemen aan ons project zeer erkentelijk. Ik had het
genoegen een groot deel van jullie zelf op school te mogen bezoeken. Vooral de leerkrachten
wil ik enorm danken voor hun gastvrijheid, tijd, moeite en betrokkenheid. Zonder u was dit
proefschrift er niet geweest.
Tot slot mijn familie. Lieve Meindert en Marcia, wat is het fijn om in goede en slechte tijden op
je familie terug te kunnen vallen. Bedankt dat jullie er altijd en onvoorwaardelijk zijn. Hetzelfde
geldt voor jullie, Paula en Ton, dank voor jullie goede zorgen, interesse, betrokkenheid,
heerlijke etentjes en gezellige uitjes. Jullie betekenen heel veel voor me! Lieve Bob, Florien, Pa
en Ma, jullie zijn mijn alles. Bob, wat leer ik veel van jouw nuchtere kijk op het leven en jouw
relativeringsvermogen in tijden van stress. Jij weet altijd het juiste te doen en te zeggen. Een
betere zwager had ik me niet kunnen wensen! Flo, grote zus, jij bent mijn beste vriendin.
Bedankt voor je wijze raad en steun, altijd en overal. Zonder jou weet ik me geen raad. Ik ben
enorm trots op je. Sprokkeltje, over niet al te lange tijd ben jij de nieuwste telg in de familie.
Mag ik je vaak komen voorlezen en knuffelen? Mama, weet je nog dat we samen een boek
zouden schrijven? Nou, hier is hij dan eindelijk! Een thriller is het niet geworden (hoewel de
meningen daarover ongetwijfeld verdeeld zullen zijn), maar ik hoop dat dit boekje iets van
jouw creativiteit en warmte bevat. Ik mis je nog elke dag, maar weet dat je altijd dicht bij me
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
DANKWOORD (ACKNOWLEDGMENTS IN DUTCH)
222
bent. Allerliefste paatje, dit boekje draag ik op aan jou. Ik kan niet vaak genoeg herhalen hoe
trots ik op je ben en hoeveel ik van je houd. Net als mama heb jij me de ingrediënten
meegegeven waarmee ik dit proefschrift heb kunnen schrijven. Hard werken, doorzetten, niet
opgeven en schouders eronder: falen is geen optie. Geen Zee te hoog!
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
223
ABOUT THE AUTHOR _________________________________________________________________________ Marjolein Zee was born on June 20, 1987, in Hoorn, the Netherlands. After completing
secondary school at the RSG Enkhuizen in 2005, she continued her education at the University
of Amsterdam (UvA), where she studied Media and Culture for half a year. In the spring of
2006, she spent some time working at Bricomarché Lourches, France, to brush up her French.
Later that year, however, she decided to return to the UvA, earning both her honors certificate
and bachelor’s degree in Educational Sciences in 2009 (Cum Laude). During her bachelor’s,
she gained experience as a student mentor and later, as a coordinator of the student mentoring
program for first-year undergraduates in the Pedagogical and Educational Sciences. For this
work, she received the Student-of-the-Year Award in 2009. From 2009 to 2011, she attended
her Research Master studies in Educational Sciences at the UvA and contributed as a junior
teacher to several first-year courses on education. Under the supervision of dr. Helma Koomen
and dr. Ineke van der Veen, she ultimately completed her studies with a thesis on the quality of
student–teacher relationships in upper elementary school.
After having worked as a junior educationist at the Center for Evidence-Based Education,
Academic Medical Center, Amsterdam, Marjolein started in September 2013 on a four-year
PhD project at UvA’s Research Institute of Child Development and Education, with prof. dr.
Peter de Jong and dr. Helma Koomen as her supervisors. She completed her dissertation in
January 2016 and subsequently started as a postdoctoral researcher at the same department,
continuing her research on student-specific teacher self-efficacy and student–teacher
relationships, and teaching various courses in the College and Graduate School of Child
Development and Education.
502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee502802-L-bw-Zee
224
LIST OF PUBLICATIONS
_________________________________________________________________________
INTERNATIONAL PEER-REVIEWED PUBLICATIONS Zee, M., Koomen, H. M. Y., & van der Veen, I. (2013). Student–teacher relationship quality and academic
adjustment in upper elementary school: The role of student personality. Journal of School Psychology, 51,
517–533. doi:10.1016/j.jsp.2013.05.003
Zee, M., de Boer, M., & Jaarsma, A. D. C. (2014). Acquiring evidence-based medicine and research skills in the
undergraduate medical curriculum: Three different didactical formats compared. Perspectives on Medical
Education, 3, 357–370. doi:10.1007/s40037-014-0143-y
Jellesma, F. C., Zee, M., & Koomen, H. M. Y. (2015). Children’s perceptions of the relationship with the teacher:
Associations with appraisals and internalizing problems in middle childhood. Journal of Applied
Developmental Psychology, 36, 30–38. doi:10.1016/j.appdev.2014.09.002
Zee, M., de Jong, P. F., & Koomen, H. M. Y. (2016). Teachers' self-efficacy in relation to individual students with
a variety of social-emotional behaviors: A multilevel investigation. Journal of Educational Psychology.
Advance online publication. doi:10.1037/edu0000106
Zee, M., & Koomen, H. M. Y. (2016). Teacher self-efficacy and its effects on classroom processes, student
academic adjustment, and teacher well-being: A synthesis of 40 years of research. Review of Educational
Research. Advance online publication. doi:10.3102/0034654315626801
Zee, M., Koomen, H. M. Y., Jellesma, F. C., Geerlings, J., & de Jong, P. F. (2016). Inter- and intra-individual
differences in teachers' self-efficacy: A multilevel factor exploration. Journal of School Psychology, 55, 39–56.
doi:10.1016/j.jsp.2015.12.003
PAPERS UNDER REVIEW Zee, M., & de Bree, E. H. (2015). Self-regulation and academic achievement in middle childhood: The role of student–teacher
relationships. Manuscript in revision.
Zee, M., de Jong, P. F., & Koomen, H. M. Y. (2015). Students’ disruptive behavior and the development of teachers’ self-
efficacy: The role of teacher-perceived closeness and conflict in the student–teacher relationship. Manuscript submitted
for publication.
POPULAR van Viersen, S., Zee, M., & Hakvoort, B. E. (2016). Advies Platform Onderwijs2032 ontbeert verbinding tussen
wetenschap en praktijk. The Post Online. http://politiek.tpo.nl/2016/03/14/advies-platform-
onderwijs2032-ontbeert-verbinding-wetenschap-en-praktijk/
502802-L-os-Zee502802-L-os-Zee502802-L-os-Zee502802-L-os-Zee Processed on: 3_25_2016Processed on: 3_25_2016Processed on: 3_25_2016Processed on: 3_25_2016
Marjolein Zee
eeeeeeeeeee
Aansluitend bent u van hartewelkom op de receptie.
eeeeeeeeee:Britt [email protected] [email protected]
Mar
jole
in Z
ee
eee
e eee
eeee
ee eeee
eeeeeeeeeeee
eeeee
ee eeeeeeeeeee
ee
voor het bijwonen van de openbare verdediging
van het proefschrift
eeee eeeeeee ee eeeeeeee eeeeeeee eeeeeee eeeeeeeeeeeee
op dinsdag 24 mei 201614:00 in de Agnietenkapel,
Oudezijds Voorburgwal 231te Amsterdam.
eeee eeeeeee ee eeeeeeeeeeeeeeee eeeeeee eeeeeeeeeeeee
Marjolein Zee
eeeeeeeeeee
Aansluitend bent u van hartewelkom op de receptie.
eeeeeeeeee:Britt [email protected] [email protected]
Mar
jole
in Z
ee
eee
e eee
eeee
ee eeee
eeeeeeeeeeee
eeeee
ee eeeeeeeeeee
ee
voor het bijwonen van de openbare verdediging
van het proefschrift
eeee eeeeeee ee eeeeeeee eeeeeeee eeeeeee eeeeeeeeeeeee
op dinsdag 24 mei 201614:00 in de Agnietenkapel,
Oudezijds Voorburgwal 231te Amsterdam.
eeee eeeeeee ee eeeeeeeeeeeeeeee eeeeeee eeeeeeeeeeeee