longitudinal associations between components of self

Longitudinal Associations Between Components of Self-Regulation,

Intelligence and Early Academic Skills

Submitted in Fulfillment of the Requirements for the Degree of

Doctor of Philosophy

in the Department of Psychology

University of Freiburg, Germany

Presented by

Fitim Uka

Summer semester, 2017

Associations Between Self-Regulation, Intelligence and Academic Skills 2

Dekan: Prof. Dr. Alexander Renkl

Wirtsschafts- und Sozialwissenschaftliche Fakultät

Erstgutachter: Prof. Dr. Alexander Renkl

Zweitgutachter: Prof. Dr. Antje von Suchodoletz

Datum des Promotionsbeschlusses: 03.05.2017


„Ich erklare hiermit, dass ich die vorliegende Arbeit ohne unzulassige Hilfe

Dritter und ohne Benutzung anderer als der angegebenen Hilfsmittel angefertigt habe.

Die aus anderen Quellen direkt oder indirekt ubernommenen Daten und Konzepte

sind unter Angabe der Quelle gekennzeichnet. Insbesondere habe ich hierfur nicht die

entgeltliche Hilfe von Vermittlungs- beziehungsweise Beratungsdiensten

(Promotionsberater oder anderer Personen) in Anspruch genommen. Niemand hat von

mir unmittelbar oder mittelbar geldwerte Leistungen fur Arbeiten erhalten, die im

Zusammenhang mit dem Inhalt der vorgelegten Dissertation stehen. Die Arbeit wurde

bisher weder im In- noch im Ausland in gleicher oder ahnlicher Form einer anderen

Prufungsbehorde vorgelegt.“

Fitim Uka

______________________ 03.05.2017


Table of Contents

Acknowledgment ........................................................................................................... 9

Summary ...................................................................................................................... 10

Introduction .................................................................................................................. 12

Definitions and Conceptualizations of Self-Regulation .......................................... 15

Development of Self-Regulation during Early Childhood ...................................... 20

Context Specific Self-Regulation ............................................................................ 25

The Dynamic Skill Theory as Theoretical Framework for Understanding the

Development of Self-Regulation, Intelligence and Early Academic Skills ............. 27

Associations Between Self-Regulation and Intelligence ..................................... 28

Associations between Self-Regulation and Emerging Academic Skills .............. 32

Measuring Self-Regulation ...................................................................................... 34

The Present Study .................................................................................................... 36

Method ......................................................................................................................... 41

Participants ............................................................................................................... 41

Procedure ................................................................................................................. 50

Measures .................................................................................................................. 52

Analytic Approach ................................................................................................... 60

Results .......................................................................................................................... 65

Descriptive Statistics and Correlations .................................................................... 67

Reciprocal Associations: Self-regulation and Intelligence ...................................... 72

Associations between Self-Regulation Components and Different Aspects of

Intelligence ............................................................................................................... 81


Reciprocal Associations: Self-regulation and Early Academic Skills ..................... 83

Discussion .................................................................................................................... 89

Interrelations Among Self-Regulatory Processes Across Different Contexts ......... 89

Bidirectional relationship between self-regulation components and intelligence ... 92

Relations Between Subcomponents of Self-Regulation and Subcomponents of

Intelligence ............................................................................................................... 95

Bidirectional relationship between self-regulation and early academic skills ......... 97

Implications for Educational Practice ...................................................................... 99

Limitations and directions for future research ....................................................... 101

Conclusions ................................................................................................................ 105

References .................................................................................................................. 107

Appendix B – Publications and Conference Contributions ....................................... 136


List of Figures Figure 1. Planned cross-lagged models representing the no-coupling model, two

different unidirectional coupling models and the bidirectional coupling mode .. 63

Figure 2. Planned structural equation modeling with self-regulation components

(inhibitory control, working memory, attention shifting) predicting aspects of

intelligence (verbal comprehension, visual spatial, fluid reasoning, working

memory, processing speed). ................................................................................. 64

Figure 3. Four different models of inhibitory control and intelligence representing the

(a) no-coupling model, (b and c) two different unidirectional coupling models

and (d) the bidirectional coupling model ............................................................. 78

Figure 4. Four different models of working memory and intelligence representing the


and (d) the bidirectional coupling model. ............................................................ 79

Figure 5. Four different models of attention shifting and intelligence representing the


and (d) the bidirectional coupling model ............................................................. 80

Figure 6. Structural equation modeling with self-regulation components (inhibitory

control, working memory, attention shifting) predicting aspects of intelligence

(verbal comprehension, visual spatial, fluid reasoning, working memory,

processing speed) ................................................................................................. 82

Figure 7. Four different models of self-regulation and vocabulary skills representing

the (a) no-coupling model, (b and c) two different unidirectional coupling

models and (d) the bidirectional coupling model.. .............................................. 87


Figure 8. Four different models of self-regulation and math skills representing the (a)

no-coupling model, (b and c) two different unidirectional coupling models and

(d) the bidirectional coupling model .................................................................... 88


List of tables

Table 1. Descriptive Statistics for Participants who Completed all Assessments to

Those with Missing Data ..................................................................................... 44

Table 2. Descriptive Statistics for Parent’s Background Variables ............................ 48

Table 3. Measures Used in the Study to Assess Self-Regulation, Intelligence and Early

Academic Skills .................................................................................................... 59

Table 4. Factor Loadings of Self-Regulation Measures: One-Factor Model .............. 66

Table 5. Factor Loadings of Self-Regulation Measures: Multi-Factor Model ............ 67

Table 6. Descriptive Statistics of Direct Measures of Self-Regulation (raw data) ...... 69

Table 7. Correlation Matrix Between Self-Regulation Across Different Contexts and

Academic Outcomes ............................................................................................. 71

Table 8. Descriptive Statistics for Intelligence (raw data) .......................................... 73

Table 9. Model Fit Results for the Full Sample: Intelligence and self-regulation

components: (a) Working Memory, (b) Inhibitory Control, (c) Attention Shifting

.............................................................................................................................. 75

Table 10. Descriptive Statistics of Early Academic Skills (raw data) ......................... 84

Table 11. Model Fit Results for the Full Sample: Self-regulation and Early Academic

Skills: (a) Language Skills and (b) Math Skills ................................................... 85


Acknowledgment

First of all, I would like to express my gratitude to Prof. Antje von

Suchodoletz, who encouraged me to pursue PhD studies and taught me the most

important and valuable information about psychology and research. This work would

not have the spirit it has without the help, motivation and support provided by Prof.

Antje von Suchodoletz. It has been an honor to be her PhD student and I will always

appreciate the wonderful example of hard work that she has provided for me. In other

words, Prof. Suchodoletz gave me the chance to develop a scientific carrier, by

motivating and helping me to present in different international conferences,

encouraging and supporting me with the article writing and finally by providing a

great stimulation to finish the PhD thesis.

I am also very grateful to Prof. Dr. Alexander Renkl for accepting my PhD

proposal and admitting me as PhD student. The opportunity that he gave to me to

pursue PhD is essential for my current and future academic carrier. Also, I would like

to acknowledge helpful suggestions from Prof. Dr. Alexander Renkl in every

presentation and meeting I had with him.

Profound gratitude goes to students who helped this project voluntarily in

different ways, including coordination and data collection. As such, I would like to

thank Dritëro Avdijaj, Dafina Kajtazi, Bardha Çelaj, Yllka Bega, Shkëndije Maxhuni,

Burbuqe Rushiti, Arbenita Shasha, Egzona Maloku, Arbeni Imeri, Lisjan Hasaj,

Jetbardha Selmani, Genta Azizi, Gentiana Cani, Diana Morina, Albulena Murati for

their help.

Finally, I would like to acknowledge with gratitude, the support and love of

my family – my wife and son, father, mother and siblings. They all kept me going and

I am aware that this would not have been possible without their support.


Summary

Self-regulation is central to performance in a variety of domains including

school readiness and successful school adjustment, academic achievement, and

educational outcomes (Blair & Razza, 2007; McClelland et al., 2007). Despite the

great advancement on the knowledge regarding the positive impact of self-regulation,

some questions still remain unclear. First, there is an ongoing controversy about

whether the structure of self-regulation is unitary or involves several distinct

subcomponents. This controversy has led to other important yet open questions

regarding the relation of subcomponents of self-regulation with intelligence and early

academic skills, for example. Second, the measurement of self-regulation is debated

and less is known how closely different measures of self-regulation across various

contexts are related. Third, the majority of self-regulation research has been

conducted in the United States. Only recently has research on self-regulation been

extended internationally. Studies from Europe and Asia suggest differences in self-

regulation across cultures and that an individual’s self-regulation may vary across

different contexts (Suchodoletz et al., 2013; Wanless et al., 2013). Addressing these

questions, the present study investigated the structure of self-regulation in preschool

children across multiple developmental contexts and tested (1) bidirectional

associations between self-regulation and intelligence, as well as (2) bidirectional

associations between self-regulation and early academic skills.

A sample of 150 preschool-aged children from Kosovo, a lower-middle-

income region in southeastern Europe (t1: 50% girls, Mage = 4.58 years, SD = .08)

participated in three waves of data collection every six months. The study involved

various self-regulation measures, including direct assessments and informant ratings,

measures of intelligence, and early academic skills.


Confirmatory factor analysis (CFA) was chosen to examine the structure of

self-regulation (i.e., single-factor model vs. multifactor model). Results supported a

multi-factor model of self-regulation across different contexts. Furthermore, results

indicated moderate interrelations between self-regulation assessed in a structured one-

to-one context and informant ratings. To address the research question on the

bidirectional relationship between subcomponents of self-regulation and intelligence,

respectively early academic skills, autoregressive cross-lag path models were used.

Study findings provided initial evidence for a bidirectional relation between inhibitory

control and intelligence across time, but not for two other subcomponents of self-

regulation (working memory and attention shifting). Regarding early academic skills,

results showed strong concurrent associations between self-regulation and math skills

at each time point but not for bidirectional associations across time. For vocabulary,

results provide preliminary evidence for longitudinal bidirectionality.

In sum, results of the present study supported the assumption that an

individual’s self-regulation might be dependent on the specific context in which the

behavior is observed. Also, results suggested that not all components of self-

regulation are equally related to intelligence, showing a complex pattern of

bidirectional associations between self-regulation and intelligence. Finally, our

findings point to differential associations between self-regulation and emerging

academic skills over the preschool period. Overall, the study adds to current efforts to

understand skill formation of children in low- and middle-income countries and lay

the groundwork for initiating a campaign in Kosovo focusing on the importance of

self-regulation for children’s learning and development.

Keywords: self-regulation, intelligence, early academic skills


Introduction

Self-regulation - manifested in children’s ability to monitor, modulate, and

direct their cognitive functions, attention, emotions, and behavior - is critical for

children’s school readiness and successful adjustment (e.g., McClelland et al., 2007;

Cameron Ponitz, McClelland, Matthews, & Morrison, 2009). Being able to follow

directions, control attention, successfully communicate needs, desires, and thoughts,

and being sensitive to other people’s feelings are essential skills for positive

adaptation and adjustment to school (Blair & Diamond, 2008; Coolahan, Fantuzzo,

Mendez, & McDermott, 2000; Ladd, Birch, & Buhs, 1999). The marked improvement

of self-regulation is shown between ages 3 and 5, when children enter more structured

learning environments (e.g., Blair & Razza, 2007; Duncan et al., 2007; McClelland et

al., 2007; Welsh, Nix, Blair, Bierman, & Nelson, 2010). It has been suggested that the

development of self-regulation is associated with the maturation of prefrontal cortex

(Anderson, 2002), which is responsible for intelligence and early academic skills as

well (Duncan et al., 2000; Miller, 2000; Miller & Cohen, 2001; Sakai, 2005).

Moreover, research suggests the adaptability of neural processes involved in self-

regulation. According to Duncan and Miller (2002) neural coding adapts to the

context in order to fit the specific behavioral demands of a given context. Thus, self-

regulatory behaviors might be different from one context to another. The present

study aims to extend the literature, by (1) investigating interrelations among self-

regulatory processes across different contexts and (2) examining bidirectional

associations between self-regulation and intelligence, respectively emerging academic

skills.

The investigation of interrelations between different self-regulatory processes

is particularly important, considering the evidence suggesting that contexts might


affect the amount and type of self-regulation displayed (Schunk, 2005). This is in line

with a growing body of research supporting the dynamic skill theory (Fischer &

Bidell, 2006), which suggests that skills are context specific, but they gradually

extend to new contexts through their associations with other skills. Recent research

provided initial evidence showing moderate to strong cross-sectional relations of self-

regulation with intelligence and early academic skills (e.g., Brydges, Reid, Fox, &

Anderson, 2012; Bohlmann, Maier, & Palacios, 2015; Friedman et al., 2006; Fuhs,

Nesbitt, Farran, & Dong, 2014). However, some studies suggest self-regulation as

leading indicator of intelligence and early academic skills (e.g., Brydges, Reid, Fox,

& Anderson, 2012; Fuhs, Nesbitt, Farran & Dong, 2014), whereas others show

intelligence or early academic skills as predictor of self-regulation (e.g., Bohlmann,

Maier, & Palacios, 2015; Salthouse, 2005). Moreover, previous research has shown

that not all aspects of self-regulation are equally related to measures of intelligence

and early academic skills (e.g., Friedman et al., 2006; Fuhs et al., 2014).

Consequently, this study seeks to examine longitudinal, bidirectional associations

between subcomponents of self-regulation (i.e., working memory, inhibitory control

and attention shifting) and intelligence, respective early academic skills among

preschoolers in Kosovo, thus aiming to identify the leading indicator in these

relationships.

It is well documented that self-regulation, intelligence and early academic

skills undergo significant developmental changes during the early childhood years

(Blair & Raver, 2015; Farkas & Beron, 2004). There are recent studies suggesting that

there might be simultaneous growth and reciprocal relations between self-regulation

and academic skills (Bohlmann, Maier, & Palacios, 2015; Fuhs, Nesbitt, Farran, &

Dong, 2014). However, only a few studies investigated bidirectional relationships


between self-regulation, intelligence and academic skills in early childhood; however,

these findings were inconsistent (e.g. Bohleman, 2015; Fuhs, Nesbitt, Farran, &

Dong, 2014). Prior research has been limited in its ability to test the dynamic nature

of self-regulation with intelligence and early academic skills across time due to the

sample size or cross-sectional study designs (Bohlmann et al., 2015). The present

study aims to overcome these limitations by examining reciprocal relations of self-

regulation with intelligence and early academic skills longitudinally with a sample of

150 children. By using three waves of data collection, this study sought to identify the

variability in these relations across time. Most prior research has been limited to one

source of information. The current study, in contrast, uses a wide range of self-

regulation measures (e.g., direct assessments and informant ratings) to investigate the

research questions. Moreover, the literature on self-regulation and its relation to

intelligence and early academic skills is dominated by studies conducted in the U.S.

and other well developed countries (e.g., Germany, Finland and France). As such, it is

an open question if these findings generalize to other cultures with low-to-modest

income. Kosovo is classified as a low-and-middle-income country (LAMIC) and the

poorest country in Europe (UNICEF Kosovo, 2011). The economic status has

persistently affected the education system and as a result most children in Kosovo still

have no access to formal education programs in early childhood. From a cultural

psychology perspective, the Kosovo society is characterized by predominantly

collectivist norms and collectivist values (valuing obedience, gratefulness, economic

contributions to family), which differs from Western societies and their individualist

values (autonomy, self-reliance). As such, we add to the existing literature by

addressing the research questions in a non-Western, understudied population. By

doing so, this study will contribute to a more culturally informed understanding of the


factors associated with self-regulation in young children and ways to promote self-

regulation in low-income populations. Importantly, research suggests that children

who are disadvantaged or experiencing demographic risk factors may benefit more

from interventions (Schmitt, McClelland, Tominey, & Acock, 2015; Tominey &

McClelland, 2011). Thus, the study can facilitate the understanding of self-regulation

within the culture in Kosovo and inform specific interventions and policies.

Definitions and Conceptualizations of Self-Regulation

There has been increasing interest in the scientific study of self-regulation and

several developmental models have emerged. In the literature many different terms

are used to refer to self-regulation and the term self-regulation is inter-changeably

used with the terms of, for example, self-control, self-discipline, executive functions

and willpower (e.g., Duckwoth & Carlson, 2013). Self-regulation is generally defined

as one’s ability to voluntarily control attentional, emotional, and behavioral impulses

in the service of personally valued goals and standards (Duckworth & Carlson, 2013).

However, considerable debate remains about its definition and specific components

(Cole, Martin, & Dennis, 2004; Kochanska, Murray, & Harlan, 2000). In this chapter

different definitions and conceptualizations of self-regulation will be discussed.

Early conceptualizations describe self-regulation as “one of the really central

and significant cognitive-developmental hallmarks of the early childhood period”

(Flavell, 1977, p. 64). In pioneering research, self-regulation was defined as the

ability to comply with a request, to initiate and cease activities according to

situational demands, to modulate the intensity, frequency, and duration of verbal and

motor acts in social and educational settings, to postpone acting upon a desired object

or goal, and to generate socially approved behavior in the absence of external

monitors (Block & Block, 1979; Fenichel, 1945; Greenacre, 1950; Luria, 1959, I960,


1961; Masters & Binger, 1978; Meichenbaum & Goodman, 1971; Mischel, 1974;

Mischel & Patterson, 1979; Parke, 1974). Early approaches to self-regulation focused

on goal directed actions assuming that actions are selected based on a prospective

consideration of possible outcomes and future lines of action.

Moving from the early conceptualizations, Kopp (1982) took a step further

and looked at self-regulation from a developmental perspective, arguing that self-

regulation is the process through which children increasingly acquire the ability to

regulate their own arousal, emotion, and behavior. Self-regulation is considered to be

adaptive to changes in the environment. The development of self-regulation is thought

to be linked to the maturation of the central nervous system during infancy, which

allows for normative improvements in the capacity to maintain behavioral and

physiological organization in the face of distressing events (Kopp 1982). According

to Kopp (1982), self-regulation develops through early experiences and social

interactions with caregivers and other significant individuals. The development of

children’s self-regulation is characterized by a shift from “other” regulation (i.e., by

parents and caregivers) to being able to control one’s own actions and reactions (i.e.,

internalization; Kopp, 1982). Children learn to detach the control of their behaviors

from the environment and become increasingly able to self-regulate behaviors that

were previously regulated by caregivers.

Another approach to self-regulation in children is to consider it as the

developmental integration of emotion and cognition in early childhood (Derryberry &

Rothbart, 1988; Posner & Rothbart, 1998; Rothbart, Ellis & Posner, 2004). A

particular prominent developmental model is the psychobiological model of

temperament (Rothbart, Ellis, & Posner, 2004). In this model, self-regulation is

considered a crucial component of temperament including two components, reactivity


and effortful control. Reactivity is defined as characteristics of the individual's

reactions to stimulus change, while effortful control is considered the ability to inhibit

a dominant, proponent response to perform a subdominant, less salient response

(Rothbart & Bates, 1998). The relation between reactivity and effortful control is seen

as an inverted U-shaped curve (Arnsten, 2009). At moderate increases in emotional

and physiological reactivity, attention is increased and effortful control is maximized,

whereas at very low or very high levels of reactivity control of attention is decreased

and effortful control is less likely to occur.

Metcalfe and Mischel (1999) propose a distinction between hot and cool

aspects of self-regulation summarized in the neural network model. Two

complementary subsystems enable self-regulation - the cool, cognitive “know” and

the hot, emotional “go” system. The cool system is described as affectively neutral,

reflective, slow acting and late developing, whereas the hot system is defined as

affectively engaged, reflexive, fast acting, early developing, and under stimulus

control (Metcalfe & Mischel, 1999). Following this model, Willoughby et al. (2013)

tested whether preschool children’s performance on self-regulation tasks could be

distinguished into hot and cool components. Results showed that children’s

performance on self-regulatory tasks was better represented using a two-factor (hot

vs. cool) model compared to a single-factor model. More importantly, researchers

have found distinguished contribution of hot and cool aspects of self-regulation on

academic achievement and social competence. Several studies suggest that the cool

aspect of self-regulation is critical for school readiness and classroom functioning,

with notable contributions to math achievement (Blair, 2010; Brock et al., 2009;

Gathercole & Alloway, 2008; Rimm-Kaufmann et al., 2009). On the other hand, it

was found that the hot aspect of self-regulation upon kindergarten entry does not


significantly add to achievement growth in elementary school (Duncan et al., 2007).

Brock et al. (2009) also showed that hot self-regulation did not predict any

achievement or behavior outcomes when examined concurrently with cool self-

regulation. Moreover, a growing number of neuroimaging studies support the

differentiation between hot and cool aspects of self-regulation (e.g., Bush, Luu, &

Posner, 2000; McClure, Laibson, Loewenstein, & Cohen, 2004; Sakagami & Pan,

2007). Results suggest that the dorsolateral prefrontal cortex and anterior cingulate

cortex are associated with cool, cognitive processing, and the orbito-frontal/ventral

lateral prefrontal cortex and posterior cingulate cortex are associated with hot aspects

of self-regulation (Bush, Luu, & Posner, 2000; McClure, Laibson, Loewenstein, &

Cohen, 2004; Sakagami & Pan, 2007).

In the Cognitive Complexity and Control theory (Zelazo & Frye, 1998),

developmental control is the term used to refer to self-regulation. The Cognitive

Complexity and Control theory (e.g., Frye, Zelazo, & Burack, 1998; Zelazo & Frye,

1998) emphasizes the importance of hierarchical structure of rule systems. According

to this theory, developmental control over behavior is reflected in an increase of

complexity in represented rule structures (described as consciousness) and reflective

awareness of those rules (described as executive functioning). Zelazo and Frye (1998)

explain consciousness as the extent to which the representation of rules is sufficiently

explicit and thus allows children’s reflection and control. When children are young

they lack the conscious representations that would allow them to keep the required

rules in mind and to exert developmental control. Once a rule system at a particular

level of complexity is acquired, the child is able to exercise control over his or her

reasoning and behavior. According to this theory, development is reflected as an

increase in complexity of represented rule structures, as such self-regulation is age-


dependent. These age-related changes in maximum rule complexity are, in turn, made

possible by age-related increases in the degree to which children can reflect on the

rules they represent (Zelazo & Frye, 1998).

Another line of thinking that is extensively supported by research is the

conceptualization of self-regulation as a set of interrelated skills (e.g., Miyake et al.,

2000). Considering the structure of self-regulation, research suggests that self-

regulation includes both top-down planning processes (e.g., executive functions) and

bottom-up regulation of more reactive impulses, such as behavioral regulation (Blair

& Raver, 2015). Behavioral regulation requires the use of basic executive functions,

such as attention shifting, working memory, and inhibitory control (McClelland et al.,

2010; Cameron Ponitz et al., 2009). Attention shifting is defined as the ability to adapt

strategies to changing situational demands (Zelazo, Frye, & Rapus, 1996). Working

memory involves holding information in mind and mentally working with it

(Diamond, 2013) and it is known as the ability to guide ongoing or later behavior

(Wieb, Epsy, & Charak, 2008). The last core component is inhibitory control,

described as the ability to keep irrelevant or misleading information from interfering

with performance, which allows the person to suppress a proponent response

(Diamond, 1990; Harnishfeger & Bjorklund, 1993). Models developed in recent years

consider five individual characteristics that are interrelated: genes, stress physiology,

emotional reactivity and regulation, attention, and executive functions (Blair, 2014;

Blair & Ursache, 2011). Individual variation in the level of genes influences

physiological, emotional, attentional, and executive control responses to stimulation,

while individual differences at each of these levels feed forward to influence activity

at higher levels. As such, increased emotional reactivity will influence the demand on

the control of attention, and the control of attention will influence the demand on


executive functions. In contrast, activity at each level also feeds back on the level

below (Blair & Raver, 2015). For instance, executive functions can help to focus

attention, and through attention enable the regulation of emotion and stress

physiology, while through stress physiology, the feedback system extends to the

genetic level to influence gene expression (Blair & Raver, 2015). Following feed-

forward and feed-backward processes between different levels, the self-regulation

system is understood to come to a particular set point or level.

Taken together, self-regulation is a multidimensional construct involved in the

performance of goal-directed actions that includes multiple, interrelated regulatory

processes such as an individual’s abilities to focus and maintain attention, to control

thoughts, emotions and actions, and to regulate one’s stress response physiology

(Blair, Calkins, & Kopp, 2010; Blair & Raver, 2015; Calkins, 2007; McClelland &

Cameron, 2012; McClelland et al., 2010). For the purpose of the present study, self-

regulation is used to describe different self-regulatory components, such as inhibitory

control, working memory, attention shifting and delaying of gratification, and the

integration and application to overt behavior.

Development of Self-Regulation during Early Childhood

Developmental research indicates that self-regulation significantly increases

across childhood (Raffaelli, Crockett, & Shen, 2005). Directing and focusing attention

to certain objects are considered the first signs of self-regulation that children show

before the end of the first year of their life (Diamond, 1990; Thompson & Meyer,

2007). Individual differences in self-regulation emerge between ages 2 and 3

(Carlson, Mandell, & Williams, 2004). Marked improvements of self-regulation can

be seen between ages 3 and 5, when children enter more structured learning

environments (e.g., Blair & Razza, 2007; Duncan et al., 2007; McClelland et al.,


2007; Welsh, Nix, Blair, Bierman, & Nelson, 2010). By the time children enter formal

schooling, usually when they are five or six years old, substantial individual

differences in self-regulation exist that tend to persist over time (Vaszonyi & Huang,

2010). It has been suggested that these developmental changes are associated with

physiological maturation and changes to the structural organization in prefrontal brain

regions (Blair & Urasche, 2011). Initial support for the idea that self-regulation

growth is related to changes in the prefrontal cortex derived from indirect evidence,

primarily studies with brain damage patients (Carlson, 2005; Espy et al., 2011). In

particular, frontal cortical damage was found to be associated with impairments in

goal-directed behavior (Rabbit, 1997; Osório et al., 2012; Welsh et al., 2010).

Research has shown that frontal-damaged patients were unable to flexibly shift mental

sets, focus attention on relevant stimuli while ignoring irrelevant ones, and lacked

planning and monitoring skills (Welsh et al., 2006). Moreover, studies investigating

frontal lobe function in the normal brain have assumed that the flexible adaptation of

neural networks in the prefrontal cortex play a central role in orchestrating neural

processes to enable goal-directed action (Blair et al., 2012; Blair & Ursache, 2011;

Duncan, 2006).

The results of studies that investigated age-related changes in self-regulation

during early childhood were consistent, showing a rapid development of different

aspects of self-regulation in this period of life. Also, Wiebe, Epsy and Clark (2008)

found that older children outperformed younger children in the self-regulation tasks.

Using longitudinal data Willougby et al., (2013) found a significant magnitude of

change in self-regulation skills between 3 and 5 years of age. Age effects were found

in all three subcomponents of self-regulation (e.g., Belanger, Belleville, & Gauthier,

2010; Mayr & Liebscher, 2001; Verhaeghen & Basak, 2005). For example, research


suggests that between the ages of 3 and 5 years there is a gradual progression in the

ability of children to deal with conflict, as measured in Dimensional Change Card

Sorting (DCCS) task (Carlson, 2005; Diamond, 2001, 2006; Frye et al., 1998; Zelazo

et al., 2003). Three-year old children have extreme difficulty redirecting attention,

older children seem to acquire the ability to inhibit this tendency and succeed in

switching from one sorting dimension to another (Kirkham et al., 2003). Using tasks

that require children to suppress a dominant response tendency, several studies have

documented substantial age-related improvements in inhibitory control and attention

shifting over the preschool period (e.g., Carlson & Moses, 2001; Klenberg, Korkman,

& Pekka, 2001; Stahl & Pry, 2005). Clark et al., (2010) found that in the period from

3 to 4 years old, children’s accuracy in inhibitory control and attention shifting tasks

improves from 40% to over 70%. However, the evidence indicates differential trends

in the development of self-regulation skills (Huizinga, Dolan, & van der Molen,

2006). Thus, different self-regulation skills have been shown to have different

developmental trajectories and the structure of self-regulation may change from

childhood to adulthood (Hughes, Ensor, Wilson, & Graham, 2010; Jurado & Roselli,

2007; Wiebe et al., 2011).

In addition, socioeconomic and sociocultural factors are shown to be

important for development of self-regulation. Different theories, including Social

Cognitive Theory (Bandura, 1977), point to the impact of social factors on self-

regulatory processes. According to this theory, human functioning is characterized as

the interplay of self-generated and external sources. According to this view, social

systems are the product of human activity, and social systems, in turn, help to

organize, guide, and regulate human behaviors (Bandura, 2008). There has been

growing evidence supporting Social Cognitive Theory by showing that low-income


populations are at an increased risk for lower self-regulation (Rhoades, Greenberg,

Lanza, & Blair, 2011; Sektnan, McClelland, Acock, & Morrison, 2010). Different

studies suggested the adverse effects of socioeconomic disadvantage on children’s

self-regulation skills (Evans & Rosenbaum, 2008; Lengua, Honorado, & Bush, 2007;

Mezzacappa, 2004; Noble, Norman, & Farah, 2005; Wanless, McClelland, Tominey,

& Acock, 2011). For example, Sektnan et al. (2010) showed that family

socioeconomic status indexed by ethnic minority status, low maternal education, low

family income, and high maternal depressive symptoms, had significant negative

effects on both parent- and teacher-rated self-regulation in preschool and kindergarten

children. Other studies found that socially disadvantaged children performed less

proficiently in self-regulation compared to their more advantaged peers (Mezzacappa,

2004; Wanless, McClelland, Tominey, & Acock, 2011). In addition, it has been

shown that children growing up in a disadvantaged environment experience more

stress, which is negatively related to their self-regulation skills (Blair, 2010; Blair &

Ursache, 2011; Wanless, McClelland, Tominey, & Acock, 2011). Approaches to

measure children’s stress response physiology using their salivary cortisol levels

confirmed that high child stress is associated with lower self-regulatory skills (Blair,

2012). As such, socioeconomic status affects children’s level of stress and in turn

their self-regulatory skills (Lengua, Zalewski, Fisher, & Moran, 2013).

However, self-regulation is not only influenced by socio-economic risk

factors, but also relies on sociocultural factors. Over the past decade, great efforts

have been made to identify regularities in diverse patterns of human development

across different sociocultural contexts (Rogoff, 2003). In particular, recent years have

seen massive growth in the number of studies on self-regulation in samples outside

the United States, and findings from these studies have improved researchers’


understanding of self-regulation within different cultures (Gestsdottir et al., 2014;

Hughes et al., 2010; Suchodoletz et al., 2013; Wanless et al., 2011). For example,

Asian children have been shown to have stronger self-regulation skills than their U.S.

and British counterparts (Lan, Legare, Cameron Ponitz, Li, & Morrison, 2011; Oh &

Lewis, 2008; Sabbagh, Xu, Carlson, Moses, & Lee, 2006). These differences were

attributed to the fact that children growing up in a Confucian society are more

oriented toward perspective taking and controlling their impulses compared to

children in Western cultures (Lewis et al., 2009; Oh & Lewis, 2008). In another study,

the self-regulation of similar-aged children from South Korea, Taiwan, China, and the

United States was tested (Wanless, McClelland, Acock, et al., 2011; Wanless et al.,

2013). The Chinese children scored higher on different measures of self-regulation

than children from all other countries (Wanless, McClelland, Acock, et al., 2011;

Wanless et al., 2013). Such findings of cultural differences in children’s self-

regulatory capacities support the assumption that sociocultural factors may shape

different development trajectories for self-regulation.

Altogether, sociocultural and socioeconomic factors may shape children’s

self-regulation skills development. Thus, it has been suggested that children in low-

and middle-income countries in particular fail to reach their developmental potential

(Grantham-McGregor et al., 2007). For example, in former socialist countries in

Europe, the political situation, economic transitions, and conflicts of the 1990s

continue to have effects on children’s development. In many of these countries, such

as Kosovo, early childhood education has been underestimated and neglected, which

is shown in the low investments from government, lack of physical infrastructure and

shortage of staff (Gjelaj, 2013; UNICEF Kosovo, 2011).

In Kosovo, children younger than the age of 5 attend kindergarten (i.e.,


nursery school and preschool in American terms), whereas preprimary school (i.e.,

kindergarten in American terms) is for children ages 5–6 years. The majority of

existing early childhood education classes are located in urban centers such as Pristina

or Mitrovica. As such, children living in villages and in the countryside have limited

access to early education (UNICEF Kosovo, 2011). In addition, the costs of education

place significant economic stress on many families in Kosovo (Sommers & Buckland,

2004). As a result, even more than a decade after the armed conflict in the region, less

than 10% of 3- to 6-year-old children attend early childhood education programs

(UNICEF Kosovo, 2011). Thus, many children might not enter primary school with

the skills necessary to be successful at school (Organisation for Economic

Cooperation and Development, 2006).

Context Specific Self-Regulation

Early self-regulation research has focused on self-regulation as a relatively

stable attribute of an individual, largely independent of the context in which self-

regulatory behaviors are observed (Lezak et al., 2004). Duncan and Miller (2002)

provided a framework that suggests the adaptability of the neural processes involved

in voluntary goal-directed action. They hypothesized that neural coding adapts to the

context in order to fit the specific behavioral demands. Similarly, the dynamic skill

theory assumes that psychological structures are organized to perform specific

functions in particular settings (Fischer & Bidell, 2006). Thus, self-regulatory

behaviors might be different from one context to another. Based on the assumption

that context affects the amount and type of self-regulation displayed (Schunk, 2005),

recent investigations of self-regulation have begun to consider that self-regulation

might differ across contexts (McClelland, et al., 2010; Toplak, West, & Stanovich,

2013). One primary finding of this line of research has been that different types of


self-regulation measures assess different context-specific aspects of the same

construct (Toplak et al., 2013). As noted by Toplak et al. (2013), performance-based

and rating measures of self-regulation assess different cognitive and behavioral

aspects. The performance-based assessments measure children’s efficiency of

cognitive abilities, while the rating assessments measure a child’s ability to pursuit

and achieve a goal (Toplak et al., 2013). Accordingly, when evaluating an

individual’s self-regulation, one needs to consider the demands of the specific context

in which it is being observed (McClelland et al., 2010).

In early childhood, contexts such as the family or the classroom place different

expectations and demands on children’s self-regulation, while simultaneously

providing different levels of support (Graziano, Reavis, Keane, & Clakins, 2007). For

example, it has been found that the amount of guidance provided by a parent is

positively related to age-specific indices of child self-regulation (Bernier, Carlson, &

Whipple, 2010). In contrast, the classroom context usually lacks extensive individual

supervision, and children are expected to complete assignments independently

(Graziano et al., 2007). Accordingly, the way in which parents and teachers evaluate

the same child’s self-regulation might differ. Moreover, the goals of each context, as

well as the goals of a person acting in a specific context compared to his or her goals

acting in another context might vary and thus lead to a different choice of actions.

For example, the goals of the child in the classroom may differ from the goals of the

child at home, leading to different self-regulatory behaviors. This is in line with the

dynamic skill theory arguing that people act differently in different situations, with

different people, in different emotional states (Fischer & Bidell, 2006).


The Dynamic Skill Theory as Theoretical Framework for Understanding the

Development of Self-Regulation, Intelligence, and Early Academic Skills

The majority of developmental models describe cognitive skills, including

self-regulation, in static and formal terms. However, considering development as

dynamic process of change provides a better understanding of variability and stability

in people’s skill formation (Fischer & Bidell, 2006). The dynamic skill theory

(Fischer & Bidell, 2006) considers pervasive variability as a hallmark of the nature of

human behavior and goal-directed action. By defining psychological structure as the

organization or pattern of activities that explain human behavior, Fisher and Bidell

argue that psychological structures are not static and not separately existing entities

and therefore depend on the situation, context and other people (Fischer & Bidell,

2006). Moreover, the dynamic skill theory considers each skill as the capacity to act

in an organized way in a specific context, which is built up gradually through the

practice of real activities in real contexts. Therefore, skills are both action-based and

context-specific. People do not have abstract, general skills; they develop skills for

specific contexts (Fischer & Bidell, 2006).

Another important aspect of dynamic skill theory is the argument Fischer and

Bidell make about skill development. They propose that skills are not developed

atomistically but are necessarily integrated with other skills (Fischer & Bidell, 2006).

For example, a skill – such as solving a math problem- draws on and unites systems

of working memory, attention focusing, intelligence, planning, scripts, and so forth.

Each of these skills, according to dynamic skill theory, should work with other skills

to enable an individual to solve a math problem. Based on this theory, skills should

automatically be integrated and function in concert with other skills in order to

produce successful performance. According to this view, self-regulation is dependent


on other skills, such as intelligence and early academic skills, but in turn provides

substantial influence on intelligence and early academic skills.

However, the integrated skills in dynamic skill theory are not simply

interdependent, but also interparticipatory (Fischer & Bidell, 2006). True integration

means that each skill participates in one another’s functioning (Fischer & Bidell,

2006). Throughout the development of skills, each skill component regulates each

other. As such, the nature of development is dynamic, where one set of developing

skills influences another set of developing skills (Fischer & Bidell, 2006). Moreover,

dynamic skills theory describes the interactive nature of developing skills, allowing

for the interaction to change over time and across contexts. In the present study, self-

regulation, intelligence and early academic skills are viewed from a dynamic skill

framework, suggesting that all three are dynamic, constructive, and contextually

embedded, giving rise to patterns of variability in developmental pathways.

Associations Between Self-Regulation and Intelligence

The development of self-regulation and intelligence are of fundamental

importance, as processes related to these constructs often influence how successful an

individual is when performing a complex task (Miyake et al., 2000). Both factors are

shown to be highly important for academic achievement (Blair & Razza, 2007). From

the dynamic skill perspective it means that successful school performance depends on

all relevant skills, including self-regulation and intelligence, working in concert.

Accordingly, a substantial research base has documented developmental associations

between self-regulation and intelligence (e.g. Duncan et al., 2000, Bishop et al.,

2008). Evidence from functional neuroimaging studies indicates that the prefrontal

cortex plays a central role in executive control processes involved in human

intelligence (Miller, 2000; Miller & Cohen, 2001). Recent neuroimaging studies


provide further evidence for the prefrontal cortex as a common neurological structure

for both self-regulation and intelligence (Abreu et al., 2014). The prefrontal cortex

was activated for the performance on tests of intelligence (Duncan et al., 2000,

Bishop et al., 2008) and self-regulation (Duncan 2006; Duncan & Owen, 2000;

Miller, 2000; Miller & Cohen, 2001), suggesting that the same neural structure might

be responsible for reciprocal growth in intelligence and self-regulation. However,

there is an ongoing debate regarding the timing, strength, and direction of relations.

Similar to self-regulation, intelligence is also conceptualized as a

multicomponent construct, which covers a broad spectrum of cognitive skills

presumed to be relevant for educational success (Schonemann, 2005). According to

Friedman et al (2006), there is a considerable overlap of the definitions of self-

regulation and intelligence, suggesting that they rely on each other for the

development (Davis, Pierson, & Holmes Finch, 2011; Decker, Hill & Dean, 2007;

Wood & Liossi, 2007). For example, Decker and colleagues (2007) showed that both

fluid reasoning and self-regulation are involved in the application of reasoning

strategies to novel or unusual situations. Similarly, working memory has been

regarded as a core function of self-regulation and intelligence alike (Friedman et al.,

2006; Miyake et al., 2000; Roberts & Pennington, 1996) and many studies reported

high correlations between intelligence and working memory (e.g., Colom, Flores-

Mendoza, & Rebollo, 2003; Kyllonen, 2002). Other studies support the assumption

that self-regulation and intelligence are distinct (Ardila et al., 2000; Crinella & Yu,

2000; Friedman et al., 2006). For example, only low to moderate correlations have

been found between measures of self-regulation and intelligence (Ardila et al., 2000).

Moreover, Crinella and Yu (2000) found that intelligence scores for children with

attention deficit-hyperactivity disorder (ADHD), characterized by self-regulation


dysfunction, do not differ from children without ADHD. It has also been found that

frontal lobe patients often have clear self-regulation deficits, but intelligence may be

preserved (Barkley, 2001). Godoy, Dias and Seabra (2014) argue that self-regulation

covers “how” an individual does something, while intelligence cover “what and how

much” an individual is capable of. Together, it has been concluded that there is a

reasonable dissociation between intelligence and self-regulation (Ardila et al., 2000;

Barkley, 2001).

Despite a relatively large body of research documenting associations between

self-regulation and intelligence among older children and adults and a growing debate

about the leading indicator in this relationship (Ardila, Pineda, & Rosselli, 2000;

Boone, Ghafferian, Lesser, Hill-Gutierrez, & Berman, 1993; Friedman et al., 2006;

Johnstone, Holland, & Larimore, 2000; Waldmann et al., 1992), there is only limited

research on the bidirectionality between these two skills. While some studies

document the impact of self-regulation components on intelligence (Brydges, Reid,

Fox, & Anderson, 2012), other studies provide evidence for the opposite hypothesis-

that aspects of intelligence predict self-regulation (e.g., Arffa, 2007; Salthouse, 2005).

However, the majority of studies on the relationship between self-regulation and

intelligence have been conducted with adults (e.g. Friedman, 2006; Johnstone,

Holland, & Larimore, 2000) and literature on the bidrectionality between self-

regulation and intelligence in young children is scarce. This is surprising given the

significant developmental changes that self-regulation and intelligence undergo

during the early childhood years (Duncan et al., 2000; Raffaelli et al., 2005). Studies

with adults suggest that different components of self-regulation may be differentially

related to measures of intelligence with some aspects of self-regulation, such as

working memory, showing stronger associations with intelligence than others


(Friedman et al, 2006). For example, high correlations between working memory and

intelligence have been found in adults (Friedman et al., 2006) and children (Arffa,

2007). This pattern of results was replicated by a meta-analysis indicating an average

correlation of .48, between working memory and intelligence (e.g., Ackerman, Beier,

& Boyle, 2005). There is considerably less evidence on the relationship between other

components of self-regulation and intelligence (Benedek et al., 2014; Polderman et

al., 2009). Results indicate that not all aspects of intelligence were equally important

for self-regulation (e.g., Delis, Kaplan, & Kramer, 2001; Salthouse, 2005). For

example, Salthouse (2005) found that two aspects of intelligence, i.e., reasoning

ability and processing speed, were related to self-regulation whereas other aspects of

intelligence (i.e., spatial visualization, episodic memory and vocabulary) were not

related to measures of self-regulation. One study reported a strong correlation

between attention shifting and intelligence among college students (Salthouse et al.,

1998). Aside from working memory, other studies did not find evidence for a

significant relationship between self-regulation and intelligence (Rockstroh &

Shweizer, 2001; Miyake et al., 2000; Friedman et al., 2006). From a dynamic skill

perspective, lack of significant associations between aspects of self-regulation and

intelligence is due to the structure of the two components, which may change over

time and might not follow the same pattern of development (Fischer & Bidell, 2006).

The lack of significant associations between some components of self-regulation and

specific aspects of intelligence in some studies has been attributed to methodological

problems. Despite being widely used, most intelligence measures do not capture the

sets of skills required for intelligent behaviors and are not sensitive to the most

important elements of intelligence: acting purposefully (i.e., controlling and planning

behavior) and thinking rationally (i.e., organizing and directing cognition) (Ardila et


al., 2000; Friedman et al., 2006).

The dynamic skill theory argues that skills are changeable over time.

Consistent with this assumption, it has been shown that different self-regulation

components have different developmental trajectories (Jurado & Roselli, 2007).

Similarly, intelligence components are shown to have distinct development

trajectories, with crystallized intelligence remaining generally robust and steady while

fluid intelligence declining with age (Horn & Cattell, 1967; McArdle et al., 2002). It

remains unclear how the various self-regulation components may influence

intelligence development or may be influenced by the development of intelligence. In

general, results of different studies suggest a dynamic and changeable relationship

between aspects of self-regulation and intelligence. However, studies on the

relationship between self-regulation and intelligence among children lack variety of

measures, which are able to tap significant components of both constructs. Thus, in

this study we use a variety of self-regulation and intelligence measures. Also, the

majority of the studies have used cross-sectional designs, which do not allow

conclusions regarding the reciprocal associations of these two skill sets over time. As

such, the current study addresses the bidirectionality of the relationship between self-

regulation and intelligence using a longitudinal design.

Associations between Self-Regulation and Emerging Academic Skills

In addition to associations between self-regulation and intelligence, research

also suggests a strong relationship between self-regulation and early academic skills.

Numerous studies showed that self-regulation skills are positively associated with

concurrent and later academic skills (McClelland & Cameron, 2012). For example,

one study found that higher levels of self-regulation at the onset of kindergarten

predicted higher fall and spring levels of math and vocabulary skills (Cameron Ponitz,


McClelland, Matthews, & Morrison, 2009). Moreover, McClelland and colleagues

(2007) reported that growth in self-regulation predicted growth in math and

vocabulary skills over the prekindergarten year. As suggested in the dynamic skills

theory, self-regulation skills are dynamic and constantly adjusting their relationship

with other structures, such as early academic skills. Simultaneous growth of self-

regulation and emerging academic skills necessitates the investigation of the strength

and direction of effects between the development of self-regulation and academic

skills. Similar to these findings, dynamic skill theory suggests that self-regulation

skills develop gradually and other skills, including early academic skills, contribute to

the self-regulation skills development. In turn, self-regulation contributes to the early

academic skills development. Throughout the coordination and mutual regulation, it

can be argued that self-regulation and early academic skills depend on each other.

Recent studies provide initial support for bidirectionality among these developing

skills (Bohlmann, Maier, & Palacios, 2015; Fuhs et al., 2014). However, there is

considerable debate regarding the direction and strength of the associations. For

example, Bohlmann et al. (2015) provide support for the assumption that vocabulary

serves as a leading indicator of self-regulation skills. Consistent with this assumption,

another study found that children with higher vocabulary skills showed greater growth

in self-regulation in comparison to their peers with lower vocabulary skills (Ayoub,

Vallotton, & Mastergeorge, 2011). However, other studies found that self-regulation

skills at the beginning of the preschool years predicted gains in both mathematics and

literacy (e.g., Welsh et al.,2010). Moreover, they found support for a bidirectional

association, with math skills predicting self-regulation gains. However, this pattern of

results was not found for vocabulary skills, thus suggesting differential reciprocal

growth of self-regulation and academic skills that depend on the specific academic


content areas. The hypothesis of differential reciprocal growth is substantiated by

other recent studies that found evidence for bidirectional associations for math, but

not for literacy (Fuhs et al., 2014; Weiland et al., 2014). In addition, Weiland et al.

(2014) found that early self-regulation predicted later vocabulary outcomes but early

vocabulary did not predict later self-regulation. However, results regarding

bidirectional associations between self-regulation and literacy skills are controversial

as another study reported an effect of early literacy skills on later self-regulation but

no significant association between early self-regulation and later literacy skills (Fuhs

& Day, 2011). Children who learn academic skills quickly might be more attentive

and focused on challenging tasks and activities, which in turn, has a positive impact

on the development of their self-regulation skills (Fuhs et al., 2014). Together, these

results suggest differential associations dependent on the academic content area.

Despite initial support for bidirectional associations, more research is needed to

determine whether the relationship between self-regulation and academic skills is

causal, whether the strength of the associations changes when children grow older,

and whether effects are specific to an academic content area.

Measuring Self-Regulation

Over the past two decades, self-regulation research has produced an

extraordinary range of measures used to assess self-regulation (Duckworth & Kern,

2011; McClelland et al., 2010). At their core, the varied operationalizations of self-

regulation seek to assess voluntary processes that serve personally valued goals and

standards (Duckworth & Kern, 2011). Several new methods have recently been

developed to assess young children’s self-regulation, including informant reports

(e.g., from parents or teachers), assessor ratings, and performance-based measures

(Garon, Bryson, & Smith, 2008; McClelland & Cameron, 2012; Smith-Donald,


Raver, Hayes, & Richardson, 2007; Willoughby, Pek, Blair, & the Family Life

Project Investigators, 2013). Most measures of early childhood self-regulation have

been shown to independently predict children’s intelligence and emerging academic

skills, such as vocabulary, literacy, and math (Blair & Razza, 2007; McClelland et al.,

2007; Suchodoletz et al., 2013; Wanless et al., 2011).

Informant-report questionnaires and direct performance-based methods are the

most common approaches for assessing self-regulation in young children (Duckworth

& Kern, 2011; McClelland et al., 2010). Informant-report questionnaires provide

important information on the complex self-regulatory behaviors that occur in typical

everyday situations (Toplak et al., 2013). In addition, different informants, such as

parents or teachers, observe a child’s behavior in different contexts, providing

different perspectives on the same child’s self-regulation. However, informant ratings

can involve bias. For example, different teachers or parents might judge the same

behavior differently and interpret the very same questionnaire item differently

depending on their own everyday experiences with the child (McClelland et al., 2010;

Toplak et al., 2013).

In contrast, performance-based assessments are highly standardized in most

instances, with regard to both stimulus presentation and response completion (Garon

et al., 2008; McClelland et al., 2010; Toplak et al., 2013; Willoughby et al., 2013).

There are several performance-based measures of self-regulation, including delay of

gratification paradigms (Mischel, 1974; Neubauer, Gawrilow, & Hasselhorn, 2012),

the Stroop test (Jensen & Rohwer, 1966), the Head–Toes–Knees–Shoulders task

(Cameron Ponitz, McClelland, Matthews, & Morrison, 2009), and task-switching

tests such as the Dimensional Change Card Sort (Diamond, Carlson, & Beck, 2005;

Hongwanishkul, Happaney, Lee, & Zelazo, 2005). Most recently, task batteries that


assess different aspects of self-regulation have been developed to improve the quality

of performance-based measurement in early childhood (Willoughby et al., 2013).

However, associations between informant-reported self-regulation and

performance-based assessments of self-regulation have been modest or even

nonsignificant (e.g., Duckworth & Kern, 2011; McClelland et al., 2007; Toplak et al.,

2013; Suchodoletz, Trommsdorff, & Heikamp, 2011; Wanless et al., 2011).

McClelland and colleagues (2010) have suggested that the lack of association

between these two self-report measures could be due to contextual differences. For

example, a child scoring high on a performance-based assessment administered in a

structured laboratory setting might demonstrate higher self-regulation than in a less

structured classroom setting that includes various distractions (Graziano et al., 2007;

McClelland et al., 2010). Accordingly, Toplak et al. (2013) argued that different

measures might tap into different but nonredundant context-specific facets of self-

regulation. According to dynamic skill theory, skills vary depending on the context,

situation and other people (Fischer & Bidell, 2006). Therefore, self-regulation skills

may vary across home, school and testing environment. Also, self-regulation skills are

influenced by the presence of other people, such as teacher, parent or assessor

(Fischer & Bidell, 2006). The goal of the present study was therefore to examine

interrelations among self-regulatory processes across different contexts using the CFA

approach applied in previous studies on the structure of the self-regulation construct

among preschoolers.

The Present Study

Processes of self-regulation that enable successful goal-directed action in

young children include working memory, inhibitory control, shifting, and delaying of

gratification (for an overview, see McClelland et al., 2010). Recent evidence suggests


that self-regulatory processes might be different from one context to another (Wiebe,

Espy, & Charak, 2008). Moreover, the context might affect the amount and type of

self-regulation displayed (Schunk, 2005). The dynamic skills theory also suggests that

skills are context specific, but they gradually extend to new contexts through close

collaboration and association with other skills (Fischer & Bidell, 2006). In line with

this theory, studies have documented a positive associations between self-regulation

and intelligence (e.g. Duncan et al., 2000, Bishop et al., 2008) and self-regulation

early academic skills (e.g., Cameron Ponitz et al., 2009; McClelland & Cameron,

2012). However, the dynamic skills theory considers that skills contribute to one

another, thus true integration of skills involves interaction between constructs and

interdependence. In contrast to the dynamic skill theory, most studies considered self-

regulation as a leading indicator and have ignored the possibility of intelligence or

early academic skills to be a leading indicator in this relationship. Moreover, most of

the work on the relationship between self-regulation and intelligence, and early

academic skills has been restricted to single points in time, which precludes a

developmental analysis of the growth of self-regulation, intelligence and early

academic skills. Given accumulating evidence for the importance of such skills,

specifying the developmental trajectories, in this context the direct and indirect effects

over time is of substantial importance for early intervention and educational

remediation efforts. Extending previous research on the immediate short-term

associations between self-regulation and intelligence, as well between self-regulation

and early academic skills among children and adults (Ardila, Pineda, & Rosselli,

2000, Duan et al., 2010; Friedman et al., 2006), the present study estimated cross-lag

effects to determine the causal flow among the constructs over the preschool period.

Therefore, the current study aims to (1) identify the structure of self-regulation and


interrelations among self-regulatory processes across different contexts and to (2)

examine the bidirectional associations between self-regulation and intelligence, as

well as between self-regulation and early academic skills.

To address the first research question on the structure of self-regulation and

interrelations among self-regulatory processes, a battery of performance-based

measures and informant ratings was administered. Measures were selected to provide

information about these self-regulatory processes in various contexts, including

performance-based self-regulation in a structured one-to-one context, teacher- rated

classroom self-regulation in the school context, and parent-rated self-regulation in the

home context. Using a CFA approach, we first examined the structure of self-

regulation across these multiple contexts. CFA has been proposed as the method of

choice for testing questions about the dimensionality of a construct (Willoughby et

al., 2013). Following CFA approaches from previous studies of young children

(Hughes et al., 2010; Wiebe et al., 2008, 2011), we compared the model fits of a

single-factor model (i.e., all measures in the battery loading on a single factor,

independent of the context in which self-regulation had been observed/reported) and a

multifactor model (i.e., measures grouped on the basis of the context in which self-

regulation had been observed/reported) to determine which model better represented

the observed data patterns. On the basis of previous summaries of research

(Duckworth & Kern, 2011; Toplak et al., 2013), we expected to find moderate

interrelations among self-regulatory processes across different contexts. We extended

prior research by including self-regulation across different contexts in a single model

and not in several independent models, as has been done in many previous studies.

Moreover, we expanded previous research on self-regulation, by investigating

the bidirectional relationship between self-regulation and intelligence, and self-


regulation and early academic skills. Although studies with adults suggest that

different components of self-regulation are related to measures of intelligence in

different ways (Friedman et al., 2006), to the best of our knowledge no such studies

existed to date for preschoolers. To fill this gap, another goal of the present study was

to investigate bidirectional relations between self-regulation components (inhibitory

control, working memory, attention shifting) and intelligence over the preschool

period. We aimed to identify evidence for a robust bidirectional model between self-

regulation and intelligence by hypothesizing that (1) the effect of prior self-regulation

on subsequent intelligence would be positive even after controlling for the effect of

prior intelligence and (2) the effect of prior intelligence on subsequent self-regulation

would be positive even after controlling for the effect of prior self-regulation. On the

basis of previous research with adults (Friedman et al., 2006), we expected the

relations to be the strongest for working memory. To extend previous work on the

relationship between self-regulation and intelligence, we used multiple intelligence

measures (Raven’s Progressive Matrices Test and Wechsler Preschool and Primary

Scale of Intelligence) at the last wave of data collection to address the question of

how each component of self-regulation is related to specific aspects of intelligence,

such as verbal comprehension, visual spatial processing, fluid reasoning, and

processing speed, and whether the associations differ for various aspects of

intelligence. Based on the previous findings (e.g. Arffa, 2007; Friedman et al., 2006;

Salthouse, 2005) we expected to find the strongest relationship between working

memory and fluid reasoning, respectively processing speed.

Finally, we examined bidirectional associations between self-regulation and

academic skills. Based on prior studies (Bohlmann et al., 2015; Fuhs et al., 2014), we

predicted that in addition to concurrent associations, there would be bidirectional


associations between self-regulation and academic skills across time. As such, we

hypothesized that (1) the effect of prior self-regulation on subsequent academic skills

(math and vocabulary) would be positive even after controlling for the effect of prior

academic skills and (2) the effect of prior academic skills on subsequent self-

regulation would be positive even after controlling for the effect of prior self-

regulation. Based on the previous studies, we expected to see the strongest

relationship between self-regulation skills and math.

We chose to examine these research questions in preschool-age children

because rapid growth in self-regulation capacities has been documented for this age

period (Best & Miller, 2010; Center on the Developing Child at Harvard University,

2011). Moreover, we addressed the research questions in a population that is

underrepresented in research, thereby contributing to the literature on the cross-

cultural applicability of the different measures of self-regulation in young children.

Method

The present study was part of the larger project “Self-Regulation Development

in Preschool and the Transition to Elementary School” (Principal Investigator: Dr.

Antje von Suchodoletz). This project was part of the research program of the research

group “The Empirics of Education – Behavioral and Economic Perspectives” in the

context of the German Excellence Initiative at the University of Freiburg, Germany.

The primary objective of the project was to investigate developmental trajectories of

self-regulation skills in children and their contribution to early academic skills in

preschool and elementary school. The project was conceptualized as a cross-

sequential study with several waves of data collection between fall-winter of 2009-

2010 and summer of 2013. An important part of this project was the investigation of

the research question in a cross-cultural design. Thus, the author of the present

dissertation conducted a longitudinal study with preschool children in Kosovo, while

taking into account the general conditions of the project.

The reported study includes three waves of data collection in Kosovo between

Spring 2012 and Spring 2013. At all three measurement points, direct assessments of

self-regulation and emerging academic skills were administered to the participating

children in one-on-one sessions. Moreover, in each wave of data collection, parents

and teachers were asked to provide additional information by completing reports on

children’s self-regulation.

Participants

Participants of the present study were recruited from preschool institutions in

three urban centers in Kosovo (Prishtina, Mitrovica and Vushtrri). The Ministry of

Education in Kosovo granted permission for conducting the study and data collection

during regular kindergarten days. Initially, principals of 43 early childhood education

Associations between Self-Regulation, Intelligence and Academic Skills 42

centers in the northeastern part of Kosovo that were licensed by the country’s

Ministry of Education were contacted and informed about the study. The information

letter contained a short explanation of the overall project, the specific study goals,

policies and practices followed to protect each child’s data, the procedure of data

collection and description of the child, parent and teacher responsibilities in the

project. In total, 20 institutions (46.5%) agreed to participate in the study. The main

reasons institutions cited for not participating in the study were a lack of available

space for the research (15 institutions) and daily schedules that were too inflexible to

allow the children to be taken out of the classroom for the assessment (8 institutions).

In general, early childhood education institutions in Kosovo have one single class for

4-5 year old children, with approximately 15–25 children per classroom. To recruit

the sample of children, the research team made an announcement in these classes and

sent an information letter home to parents. In total, 380 parents were contacted, and

39.4% (n = 150) of them gave written, informed consent for their child’s participation

prior to data collection. Reasons for the low response rate might be related to

stereotypes and prejudices regarding research in general and research with young

children in particular. This was the first study of this kind to be conducted in Kosovo

since the armed conflict in the late 1990s. The sample consisted of typically

developing preschoolers, and none of the parents indicated that their child had an

Individualized Education Plan. As such, all 150 children, whose parents signed the

consent letter, participated in the study.

At the first measurement point, 150 children (50.7% girls) between 4 and 5

years of age participated (Mage = 54 months, SD = 3.7). At time 2, children (N = 145)

were on average 61 months old (SD = 4.1), and at time 3, children (N = 105) were on

average 68 months old (SD = 4.8) (Table 1). To address the high drop out between the


second and third wave of data collection, we conducted t-tests to compare those who

completed all assessments to those with missing data. There were significant

differences between the two groups regarding scores children obtained on KABC

memory subtest at time 3, t(103)= -2.30, p < .05. In addition to that, there were no

significant differences in other self-regulation, intelligence, and early academic skills

assessments (Table 1).

Table 1. D

escriptive Statistics for Participants who C

ompleted all Assessm

ents to Those with M

issing Data

Measure

Total

Group

M

ean differences

W

ith all data

With m

issing data

M

SD

n

M

SD

n

M

SD

n

95% C

I t

df

Self-regulation measures

HTK

S (t1) 130

16.64 130

15.65

12.23 82

18.17

12.18 53

-6.78, 1.73 -1.17

133

HTK

S (t2) 147

26.29 147

26.47

11.82 92

26.00

12.92 55

-3.65, 4.59 .22

145

HTK

S (t3) 102

30.90 102

32.13

9.00 47

29.85

11.62 55

-1.86, 6.41 1.09

100

Pencil Tap (t1) 147

10.46 147

10.43

4.76 92

10.50

5.52 56

-1.76, 1.63 -.08

146

Pencil Tap (t2) 147

13.47 147

13.14

3.45 91

14.00

2.85 56

-1.94, .23 -1.57

145

Pencil Tap (t3) 106

14.97 106

14.94

1.65 50

15.00

1.62 56

-.69, .57 -.19

104

KA

BC

mem

ory (t1) 8.17

2.31 149

8.07

2.28 91

8.32

2.36 59

-1.02, .51 -.663

148

KA

BC

mem

ory (t2) 9.17

2.55 144

8.85

2.57 85

9.64

2.48 59

-1.64, .05 -1.86

142

(continued)

Associations betw

een Self-Regulation, Intelligence and Academic Skills 45

Measure

Total

Group

M

ean Difference

W

ith all data

With m

issing data

M

SD

n

M

SD

n

M

SD

n

95% C

I t

df

KA

BC

mem

ory (t3) 9.68

2.39 105

9.07

2.59 46

10.15

2.14 59

-2.00, -.17 -2.30*

103

DC

CS (t1)

12.64 4.76

135

12.03 5.02

76

13.42 4.31

60 -3.00, .22

-1.71 134

DC

CS (t2)

20.21 3.40

148

20.82 3.25

89

19.29 3.45

59 .43, 2.64

2.74 146

DC

CS (t3)

22.19 2.68

104

22.32 2.12

44

22.10 3.05

60 -.84, 1.28

.41 102

Academ

ic skills

KA

BC

math (t1)

9.32 4.07

145

9.24 4.01

89

9.45 4.22

56 -1.59, 1.17

-.30 143

KA

BC

math (t2)

13.54 4.63

145

13.24 4.74

89

14.02 4.45

56 -2.34, .78

-.99 143

KA

BC

math (t3)

16.99 4.46

105

16.78 5.06

50

17.18 3.87

55 -2.14, 1.33

-.46 103

PPVT (t1)

31.82 16.63

150

81.91 13.79

101

84.10 11.89

49 -6.73, 2.35

-.953 148

PPVT (t2)

56.78 17.81

116

80.31 17.40

67

82.12 18.47

49 -8.46, 4.84

-.539 114

(continued)

Associations betw


Measure

Total

Group

M

ean Differneces

With all data

W

ith missing data

M

SD

n

M

SD

n

M

SD

n 95%

CI

t df

PPVT (t3)

69.52 22.03

104

83.64 15.26

55

83.96 16.45

49 -6.49, 5.84

-.104 102

Intelligence

CPM

(t1) 31.67

30.48 136

28.99

31.20 91

37.09

28.53 45

-19.04, 2.83 -1.46

134

CPM

(t2) 53.05

28.78 146

51.16

29.27 101

57.29

27.51 45

-16.31, 4.05 -1.19

144

CPM

(t3) 57.71

28.59 96

53.88

27.90 51

62.04

29.04 45

-19.72, 3.38 -1.40

94

HTK

S = Head-Toes-K

nees-Shoulders task. KA

BC

– Kaufm

an Assessm

ent Battery for C

hildren. DC

CS = D

imensional C

hange Card Sort. PPV

T

– The Peabody Picture Vocabulary Test. C

PM = C

oloured Progressive Matrices.

* p < .05.

Parents completed a demographic questionnaire, indicating information on the

nationality, level of education, employment status, and family income (Table 2). All

children were Albanian, and all parents were of Albanian nationality. The majority of

them (93.8%) were Muslims, 4% Catholics and 2.2% were declared atheists. The

average time that children spend in education centers was 7.2 hours, with many

children (79%) staying 7 or more hours per day in early childhood education centers.

Parent’s average age was 34.6 years (SD=4.9) at the first time point.

All parents were married. When asked about the number of children, the majority of

them (80.4%) reported to have two or more children, while only 19.6% had one child.

Regarding their education status, half of the parents (56%) held a university degree,

14.1% of them had finished their postgraduate studies, and 26.8% had a high school

diploma, while 3.1% reported elementary school as their highest level of education

level. As expected, most parents of the participating children were employed (88%),

compared to the general population in Kosovo, that reported an unemployment rate of

35-39% among the general population in Kosovo (Department of Work and

Employment in Government of Kosovo, 2011). More than half of parents (66%)

reported a monthly family income of less than 800 Euros. Compared to the general

population income, this is relatively high, as the poverty line per inhabitant is €1.72

per day (Kosovo Agency of Statistics, 2011).

Associations betw


Table 2. D

escriptive Statistics for Parent’s Background Variables

Variable

N

%

M

SD

Min

Max

% M

issing

Children’s age in month (t1)

150

54 3.7

48 60

0.00


145

61 4.1

55 65

3.33


105

68 4.8

65 72

30.00

Parent’s Age (t1)

97

34.59 2.81

26 46

35.33

Parent’s education (t1) 97

35.33

Elementary school

3.1

High School

26.8

Bachelor or Postgraduate

70.1

Parent’s Occupation (t1)

97

35.33

Employed

88.7

Not Em

ployed

11.3

Family m

onthly income (t1)

97

626.36 866.73

65.00 5000.00

35.33

(continued)

Associations betw


Variable

N

%

M

SD

Min

Max

% M

issing

<400 euro

28.9

400 – 800

37.1

>800 euro

34.0

Note. N

= Total num

ber of participants.


Procedure

The present study included three waves of data collection with approximately

6 months between the measurement points (time 1: spring preschool 2012; time 2: fall

kindergarten 2012; time 3: spring kindergarten 2013). At each time point, direct self-

regulation measures and informant ratings of children’s self-regulation were

administered. In addition, children completed intelligence tests and early academic

skill measures. The self-regulation battery consisted of five tasks: Pencil Tap, Head-

Toes-Knees-Shoulders (HTKS), Dimensional Change Card Sort (DCCS), Kaufman

Assessment Battery for Children number recall (KABC) and Watch and Wait task.

Academic skills were measured with the Peabody Picture and Vocabulary Test and

the KABC math subtest. Intelligence was assessed using the Raven’s Progressive

Matrices Test. At each time point, the tasks were administered in a fixed order. Self-

regulation measures were administered first, followed by the academic skills

measures, and lastly the intelligence test. To address the research question on the

relation between self-regulation components and specific aspects of intelligence, the

complete Wechsler Preschool and Primary Scale of Intelligence (WPPSI-IV) was

given at Time 3. Due to time and resource restrictions, the WPPSI-IV could not be

completed with all children in the sample. We therefore randomly selected half of the

children listed in the dataset and administered the WPPSI-IV with 57 children. A t-

test indicated no significant differences between children who completed the WPPSI

and the ones who did not regarding any of the variables of interest. Also, to address

the research question on the longitudinal associations between (1) self-regulation

skills and intelligence, as well as the longitudinal association between (2) self-

regulation skills and early academic skills, only selected direct measures of self-

regulation (i.e. Head Toes KS, Pencil Tap, DCCS and KABC memory), intelligence


(i.e. CPM) and early academic skills (i.e. PPVT and KABC math) were used in the

longitudinal analysis.

On average, completion of the tasks took approximately 1 hour and 15

minutes per session (including practice trials and breaks). The WPPSI that was

administered in the third wave of data collection took approximately 2 hours and was

administered on another day of the same week. Every child was rewarded with a

small toy for participating.

In order to avoid cultural bias, all measures used in the study, including

instructions, were translated into the Albanian language, using the procedure of

translation and back-translation suggested for cross-cultural research (Brislin, 1970).

Two parallel translators were involved in the independent translation of each measure.

After the first draft, translators and the author of this thesis compared two versions.

According to the procedure (Brislin, 1970), all translators reconciled discrepancies

and agreed on the final version, which tapped the best of the independent translations

or alternatively, appeared in the course of discussion. The final versions of the

translated measures where then piloted with children prior to data collection. These

data were not included in the sample to ensure that information and measures were

well adopted, were easy to follow, and clear for children.

All participants were tested individually during a normal school day in a quiet

area in their school. Regular empty classes, art spaces, or teacher’s offices were

adapted to the needs of the study and used to provide a comfortable space for children

to complete the assessments. When administering the measures, the assessor always

read the instructions to the child and tested child’s understanding of the task with

practice trials. During the test trials the scores were immediately recorded on the

score sheet. In addition to the direct assessments, the informant report measures were


used at each time-point. Assessors completed the Preschool Self-Regulation

Assessment–Assessor Rating in the testing environment, right after finishing test

battery. Parents and teachers completed the questionnaires indicating children’s self-

regulation skills mostly at home, and sent them back in closed envelopes.

During the process of data collection, the research team ensured data

protection and confidentiality. It was not possible to match the data with any

identifying information. Only the author of this dissertation and the PI of the project

had access to anonymous data collected. All data was assigned with the code

numbers. Consent forms, protocol sheets and sealed envelopes containing

demographic questionnaires will be stored in different locked cabinets for a minimum

duration of 5 years.

A group of 10 trained students of psychology, all of them native speakers of

Albanian, administered the tasks. All students had finished a mandatory course on

developmental psychology prior to data collection. In addition to that, the author of

the present dissertation and the principal investigator provided a 3-day intensive

workshop to train the students in administration and scoring of each measurement.

Their testing skills were continuously evaluated during the training and feedback was

provided to enhance the quality of instruction and standardization. Before collecting

the data, each student administered the test battery with 5 randomly selected children

for the pilot sample to ensure standardized administration. A multivariate analysis of

variance indicated no significant differences between the assessors on any of the

study’s variables of interest.

Measures

Direct measures of self-regulation. The Head–Toes–Knees–Shoulders Task

(Cameron Ponitz et al., 2009) was used to measure children’s behavioral regulation.


In this 10-minute task, the child is instructed to do the opposite of what assessor says:

“When I say to touch your head, instead of touching your head, I want you to touch

your toes. When I say to touch your toes, I want you to touch your head, so you’re not

doing the same thing that I say to do’’. The assessor initially models the correct

response and provides feedback practice trials. However, no feedback is given during

the testing trials. When children responded incorrectly during the practice trials, they

were reminded of the instructions. In total, the HTKS consists of two parts with 10

items each. In the first part, two commands are given, “Touch you head” and “Touch

your toes”. The second part introduces two additional paired commands, “Touch your

knees” and “Touch your shoulders”. Items are scored with 0 for incorrect responses, 1

for self-corrected responses (initially responding incorrectly, but then correcting

him/herself), and 2 for correct responses. Scores range between 0 and 40 with higher

scores indicating higher levels of self-regulation. Internal consistency was high in the

present data (Cronbach’s alpha t1 = .88; Cronbach’s alphat2 = .86; Cronbach’s alphat3

= .85). In total, 20% of children (n = 30) were double coded. Interrater agreement

measured by Cohen coefficient was good, ĸ = .78.

The Pencil Tapping Task (Blair & Razza, 2007; Smith-Donald et al., 2007)

was used to measure children’s inhibitory control. In this task, the child is instructed

to tap with a pencil once when the assessor taps twice and to tap twice when the

assessor taps once. After a series of practice trials, in which the assessor provides

feedback to the child, 16 test trials were administered in counterbalanced sequence

and no feedback was provided to the child. Each correct response was coded with 1

point. Children received a score of 0, when they tapped multiple times regardless of

what assessor did or when they imitated the assessor, rather than doing the opposite.

The total score represents the total number of correct responses. Higher scores


indicate higher levels of self-regulation. Internal consistency was high in the present

study (Cronbach’s alphat1 = .91; Cronbach’s alphat2 = .86; Cronbach’s alphat3 = .67).

The Number Recall subtest of the Kaufman Assessment Battery for Children,

Second Edition (Kaufman & Kaufman, 2004), was used as a standardized measure of

working memory, which is suitable for use with preschool children. In this task,

children are instructed to repeat sets of numbers that increase in length (from two

numbers [e.g., 10-5] to five numbers [e.g., 1-5-2-9-4-3]). The subtest consists of five

blocks with three items each (in total, 15 items). Children get a score of 1 only when

they are able to repeat all numbers. According to the manual, testing should stop

when the child responds incorrectly to all items of one block. Cronbach’s alpha in the

present study was .70, .61 for Time2 and .62 for Time3.

The Dimensional Change Card Sort (Frye, Zelazo, & Palfai, 1995;

Hongwanishkul et al., 2005) is a widely used measure for assessing preschool

children’s attention shifting. In the task, children are required to sort a series of

trivalent test cards (varying in shape, color, and size) into boxes with three target

cards (big blue dog, small red fish, medium yellow bird) and one box with a distractor

card (big green frog). Following practice trials, children were first instructed to sort

the cards by shape. Once they had completed six items of each part, the children were

told to stop and switch to the next part. No feedback was provided during the test

trials. In the second part, they were asked to sort the cards according to color and in

the third part, they were instructed to sort the cards by size. Each correct response was

scored with 1 point. If the child received 5 points or more for the first three parts, a

fourth part consisting of another six items was administered. In this part, some cards

had black borders. The children were instructed to sort cards with a black border by

size and those without a black border by color. The total score (0–24 points) indicates


the total number of correct responses in all four parts. Higher scores reflected higher

levels of self-regulation. Cronbach’s alpha in the present data was .76 for Time1, .70

for Time2 and .75 for Time3.

The Watch-and-Wait Task (Neubauer et al., 2012) is a newly developed delay

of gratification task that adapts the procedure of the waiting task (Mischel, 1974).

First, children were asked to choose two toys from a selection of different toys. They

are told that they can have one toy immediately or both toys if they watch an

hourglass for 15 min without looking away or talking. The children are informed that

they will receive cards if they break the rules. A yellow card signals that they can

continue with the task, and a red card means that the task is over and that they will

receive only one toy. The first and second times children look away from the

hourglass or talk, they receive a yellow card; the third time they receive a red card. If

the child succeeds in watching the hourglass for 15 min (900 s), he or she gets both

toys. For the analyses, we followed previous research (e.g., Neubauer et al., 2012) and

used the time in seconds that elapsed before the child received the first yellow card. A

score of 900 s indicates that the child finished the task without breaking the rules, thus

reflecting high levels of self-regulation.

Informant ratings of children’s self-regulation. Teachers rated the

children’s self- regulation in the classroom by completing the Child Behavior Rating

Scale (Bronson, Tivnan, & Seppanen, 1995). The original version of the Child

Behavior Rating Scale consists of 32 items. In the present study, 17 items were used

to measure children’s self-regulation in academic tasks (10 items; e.g., “observes

rules and follows directions without requiring repeated reminders”) and social

situations (7 items: e.g., “takes turns in a game situation with toys, materials, and

other things without being told to do so”). Each item was rated on a 5-point Likert


scale (1 = never to 5 = always). Higher scores indicate higher levels of self-regulation

in the classroom. Both scales demonstrated good internal consistency in the present

study (classroom self-regulation in academic tasks: Cronbach’s α = .92, classroom

self-regulation in social situations: Cronbach’s α = .76).

The Children’s Behavior Questionnaire (short version; Putnam & Rothbart,

2006) and the Emotion Regulation Checklist (Shields & Cicchetti, 1997) were used to

assess parents’ ratings of their children’s self-regulation at home. The Children’s

Behavior Questionnaire short version consists of 36 “My child ... ” statements, with

12 items measuring children’s effortful control (e. g., “when drawing or coloring in a

book, shows strong concentration”). Parents are asked to indicate on a 7-point Likert

scale whether the statement about their child is not true (1) or it is true (7). Higher

scores reflect higher levels of parent-rated child self-regulation. Internal consistency

was good in the present study (Cronbach’s α = .76). Children’s self-regulation of their

emotions was measured with 15 items from the Emotion Regulation Checklist (e.g.,

“My child exhibits wide mood swings”). Each item was rated on a 4-point Likert

scale (1 = never to 4 = almost always). For purposes of clarity, the scale was recoded

so that higher numbers reflect higher levels of parent-rated child self-regulation of

emotions. Cronbach’s alpha (.77) indicated good internal consistency.

The Preschool Self-Regulation Assessment–Assessor Rating (Smith-Donald et

al., 2007) was used to rate children’s self-regulation during the tasks of the direct

assessment battery. This measure consists of two subscales: Attention/Impulse

Control (18 items; e.g., “careful, interested in accuracy; not careless”) and Emotion

Regulation (8 items; e.g., “shows pleasure in accomplishment and active task

mastery”). Each item is coded using a 4-point Likert scale, with higher scores

reflecting higher levels of self-regulation. Internal consistency was high in the present


study (impulse control: Cronbach’s α = .92, emotion regulation: Cronbach’s α = .82).

Intelligence. The children’s intelligence was assessed with the Coloured

Progressive Matrices (CPM; Raven, Court, & Raven, 1984). The CPM consists of 36

colored items, which are divided equally into three sets (A, AB, B). In each item

subjects are presented with an incomplete design and six alternatives to chose from

that best completed the design. The items increase in difficulty. Children were not

allowed to use paper to work out any of the problems. No feedback was provided and

each child was asked to answer each question before moving on to the next item.

Thus, children completed all three sets with 36 items. No time limits are imposed at

any point. Each correct response was coded with 1 point and a wrong response is

given zero. Sum scores (number of correct responses, out of 36) were transformed

into age-standardized T-values. In the current data the internal consistency was

acceptable, (Cronbach’s alphat1 = .72; Cronbach’s alphat2 = .76; Cronbach’s alphat3 =

.80).

The Wechsler Preschool and Primary Scale of Intelligence (WPPSI-IV) is an

intelligence test designed for use with children between 2.6 and 7.7 years. The

WPPSI-IV is a psychometrically strong instrument with extensive evidence provided

to support both the reliability of scores and the validity of a wide range of score-based

inferences (Wechsler, 2012). WPPSI-IV implies several game-like activities and

contains stimuli that are visually engaging for children. The scores derived at the

Primary Index Scale level are most commonly used for a comprehensive description

and evaluation of a child’s cognitive abilities (Wechsler, 2012). As such, five primary

index scores describe children’s cognitive abilities: (a) the Verbal Comprehension

Index (VC); (b) the Visual Spatial Index (VS); (c) the Fluid Reasoning Index (FR);

(d) the Working Memory Index (WM); and (e) the Processing Speed Index (PS).


Reliability coefficients of WPPSI-IV ranged from .85 to .93 for the subscales and

from .86 to .94 for the composite scores (Wechsler, 2012). In the present data, internal

consistency reliability of WPPSI-IV ranged from .65 to .95 for the Primary Index

Scales, while all alpha coefficients for subscales were good ranging between .62 and

.90, with the exception of Object Assembly (α =.52).

Vocabulary Skills Measures. The Peabody Picture Vocabulary Test (PPVT;

Dunn & Dunn, 2007) was used to assess children’s vocabulary. The PPVT contains

228 test items. Children are asked to point to the correct picture out of 4 options

presented after the assessor says its name (e.g., pen, ball, doll). The total score

represents the total number of correct answers with higher scores reflecting higher

vocabulary. Internal consistency was good (Cronbach’s alphat1 = .85).

Math Skills Measures. The Math subtest of the Kaufman Assessment Battery

for Children, Second Edition (Kaufman & Kaufman, 2004) was used to assess

children’s ability to solve arithmetic problems, such as counting and comparing

quantities. The task consists of 25 items (for example, “There are six elephants in the

zoo. If four of them left how many elephants would remain?”). The total score

represents the total number of correct answers with higher scores reflecting higher

math skills. Internal consistency was acceptable (Cronbach’s alphat1 = .70).

Associations betw


Table 3. M

easures Used in the Study to Assess Self-Regulation, Intelligence and Early Academ

ic Skills

Variables

Measure

Direct m

easures of self regulation The H

ead–Toes–Knees–Shoulders Task (C

ameron Ponitz et al., 2009)

Pencil Tapping Task (B

lair & R

azza, 2007; Smith-D

onald et al., 2007)

K

aufman A

ssessment B

attery for Children, Second Edition (K

aufman &

Kaufm

an, 2004)

D

imensional C

hange Card Sort (Frye, Zelazo, &

Palfai, 1995; Hongw

anishkul et al., 2005)

W

atch-and-Wait Task (N

eubauer et al., 2012)

Informant ratings of children’s

self-regulation

Child B

ehavior Rating Scale (B

ronson, Tivnan, & Seppanen, 1995)

Children’s Behavior Questionnaire (short version; Putnam

& Rothbart, 2006)

Preschool Self-Regulation A

ssessment–A

ssessor Rating (Sm

ith-Donald et al., 2007)

Intelligence C

oloured Progressive Matrices (R

aven, Court, &

Raven, 1984)

Wechsler Preschool and Prim

ary Scale of Intelligence (Wechsler et al., 2012)

Vocabulary Skills

Peabody Picture Vocabulary Test (D

unn & D

unn, 2007)

Math Skills

Kaufm

an Assessm

ent Battery for C

hildren, Second Edition (Kaufm

an & K

aufman, 2004)


Analytic Approach

We began our analyses by examining descriptive statistics using the Statistical

Package for the Social Sciences (SPSS, 21). The amount of missing data was

considerable, especially for some of the indicators gathered by the informants. The

propensity of missingness was modeled via logistic regression, and no systematic

patterns were seen in the data. Thus, the data were considered to be missing

completely at random. Consequently, we handled the missing data using the full

information maximum likelihood method in Mplus (Muthen & Muthen, 1998–2010).

The advantage of FIML is the flexibility to handle missing data when data from at

least one measurement point is available which was true for all children in our sample

(Acock, 2012). All statistical models performed in Mplus were evaluated using the

maximum-likelihood ratio-test statistic and indices of model fit, i.e., root mean square

error of approximation (RMSEA), comparative fit index (CFI), the Tucker Lewis

Index (TLI) and standardized root-mean-square residual (SRMR). RMSEA values

less than .08 indicate an acceptable fit to the data. For SRMR, we used a cutoff value

of <.08 (Hu & Bentler, 1999; Kelloway, 1998). In the case of CFI and TLI, we took

values >.90 to indicate an acceptable model fit (Hu & Bentler, 1999; O’Boyle &

Williams, 2011). The data were examined by means of a variety of diagnostic

measures (residuals plots, histograms of residuals, and scatterplots), and assumptions

were met for both normality and linearity.

First, the structure of self-regulation across multiple contexts was examined

using CFAs. Models derived from previous research were compared (i.e., single-

factor vs. multifactor model). The best fitting model was selected on the basis of fit

statistics and chi-square test indices. Multilevel modeling was used to control for the

clustering of the data (i.e., children nested in classrooms).


To address the research question on the longitudinal relationship between self-

regulation and intelligence, respectively early academic skills, autoregressive cross-

lag path models (ACL; Jöreskog & Sorbom, 1979) were estimated in Mplus. Four

separate autoregressive cross-lag models were estimated to determine which model

best represented (a) the associations between self-regulation components and

intelligence and (b) the associations between self-regulation and emerging academic

skills: no coupling (i.e. no cross-lag associations), unidirectional coupling (self-

regulation predicting intelligence/emerging academic skills), unidirectional coupling

(intelligence/emerging academic skills predicting self-regulation), and bidirectional

coupling (i.e. full coupling).

In the first ACL model (no coupling), repeated measured variables (e.g. self-

regulationt1, self-regulationt2 and self-regulationt3) were connected through auto

regressions. The strength of the autoregressive effects as well as the stability of the

individual differences across time was indicated by the size and significance of the

autoregressive coefficients (Geiser, 2013). Concurrent self-regulation and

intelligence/early academic skills were allowed to correlate at each of the three time

points. The coefficient of correlation was used to estimate the size and significance of

associations (Figure 1a). In two subsequent ACL models (unidirectional coupling), in

addition to autoregressive and correlational paths, regression paths were added (model

2) from intelligence/early academic skill to self-regulation (Figure 1b) or from self-

regulation to intelligence/early academic skills (Figure 1c). In the fourth model (full

coupling), autoregressive path, correlational path and bidirectional cross-lag paths

were estimated (Figure 1d). The cross-lagged effects of the ACL allowed determining

the effects of prior self-regulation on the subsequent intelligence/early academic skills

and vice versa. Standardized regression coefficients were used as measures of the


effect size with β < .10 indicating a small effect, a β of around .30 a medium-sized

effect and β >.50 indicating a large effect (Kline, 2005). Within the model no

constraints or covariates were added.

Associations betw


a)

b)

c)

d)

Figure 1. Planned cross-lagged models representing the no-coupling m

odel, two different unidirectional coupling m

odels and the bidirectional

coupling mode


In order to test how each component of self-regulation is related to specific

aspects of intelligence, a structural equation model was performed. In the model, we

allowed each component of self-regulation to be related to five Primary Index Scales

measured by WPSSI at Time 3, with a subsample of 57 children. Also, each aspect of

self-regulation was independently related to the intelligence as measured by Raven’s

Coloured Progresive Matrices (see Figure 2).

Figure 2. Planned structural equation modeling with self-regulation components

(inhibitory control, working memory, attention shifting) predicting aspects of

intelligence (verbal comprehension, visual spatial, fluid reasoning, working memory,

processing speed).


Results

The present study had two goals: (1) to investigate the structure of self-

regulation in young children across multiple important developmental contexts in

early childhood and (2) to test the bidirectional associations between self-regulation

and intelligence, respectively early academic skills, over the preschool period.

Structure and interrelations among self-regulatory processes

The first research question examined the structure and interrelations among

self-regulatory processes across different contexts. The structure of self-regulation

across multiple contexts was investigated using a confirmatory factor analysis

approach. Following CFA approaches from previous studies of young children

(Hughes et al., 2010; Wiebe et al., 2008, 2011), we compared the model fits of a

single-factor model (i.e., all measures in the battery loading on a single factor,

independent of the context in which self-regulation had been observed/reported) and a

multifactor model (i.e., measures grouped on the basis of the context in which self-

regulation had been observed/reported) to determine which model better represented

the observed data patterns.

Factor Solutions: Self-Regulation Across Different Contexts

First, a unitary model with all self-regulation measures across the different

contexts loading on a single factor was tested. The single-factor model did not fit the

data well, as indicated by the fit statistics (RMSEA=.15, CFI=.50, SRMR=.12). As

can be seen in Table 4, factor loadings were statistically significant, with the

exception of parent-rated self-regulation at home.


Table 4. Factor Loadings of Self-Regulation Measures: One-Factor Model

Indicator Standardized factor loading SE

HTKSa 0.66** 0.06

Pencil tap 0.67** 0.08

DCCSb 0.50** 0.08

Number recall 0.51** 0.10

Watch and wait task 0.29** 0.06

Emotion regulation during assessment 0.42** 0.13

Impulse control during assessment 0.57** 0.10

Self-regulation in academic tasks 0.40* 0.18

Self-regulation in social situations 0.38* 0.14

Emotion regulation at home -0.03 0.15

Effortful control at home 0.05 0.16

Note. a Head-Toes-Knees-Shoulders task. b Dimensional Change Card Sort.

** p < .01. * p < .05.

Next, we tested a multifactor model with measures grouped on the basis of the

context in which self-regulation had been observed. As the factors were all estimated

simultaneously, it was possible to estimate factors with only two indicators as well as

the correlations between the factors. The model showed good fit (RMSEA=.06,

CFI=.94, SRMR=.07), and all factor loadings were statistically significant (Table 5).

A comparison of the Bayesian information criteria indicated that the fit of the

multifactor model was significantly better than that of the simple one-factor model.


Table 5. Factor Loadings of Self-Regulation Measures: Multi-Factor Model

Indicator Standardized factor loading SE

Performance-based self-regulation

HTKSa 0.71** 0.06

Pencil tap 0.64** 0.09

DCCSb 0.56** 0.07

Number recall 0.52** 0.07

Watch and wait task 0.29** 0.06

Assessor-rated self-regulation during assessment

Emotion regulation 0.62** 0.16

Impulse control 1.00** 0.19

Teacher-rated self-regulation in the classroom

Self-regulation in academic tasks 0.65** 0.14

Self-regulation in social situations 1.03** 0.17

Parent-rated self-regulation at home

Emotion regulation 0.64** 0.16

Effortful control 0.47** 0.15

Note. a Head-Toes-Knees-Shoulders task. b Dimensional Change Card Sort.

** p < .01.

Descriptive Statistics and Correlations

Descriptive statistics of the different direct measures of self-regulation,

including mean and standard deviation, are provided in Table 6. Bivariate correlation

coefficients (Table 9) showed that the performance-based self-regulation was

positively correlated with the assessor-rated self-regulation during the assessment and

classroom self-regulation but not with self-regulation at home. Classroom self-


regulation was also related to self-regulation at home but not to the assessor-rated

self-regulation during the assessment. Assessor-rated self-regulation during the

assessment and self-regulation at home were not related. The results were supportive

of our hypothesis confirming only moderate associations between self-regulatory

processes across different contexts

Associations between Self-Regulation, Intelligence and Academ

ic Skills 69

Table 6. D

escriptive Statistics of Direct M

easures of Self-Regulation (raw data)

Variable

N

Mean

SD

Min

Max

% M

issing

HTK

Sa (behavioral regulation) – T1

130 16.64

12.22 0.00

40.00 11.34

HTK


147 26.29

12.19 0.00

40.00 2.00

HTK


102 30.90

10.51 0.00

40.00 32.00

Pencil tap (inhibitory control) – T1 147

10.46 5.06

0.00 16.00

2.00


13.47 3.51

2.00 16.00

2.00


14.97 1.62

7.00 16.00

29.44

DC

CS

b (attention shifting) – T1

135 12.64

4.76 0.00

23.00 9.40

DC

CS b (attention shifting) – T2

148 20.21

3.40 9.00

24.00 1.44

DC

CS

b (attention shifting) – T3 104

22.19 2.68

11.00 24.00

30.77

Num

ber recall (working m

emory) - T1

149 8.17

2.31 3.00

14.00 0.77

Num


emory) - T2

144 9.17

2.55 3.00

15.00 4.00

Num


emory) - T3

105 9.68

2.39 5.00

15.00 30.00

(continued)


ic Skills 70

Variable

N

Mean

SD

Min

Max

% M

issing

Watch and w

ait taskc – T1

143 165.69

177.44 15.00

900.00 4.02

Assessor-rated self-regulation during assessm

ent

Emotion regulation

97 16.82

4.20 6.00

24.00 34.91

Inhibitory control 95

40.02 9.52

12.00 52.00

36.22

Teacher-rated self-regulation in the classroom

Classroom

self-regulation in academic tasks

106 3.81

0.69 1.90

4.90 28.60

Classroom

self-regulation in social situations 106

3.77 0.63

1.86 4.86

28.61

Parent-rated self-regulation at home

Emotion regulation

109 2.19

0.41 1.40

3.27 26.82

Effortful control 108

5.90 0.67

3.42 7.50

27.53

Note. N

= Total num

ber of participants. M =

Mean. SD

= Standard D

eviation


ic Skills 71

Table 7. C

orrelation Matrix Betw

een Self-Regulation Across Different C

ontexts and Academic O

utcomes

Variable

1 2

3 4

1. Performance-based self-regulation

--

2. Assessor-rated self-regulation during assessm

ent .60**

--

3. Teacher-rated self-regulation in the classroom

.34** .16

--

4. Parent-rated self-regulation at home

-.04 .69*

.12 --

Note. ** p < .01. * p < .05


Bidirectional Associations Between Self-Regulation, Intelligence, and Academic

Skills

The second research question investigated longitudinal associations between self-

regulation and intelligence, as well as self-regulation and emerging academic skills

over the preschool period using a series of autoregressive cross-lag path models.

Following previous research (Bohlmann et al., 2015), four autoregressive cross-

lagged regression models were estimated to determine which model best represented

(a) the associations between self-regulation and intelligence and (b) the associations

between self-regulation and emerging academic skills: no coupling (i.e. no cross-lag

associations), unidirectional coupling (i.e. self-regulation predicting

intelligence/emerging academic skills), unidirectional coupling (i.e.

intelligence/emerging academic skills predicting self-regulation), and bidirectional

coupling (i.e. full coupling). Additionally, a structural equation model explored how

different self-regulation components are related to specific aspects of intelligence.

Reciprocal Associations: Self-regulation and Intelligence

First, the bidirectional relations between self-regulation and intelligence were

tested. Descriptive statistics for the intelligence measure across three time points are

presented in Table 9.

Associations Between Self-Regulation, Intelligence and Academ

ic Skills 73

Table 8. D

escriptive Statistics for Intelligence (raw data)

Variable

N

Mean

SD

Min

Max

% M

issing

IQ – T1

136 31.67

30.48 1.00

99.00 9.33

IQ – T2

146 53.05

28.78 1.00

100.00 2.66

IQ – T3

96 57.71

28.59 1.00

100.00 36.00

Note. N

= Total num


Mean. SD

= Standard D

eviation


Model fit of the four different models (no coupling, two unidirectional

coupling and bidirectional coupling) for each aspect of self-regulation (i.e., inhibitory

control, working memory and attention shifting) and intelligence are presented in

Table 10. The full cross- lagged model showed the best model fit to the data for the

inhibitory control and intelligence (CFI = 0.99, RMSEA = 0.05, SRMR = 0.02), as

well as for working memory and intelligence (CFI = 0.98, RMSEA = 0.06, SRMR =

0.03). In contrast, for attention shifting all four models showed equally acceptable fit:

no coupling (CFI=.97, TLI =.95, RMSEA = .05, SRMR = .06), unidirectional

coupling with attention shifting predicting intelligence (CFI=.97, TLI =.93, RMSEA

= .06, SRMR = .05), unidirectional coupling with intelligence predicting attention

shifting (CFI=.97, TLI =.94, RMSEA = .06, SRMR = .05) and bidirectional coupling

(CFI=.97, TLI =.90, RMSEA = .07, SRMR = .04). Figures 3, 4 and 5 displays the

results for the three autoregressive cross lag models, broken down by self-regulation

components.


Table 9. Model Fit Results for the Full Sample: Intelligence and self-regulation

components: (a) Working Memory, (b) Inhibitory Control, (c) Attention Shifting

Model x2 (df) CFI/TLI RMSEA >CI@ SRMR

(a) Inhibitory control and intelligence

1. no coupling 40.20 (8)*** .80/.64 .14 >.10-.19@ .13

2. unidirectional (IC Æ IQ) 22.89 (6)*** .89/.75 .12 >.07-.17@ .10

3. unidirectional (IQ Æ IC) 23.81 (6)*** .89/.74 .12 >.07-.18@ .07

4. bidirectional coupling 6.24 (4) .99/.95 .05 >.00-.13@ .03

(b) Working memory and intelligence

1. no coupling 22.46 (8)** .92/.86 .10 >.05-.14@ .08

2. unidirectional (WM Æ IQ) 10.88 (6) .97/.94 .06 >.00-.12@ .07

3. unidirectional (IQ Æ WM) 18.93 (6)** .93/.83 .10 >.05-.16@ .06


(c) attention shifting and intelligence

1. no coupling 12.27 (8) .97/.95 .05 >.00-.11@ .06

2. unidirectional (IC Æ IQ) 10.55 (6) .97/.93 .06 >.00-.12@ .05

3. unidirectional (IQ Æ IC) 10.07 (6) .97/.94 .06 >.00-.12@ .05



Inhibitory control and intelligence. The full coupling bidirectional model

testing the bidirectional associations between inhibitory control and intelligence

showed that all autoregressive paths were significant, indicating that children’s skills

at the previous assessment were positively associated with their respective skills at the

subsequent assessment (Figure 3d). The bidirectional cross-lagged model (see Figure

3d) indicated that inhibitory control and intelligence are significantly correlated at the

first measurement point (r = .329, p < .01), but not at two other subsequent

measurement points. It was found that inhibitory control at Time 1 had a negative

association with intelligence at Time 2. In contrast, inhibitory control at Time 2 had a

positive direct influence on intelligence measured at Time 3 (β = - .24, SE = .08, p <

.01). On the other hand, the path from Time 1 intelligence to Time 2 inhibitory

control was nonsignificant. The relationship between intelligence (Time 2) and

inhibitory control (Time 3) turns significant in the subsequent occasions (β = .34, SE

= .07, p < .01).

Working memory and intelligence. The full coupling bidirectional model



subsequent assessment (Figure 4d). With respect to the research question concerning

bidirectional relationship between self-regulation components and intelligence, we

found that paths from IQ (Time 1 and Time 2) to working memory (Time 2 and Time

3) were not significant. Also, at Time 1 and Time 2, there are no significant

correlation between working memory and intelligence. However, the significant

correlation between intelligence and working memory were found on Time 3 (r =

.297, p < .01). Therefore, working memory at Time 1 had a negative direct influence

on intelligence at Time 2 (β = - .12, SE = .06, p < .05), while working memory at


Time 2 had a positive direct influence on intelligence at Time 3 (β = .18, SE = .06, p <

.01).

Attention shifting and intelligence. All four models for attention shifting and

intelligence fitted the data equally well. Consistent across all models, all

autoregressive paths were significant, indicating that children’s skills at the previous

assessment were positively associated with their respective skills at the subsequent

assessment (Figure 5d). The results of this autoregressive cross-lag, showed only

associations between attention shifting and intelligence at Time 1, but not at Time 2

and Time 3. Also, all the paths from attention shifting to intelligence and vice versa

were nonsignificant.


ic Skills 78

a)

b)

c)

d)

Figure 3. Four different models of inhibitory control and intelligence representing the (a) no-coupling m

odel, (b and c) two different

unidirectional coupling models and (d) the bidirectional coupling m

odel. Note. * p < .05. ** p < .01. Solid lines indicate significant paths.

Dashed lines indicate non-significant paths. Estim

ates provided are standardized coefficients.


ic Skills 79

a)

b)

c)

d)

Figure 4. Four different models of w

orking mem

ory and intelligence representing the (a) no-coupling model, (b and c) tw

o different






ic Skills 80

a)

b)

c)

d)

Figure 5. Four different models of attention shifting and intelligence representing the (a) no-coupling m







Associations between Self-Regulation Components and Different Aspects of

Intelligence

To identify the contribution of each component of self-regulation on the

subcomponents of intelligence, structural equation modeling was employed. In the

structural equation model, each aspect of self-regulation was specified as predictor of

aspects of intelligence, such as verbal comprehension, visual spatial, fluid reasoning,

working memory and processing speed, while controlling for the effects of

intelligence as measured by Raven’s Coloured Progressive Matrices. The structural

equation model predicting aspects of intelligence showed good model fit (CFI=0.98,

RMSEA=0.06, SRMR=0.07). The model itself is illustrated in Figure 6, and it reveals

which factors are significant predictors of self-regulation.

The results show that intelligence measured by CPM was positively correlated

with inhibitory control (r = .407, p < .01), attention shifting (r = .224, p < .05) and

working memory (r = .379, p < .01). Inhibitory control was significantly related to

fluid reasoning (.30) and marginally related to visual spatial (.35) and processing

speed (.21). Attention shifting was also significantly related to fluid reasoning (.36)

and marginally significant path was found for processing speed (.28). Both path

coefficients, relating working memory with verbal comprehension (.25) and

processing speed (.26) are only marginally significant. As can be seen from the figure

2, working memory measured by the WPPSI is not predicted by any of three aspects

of self-regulation.


Figure 6. Structural equation modeling with self-regulation components (inhibitory

control, working memory, attention shifting) predicting aspects of intelligence (verbal

comprehension, visual spatial, fluid reasoning, working memory, processing speed).

RMSEA = 0.066, CFI = 0.978, TLI = 0.855, SRMR = 0.067.

+p < .10, * p < .05, ** p < .01


Reciprocal Associations: Self-regulation and Early Academic Skills

Research question two also investigated the reciprocal associations between

self-regulation and early academic skills. The descriptive statistics for the early

academic skills (math and vocabulary) are presented in table 10.


ic Skills 84

Table 10. D

escriptive Statistics of Early Academic Skills (raw

data)

Variable

N

Mean

SD

Min

Max

% M

issing

Vocabulary – T1

150 31.82

16.63

0.00

Vocabulary – T2

116 56.78

17.81

22.66

Vocabulary – T3

104 69.52

22.03

30.66

Math – T1

145 9.32

4.07 1.00

20.00 3.33

Math – T2

145 13.54

4.63

3.33

Math – T3

105 16.99

4.46

30.00

Note. N

= Total num


Mean. SD

= Standard D

eviation.


Model fit of the four different models (no coupling, two unidirectional

coupling and bidirectional coupling) for a) self-regulation and vocabulary skills and

b) self-regulation and math skills are presented in Table 11. Results showed that the

full cross-lagged model provided the best model fit to the data on all model fit

indices: vocabulary (CFI = 0.95, TLI = 0.81, RMSEA = 0.08, SRMR = 0.05), and

math skills (CFI = 1.00, TLI = 1.05, RMSEA = 0.00, SRMR = 0.02). Moreover,

bidirectional coupling models were the most parsimonious models with the lowest

Chi-square compared to all other models.

Table 11. Model Fit Results for the Full Sample: Self-regulation and Early Academic

Skills: (a) Language Skills and (b) Math Skills

Model x2 (df) CFI/TLI RMSEA >CI@ SRMR

(a) Self-regulation and language skills

1. no coupling 22.09 (8)** .85/.74 .09 >.05-.14@ .11

2. unidirectional (Lang Æ SR) 15.16 (6)* .90/.77 .09 >.03-.14@ .08

3. unidirectional (SRÆ Lang) 16.11 (6)** .89/.75 .09 >.04-.15@ .08


(b) Self-regulation and math skills

1. no coupling 20.67 (8)** .91/.85 .09 >.04-.14@ .10

2. unidirectional (Math Æ SR) 5.78 (6) 1.00/.1.00 .00 >.00-.09@ .04

3. unidirectional (SR Æ Math) 19.38 (6)** .91/.79 .11 >.05-.16@ .10

4. bidirectional coupling 1.98 (4) 1.00/1.04 .00 >.00-.08@ .01

Note. *** p <. 001, ** p <. 01, * p <. 05


Self-regulation and vocabulary skills. The full coupling bidirectional model

(Figure 7d) showed that all autoregressive paths were significant, indicating that

children’s skills at the previous assessment were positively associated with their

respective skills at the subsequent assessment. Results testing associations between

self-regulation and vocabulary showed that self-regulation and vocabulary were

significantly related at time 1 (β = .30, SE = .07, p < .05). All other concurrent

associations, however, were not significant. Further, results indicated that self-

regulation at time 1 was significantly associated with vocabulary at time 2 (β = .17,

SE = .08, p < .05). The path from vocabulary time 2 to self-regulation time 3 was also

significant (β = .24, SE = .09, p < .05), supporting a bidirectional association between

self-regulation and vocabulary.

Self-regulation and math skills. The full coupling bidirectional model testing

the bidirectional associations between self-regulation and vocabulary (Figure 8d)



subsequent assessment (t1: β = .49, SE = .07, p < .05; t2: β = .27, SE = .08, p < .05; t3:

β = .44, SE = .11, p < .05). Also all concurrent associations between self-regulation

and math were significant at all three time points. However, the only significant cross-

lag path was from math time 2 to self-regulation time 3 (β = .37, SE = .10, p < .05),

which did not support bidirectional associations between self-regulation and math

skills.


ic Skills 87

a)

b)

c)

d)

Figure 7. Four different models of self-regulation and vocabulary skills representing the (a) no-coupling m







ic Skills 88

a)

b)

c)

d)

Figure 8. Four different models of self-regulation and m

ath skills representing the (a) no-coupling model, (b and c) tw

o different unidirectional

coupling models and (d) the bidirectional coupling m

odel. Note. * p < .05. ** p < .01. Solid lines indicate significant paths. D

ashed lines indicate

non-significant paths. Estimates provided are standardized coefficients.


Discussion

The purpose of the present study was to investigate the structure of self-

regulation in young children across multiple important developmental contexts in

early childhood and to identify the possible interrelations between self-regulation

across different contexts. Moreover, the current study aimed to explore the

bidirectional associations between children’s self-regulation and intelligence,

respectively early academic skills. Findings of the present study supported a multi-

factor model of self-regulation. Further analysis indicated moderate interrelations

between self-regulation assessed in a structured one-to-one context and informant

ratings. Extending previous empirical evidence on the relationship between self-

regulation components and intelligence, findings suggests a bidirectional relationship

between inhibitory control and intelligence across time, but not for two other

subcomponents of self-regulation (working memory and attention shifting).

Moreover, the study provides evidence for a bidirectional relationship between self-

regulation and vocabulary. On the other hand, results point only to strong concurrent

associations between self-regulation and math skills, but not bidirectional associations

across time. Previous research on the structure of self-regulation and its association

with intelligence and early academic skills has mostly been conducted in the United

States and other well-developed countries (e.g., Bohleman, 2015; Fuhs, Nesbitt,

Farran, & Dong, 2014). As such, the current study added to the existing literature by

addressing these research questions in non-Western understudied population.

Interrelations Among Self-Regulatory Processes Across Different Contexts

The findings of the present study support the assumption that an individual’s

self-regulation might be dependent on the specific context in which the behavior is

observed. Several measures selected to provide information about self-regulation


processes in various contexts were administered to a sample of young Albanian

children from Kosovo. The CFA approach we used provided preliminary evidence

that different measures of self-regulation assess different context-specific aspects of

the same construct. The findings of a context-specific model of self-regulation

suggest a selective adaptability of self-regulation processes to the given behavioral

demands in a specific task context. Self-regulatory behaviors might be expressed

differently across contexts in response to different expectations and goals within a

given environment.

Bidel and Fischer (2006) argue in the dynamic skill theory that skills changes

over time and adapts to the specific context in which the behavior is expressed. The

complexity of self-regulation varies in similar ways for different contexts and states

(Bidel & Fischer, 2006). Thus, the same child may show good self-regulation

behaviors in one context (i.e., home) and fail to replicate the same performance on

self-regulation tasks in another context (i.e., school). Blair and Ursache (2011) have

proposed that self-regulation needs to be examined from the perspective of biological

sensitivity to certain contexts. In this model, it is argued that adaptive prefrontal

activation facilitates self-regulation in specific contexts. This corresponds to Duncan

and Miller’s (2002) adaptive coding model, which emphasizes flexibility of neural

responses to fit a behavioral context. It has also been suggested that early experiences

can potentially promote or hinder the expression of self-regulatory behaviors that are

likely to meet context-specific expectations and task demands (Blair & Ursache,

2011). Fischer and Bidell (2006) argue that people build a skill for a certain task and

then repeatedly rebuild a more complex skill for coping with the task in a different

context. According to this view, when children’s self-regulation skills fail to succeed

in a certain context, they will try different strategies and they will rebuild the skill to


meet context-specific expectations. As such, it seems important for future work on

self-regulation to examine, which particular types of experiences may lead to optimal

adaptation of self-regulatory behaviors to different contexts.

The present study found moderate interrelations among self-regulatory

processes across different contexts, thus confirming previous research on the

convergent validity of self-regulation measures (Duckworth & Kern, 2011; Toplak et

al., 2013). Duckworth and Kern (2011) suggest that the heterogeneity in correlations

between self-regulation measures is partly explained by the type of measure used.

Thus, it is possible that the moderate correlations found in the present data may be

due to the fact that different types of measures were used to assess self-regulation

skills in the various contexts. For example, the performance-based measures assess

self-regulation in a highly structured one-on-one situation. In contrast, informant

ratings are based on observations of how self-regulation skills are applied

independently in situations with limited external support (Gestsdottir et al., 2014;

Toplak et al., 2013). Consequently, failure in performance-based tasks (e.g., time in

seconds before turning away from an hourglass) might not necessarily imply that a

child will fail in everyday situations that require self-regulation (e.g., concentrating on

a book’s story when being read to by the parent) or success in everyday situation tha

require self-regulation might not imply that child will succed in performance based

tasks. Although preliminary, our results support the assumption that, to some extent,

different types of measures are able to examine different processes and behavioral

correlates related to self-regulation in different contexts (Toplak et al., 2013).

According to Fischer and Bidell (2006), research needs to combine multiple tasks and

assessment contexts so that it can capture the range of levels and competences,


pathways, and social interactions that characterize development. Thus, future studies

might benefit from including different types of measures within each context.

Bidirectional relationship between self-regulation components and intelligence

Another question of interest in this study concerned the bidirectional

associations between children’s self-regulation subcomponents and intelligence

throughout preschool, assuming that self-regulation may contribute to beneficial

changes on intelligence over time, while on the other hand intelligence might

influence self-regulation development. Model fit of the four different models (no

coupling, two unidirectional coupling and bidirectional coupling) supported the

bidirectional relationship between aspects of self-regulation and intelligence. For

inhibitory control and working memory, the full bidirectional model showed the best

fit. Additionally, all four models of attention shifting and intelligence revealed

acceptable fit. Findings support the dynamic skill theory, which suggests that complex

skills are interdependent, work in an organized way and build a developmental web

where developing skills in one context influence developing skills in another (Fischer

& Bidell, 2006).

With respect to the bidirectional relationship between self-regulation

components and intelligence, growth in some aspects of self-regulation were

interrelated with the growth of the intelligence in the preschool years. Findings are in

line with previous studies, which have shown that self-regulation is involved in all

kinds of higher-order cognition, including intelligence (Miller & Wallis, 2009). From

a theoretical perspective, humans control their response to a novel situation by

invoking self-regulation skills, which helps to coordinate thoughts and action into a

purposeful behavior (Miller & Wallis, 2009). In other words, self-regulation allows


humans to adaptively respond to situations that require higher order cognition

(Barkley, 2001; Cohen, Bayer, Jaudas, & Golwitzer, 2008).

Examining each component of self-regulation separately, we found that

inhibitory control and working memory improvements were significantly predictive

of later intelligence gains in preschool, whereas only intelligence gains around age 5

were related to continued inhibitory control gains in preschool.. As such, findings

confirm that components of self-regulation are differentially related to measures of

intelligence and that some components of self-regulation might be more associated

with intelligence than others (Friedman et al, 2006). Regarding working memory,

previous studies have also found that this construct is highly related to intelligence

(Colom et al., 2008). Researchers have argued that intelligence and working memory

are highly related because they share capacity limits and both mechanisms could rely

on discrete brain regions belonging to frontal and parietal areas (Colom et al., 2005).

In their study, Friedman et al (2006) found that working memory was highly

correlated with the intelligence measures, but inhibitory control and attention shifting

were not. In addition to the findings of Friedman et al (2006), we have found that

inhibitory control is also an important predictor for intelligence among children, thus

confirming results of previous studies (e.g., Polderman et al., 2009). Polderman and

colleagues (2009) found that the ability to keep irrelevant or misleading information

(in working memory?) contributes in the process of solving new problems. As

expected, the longitudinal relationship between attention shifting and intelligence was

not significant, apart of concurrent significant association between these two

components at Time 1. As such, attention shifting continues to be salient for

intelligence, arguing that inhibitory control and working memory are more important

for intelligence tasks. A possible explanation might be that not all components of self-


regulation are able to stretch the limits of their current patterns of organization, and to

actively guide, influence, or reorganize the relations with other skills (Fischer &

Bidell, 2006). Looking especially to the concurrent relationship between intelligence

and specific components of self-regulation (correlations between factors), it may be

the case that inhibitory control is important for the performance on the intelligence

tests in early childhood and when children are about to finish preschool, working

memory becomes more important for intelligence tasks.

Finding different patterns of bidirectionality between each subcomponent of

self-regulation and intelligence indicates that the development of both skills is

dynamic and changeable over time, and only at a certain age do these two constructs

become interdependent, thus providing initial support for the dynamic skill theory.

According to the dynamic skill theory, the integrated skills are not simply

interdependent, but also interparticipatory, in that true integration means that the

systems participate in one another’s functioning and are interdependent (Fischer &

Bidell, 2006). Considering previous findings, we expected an interdependent

relationship between self-regulation components and intelligence. Further, we

hypothesized that the effect of prior self-regulation on subsequent intelligence would

be positive even after controlling for the effect of prior intelligence. Our study

confirms that for some components of self-regulation (i.e., working memory and

inhibitory control) such interdependent and interparticipatory integration with

intelligence is possible around age of 5. However, the positive effect of aspects of

self-regulation on intelligence was not consistently found in our data. The lack of

continuous interrelations between two constructs can also be justified with the

dynamic skill theory, which presumes that the nature of the interaction between skills

and abilities change over time and across contexts. Fischer and Bidell (2006) argue


that children’s development skills are marked by variability in relation to other skills.

Thus, results point to a changing pattern of the relationship between self-regulation

and intelligence over time, suggesting that the development of self-regulation and

intelligence might synchronize relatively late in early childhood. It may be that such a

malleable bidirectional relationship between self-regulation components and

intelligence is influenced by the fact that different self-regulation skills have been

shown to have different developmental trajectories and the structure of self-regulation

may change from childhood to adulthood as indicated in previous studies (Hughes,

Ensor, Wilson, & Graham, 2010; Jurado & Roselli, 2007; Wiebe et al., 2011). Also, it

may be that direct measures of self-regulation cannot provide a clear picture of

dynamic interaction of skills across contexts, thus different approaches on assessing

self-regulation can provide a better understanding of their dynamic interaction.

Relations Between Subcomponents of Self-Regulation and Subcomponents of

Intelligence

One important extension of prior work is the examination of effects that each

self-regulation component has on subcomponents of intelligence. A majority of

previous studies have used composite scores of intelligence test (Demetriou et al.,

2014; Engel de Abreu, Conway, & Gathercole, 2010). However, the composite score

approach does not allow for looking at the factors of intelligence that are mostly

affected by self-regulation components. As such, moving to the subcomponents of

intelligence, the study can provide possible explanations on the lack of a relationship

between self-regulation and intelligence or overlapping between components of these

two constructs.

In contrast to our expectations, only inhibitory control and attention shifting

were related to fluid reasoning, an important factor of intelligence, which allows


children to apply reasoning strategies to novel situation. These findings are in line

with the explanation of Decker et al. (2007), who considered that both fluid reasoning

and self-regulation share the common element in that both involve the application of

reasoning strategies to novel or unusual situation. Similar to the study by Decker et al.

(2007), it may be the case that the common measured element among self-regulation

tests is similar to the common element measured in fluid reasoning tests. In addition

to that, our study confirms previous findings (e.g., Salthouse, 2005), which show

reasoning ability and processing speed being the most related aspects of intelligence

to self-regulation.

Importantly, in this study was found that aspects of self-regulation were

independently related to separate aspects of intelligence. In contrast with thoughts for

two overlapping concepts, our findings support previous studies (e.g., Ardila 1999;

Ardila et al., 2000; Crinella & Yu, 2000; Friedman et al., 2006) that self-regulation

and intelligence are distinct. Moreover, findings support the conclusion of Godoy,

Dias and Seabra (2014), which demonstrated that self-regulation and intelligence do

have separate roles. According to their assumptions self-regulation covers “how” an

individual does something, while intelligence cover “what and how much” an

individual is capable of. Also, evidence for the relationship between several

subcomponents confirms the hypothesis of dynamic skill theory that although skills

may have distinct roles, they are interdependent.

In previous studies, the lack of significant relationship between all

components of self-regulation and intelligence were attributed to differences between

the tests used to assess self-regulation and intelligence. It was concluded that the

traditional intelligence tests did not appropriately evaluate self-regulation (Ardila et

al., 2000; Friedman et al., 2006) and missed important abilities that are required in


intelligent behavior (Friedman et al., 2006). Following this line of thinking, we

applied two different tests of intelligence in the current study. However, results point

to only some moderate effects of subcomponents of self-regulation on components of

intelligence, showing that not all aspects of self-regulation are important predictors of

intelligence. As such, not only traditional intelligence tests (i.e., Raven’s Coloured

Progressive Matrices), but also newly developed tests of intelligence (i.e., Wechsler

Preschool and Primary Scale of Intelligence) do not appropriately evaluate the

influence of self-regulation on intelligence. Therefore, it may not be possible to

measure components of self-regulation with intelligence tests and vice versa.

Bidirectional relationship between self-regulation and early academic skills

An important goal of this study was to extend previous research on the

bidirectionality in self-regulation and academic skills in different academic content

areas. Similar to the results found for self-regulation and intelligence, the model fit

favored bidirectional coupling, thus supporting the bidirectional relationship between

self-regulation and early academic skills. Results partly confirm our hypothesis

suggesting bidirectional associations between self-regulation and vocabulary skills.

However, full bidirectionality as it has been previously found Bohlmann et al. (2015)

could not be established. It might be that patterns of bidirectionality also depend on

the specific language domain tested. Bohlmann et al. (2015) focused on expressive

language whereas our study tested receptive language skills. Interestingly, however,

our results regarding concurrent associations between self-regulation and vocabulary

replicated findings by Bohlmann et al. (2015).

The results showing that self-regulation at Time 1 predict growth on

vocabulary skills Time 2, whereas in turn, self-regulation at Time 2 predicted growth

on subsequent self-regulation supports the rational behind the dynamic skill theory.


The dynamic skill theory points to the dynamic nature of children’s skills

development, which is influencing and influenced by other skills (Fischer & Bidell,

2006). Therefore, it could be that self-regulation and vocabulary skills organize their

activities into integrated systems with sequences of coordination and regulations that

build on each other, meaning that one skill may function as a compensating factor for

the supportive development of the other skill.

Our findings correspond with a growing body of brain research showing an

overlap in the neural processes involved in language and self-regulation skills

(Poeppel et al., 2012; Rogalsky & Hickok, 2011). Moreover, our study results are

consistent with the dynamic neural plasticity hypothesis that points to the intersection

of language learning ability with the cognitive processes required for learning

(Blumstein & Amso, 2013). Furthermore, our study confirms language as a key tool

in children’s self-regulation, as it supports children in focusing their attention during

play activities, remembering the steps in view of goal-oriented actions, and persisting

in the face of difficulty through the use of private speech (or self-talk) (Vygotsky,

1962).

Regarding self-regulation and math skills, consistent with earlier research

(Cadima et al., 2015; McClelland et al., 2014; Wanless et al., 2011), higher levels of

self-regulation were associated with higher levels of math skills at all three-time

points. However, results did not support our assumption for bidirectional associations

between self-regulation and math skills. Thus, the pattern of bidirectionality between

self-regulation and academic skills found in our sample of children from Kosovo

differs from the pattern reported for a sample of young American children (Fuhs et

al., 2014). In contrast to the expectations, we only found math (time 2) as significant

predictor of subsequent self-regulation. Although it may be true that self-regulation


depends on math skills, an alternative explanation could be that the association is a

product of early childhood classroom demands, which focus more on the mathematic

instruction compared to self-regulation skills. An important area for future work

might be assessing how different sub-skills within the mathematics content area are

associated with self-regulation skills in young children. In general, our results support

the idea that intervention programs targeting self-regulation may enhance the

performance on early academic skills. However, bidirectional associations between

self-regulation and vocabulary and the influence of math on self-regulation skills

found in our study suggests that specific academic activities may actually enhance

children’s self-regulation skills.

One important extension of prior work is the examination of bidirectional

associations between self-regulation and academic skills through preschool because

only a few prior studies assessed such bidirectional associations in this age-group.

Also, the current study is unique in extending these analyses across two main

academic content areas (vocabulary and math). However, more research is needed to

disentangle the bidirectionality between these developing skills, particularly because

results on different academic content areas remain inconsistent. An alternative

explanation for the current findings could be that the results reflect specific (and

possibly overlapping) characteristics that are inherent to the measures used in the

present study. For example, shared measurement variance may have influenced the

pattern of results.

Implications for Educational Practice

Longitudinal studies emphasize the importance of early learning-related skills

for later achievement and school performance (Grissmer, Grimm, Aiyer, Murrah, &

Steele, 2010). Individual differences in self-regulation become increasingly apparent


when children enter preschool and tend to persist throughout childhood and

adolescence (Heckman, 2006; McClelland et al., 2010). Thus, it seems necessary to

systematically assess and observe children’s self-regulation over the preschool period.

The present findings suggest that measuring self-regulation across different contexts

captures the complexity of self-regulation processes. Thus, researchers may want to

develop test batteries that assess self-regulatory behaviors across different contexts. It

is important to note that self-regulation includes additional modalities, such as the

regulation of the stress response system (Blair & Ursache, 2011) that could not be

included in the present study but may vary in response to demands and expectations

of different contexts. Even though newly developed test batteries have greatly

improved the quality of measurement in young children (see, for example,

Willoughby et al., 2013), measures that tap into self-regulation processes at biological

levels are still limited and need further research.

Together with previous research on self-regulation in young children from

diverse cultures (Cameron Ponitz et al., 2009; Suchodoletz et al., 2013, Wanless et al.,

2011), the present study provides preliminary evidence for the predictive utility of

direct measures among children from Kosovo. Thus, the study adds to current efforts

to understand children’s skill formation in low- and middle-income countries. For

example, to our knowledge this is the only recent study on early childhood

development in Kosovo. As such, the results of the present study might lay the

groundwork for initiating a campaign focusing on the importance of early learning-

related skills (such as self-regulation) for children’s learning and development.

Consequently, promoting young children’s self-regulation should be of particular

interest for early childhood education experts aiming to help children develop the

skills they need to be successful in school (Blair & Ursache, 2011; McClelland et al.,


2010). For example, the battery of self-regulation measures used in the present study

can easily be used in preschool settings and can thus be applied by regular staff,

providing preschool teachers and education experts with information about children’s

self-regulatory functioning.

With regard to practical implications, our findings suggest that both self-

regulation and early academic skills are important aspects of children’s school

readiness. However, current education in Kosovo has a strong focus on promoting

emergent academic skill whereas teachers do not see the importance of self-regulation

for children’s school readiness. Consequently, the findings may be used for

curriculum development in Kosovo to promote early self-regulation skills among

young children. Recent studies show that self-regulation can be improved in

kindergarten with circle time games that help children practice self-regulation skills

(Tominey & McClelland, 2011). Tominey and McClelland (2011) found that for

children entering preschool with low levels of self-regulation in particular, treatment

group participation predicted gains in self-regulation and academic skills over the

year. These results encourage the design and implementation of interventions using

common classroom materials and routines. Moreover, training children with different

strategies, such as “making if-then plans”, has been found to benefit self-regulation

among children with impulse control deficits (Gawrilow, Gollwitzer, & Oettingen,

2010). More research is needed to identify the time needed to benefit from self-

regulation trainings and the effectiveness of such training for children with behavioral

problems.

Limitations and directions for future research

Several limitations of the present study should be taken into account when

interpreting the findings. Considering the fact that early childhood education in


Kosovo is expensive and causes economic stress for many families (Sommers &

Buckland, 2004), only parents with a stable economic status and work schedule can

manage to have their children attend early childhood education programs. The present

sample was recruited via early childhood education institutions. Thus, the findings

cannot be generalized to children who do not have access to early childhood

education. Furthermore, the sample included only children from urban centers and

children without special needs. This may lead to potential bias, since there are studies

showing significant differences between children who attend early childhood

education and the ones who do not attend early childhood education programs (e.g.,

Barnett, Howes, & Jung, 2008). Children who attend preschool have enhanced

cognitive, verbal, and social development, which is maintained into the first few years

of school (Barnett, Howes, & Jung, 2008; Frede, Jung, Barnett, Lamy, & Figueras,

2007; Hustedt, Barnett, Jung, & Thomas, 2007). Future studies should also recruit

children who cannot attend early education programs in order to identify the

relationships among self-regulation, intelligence and early academic skills beyond the

effects of preschool.

Another limitation is the low participation rate of early childhood education

institutions, as well as the relatively low response rate with regard to teacher- and

parent-report questionnaires. For example, it could have been the case that parents

who returned the questionnaires and parents who did not return the questionnaires

differed with regard to their perceptions of their children’s self-regulation. Such

differences could have potentially introduced biases into the results. In an attempt to

control for such effects, we checked whether there were differences in any of the

study variables between children for whom parent-reported data were available and

children for whom these data were not available and did not find any significant


differences.

Another limitation refers to the fact that for some factors of the multifactor

CFA model only two indicators were available. As a consequence, the models for

each instrument could not be estimated separately, and it is therefore possible that one

of the factors that were being masked by others had a poor fit. Also, the study could

not control for school and home environment variables, such as class size, teaching

strategies, or parents’ involvement, which have an effect on the development of self-

regulation and early academic skills. For example, it has been shown that the

classroom context that lacks extensive individual supervision leads to children with

lower academic success (Graziano et al., 2007). Also, compared to the general

population in Kosovo, our sample had a higher level of employment, higher levels of

monthly family income and higher levels of education (Kosovo Agency of Statistics,

2011). This may be another source of bias, since research suggests that children from

homes with fewer learning resources showed a subtle lag in inhibition and cognitive

flexibility performance that persisted at kindergarten entry age (Mezzacappa, 2004).

Other studies found that socially disadvantaged children performed less proficiently

in self-regulation compared to their more advantaged peers (Wanless, McClelland,

Tominey, & Acock, 2011). In future studies, it is important to include a wider range

of variables spanning different developmental contexts when investigating children’s

self-regulation, intelligence, and academic skills trajectories.

The current study is also limited in only three time points. However, results of

studies that investigated age-related changes in self-regulation during early childhood

were consistent, showing a rapid development of different aspects of self-regulation

and intelligence in this period of life (Jurado & Roselli, 2007). As such future studies,

should consider implying more data collection points to illustrate the development of


each subcomponent of self-regulation and intelligence and their interplay throughout

the time.


Conclusions

The preschool period is characterized with rapid growth in self-regulation,

intelligence and early academic skills, all aspects that predict later school success

(Blair & Razza, 2007; Duncan et al., 2007). The present study adds to a growing body

of theoretical and empirical evidence suggesting that an individual’s self-regulation

might be dependent on the specific context in which the behavior is observed.

Importantly, the results of this study provide empirical support for bidirectional

relationship between components of self-regulation and intelligence, respectively

early academic skills.

With respect to the bidirectional relationship between self-regulation

components and intelligence, growths in some aspects of self-regulation were

interrelated with the growth of the intelligence in the preschool years. Findings

confirm that components of self-regulation are differentially related to measures of

intelligence and that some types of self-regulation (inhibitory control and working

memory) are more associated with intelligence than others (attention shifting).

Results provided initial support for bidirectional associations between self-

regulation and vocabulary skills. Consistent with earlier research (Cadima et al.,

2015; McClelland et al., 2014; Wanless et al., 2011), higher levels of self-regulation

were associated with higher levels of math skills at all three-time points, but no

bidirectional associations between self-regulation and math skills. The results suggest

that self-regulation predicts vocabulary skills, and in turn, vocabulary skills and math

skills predict self-regulation. This speaks to the dynamic nature of skills development

where developing skills influence one another and the nature of that influence

changes over time.


While interventions targeting self-regulation have demonstrated positive

benefits for early academic skills (e.g., the Chicago School Readiness Project; Raver

et al., 2011), the present findings suggest that the development of intervention

programs targeting early academic skills are likely to have a positive impact on

development of self-regulation skills. Moreover, the results could potentially inform

future research investigating how teachers and parents may promote children’s self-

regulation to fit different contexts and determining which principles of self-regulation

that are particularly important for early academic achievement may generalize across

contexts. Finally, these findings add to a growing literature that has demonstrated the

importance of self-regulatory skills for early academic skills in the U.S. and Asia, and

to the literature focusing on the role of self-regulation in early childhood in Europe.


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Appendix A - Curriculum Vitae

MSc. Fitim Uka Nationality: Albanian Date of birth: 21.03 1987 (29 years old) Adress: Street Gjon Serreqi, No. 62, 10000 Prishtinë, Kosovo Email: [email protected]

_____________________________________________________________________

Education

since 2013

Albert-Ludwigs-University Freiburg, Germany

PhD student in psychology

(Advisors: Prof. Dr. Alexander Renkl, Dr. Antje von Suchodoletz)

2011 – 2013

Ludwig Maximilian University – Munich, Germany

Master Studies in Psychology program for Learning Sciences

(Advisor: Prof. Dr. Moritz Heene)

2005-2008

University of Prishtina, Kosovo

Faculty of philosophy, department of Psychology

Bachelor studies in general psychology

2010 - 2011

Kosova Health Foundation, Prishtinë, Kosova

Professional training for Family Therapy

Work experience

Since 2014 Public University “Hasan Prishtina”, Faculty of Philosophy Assistant in master program of Clinical Psychology and School Psychology. Subjects: “Clinical Practice I and II”, “Psychotherapy with children and adolescents”.


2013 – 2014 Public University “Hasan Prishtina”, Faculty of Education Lecturer in BA program. Subject “General Psychology”

Since 2012 Private Bearer of Higher Education “qeap heimerer”, Prishtinë, Kosovo Lecturer for subjects “General psychology”, “Research Methods”, “Academic writing” and “Behavioral sciences”. Leader of Research and Quality Management Office.

Since 2013 Clinical Psychology Praxis “UNI” Psychologist

05/2012 – 09/2012 Ludwig Maximilian University, Munich, Germany Tutor for courses “Measuring Learning and Change 2” and “Educational and Psychological Testing 2” (Prof. Dr. Maximilian Sailer and Dr. Moritz Heene).

10/2012 – 12/2012 Ludwig Maximilian University, Munich, Germany Tutor for the course “Statistics” (Dr. Moritz Heene).

03/2009 – 08/2011 Psycho-Social and Medical Research Center, Prishtinë, Kosovo Executive director

10/2010 – 10/2011 Psychological magazine “Psika” Editor and editor in chief

2005 –2008 Daily Newspaper “Infopress” Journalist

2008 – 2011 Daily newspaper “Tribuna” Editor in chief


Grants and scholarships

2015 Grant from Jacobs Foundation to attend the Society for Research in Child Development, Biennial Meeting: Philadelphia, Pennsylvania, U.S.A., 2015

2014 Grant from International Society for Intelligence Research to attend the 15th

Annual Conference of International Society for Intelligence Research: Grazz, Austria.

2014 Scholarship from the Ministry of Education in Kosovo for PhD studies.

2013 Jacobs Foundation Fellowship to attend the 16th European Conference on Developmental Psychology: Laussane, Switzerland, 2013.

2006 – 2007 Scholarship from the University of Prishtina for distinguished results in BA studies.

2007 – 2008 Scholarship from the University of Prishtina for distinguished results in BA studies. ___________________________________________________________________________ Achievements

2014 Winner of the price “Best research” at the International Conference for Health Sciences “KISCOMS IV”

2014 Winner of the second place for the “Best paper award” at the fifth International Symposium for Health Sciences, Prishtinë, Kosovo.

Professional affiliations

Since 2013 Member of Early Research Union of European Association of Developmental Psychology

Since 2011 Researcher at Southeast institute for advancement in health


Appendix B – Publications and Conference Contributions

von Suchodoletz, A., Uka, F. & Larsen, L. (2015): Self-Regulation Across Different

Contexts: Findings in Young Albanian Children. Early Education and

Development, DOI: 10.1080/10409289.2015.1012189.

Dimitrova, R., Crocetti, E., Buzea, C., Kosic, M., Tair, E., Tausova, J., Uka, F.,

Jordanov, V. (2015). The Utrecht-Management of Identity Commitments Scale

(U-MICS): Measurement invariance and cross-national comparisons of youth

from six European countries. European Journal of Psychological Assessment,

2-9. DOI: 10.1027/1015-5759/a000241.

Uka, F. & von Suchodoletz, A. (2016). Associations between Preschooler’s Behavior

Regulation and Emerging Academic Skills in Kosovo. International

Psychology Bulletin, 20, 12-17.

Books published in Albanian language:

Uka, F. (2015). Shkrimi Akademik për shkencat sociale dhe shëndetësore. Prishtinë:

qeap-heimerer.

Papers and posters presented in local and international conferences:

Uka, F., & Suchodoletz, A. v. (2016). Associations between Preschooler’s Behavior

Regulation and Emerging Academic Skills in Kosovo. Paper presented at

EARLI Sig 15, Leuven, Belgium.

Uka, F., & Suchodoletz, A. v. (2016). Associations between teacher characteristics

and observed classroom quality in kindergarten classrooms in Kosovo. Paper

presented at EARLI SIG 5, Porto, June, 2016.

Uka, F., Avdyli, D., Kastrati, E., Rexha, F. & Zenullahu, A. (2016) “Why am I

aggressive?” The impact of the violent electronic games. Paper presented at

20th European Congress for Adolescent’s Health, Prishtina, September, 2016.


Uka, F., & Suchodoletz, A. v. (2015). The role of self-regulation for early academic

skills over the preschool period. Paper presented at 17th European Conference

on Developmental Psychology. Braga, Portugal.

Uka, F., & Suchodoletz, A. v., Larsen, R. (2015). A cross-lagged analysis of

longitudinal associations between executive functions and intelligence among

preschoolers from Kosovo. Poster presented at the Biennial Meeting of the

Society for Research in Child Development. Philadelphia, Pennsylvania,

U.S.A.

Uka, F. (2015). The effects of perfectionism and procrastination on achievement

emotions. Poster presented at 17th European Conference on Developmental

Psychology. Braga, Portugal.

Rexhepi, G., Hasaj, K., Abazi, A., & Uka, F. (2015). Intelligence beyond

anticipation: Is it a protective factor against depression? Paper presented at 5th

International Congress of Health Sciences KISCOMS. Awarded as a second

best paper of Congress.

Uka, F., & Rexhepi, G. (2015). The influence of therapeutic relationship with patients

on health professionals’ socio-emotional health. Paper presented at 6th

Symposium of Health Sciences in European Conference on Developmental

Psychology. Braga, Portugal.

Dimitrova, R., & Stefenel, D., Uka, F., Zahaj, S., & Abubakar, A. (2015). The

protective influence of family connectedness and national identity for well-

being of Roma adolescents in Albania, Bulgaria, Kosovo and Romania. Paper

presented at 17th European Conference on Developmental Psychology. Braga,

Portugal.

Uka, F. Maxhera, F., & Musliu, L., (2015) Satisfaksioni me jetën dhe depresioni tek

pacientët në hemodializë. Paper presented at the International Conference

“Week of Science”, Prishtinë, Kosovo.


Telaku, N., Uka, F., Maxhera, F., Meholli, F., & Hamiti, A. (2015). Ndikimi i imazhit

për trupin në shprehitë e të ushqyerit tek adoleshentët. Paper presented at the

International Conference “Week of Science”, Prishtinë, Kosovo.

Uka, F., & Suchodoletz, A. v. (2014). Cross-sectional study on the relations between

intelligence and executive functions among Kosovar preschool children.

Poster presented at the 15th Annual Conference of International Society for

Intelligence Research. Grazz, Austria.

Uka, F., & Suchodoletz, A. v. (2014). Longitudinal associations between executive

function and early math skills . Poster presented at the International

Conference of Executive Functions. Stuttgart, Germany.

Uka, F., & Suchodoletz, A. v. (2014). Challenges on measuring self-regulation: Do

direct measures and informant ratings assess the same construct? Poster

presented at the 3rd Biennial EARLI Conference of SIG 5 – Learning and

Development in Early Childhood. Jyvaskyla, Finland.

Uka, F., Uka, S. & Miftari, Z. (2014). Engagement of adolescents in risky behaviors

and their influence on life-satisfaction. Paper presented at the 18th European

Congress of International Association for Adolescent Health (IAAH). Paris,

France.

Uka, S., Uka, F. & Syla, S. (2014). Prevalence and antecedents of violent behavior

among adolescents in Kosovo. Poster presented at the 18th European Congress

of International Association for Adolescent Health (IAAH). Paris, France.

Uka, F., Uka, S., Syla, S., Vitija, A., Kamberi, E, & Dermaku, E. (2014). Vlerësimi i

njohurive dhe shkathtësive të Mjekëve Familjarë në menaxhimin e Pacientit

"Misterioz” me kokëdhimbje. Paper presented at the 5th Symposium of Health

Sciences. Prishtinë, Kosovë. Awarded as a second best paper of

Symposium.


Uka, F., Beqiri, P., Brajshori, N., Kamberi, E., & Dermaku, E. Lohaj, K. (2014).

Health profesionals performance. What matters for patient’s satisfaction?

Paper presented at 4th International Congress of Health Sciences KISCOMS.

Awarded “Best paper”. Prishtinë, Kosovo.

Uka, F., & Suchodoletz, A. v. (2013). Assessing preschool children’s self-regulation:

Examining differences among parents, teachers, assessors, and direct

assessments. Poster presented at 16th European Conference on Developmental

Psychology. Laussane, Switzerland.

Uka, F. (2013). Adressing measurement nonequivalence in preschool children’s self

regulation assessment. Paper presented at the Annual conference of the

International Society for the Study of Behavioral Development. Budapest,

Hungary.

Uka, F., Baruti, Sh., Mehmetaj, G., Shabani, R., & Tahiri, Sh. (2013). Development

of early speech and language skills among infants and toddlers. Paper

presented at the International Conference of “Education, ICT and Knowledge

Society”. Vlorë, Albania.

Uka, F., Mustafa, A., Rexhaj, D., Hasanramaj, A., & Qenaj, B. (2013). The influence

of communication on patient’s satisfaction with healthcare services. Paper

presented at the international conference “Week of Science”, Prishtinë,

Kosovo.

Uka, F., Beqiri, P., Brajshori, N., Kamberi, E., & Dermaku, E. (2014). The influence

of therapeutic relationship on patient’s satisfaction with healthcare service.

ICTEA conference, Polytechnic University of Tirana, Tirane, Albania.

longitudinal associations between components of self

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