can’t learn, won’t learn? an ... - university of salford
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CAN’T LEARN, WON’T LEARN?
AN EXTENSION OF DWECK AND LEGGETT’S MOTIVATIONAL FRAMEWORK
A dissertation submitted to The University of Manchester for the degree of Master of Science in the Faculty of Humanities
2012
JOHN HUDSON
MANCHESTER BUSINESS SCHOOL
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Table of Contents List of tables........................................................................................................ 3
List of figures ...................................................................................................... 4
Abstract ............................................................................................................... 5
Declaration .......................................................................................................... 6
Intellectual property statement ............................................................................. 7
Dedication ........................................................................................................... 8
Acknowledgements ............................................................................................. 9
1.0 Introduction ................................................................................................. 10
2. 0 Literature review ........................................................................................ 14
2.1 Mindset ................................................................................................... 14
2.2 Goal orientation ....................................................................................... 19
2.2.1 Mindset and goal orientations ........................................................... 21
2.3 Locus of control ....................................................................................... 24
2.3.1 Locus of control, mindset and goal orientations: mediation? ............. 26
2.4 Self-efficacy ............................................................................................ 28
2.4.1 Locus of control and self-efficacy ..................................................... 29
2.4.2 Mindset, goal orientations and self-efficacy ...................................... 30
2.5 Overview and hypothesised model ........................................................... 32
2.6 Study aims ............................................................................................... 35
3. 0 Method ....................................................................................................... 37
3.1 Study design ............................................................................................ 37
3.2 Sample .................................................................................................... 37
3.3 Data collection instruments ...................................................................... 39
3.3.1 Mindset ............................................................................................. 39
3.3.2 Locus of control ................................................................................ 40
3.3.3 Goal orientation ................................................................................ 41
3.3.4 Public speaking self-efficacy ............................................................. 42
3.3.5 Public speaking measures.................................................................. 42
3.4 Procedure ................................................................................................ 44
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4.0 Results......................................................................................................... 46
4.1 Preliminary analyses ................................................................................ 46
4.2 Confirmatory factor analysis .................................................................... 48
4.2.1 Item Parcels ...................................................................................... 49
4.3 Structural equation models (SEM) ........................................................... 55
4.3.1 Missing data...................................................................................... 56
4.3.2 Structural models .............................................................................. 57
4.3.3 Additional analysis ........................................................................... 65
5 Discussion ...................................................................................................... 67
5.1 Summary of analysis and results .............................................................. 67
5.2 Discussion of results ................................................................................ 69
5.2.1 Mindset and locus of control ............................................................. 69
5.2.2 Relationships from LoC to goal orientations and PSSE ..................... 73
5.2.3 Goal orientations and dependent variables......................................... 75
5.2.4 Public speaking self-efficacy and dependent variables....................... 78
5.3 Limitations .............................................................................................. 79
5.4 Future research ........................................................................................ 81
5.5 Implications ............................................................................................. 84
5.6 Conclusion .............................................................................................. 86
6. References .................................................................................................... 88
7.0 Appendices ............................................................................................ 108
7.1 Appendix A: Questionnaire ................................................................ 108
7.2 Appendix B: Missing data analysis and demographic differences between groups with missing and complete data .................................................... 112
7.3 Appendix C: Confirmatory factor analyses on individual scales ......... 114
Word count: 15, 729
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List of tables
Table 3.1 Participant demographic details Page 38
Table 4.1 CFA fit statistics of scales using individual items Page 51
Table 4.2 Means, standard deviations and correlations between latent and observed variables
Page 54
Table 4.3 Assessments of construct convergent and discriminant validity: AVE, MSV and ASV
Page 55
Table 4.4 Fit statistics for structural models Page 62
Table 7.1 Comparison of demographics between two groups with complete and incomplete data for dependent variables
Page 113
Table 7.2 SPSS output showing mean differences of independent variables between two groups with complete and incomplete data
Page 114
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List of figures
Figure 2.1: Hypothesised model and direction of relationships Page 35
Figure 4.1. Measurement model Page 53
Figure 4.2:
SEM model 1: LoC mediating the effect sof mindset on goal orientations
Page 59
Figure 4.3 SEM model 2: covaried mindset and LoC model
Page 61
Figure 4.4: Final model Page 64
Figure 4.5: Test of significance of indirect effects of mindset on learning and avoidance goal orientations.
Page 66
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Abstract
Does it matter whether we believe our abilities are fixed or malleable? This study
aimed to test and extend Dweck and Leggett’s (1988) motivational framework,
which proposes that this belief is directly related to the type of goals people
pursue. However, empirical support has not been as robust as its theoretical basis
would imply, and it was contended that locus of control would mediate this
relationship and be predictive of goal orientations and self-efficacy, shown to
influence the self-regulatory strategies people use. This study answered calls for
greater integration of a range of social cognitive constructs by investigating their
influence in relation to public speaking and level of practice at a network of
public speaking clubs. Based on a sample of 161 people, confirmatory factor
analysis and structural equation modelling were used to assess the hypothesised
model. Results supported the mediating role of locus of control, as well as the
impact of goal orientations on long-term membership at the clubs. The latter also
had a positive relationship with public speaking self-efficacy. In common with
previous research, self-efficacy was also significantly related to the amount of
practice people engaged in, but contrary to expectations, goal orientations were
not. A number of methodological issues were identified and discussed alongside
the potential implications of this research. This was the first study to test the
addition of a mediating construct in Dweck and Leggett’s popular framework and
these results may illuminate equivocal findings of previous research looking at
mindset and goal orientation.
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Declaration
I declare that no portion of the work referred to in the dissertation has been
submitted in support of an application for another degree or qualification of this
or any other university of other institute of learning.
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Intellectual property statement
i. The author of this dissertation (including any appendices and/or schedules to this dissertation) owns certain copyright or related rights in it (the “Copyright”) and he has given the University of Manchester certain rights to use such Copyright, including for administrative purposes.
ii. Copies of this dissertation, either in full or in extracts and whether in hard or electronic copy, may be made only in accordance with the Copyright, Designs and Patents Act 1988 (as amended) and regulations issued under it or, where appropriate, in accordance with licensing agreements which University has from time to time. This page must form part of any such copies made.
iii. The ownership of certain Copyright, patents, designs, trademarks and other intellectual property (the “Intellectual Property”) and any reproductions of copyright works in the dissertation, for example graphs and tables (“Reproductions”) which may be described in this dissertation, may not be owned by the author and may be owned by third parties. Such Intellectual Property and Reproductions cannot and must not be made available for use without the prior written permission of the owner(s) of the relevant Intellectual Property and/or Reproductions.
iv. Further information on the conditions under which disclosure, publication and commercialisation of this dissertation, the Copyright and any Intellectual Property and/or Reproductions described in it may take place is available in the University IP Policy (see http://documents.manchester.ac.uk/display.aspx?DocID=487), in any relevant Dissertation restriction declarations deposited in the University Library, The University Library’s regulations (see http://documents.manchester.ac.uk/library/aboutus/regulations) and in The University’s Guidance for the Presentation of Dissertations.
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Dedication
To my absolutely wonderful mum and family. Thank you for all the love and
support you give me. This is for you.
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Acknowledgements
From a cast of thousands… Thanks to Paul Irwing, my supervisor, for his
patience when facing a barrage of confusing statistical questions and his
willingness to put up with my regular digressions into the world of sport.
To the lecturers and staff at MBS: Thanks for letting me in in the first place, and
for looking after me while I was here. All of you have helped make the last year
time well spent.
Thanks also to my classmates, who really are a great bunch of people and have
kept each other’s spirits up throughout.
Finally, huge thanks to my good friend Tom who has had to listen to my
digressions into the world of factor loadings and beta coefficients, when he would
rather have been talking about the Tour de France. Or the Olympics. Or
anything,
Thank you, one and all.
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1.0 Introduction
“Whether you think you can or cannot, you are right”
Henry Ford.
Ford’s maxim is a perennial favourite of motivational speakers and is not without
empirical support. It echoes one of the central tenets of Bandura’s (1977) self-
efficacy theory, while implicit in the statement is the belief that you have some
control, which relates to Rotter’s (1966) concept of locus of control. Both have a
strong evidence base and are among the most researched constructs in
psychological literature (Judge, Erez, Bono & Thoresen, 2002), playing a role in
persistence, performance and resilience (Bandura, 1997; Levenson, 1981; Stevens
& Gist, 1997). Meanwhile, a growing body of research has indicated there may
be a key variable underpinning these and influencing whether we truly believe we
can. Or cannot.
Mindset is a term coined by Carol Dweck (2006), and her theory asserts that
individuals hold one of two divergent perspectives regarding the fixedness or
malleability of personal characteristics (Dweck, 2000). People with an entity
mindset perceive personal abilities and characteristics as fixed traits, while
individuals with an incremental perspective believe these can be developed by
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dint of their own efforts (Dweck, 2006). This theory has its roots in educational
settings and although the majority of research was therefore conducted in
academic contexts (e.g. Aronson, Fried & Good, 2002; Howell & Buro, 2009;
King, 2012; Mueller & Dweck, 1998), there is a growing evidence base for its
applications in other domains (e.g. Beer, 2002; Burnette, 2010; Heslin, Latham
and VandeWalle, 2005). Dweck (2000) asserts that these opposing views
influence how learning situations are cognised (e.g. Mueller & Dweck, 1998) and
these notions of ability initiate self-regulatory mechanisms in either self-
enhancing or self-impeding ways (Bandura, 1997; Wood & Bandura, 1989).
Consequently, the contrasting mindsets have been linked with both learning
involvement (e.g. Hong, Chiu, Dweck, Lin & Wan, 1999), procrastination
(Howell & Buro, 2009) and performance outcomes (Blackwell, Trzensiewski and
Dweck, 2007). This research has also tended to support the adaptive benefits of
holding the incremental rather than entity perspective of one’s own abilities.
However, although studies in academic contexts have predominated, mindset-
related research has clear relevance to other domains such as the workplace,
where learning and development can be so crucial to the success of both
individuals and organisations (Buckley & Caple, 2004). Overall, there appears to
be solid support for the impact of mindset and, encouragingly, research has begun
to show that mindset itself may be malleable and therefore amenable to
interventions (e.g. Blackwell et al, 2007; Heslin, VandeWalle & Latham, 2006).
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Mindset also forms part of Dweck and Leggett’s (1988) conceptual framework
where it is proposed as antecedent to goal orientation – i.e. whether someone
focuses on task mastery goals or goals demonstrating competence or the
avoidance of appearing incompetent. Dweck and Leggett propose that mindset
directly influences an individual’s goal orientation, consequently impacting task
strategy and persistence. This framework has proved to be influential across the
literature (e.g. Bandura, 1997; Skinner, 1996), although research has tended to
demonstrate relatively modest, but significant, relationships (e.g. Payne, Young &
Beaubien, 2007) despite the strong theoretical underpinning.
As mindset involves cognitions about controllability of skill acquisition or
personal change, there are clear theoretical links with locus of control, which in
turn has been is associated with self-efficacy (Bandura, 1997). However,
although research has investigated some of these relationships in the literature
(e.g. Bandura & Wood, 1989; Dupeyrat & Mariné, 2005; Payne et al, 2007), there
have been calls for greater integration of promising concepts such as mindset into
existing social cognitive theory (Funder, 2001). Presently no research has
investigated these constructs together and this study aims to extend Dweck and
Leggett’s (1988) framework and the relationships between mindset and goal
orientation by incorporating locus of control and self-efficacy.
These constructs tend to most strongly manifest their effects concerning
challenging rather than ‘routine’ tasks (Bandura, 1997), so this study examines
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their relationships relative to a common source of anxiety, public speaking. This
research will test a hypothesised model (figure 2.1) integrating these in relation to
the level of involvement/engagement from members of a network of public
speaking clubs that provide opportunities to practice and improve these skills.
This will add to the literature by integrating these constructs into a single model,
extending Dweck and Leggett’s framework. It should also provide a greater
understanding of the mechanisms by which adaptive characteristics such as
internal locus of control and higher self-efficacy are developed, and thus may
inform training and development practice.
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2. 0 Literature review
Dweck and Leggett (1988) propose that mindset directly influences whether
people prioritise learning or the demonstration of competence, and this influences
peoples’ response to challenges. Dweck and Leggett’s motivational framework
forms the basis for a hypothesised model (figure 2.1) but this study aims to go
beyond their conception: it proposes that locus of control will mediate the
relationship between mindset and goal orientation and directly influence self-
efficacy, which in turn will be positively associated with the amount of public
speaking practice participants engaged in. The literature review will follow the
broad structure provided by the hypothesised model and forms the overarching
framework. It begins with a discussion of mindset, followed by goal orientation
and their association, before considering locus of control and its role as mediator.
The final construct in the model – self-efficacy – is then discussed and the review
concludes with an overview of some existing models which have incorporated
some of these variables. Finally, this section ends with a discussion of the
dependent variables and aims of the study.
2.1 Mindset
Dweck (2000) draws parallels between mindset and Kelly’s (1955) personal
construct theory, asserting that a person’s beliefs about the fixedness or
malleability of personal characteristics and abilities provide a filter through which
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relevant information is processed. These beliefs about oneself – implicit self-
theories - form a framework which guide how we interpret and respond to the
outside world.
In a recent publication, Dweck (2006) uses the term mindset to describe the
holding of either the fixed or malleable view but it should be noted that it has
been variously termed implicit-self theory, implicit theory of ability and implicit
theory of personality in Dweck (2000) and across the literature. However, for
clarity, the term mindset is employed here throughout. Furthermore, the terms
entity and incremental – the two contrasting mindsets - are fairly consistent across
the literature and will be used here, in addition to fixed and malleable
respectively, where appropriate.
Dweck conceptualises mindset as a relatively stable, dispositional individual
difference construct (Dweck, 2000; Dweck, Chiu & Hong, 1995). It is also
proposed as being domain-specific, so that beliefs in the fixedness of intelligence
would not necessarily translate to a fixed conception of social skills. However,
this is not borne out by Spinath, Spinath, Riemann & Angleitner (2003) or
Kornilova, Kornilov & Chumakova, 2009) who report correlations between
domain-specific scales between .48 and .53, indicating that mindset in one
domain was actually reasonably predictive of mindset in another. Nonetheless,
the majority of studies have used Dweck’s implicit theory of intelligence (i.e.
intelligence-mindset) measure (e.g. Haimovitz, Wormington & Corpus, 2011;
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Mueller & Dweck, 1998; Robins & Pals, 2002; Roedel & Schraw, 1995) which is
unsurprising given the predominantly academic focus of research, although a
wider range of measures have often been employed in other domains (e.g. Beer,
2002; Burnette, 2010). However, Dweck (2000) asserts that where a task cuts
across domains it is appropriate to use the domain-general measure employed
here, as per Heslin, VandeWalle & Latham (2006). Furthermore, as this study is
investigating mindset in relation to other general dispositional measures (locus of
control and goal orientation), and is hypothesising that it is antecedent to them, a
more specific focus would not make theoretical sense.
Research has identified and supported a number of implications of mindset: from
an individual perspective, it has commonly been associated with task persistence
(Mueller & Dweck, 1998) and self-regulatory strategies (e.g. Mangels,
Butterfield, Lamb, Good and Dweck, 2006), both of which have consequences for
the development of one’s abilities. Interestingly, these appear to operate
independent of cognitive ability or confidence in ability (Dweck et al, 1995). It is
also perhaps noteworthy that mindset appears to be statistically independent of
constructs such as self-esteem (Dweck et al, 1995) and neuroticism (Spinath et al,
2003).
Incremental theorists, with their belief in the malleability of their characteristics,
are theorised to have higher control convictions (Ziegler & Stoeger, 2010). They
therefore tend to demonstrate greater task persistence relative to entity theorists
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(e.g. Nussbaum & Dweck, 2008) as well as willingness to spend time practicing
skills (Cury, Da Fonseca, Zahn & Elliot, 2008). It is via such actions that
incremental theorists exceed their entity counterparts in learning and performance
(e.g. Blackwell et al, 2007). In common with other social cognitive constructs,
these effects are theorised to exert their greatest influence in the face of
challenges and setbacks (Dweck, 2000). Mueller and Dweck’s (1998) studies
support this view, showing significant differences between the two mindsets in
response to task failures. However, as the present study is looking at dispositional
mindset it should be acknowledged that Mueller and Dweck’s research used an
intervention to induce an incremental or entity view. Nonetheless, while it is
unclear whether results would have differed if participants were grouped based on
dispositional mindset, this still highlights its impact as well as suggesting the
malleability of mindset itself.
Although Dweck’s construct is proposed to play a causal role in performance
outcomes (Dweck et al, 1995), it should be expected to stem from the contrasting
self-regulatory strategies employed by the two divergent mindsets. Mindset
should firstly influence their interpretation of events; in line with the theorised
links between personal construct theory and mindset, Molden and Dweck (2006)
assert that a person’s entity or incremental view of their abilities will influence
their perception of failures and setbacks. A fixed conception of personal
characteristics would theoretically see failure as reflecting their ability and thus
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direct attention towards managing the appearance of failure (Dweck, 2000); for
example, minimising the importance of the task and making self-serving
attributions (e.g. Kennett & Keefer, 2006) or reducing effort (Mueller & Dweck,
1998). Conversely, an incremental mindset is likely to view failure as providing
diagnostic information about their present level, as well as actions required to
improve (Molden & Dweck, 2006). This suggests mindset may influence the
allocation of attentional resources, a view underlined by Mangels et al (2006) and
cognitive neuroscience-based research. Their study looked at how people attend
– and respond - to feedback, which is a key element in learning (e.g. Ericsson,
Krampe & Tesch-Romer, 1993). Mangels and colleagues found an entity mindset
was related to lower levels of neural activity in regions of the brain associated
with feedback processing, when receiving corrective task-related feedback, than
incremental theorists. The implication being that people with an incremental
mindset paid more attention to feedback and were also more likely to utilise it.
While this certainly underpins its potential impact on learning strategies and
effort, there has been criticism for the high proportion of academic and
laboratory-based studies. Its generalisability and weaker results outside the
‘laboratory’ have also been raised (e.g. Bråten and Strømsø, 2004). However,
numerous studies have demonstrated its applicability, linking an incremental
mindset to adaptive self-regulatory strategies such as feedback seeking behaviour,
in the workplace (Heslin and Latham, 2004) and linking an entity mindset with
avoidant coping strategies in weight management (Burnette, 2010). Mindset has
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also been associated with managers’ willingness to invest time in coaching and
supporting subordinates (Heslin et al, 2006; Van Vianen, Dalhoeven & De Pater,
2011). Furthermore, Beer’s (2002) suite of studies clearly demonstrate the impact
of mindset in relation to shyness, finding that incremental theorists (i.e. those with
an incremental mindset) with equivalent levels of shyness to a group of entity
theorists, were less likely to use avoidant strategies in a challenging social
situation. This is one of the few studies looking at these types of interaction and
appears particularly relevant to the present study, as fears over public speaking
are common and are related to widely-held social-evaluative concerns (Essau,
Conradt & Petermann, 1999). These avoidance and related strategies mentioned
in Beer’s research are indicative of particular goal orientations – dispositional
individual difference constructs which have been theorised as emerging from the
two opposing mindsets (Dweck & Leggett, 1988).
2.2 Goal orientation
Although a range of models and measures have derived from goal orientation
research (e.g. Button, Mathieu & Zajac, 1996; Elliot & McGregor, 2001;
VandeWalle, 1997) these can broadly be categorised as either learning or
performance goal orientations. Learning goal orientations are concerned with
improving ability, whereas performance orientations focus on task performance
and comparison with others (Fisher & Ford, 1998; Nicholls, 1984). Dweck and
Leggett’s (1988) model links entity beliefs with a tendency towards performance
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goal orientations while those with an incremental belief are likely have learning
goal orientations (Blackwell et al, 2007; Stipek & Gralinski, 1996).
Unsurprisingly, given the ‘benefits’ of an incremental mindset, the literature has
largely highlighted the advantages of learning over performance goal orientations
(e.g. Cannon-Bowers, Rhodenizer, Salas & Bowers, 1998; Fisher & Ford, 1998;
Utman, 1997).
The various researchers looking at goal orientation have employed an array of
labels to refer to similar concepts; so learning goal orientation also appears as
mastery, approach, and task orientations, while, performance goals have also
been branded as ego, avoidance and achievement orientations, among others.
However, for clarity, this study will use the terms learning (LGO) and
performance goal orientations (PGO) to refer to these conceptually similar but
variously termed constructs.
Like mindset, LGO has been positively associated with adaptive strategies (e.g.
Ames & Archer, 1988; VandeWalle & Cummings, 1997), which given the
proposed links between them should be expected, and it is via these strategies that
LGO is similarly associated with performance outcomes (e.g. Midgeley,
Middleton & Kaplan, 2001). Meanwhile, PGO is theorised to result in shallower,
less sophisticated learning strategies (Seifert, 1995), such as the reduced
attentional resources allocated to feedback in Mangels et al (2006). Furthermore,
PGO’s define success in relation to others, thus appearing less controllable than
self-referent goals and have been theorised to represent a threat to ego (Nicholls,
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1984). Dweck and Leggett propose that for individuals with low confidence in
ability, this leads to a tendency to engage in maladaptive strategies such as task
withdrawal due to lack of belief in the worth of effort (Utman, 1997).
Conversely, Stevens and Gist’s (1997) research suggests that LGO is effective in
learning situations precisely because it mitigates these judgments and subsequent
avoidance behaviours.
2.2.1 Mindset and goal orientations
The model which links these two constructs (Dweck & Leggett, 1988) has proven
popular and there has been a fair degree of support (e.g. Ames & Archer, 1988;
Miller, Behrens & Greene, 1993; Martocchio, 1994). Robins and Pals (2002)
indicate that entity or incremental perspectives played important roles in
performance and self-esteem respectively, but did so via the learning orientations
that emerged from these (Molden & Dweck, 2006). Meanwhile, Blackwell et al’s
(2007) longitudinal study indicated that students with an entity mindset were
more likely to ascribe failures to lack of ability, than incremental theorists. With
a conception of abilities as fixed and stable entities, individuals are more likely
view a situation as a fait accompli, leading to task helplessness and responses
such as withdrawal in the face of setbacks (Duda, 2001). This is characteristic of
an avoidance goal orientation which has been proposed as a key dimension of
PGO (VandeWalle, 1997). However, despite the theoretical support for the
relationship between mindset and goal orientations - alongside the deleterious
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impact of PGO and advantages of LGO - the evidence is not unequivocal and the
hypothesised model does not include a direct path between them.
A key aspect of this research relates to the relationship between these two
constructs so it is important to acknowledge and justify the exclusion of such a
path. On the basis of the clear conceptual links between mindset and goal
orientation theory (Hafsteinsson, Donovan & Breland, 2007) a consistently strong
relationship might be expected between them. Yet, although studies have
generally supported the direction of the relationships (e.g. VandeWalle, 1997),
they have often been relatively weak (e.g. Bråten & Strømsø, 2004; Kennett &
Keefer, 2006; Payne et al, 2007). There are three potential explanations here;
firstly, that mindset simply has little explanatory value in relation to goal
orientations – while this is possible, the theoretical links discussed previously
appear compelling and suggest otherwise. Alternatively, there may be issues with
its measurement. Dupeyrat and Mariné (2005) - in common with numerous
others (e.g. Bråten and Strømsø, 2005) - treat mindset as a two-factor construct
(i.e. entity and incremental), with mixed results. However, mindset was
originally conceptualised as a unidimensional construct with high scores
representing a tendency towards an incremental perspective and low scores
representing the opposite. Subsequent validation research lends clear support to
this view (e.g. Hong et al, 1999). Although Dweck (2000) asserts that differing
mindsets may be held in relation to different domains, there is no real logic to the
view that one can hold both an entity and incremental mindset regarding the same
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task concurrently. Therefore, treatment of mindset as a two-factor construct
appears conceptually confusing and may be clouding real relationships.
Skinner (1996) asserts that inconsistent results and conclusions are likely when
potentially different constructs are described by the same terms, therefore similar
issues may also stem from the sheer number of ways that goal orientation has
been conceptualised and measured (Grant & Dweck, 2003). Yet despite this
growing number of measures (e.g. Button et al, 1996; Elliot & Harackiewicz, 1996;
VandeWalle, 1997), Carr, DeShon and Dobbins note that around 75% of the 27
studies they reviewed did not use established measures, making comparison and
assimilation of research problematic, while reliabilities are also frequently poor
(e.g. Dupeyrat & Mariné, 2005). However, goal orientation has been an
increasingly researched construct (e.g. Hafsteinsson et al, 2007; Payne et al, 2007;
VandeWalle, 1997) with sound theoretical underpinnings.
The rationale for the relationships between these constructs is sound, but not fully
borne out by research so this may be related to the varying conceptualisations and
measurement issues described. However, while this may be a factor, these
significant but weak relationships are relatively consistent across studies and an
alternative explanation is that previous theory and research has omitted a key
variable from their models. After all, mindset is a belief that it is possible to
change one’s ability/characteristic and although it has clear control-related aspects
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it does not necessarily indicate beliefs about one’s capacity to do so. And this
may be where Locus of Control can bridge the gap between mindset and goal
orientations.
2.3 Locus of control
The central tenet of mindset, regarding the belief an individual can or cannot
change their characteristics with effort and strategy, infers a sense of control; it
should therefore be expected to have a significant relationship with locus of
control (LoC). Rotter (1966) originally differentiated between people with an
internal and external LoC; ‘internals’ believe they are in control of their own fate
and are therefore more likely to take necessary steps to control it, while
‘externals’ tend to believe they are at the mercy of the environment and that
external factors are responsible (Ng, Sorensen & Eby, 2006). Although there
have been numerous subsequent models with differing factor structures (e.g.
Lefcourt, 1991; Levenson, 1981), it is the broad internal/external dichotomy that
is the focus here.
Perceived control has implications for how people respond to stressful situations
(Lazarus & Folkman, 1984) and it has been shown to reduce the effects of
potentially threatening situations into nonthreatening (Sanderson, Rapee and
Barlow, 1989). Considering the fear that is so commonly held in relation to
public speaking (Stein, Walker, & Forde, 1996) this is highly pertinent to the
present study. Externality has been linked with stressful situational appraisals
25
which can lead to the helplessness and maladaptive responses characteristic of
avoidance goal orientations (Noe, 1988). Meanwhile internal control beliefs are
associated with LGO’s (Phillips & Gully, 1997) which do not involve the same
deleterious normative evaluations that activities such as public speaking may
invoke (e.g. DiBartolo, Frost, Dixon & Almodovar, 2001).
People who believe that outcomes are determined by their own actions are also
more proactive than those with external perspectives (Bandura, 1997). On this
basis, it might also be anticipated that an internal LoC would be associated with
performance outcomes (e.g. Judge & Bono, 2001). Nonetheless, direct
relationships between LoC and behavioural outcomes have been weaker than this
view would suggest (e.g. Biddle, 1999; Smith, 1989; Tett, Jackson & Rothstein,
1991) and LoC actually exerts its effects via self-regulatory constructs such as
goal orientation, as well as self-efficacy (e.g. Ng et al, 2006; Phillips & Gully,
1997). In support of this, Skinner (1996) proposes a tripartite model of control
whereby our general beliefs about the changeability of a situation (i.e. mindset)
are mediated by beliefs about our level of influence upon it (i.e. LoC) and this in
turn impacts our self-regulatory strategies (i.e. goal orientations). Although
Skinner’s framework is necessarily general and not intended as a testable model,
it provides a useful way of organising these constructs and indicating their broad
relationships.
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2.3.1 Locus of control, mindset and goal orientations: mediation?
However, in relation to the present study, there is scant research which has
considered LoC and mindset together. Dweck and Leggett (1988), along with
Seligman (1975), suggests that when faced with ‘failure’, a fixed conception of
one’s ability is associated with a lack of control – and related attributions -
resulting in ‘helpless’ rather than ‘mastery’ responses. This is characterised by
the belief in the futility of further effort and avoidance strategies (Elliot & Dweck,
1988), which have parallels with avoidance goal orientations (AGO). So,
controllability is considered by Dweck and Leggett but does not appear as a
discrete construct in their model.
However, Garofano and Salas (2005) appear to suggest that LoC and mindset may
be treated interchangeably, which is understandable given the control-related
parallels between them (Skinner, 1996). Nonetheless, despite the dearth of
research including both constructs, Dweck and colleagues’ (1995) validation
study does indicate only a small, but significant, correlation (r = .15; p < .01)
between the internality subscale of Levenson’s (1981) LoC scale and a domain-
specific measure of mindset. This is echoed by Burnette (2010) with Wallston
and Wallston’s (1978) Health LoC scale (r = .14; p <.05). It is plausible they
would be related, but contrary to Garofano and Salas, it is contended here that
they are not the same and that a belief that one’s abilities can be changed is not
enough. It must then be followed by the conviction that one has the capacity to
exercise that change, as per Skinner (1996) and Bandura (1977), which is where
27
LoC and the self-efficacy come in. So it is asserted that an incremental mindset
should serve as a necessary - but not sufficient - precondition for these beliefs. So
this would indicate mindset as being antecedent to LoC.
The second element of the proposed mediating relationship concerns the
relationship between LoC and goal orientations. Control beliefs, such as LoC are
clearly influential in the formation of goal orientations; passive or avoidant
coping strategies, associated with PGO’s, are prompted by a general perception
that one is powerless to control outcomes (Seligman, 1975). Meanwhile a belief
that a person has control over what happens to them produces more proactive and
adaptive responses (Luthar, 1991), and has similarities with the previously
discussed constructs. According to this, an internal or external LoC would be
linked to LGO or AGO respectively. So, although LoC has often demonstrated
weaker than expected relationships with distal outcomes (Biddle, 1999), it may
simply be that LoC is mediated by more proximal constructs such as goal
orientations and self-efficacy. However, Bandura (1997) asserts that people who
see outcomes as being personally determined – analogous to an internal LoC – yet
lacking requisite expertise will have low self-efficacy and view tasks with “a
sense of futility”. Equally, Averill (1973) asserts that the mere conviction that
control is available is sufficient to encourage action. Yet these views are
somewhat simplistic and fail to acknowledge the effect of goal orientations, with
LGO shown to ameliorate the deleterious impact of low self-efficacy, while AGO
28
may mitigate the benefits of ability. For example, Stevens & Gist (1997) show
that low self-efficacy is only problematic when accompanied by a performance
goal orientation. Furthermore, there would be little point in setting challenging
goals if an individual believes that results are due to factors beyond their control.
So the hypothesised model proposes that LoC will mediate the effects of mindset
on goal orientations, while also having a direct effect on self-efficacy.
2.4 Self-efficacy
Self-efficacy has been defined as the ‘beliefs in one’s capabilities to organise and
execute the courses of action required to produce given attainments (Bandura,
1997, p.3). The key role of self-efficacy in performance has been widely asserted
(e.g. Bandura, 1986, 1997; Bandura & Locke, 2003; Bouffard-Bouchard, Parent
& Larivee, 1991; Zimmerman, Bandura & Martinez-Pons, 2003) with low self-
efficacy shown to have a detrimental impact on functioning and task performance
(Bouffard et al, 2005). Furthermore, high self-efficacy has been shown as one of
the strongest predictors of goals and behaviour (Plotnikoff, Lippke, Cournega,
Birkett and Sigal, 2008), being associated with proactivity in seeking
opportunities to practice recently learned skills (Hill, Smith & Mann, 1987). It
has also been linked to a willingness to apply nascent skills outside of the training
environment and the likelihood of trying out difficult tasks (Quinones, Ford, Sego
& Smith, 1996). It is therefore expected that self-efficacy should be related to
levels of practice in the present study
29
Although self-efficacy appears to be viewed in a way that simply suggests that
self-efficacy per se is beneficial, Bandura (2012) differentiates between ‘easy’
and ‘resilient’ self-efficacy. The former gained via easy successes and the latter
by learning how to manage failure so it is informative rather than disheartening.
It is therefore contended that an incremental mindset, internal LoC and learning
goal orientation, with their links to adaptive strategies and personal agency, would
provide fertile ground for the development of this ‘resilient’ brand of self-
efficacy. With the opposite being true for an entity mindset, external LoC and
AGO.
2.4.1 Locus of control and self-efficacy
It is affirmed by a number of sources that self-efficacy can influence effort and
persistence (e.g. Bandura, 1986; Ormrod, 2006; Zimmerman, 2000) and
individuals with an internal LoC are likely to display higher self-efficacy because
of their belief in their ability to exercise control over tasks (Ng et al, 2006).
Nonetheless, Bandura (2006) affirms that self-efficacy - a judgment of capability
- should be differentiated from LoC, which is a belief about whether outcomes are
determined by our own, or external factors. LoC has been often associated with
self-efficacy (e.g. Ng et al, 2006; Skinner, 1996), while Phillips & Gully’s (1997)
research indicated a direct causal path to self-efficacy, though not to the extent
that Averill’s (1973) assertion would indicate. It is unsurprising that LoC and
30
self-efficacy should be linked, given they can each be considered under the rubric
‘control’ (Thompson & Spacapan, 1991) and have been proposed as part of the
superordinate construct of Core Self-Evaluation (CSE; Judge et al, 2002).
However, Judge and colleagues employ a general measure of self-efficacy, which
is considered questionable given its domain specificity (Ajzjen & Fishbeing,
2005), while Ajzen (2002) also asserts the substantial empirical evidence
regarding the distinctiveness of LoC and self-efficacy.
2.4.2 Mindset, goal orientations and self-efficacy
Furthermore, based on research linking learning (and avoidance) goal orientations
positively (and negatively) with self-efficacy (Hsieh, Sullivan & Guerra, 2007)
and similarly between mindset and self-efficacy (e.g. Martocchio, 1994), a case
could be made for the inclusion of such paths in the hypothesised model (figure
2.1) as do Phillips and Gully (1997). They assert that goal orientations are
predictive of self-efficacy because they influence how failures and setbacks are
interpreted and acted upon, although their model indicated relatively modest
relationships between self-efficacy and LGO (β = .13; p <. 05) as well as PGO (β
= -.14; p < .01). However, it is the response the orientation generates rather than
the orientation itself that should influence self-efficacy, which in the case of the
present study would be the amount of cumulative practice (i.e. length of
membership) – and it is expected that this would mediate the effect of goal
orientations on self-efficacy. It is noted that numerous previous studies have
31
indicated direct effects of goal orientations on self-efficacy (e.g. Hastings &
West, 2011; Kozlowski, Gully, Brown, Salas, Smith and Nason, 2001; Leroy,
Bressoux, Sarrazin & Trouilloud, 2007; Phillips & Gully, 1997).
However, Bandura’s own causal process model emphasises the complex two-way
nature of self-efficacy and outcomes (Bandura, 1997; Bandura & Wood, 1989;
Wood & Bandura, 1989) whereby past performance influences efficacy beliefs
and self-regulatory strategies. These in turn influence task performance, thus
modifying efficacy beliefs and subsequent strategies. This feedback loop is
repeated each time, because, although high self-efficacy has been asserted as a
crucial causal factor learning and practice (e.g. Zimmerman, 2000), it is also an
outcome of it. This view supports the hypothesis that longer-term membership at
a public speaking club (and therefore more practice) would be positively
associated with self-efficacy, while self-efficacy would be positively related to the
amount of recent practice undertaken.
Models based on cross-sectional methodology omitting indicators of self-
regulatory strategies do not appear to account for any of the mechanisms by
which self-efficacy is actually developed. For example, enactive mastery
experiences - the strongest source of self-efficacy (Bandura, 1997) - are likely to
occur because of self-regulatory strategies such as willingness to practice and
persistence; such strategies and responses, according to goal orientation theory are
32
less likely when one has ability and competence-related concerns (Bandura, 1997)
that are characteristic of avoidance goal orientations.
Although self-efficacy is more complex and nuanced than just described (see
Bandura, 1997), this should highlight that previous cross-sectional research
treating goal orientations as having a direct effect appears flawed on that basis.
The same rationale could be theorised regarding mindset and it too has been
treated similarly in a number of studies (e.g. Martocchio, 1994). So while
mindset and goal orientations should provide good foundations for the
development of self-efficacy – and therefore should be correlated - their influence
should not be direct. These should influence self-efficacy via the selection of
self-regulatory strategies, which lead to more effective learning via mechanisms
such as ‘production’ and ‘reproduction’ of the desired skills (i.e. practice) and
response to feedback (Bandura, 1977). In common with many previous studies,
the present research is also cross-sectional and it is for this very reason that the
hypothesised model does not include a direct path to self-efficacy from either goal
orientation or mindset.
2.5 Overview and hypothesised model
Dweck and Leggett’s (1988) model, proposing the direct links between the two
divergent mindsets and goal orientations, has proved a useful and popular one for
researchers, but it is argued here that it should be extended to incorporate LoC in
a mediating role. Payne and colleagues’ (2007) meta-analysis includes self-
33
efficacy as well as [intelligence-related] mindset within their meta-analysis of
goal orientations. Their outline broadly follows the temporal order of this model,
with mindset and dispositional goal orientation being antecedent to task-specific
self-efficacy. Nonetheless, while Payne et al include a wide range of personality
and individual difference constructs in their analysis, they do not include LoC and
end with a call for future research to incorporate it. Similarly, Phillips and Gully
(1997) include LoC, goal orientations and self-efficacy, with only mindset absent
from their model; however they consider LoC and goal orientations as having
independent direct effects on self-efficacy which has already been queried.
Nonetheless, these and other models provide useful frameworks, but none
consider all of the constructs investigated here.
The research outlined throughout argues that mindset, locus of control and goal
orientations provide a platform for the development of self-regulatory strategies,
self-efficacy and subsequent behavioural responses and performance outcomes.
Therefore, the aim of this study is to investigate whether the hypothesised model
is predictive of practice levels and continued commitment to the learning of
public speaking skills. This is considered a particularly relevant context given the
effects of these constructs are most salient in the face of challenges, as public
speaking has been commonly identified as a fearful situation (Dwyer & Davidson,
2012). Furthermore, participation in the public speaking clubs studied here also
incorporates regular performance feedback, the response to which is strongly
influenced by factors such as goal orientations and self-efficacy (Bandura, 1997;
34
Brett & VandeWalle, 1999). Therefore, the constructs included here should have
useful explanatory value in this context and be related to the amount of practice
people engage in, as well as their long-term involvement at their public speaking
club.
The hypothesised relationships between the variables in the model are shown in
figure 2.1, with hypothesised positive and negative relationships being indicated
by (+) and (-) symbols. In summary, the hypothesised model proposes that an
incremental mindset would support an internal locus of control, which in turn
would lead to the pursuit of learning – as opposed to avoidance – goals. It is
further theorised that learning goal orientations promote development of self-
efficacy via the self-regulatory strategies they facilitate (Bandura, 1997). Goal
orientations, as well as self-efficacy, should therefore be predictive of level of
public speaking practice, while goal orientations would also be expected to be
related to length of membership at their public speaking club, which could be
considered as being related to persistence. Additionally, it is also contended that
length of membership would naturally be associated with higher public speaking
self-efficacy due to the greater amount of practice that longer-term membership is
likely to provide.
35
Figure 2.1: Hypothesised model and direction of relationships
Note: LoC = locus of control; AGO = avoidance goal orientation; LGO = learning goal orientation; Memb. = length of public speaking club membership; PSSE = public speaking self-efficacy; Practice = amount of public speaking practice
2.6 Study aims
In the introduction, it was asserted that research had investigated relationships
between mindset and some of these established constructs, but not as an
integrated whole. One of the main aims of the study is to address this and
respond to calls for greater integration of mindset with social cognitive theory.
An additional key aim is to further integrate Dweck and Leggett’s motivational
framework within this and test an extension of this by including a proposed
mediator; this has not previously been proposed or tested previously. Based on
the research and theory presented here, this could play a valuable role in
Mindset
LGO
AGO
LoC PSSE
Memb.
Practice+
_
+
+ +
+
_
_
+
+
36
explaining previously modest results and inform future research using their
framework. Therefore, in addition to the hypothesised model, an alternative non-
mediated model where both LoC and mindset have direct effects on goal
orientations is also tested to allow comparison. Finally, as the majority of
research has been conducted in relation to educational establishments or
workplaces, this will also add to the knowledge regarding how these constructs
operate together, in relation to a voluntary undertaking, where individuals have
more discretion to engage in their chosen behaviours.
37
3. 0 Method
3.1 Study design
The study was conducted using a cross-sectional survey design and the target
population were members of an organisation that oversees a network of public
speaking clubs. The independent variables were scores on measures of mindset,
LoC, AGO, LGO and Public-speaking self-efficacy (PSSE), while the two
dependent variables were the amount of public speaking practice (practice) and
length of membership (membership); these measures are discussed in section 3.3.
3.2 Sample
Demographic details are included in table 3.1 and the final sample comprised 161
participants (96 females and 65 males) between the ages of 18 and 80 (M = 37.9).
The majority were in full-time employment (56.5%), with 13% being self-
employed and 13% students. It was also noted that a large proportion of the
sample were university educated (75.1%), with the majority of these educated to
postgraduate level.
38
Table 3.1: Participant demographic details
Demographic variable Category
Frequency (total n = 161)
% of sample
Gender Female 96 59.6% Male 65 40.4%
Age
Under 20 years old 2 1.2% 20 – 29 years 42 26.1% 30 – 39 years 66 41.0% 40 – 49 years 23 14.3% 50 – 59 years 13 8.1% 60 – 69 years 12 7.5% 70 – 79 years 2 1.2% 80+ years 1 0.6%
M = 37.89; SD = 12.86; range = 62 (18 – 80)
Employment status
Full time 91 56.5%
Part time 13 8.1%
Self-employed 21 13.0%
Student 21 13.0%
Unemployed 5 3.1%
Retired 6 3.7%
Missing data 4 2.5%
Occupational level
Not currently employed 32 19.9%
Admin 12 7.5%
Service 13 8.1%
Semi-skilled 2 1.2%
Skilled 12 7.5%
Junior mgmt. 27 16.8%
Senior mgmt. 57 35.4%
Missing data 6 3.7%
Educational Level
No qualifications 0 0%
Secondary school to age 16 7 4.3%
Secondary school to age 18 4 2.5%
Non-university higher education 25 15.5%
Undergraduate university education 49 30.4%
Postgraduate university education 72 44.7%
Missing data 4 2.5%
39
3.3 Data collection instruments
Data collection was conducted via a questionnaire (Appendix A), which in
addition to the demographic information described previously, comprised the
measures described below. Unless stated otherwise, all scales were measured
using 7-point Likert scales, ranging from one (strongly disagree) to seven
(strongly agree). Reliabilities of scales in the present study were all satisfactory,
ranging from .69 to .93 and are presented in table 4.2 in the results section.
3.3.1 Mindset
Mindset was assessed using an eight-item questionnaire developed by Dweck
(2000), which includes four items where agreement indicates an incremental
mindset and four items indicating an entity mindset. Incremental items include
“You can always substantially change the kind of person you are”, while entity
items include “You can do things differently, but the important parts of who you
are can’t really be changed”. Previous research has demonstrated reliabilities in
the range of .78 (Sue-Chan and Wood, 2009) and .92 (Heslin et al, 2006). As
previously mentioned, this scale is selected in preference to domain-specific
measures of mindset (e.g. intelligence) as recommended by Dweck (2000)
because the skills used by members were thought to cut across domains, involving
speech-preparation, organisational skills as well as public-speaking itself.
40
The unidimensionality of mindset is asserted by Dweck and demonstrated by
Hong et al (1999) and Levy, Stroessner and Dweck (1998), finding that
agreement with entity items represents disagreement with incremental items and
vice versa. However, Dweck and colleagues (e.g. Dweck et al, 1995) have
previously used artificial dichotomisation to reduce continuous level data into
dichotomous entity and incremental variables. This practice has been criticised
(e.g. Cohen, 1983, MacCallum, Zhang, Preacher & Rucker, 2002) for reducing
statistical power and therefore entity mindset items (1, 2, 4 & 6) were reverse-
scored so that on all items, higher scores reflected an incremental belief and lower
scores indicated entity beliefs, as per Heslin et al (2006).
3.3.2 Locus of control
The eight-item ‘internality’ subscale of Levenson’s (1981) measure was used to
assess Locus of Control. This gauges the degree to which people perceive
themselves as being in control of their own lives, versus the belief that external
factors are responsible (Oreg, 2003). This scale includes items such as “When I
make plans, I am almost certain to make them work” and “When I get what I
want, it’s usually because I worked hard for it”. No items are negatively worded
and on all items, higher scores indicate stronger internality perceptions. Levenson
(1974) reports relatively modest reliability (α = .64), but it is selected in
preference to other scales (e.g. Rotter, 1966) as it is superior to scales such as
Rotter (1966) in terms of social desirability effects (Lefcourt, 1991), while from a
practical perspective, with fewer items, it required less of participants time.
41
3.3.3 Goal orientation
This was assessed using the learning and avoidance goal orientation subscales of
VandeWalle’s (1997) goal orientation scale; five items related to LGO (e.g. “I
enjoy challenging and difficult tasks where I’ll learn new skills”) and four items
related to the avoidance orientation (AGO) (e.g. “I would avoid taking on a new
task if there was a chance I would appear incompetent to others”. Reliabilities
for the learning and avoid subscales have been shown at .89 and .88 respectively
(VandeWalle, 1997). For all items, higher scores represented a stronger
inclination towards the relevant factor. It should be noted that the VandeWalle’s
(1997) scale includes an additional dimension ‘prove goal orientation’ – which is
concerned with the demonstration of competence, rather than avoidance of
appearing incompetent characterised by AGO. However, this orientation has
been shown to have consistently weak or inconsistent relationships (Hafsteinsson
et al, 2007; VandeWalle, Brown, Cron & Slocum, 1999). Therefore, as this
model included a number of relatively untested relationships, it was considered
appropriate to select only the two conceptually clearer orientations, AGO
(avoidance goal orientation) and LGO (Brophy, 2004).
42
3.3.4 Public speaking self-efficacy
This was assessed using Schuuhrman, Alley, Marshall and Johnstone’s (2008)
Public Speaking Self-Efficacy scale; this comprises ten items measuring different
aspects involved in public speaking. Respondents are asked to indicate their level
of confidence on a seven-point scale (1 = “not at all confident”; 7 = “completely
confident”) in relation to statements such as “When public speaking I am [level of
confidence] that I can…connect well to an audience that is not familiar with the
topic”. Schuurman and colleagues report a reliability of .90 & .89 based on two
samples (n = 773; n = 279) and all items are scored so higher scores indicate
greater levels of public speaking self-efficacy.
3.3.5 Public speaking measures
The dependent variables are based on membership of a network of public
speaking clubs. Clubs hold meetings every fortnight and provide a number of
public speaking opportunities; meetings include designated slots for members to
deliver prepared speeches on topics of their choosing, as well as a segment set
aside for impromptu speaking. In addition, all speakers are provided with
feedback and meetings include a number of other roles which involve speaking in
front of the audience, providing further opportunities to develop skills, such as
giving feedback or presiding over meetings. The two dependent variables are
described below.
43
Public speaking practice (practice)
The amount of public speaking practice undertaken by members was assessed by
asking respondents to indicate the number of times they had taken on speaking
roles at their club in the last six months. All meetings follow a standard format
which includes a set number of these roles which members can volunteer for, in
order to practice their public speaking. There are eight possible roles (e.g.
prepared speaker, speech evaluator, general evaluator) and participants indicated
how many of each of these roles they had undertaken in the previous six months.
These were summed for each participant to form a composite total ‘practice’
score.
It is acknowledged that this measure does rely on people’s recall and is why a six
month period was considered – to provide a long enough period to allow for short
spells of attendance/inactivity yet also short enough that people could recall their
involvement. Accurate, objective measures would have been preferred, but
official records were not available for confidentiality reasons. However, it was
considered at least an objective, rather than perceived, measure of practice.
Length of membership (membership)
Length of membership was intended to reflect a longer-term persistence or
commitment to improving public speaking skills. Participants were asked to
provide the number of years and months they had been a member of the public
speaking network and a composite ‘membership’ (in months) variable was
44
computed by multiplying the number of years by 12 then adding the number of
months.
3.4 Procedure
Data collection was predominantly via an online questionnaire and after
contacting the area governor responsible for clubs in the north of England and
Scotland, these were to be disseminated to all members in this area via internal
club e-mail channels. As it later transpired this was not possible, the
questionnaire was instead circulated using snowball sampling via the researchers
own networks within the clubs; a paper and pencil version of the questionnaire
was also disseminated at clubs in the Greater Manchester area by the researcher.
Furthermore, the link to the online questionnaire was also posted on club- and
public-speaking -related web-based social networks such as Facebook, LinkedIn
and Twitter to promote it to a wider range of available participants.
When participants followed the online link to the questionnaire (appendix A) they
were presented with information about the study and its voluntary nature to
ensure informed consent. No personally identifiable information was requested,
so anonymity was assured. The questionnaire comprised the measures described
previously in this section. Although the questionnaire was targeted at club
members, as it was also disseminated online via various social networks it was
possible that non-members could also complete the questionnaire. To reduce the
possibility of non-members answering questions specific to membership
45
involvement (i.e. practice and period of membership), the questionnaire included
a question asking if the respondent was a member of the public speaking clubs,
and a ‘no’ response lead to this set of questions being skipped*. Aggregated data
was then uploaded into SPSS for preparation and initial analysis, details of which
are provided in the results section.
*Is should be noted, that a technical issue with the online data collection
instrument meant that a number of participating club members indicated that they
had been unable to input information in the section regarding club membership
and participation (i.e. the dependent variables). It is not thought this affected a
large number of participants, but it is unclear and is discussed further in section
4.3.1.
46
4.0 Results
Data from the total sample of 161 were initially screened to assess missing values
and data normality. The subsequent analyses followed Anderson and Gerbin’s
(1988) two-step process, whereby confirmatory factor analysis (CFA) is used to
develop a measurement model incorporating mindset, locus of control, AGO,
LGO and PSSE, allowing factor loadings and overall fit of the model to the data
to be tested. In the second stage, structural equation modelling (SEM) enables the
testing of the hypothesised relationships between the constructs. This section
begins by discussing the initial screening of data, before describing the CFA and
SEM analyses.
4.1 Preliminary analyses
Firstly, data were prepared using SPSS 16; the four negatively-phrased mindset
items were reverse scored and composite scores computed for ‘length of
membership’ and ‘total practice’ as described in the previous section.
Prior to analysis, two cases were identified as missing a single value (items LoC1
& LoC7); this comprised only .03% of the available data and these were replaced
via single imputation using the expectation-maximisation algorithm. A second
more relevant missing data issue concerns a high proportion of missing data on
the dependent variables and the strategy for dealing with this is discussed in the
SEM section (4.3.1).
47
Although boxplots highlighted a number of outliers, only two extreme outliers (z
> 3.29) were identified; one on the ‘practice’ variable (z = 4.64) and one on
‘membership’ (z = 3.50). While these did not indicate data entry errors, it was not
clear whether the ‘practice’ outlier in particular was spurious; based on the
researcher’s knowledge of the clubs this was considered to represent an extremely
high level; it also appeared particularly problematic to the degree of kurtosis.
Although the removal or transformation of outliers has been questioned (e.g.
Cortina & Gully, 1999) it has also been commonly recommended to maximise
data normality (e.g. Tabachnick & Fiddel, 2007; Wilcox, 1997), which was
considered important given the assumptions made by SEM, but more so in light
of the missing data and the impact kurtosis in particular may have when dealing
with it (Enders, 2001b). Therefore, to reduce - but not ignore - these cases, they
were transformed by taking the next highest variable score plus one unit, as
specified by Tabachnick and Fiddel (2007), which reduced kurtosis from 5.37 to
1.41. However, additional analyses using the untransformed data were conducted
and are reported alongside the statistics for the final tested models (table 4.4).
Subsequently, skew for all variables ranged from 1.81 to -1.33, while kurtosis
ranged from 2.70 to -1.13, and were within acceptable limits for SEM (Lei &
Lomax, 2005).
48
4.2 Confirmatory factor analysis
Confirmatory factor analysis (CFA) was conducted initially to assess the
individual scales used, before testing of the final measurement model and
hypothesised structural models. Measurement and structural models (SEM) were
tested using AMOS Graphics (version 16.0.1), using Maximum Likelihood
estimation (ML). ML assumes multivariate normality and continuous level data,
which was not strictly met by the 7-point response scales used here (Bollen,
1989). However, ML has been supported even when strict multivariate
assumptions are not met (Iacobucci, 2009; 2010; Olsson, Foss, Troye & Howell,
2000). Moreover, item parcelling was employed to provide a closer
approximation of continuous level data (e.g. Jöreskog and Sörbom, 1996) and
improve indicator-to-factor ratios, which, at higher levels have been linked with
increased sampling errors and biased estimates of fit (e.g. Nasser & Wisenbaker,
2003).
To assess the adequacy of the models tested, it is widely recommended that
multiple statistics are employed (Hu & Bentler, 1990; Kaplan, 2000). In line with
this, and simulations assessing performance of fit indices with missing data (e.g.
Davey, Savla & Luo, 2005), the following are selected here: Chi-squared (χ2),
Root-Mean-Square-Error-of-Approximation (RMSEA); Comparative-Fit Index
(CFI) and Tucker-Lewis Index (TLI). Schreiber, Amaury, Stage, Barlow and
King (2006) affirm that the CFI, TLI and RMSEA are suitable for the level of
49
data used here while the standardised-root-mean square residual (SRMR) has
been recommended with ML (Hu and Bentler (1998).
To evaluate the fit between model and data, RMSEA statistics of ≤ .08 are
considered reasonable, with figures above .10 representing a poor fit (Hu &
Bentler, 1999). However, as the RMSEA has a tendency to over-reject models
with smaller samples (Byrne, 2010; Hu & Bentler, 1999), confidence intervals are
also reported here to indicate the precision of estimates. Furthermore, when the
χ2/df ratio is below two, this is generally suggestive of good fit (Ullman, 2006).
Hu and Bentler also suggest cut-off levels of ≤.08 for the SRMR with values of ≥
.95 for the CFI and TLI considered good (Hu & Bentler, 1999). However, it
should be noted that AMOS cannot produce the SRMR statistic when there is
missing data, as is the case with the dependent variables (discussed in section
4.3.1), so this is reported for the measurement model, but not available for the
structural portion of the analysis. Finally, for the testing of the structural models,
the Akaike Information Criterion (AIC) is used to assess comparative model fit.
The AIC has been identified as an effective measure for comparing non-nested
models (Kline, 2005), with the lowest AIC representing the more accurate model
(Kuha, 2004).
4.2.1 Item Parcels
Confirmatory factor analysis was first conducted on individual scales using all
items to assess item loadings and inform the development of parcels. Fit statistics
50
are presented in table 4.1 and show that none met all of the cut-off criteria, while
details of items and factor loadings are included in appendix C. Additionally,
three items on LoC (LoC1, 2 & 4) had poor loadings (.11, .30 and .34
respectively) and were removed prior to parcelling as they were not considered to
adequately represent the factor they were set to load onto.
It should also be noted that the mindset scale indicated particularly poor fit. It has
been clearly asserted as unidimensional (Hong et al, 1999), yet the poor fit and
modification indices showing correlated errors predominantly between entity-
worded mindset items (1, 2, 4 & 6) suggested a two-factor model. The fit for the
two-factor solution, with the four entity and incrementally worded items set to
load on separate factors showed substantially better fit (table 4.1). However, a
two-factor solution is theoretically unsatisfactory as already discussed and the
factors were highly correlated (r = .80). Therefore, in view of the generally good
factor loadings for the single factor CFA, items are parcelled on the basis of these
loadings and additional modifications only considered if this appeared
problematic in the measurement model.
51
Table 4.1. Fit statistics for each scale using individual items
Scale df χ2 CFI TLI SRMR RMSEA 90% CI PCLOSE
Lower Upper Mindset 20 107.348 .886 .840 .061 .165 .135 .197 .000 LoC 20 39.672 .873 .822 .068 .078 .042 .114 .094 PSSE 35 181.916 .883 .850 .059 .162 .139 .186 .000 LGO 5 13.836 .978 .956 .035 .105 .041 .173 .072 AGO 2 10.863 .972 .917 .045 .166 .079 .269 .017
Two-factor Mindset 19 34.753 .979 .970 .032 .072 .031 .109 .159
Parcels were created using the method specified by Little, Cunningham, Shahar &
Widaman, (2002), where the highest loading items are allocated to separate
parcels, and then the lowest loading items are allocated in reverse order to the
corresponding parcels. Marsh and Hau (1999) assert that each latent variable
should retain at least three indicators and for PSSE and mindset scales, this
resulted in three parcels comprising between two and four items. Where scales
only had four and five items (i.e. LoC, AGO & LGO), following Marsh and
Hau’s (1999) minimum criteria, it was necessary for one (LGO, LoC) or two
items (AGO) to remain unparcelled. In this case, it was the strongest loading that
was kept unparcelled. Full details of items and parcels are shown in appendix C.
Further CFA to assess individual scales with parcels was not possible as with only
three indicators, these were just-identified and measures of adequacy could not be
calculated. Therefore CFA was used to assess the adequacy of the full
measurement model and the selected indices suggested a good fit between model
52
and the data (²= 125.10, df = 80, p = .001; CFI = .971; TLI = .962; RMSEA =
.059; SRMR = .053). Inspection of modification indices (MI) suggested two
particular improvements, but there did not appear to be any reasonable theoretical
link; an MI of 11.89 was associated with AGO and a residual error on one PSSE
indicator (SE2) and an MI of 10.65 between residual errors on indicators of AGO
(AGO3) and Mindset (Mind2). Therefore, as per Jöreskog (1993), in the absence
of strong justification for respecifying the model no further changes were made
and this measurement model formed the basis for the subsequent analyses of
structural models. The full measurement model is displayed in figure 4.1 and also
shows the factor loadings for indicators which ranged from .62 (LoC1) to .94
(Avoid2). On the basis of the good fit shown by the measurement model, the
mindset latent variable was retained without further changes.
53
Figure 4.1: Measurement model (²= 125.10, df = 80, p = .001; CFI = .971; TLI = .962; RMSEA = .059; SRMR = .053)
Mind
.82Mind1 e13.91
.76Mind2 e14
.87
.85Mind3 e15
.92
LoC
.38LoC1 e10.62 .48LoC2 e11
.70.56
LoC3 e12
.75
.37
LGO
.75LGO1 e7.86 .60LGO2 e8
.77.85
LGO3 e9
.92
.43
.27
AGO
.52AGO1 e4.72
.89AGO2 e5
.94
.72AGO3 e6
.85
-.39
-.22
-.17
PSSE
.86SE1 e1
.93 .76SE2 e2.87
.83SE3 e3
.91
-.29
.50
.45
.27
54
Table 4.2: Means, standard deviations and correlations between latent and observed variables Variable M SD 1 2 3 4 5 6 7
1. Mindset 35.36 9.19 (.91) 2. LoC 25.15 3.77 .42 (.69)1 3. AGO 13.60 5.03 -.12 -.30 (.86) 4. LGO 28.04 4.47 .22 .53 -.16 (.86) 5. PSSE 51.22 10.65 .21 .52 -.25 .36 (.93) 6. Membership (months) 47.99 53.45 .08 .20 -.26 .28 .56 - 7. Practice (roles) 17.27 14.13 .10 .25 -.20 .24 .23 .38 - Note. alpha coefficients in parentheses 1alpha coefficient based on five items retained from initial CFA
Means, standard deviations, scale reliabilities and correlations between latent and
observed variables are shown in table 4.2. In light of the apparent similarities
between LoC and mindset, discussed previously, it is notable that the moderate
correlations (r =.42) support the assertion that they do represent distinct
constructs. Scale reliabilities were all adequate, ranging from .69 (LoC) to .93
(PSSE).
Convergent and discriminant validity were assessed using calculations from Hair,
Black, Babin and Anderson (2007). Convergent validity was evaluated by
assessing the average variance extracted (AVE) for each of the latent constructs
(Hair et al, 2007); although the majority of these comfortably exceeded the
recommended threshold of AVE > .5 (Hair et al, 2007), the LoC scale fell just
short of this (AVE = .48).
55
Table 4.3: Assessments of construct convergent and discriminant validity: AVE, MSV and ASV
Latent variable
Average Variance Extracted (AVE)
Maximum Shared Squared Variance (MSV)
Average Squared Shared Variance (ASV)
Mindset 0.81 0.14 0.08 LoC 0.48 0.20 0.15 AGO 0.71 0.15 0.08 LGO 0.73 0.25 0.16 PSSE 0.82 0.25 0.15
Meanwhile, all latent constructs indicated discriminant validity with AVE for all
scales being greater than both MSV (maximum shared squared variance) and
ASV (average squared shared variance) (table 4.3).
4.3 Structural equation models (SEM)
The approach taken to model testing was in line with Jöreskog’s (1993)
alternative models methodology, whereby models specified a priori are tested
(figures 4.2 & 4.3) and the best fitting model selected. This also follows advice
from MacCallum, Roznowski and Necowitz (1992) and Ullman (2007) who
caution against excessive modification of models to achieve best fit by ‘fitting
small idiosyncratic characteristics of the sample’. This was considered
particularly relevant in view of the present sample size and missing data issues.
Therefore, aside from the issues arising from preliminary data screening and item
retention decisions detailed previously, only the two proposed models were tested
and no further modification undertaken.
56
4.3.1 Missing data
At this stage the observed dependent variables (‘practice’ and ‘membership’)
were added to test the hypothesised structural models. However, a key issue
should be highlighted; from the total sample of 161 which provided data on the
five constructs used in measurement models, only n = 81 provided responses on
the practice variable. There were two further cases missing data for membership
meaning there was data available from n = 79 for this variable. This appeared to
stem from either the technical issue described previously (section 3.4) or
questionnaires being completed online by non-members who therefore were not
required to complete the membership-specific section. It is unclear exactly how
many are due to the former and how many are simply due to non-membership –
although it believed the latter is responsible for the greater proportion. However,
in preference to listwise deletion of cases with incomplete data, which has been
shown to be unsatisfactory in these circumstances and sample size (Enders &
Bandalos, 2001), the decision was taken to employ Full Information Maximum
Likelihood estimation (FIML), a method which has been shown to provide
reasonable results even under conditions of similar ‘missingness’.
However, before discussing this, a missing-values-analysis in SPSS, indicated
significant differences in mean scores on four of the five independent variables
between the group providing full data and the group which did not. Full details
57
are provided in table 7.2 (appendix B) and indicate that only the difference in
LGO were non-significant. These differences themselves may have been
interesting findings if missingness was purely due to membership versus non-
membership, but as this was not entirely the case it is mentioned only to highlight
the issue. Demographic comparisons of the two groups are displayed in table 7.1
(appendix B) and indicate both groups were broadly similar though the ‘missing
data’ population contained a greater proportion of students (23.8%) than the
‘complete data’ population (2.5%).
FIML is available within AMOS (Arbuckle, 1996; Arbuckle, 2007; Byrne, 2010;
Carter, 2006; Graham, 2009) and draws on information from all the available
variables in the dataset to estimate parameters (Enders, 2001a). It has been shown
as an effective solution for dealing with missing data, including when there are
distributional violations (Yuan, Wallentin & Bentler, in press). Furthermore,
simulations have shown FIML performs well across a range of conditions and has
produced satisfactory results even in small samples, or those featuring over 50%
missing data (e.g. Enders, 2010; Enders & Bandalos, 2001; Newman, 2003,
Wothke, 2000)
4.3.2 Structural models
SEM was used to test the hypothesised models including the observed dependent
variables ‘practice’ and ‘membership’. The two models tested here were broadly
58
similar, with only the relationships between mindset with LoC and the two goal
orientations varying. The first model tested is the hypothesised one, which
proposes that LoC would mediate the relationship between mindset and goal
orientations (figure 4.2). However, an alternative model broadly following
Dweck and Leggett’s (1988) framework - with the addition of LoC - was also
tested. Both models hypothesised positive paths from LGO to, ‘practice’ and
‘membership’, with the same paths from AGO hypothesised as negative. Both
‘membership’ and LoC were hypothesised to have positive paths to PSSE while
PSSE was also predicted to be positively related to ‘practice’.
59
Model 1: Locus of control as a mediator of mindset
The hypothesised model considered LoC as a mediator of mindset. This model
proposes that the belief that one’s abilities are malleable (fixed) contributes to an
internal (external) locus of control, and this influences the tendency to select
avoidance or learning goals. Across the selected indices, this model indicated
reasonable fit (²=180.346, df = 111, p<.001; CFI = .956; TLI = .939; RMSEA =
.062; AIC = 298.346) and table 4.4 indicates that even at the upper bounds of the
RMSEA 90% confidence interval, this model would still suggest an adequate fit.
Figure 4.2: SEM model 1: LoC mediating effect sof mindset on goal orientations (²=180.346, df = 111, p <.001; CFI = .956; TLI = .939; RMSEA = .062)
Mindset
.82Mind1 e1.91
.76Mind2 e2
.87
.86Mind3 e3
.93
.17
LoC
.34LoC1 e4.58
.50LoC2 e5
.71
.49LoC3 e6
.70
.09
AGO
.51AGO1 e7.71
.92AGO2 e8
.96
.70AGO3 e9
.84
.28
LGO.86
LGO3e10
.93
.59LGO2e11
.77
.74LGO1e12 .86
.48
PSSE
.83SE3e13
.77SE2e14.88
.85SE1e15
.92
.13
Membership
.17
Total practice
.42
.42
.31
.91
.11
-.22
.48
-.11
.53 -.30
e16
e17
e18
e19
e20
e21
.25
60
However, in view of the popularity of Dweck and Leggett’s framework, it was
considered important to test an alternative model as a comparison. This more
closely follows their conception of the relationships between mindset and goal
orientations.
Model 2: Covaried mindset/LoC
Although the first model indicated good fit, this model was tested to rule out an
alternative theoretical explanation: that mindset has a direct effect on goal
orientations. This model proposes that an incremental mindset has direct effects
on AGO and LGO, while LoC will also have similar direct effects (figure 4.3).
All other relationships remained the same as model 1. The fit indices suggested
an acceptable fit, based on the specified criteria (²= 179.360, df = 109, p <.001;
CFI = .955; TLI = .937; RMSEA = .064; AIC = 301.360). However, in this
model, although path coefficients between mindset and both AGO (β =-.06; p =
.53) and LGO (β =.08; p. = .38) were in the expected direction, neither were
significant. Meanwhile, LoC’s relationships with both LGO (β = .48; p < .001),
PSSE (β = .41; p < .001) and AGO (β = -.26; p < .05) were significant, as in the
previous model, suggesting that LoC fully mediates the effect of mindset on goal
orientations. The model also indicated a comparatively poorer fit than the first
model (AIC = 301.360).
61
Figure 4.3: SEM model 2: covaried mindset and LoC model (²= 179.360, df = 109, p <.001; CFI = .955; TLI = .937; RMSEA = .064)
Mindset
.82Mind1 e1.91
.76Mind2 e2
.87
.85Mind3 e3
.92LoC
.35LoC1e4
.59
.50LoC2e5
.71
.51LoC3e6 .71
.08
AGO
.51AGO1 e7.71
.92AGO2 e8
.96
.70AGO3 e9
.84
.26
LGO.87
LGO3e10
.93
.59LGO2e11
.77
.74LGO1e12 .86
.48
PSSE
.83SE3e13
.77SE2e14.88
.85SE1e15
.92
.13
Membership
.17
Total practice
.31
.91
.11
-.23
.48
-.11
e16
e17
e18
e19
e21
.25
-.26
.41
.48 -.06.08
.40
62
Table 4.4: Fit statistics for alternative models
Model df ² CMIN/DF CFI TLI RMSEA 90% CI PCLOSE AIC
1. LoC mediator 111 180.346* 1.625 .956 .939 .062 .045 .079 .109 298.346
2. Covaried mindset/LoC 109 179.360* 1.646 .955 .937 .062 .046 .080 .093 301.360
3. LoC mediator untransformed1 111 177.842* 1.602 .958 .941 .061 .044 .078 .132 295.842
4. Covaried mindset/LoC untransformed2
109 176.895* 1.623 .957 .939 .062 .045 .079 .113 298.985
Note. CFI = comparative fit index; TLI = Tucker-Lewis index; RMSEA = root-mean-square error of approximation; AIC = Akaike information criterion. 1 same as model 1, includes untransformed outliers 2 same as model 2, includes untransformed outliers * p < .001
Final model
Of the two models, the hypothesised model indicated superior fit on all of the
selected fit indices (table 4.4), though both models could be classified as
reasonable-to-good fit based on these. Both models were also similar in the
amount of variance they explained on the dependent variables, with the only
difference in explanatory value being the additional 2% and 1% of variance on
LGO and AGO respectively accounted for by the LoC-as-mediator model (model
1). Nonetheless, despite the relative similarity in fit and variance explained, the
hypothesised model was still superior on both counts and is preferred to the
alternative.
63
Further to the discussion of outliers (section 4.1), there were some differences in
the model statistics between the data with transformed or untransformed outliers;
the untransformed data actually produced better fit (see models 3 and 4 in table
4.4), though explained 1% less variance on ‘membership’ due to the reduced path
coefficient from LGO (β = .23, p < .01) than those with the transformations (β =
.25, p < .01). Nonetheless, with both datasets, each of the models supported the
hypothesis that LoC and mindset would be significantly related.
The selected model (figure 4.2, and summarised in figure 4.4) suggests the effect
of mindset on LGO and AGO is mediated by LoC and supports the hypothesised
positive relationship between incremental beliefs and stronger perceptions of
internality (β =.42; p < .001). Furthermore, the positive and negative
relationships between LoC with LGO (β = .53; p < .001) and AGO (β = -.30; p <
.01) respectively, were also as hypothesised and in view of the superior fit of this
model and the direct non-significant paths from mindset to AGO and LGO in the
alternative model, this may offer some insight into the equivocal results of
previous research which has looked at these relationships (e.g. Payne et al, 2007).
In common with previous research (e.g. Phillips & Gully, 1997) and as
hypothesised, the path between LoC and PSSE was positive and significant (β =
.42; p < .001), with an internal LoC being associated with higher levels of public
speaking self-efficacy. Interestingly however, while goal orientations were
significantly related to continued membership of their public speaking clubs and
supported both hypotheses (AGO β = -.22 p < .05; LGO β = .25; p < .05), the
64
hypothesis that goal orientations would be significantly related to the amount of
public speaking practice in the previous six months was not (AGO β = -.11 p =
.33; LGO β = .11; p = .34). Meanwhile, the apparent cumulative benefits of
practice (inferred by length of membership) on self-efficacy was also indicated by
the positive relationship between length of membership and PSSE (β = .48; p <
.001). Furthermore, following the results of the membership to PSSE path and
Bandura’s (1997) theory regarding the unfolding and dynamic nature of self-
efficacy, PSSE was also positively related to the amount of practice undertaken (β
= .31; p < .001).
Figure 4.4: Final model including standardised path coefficients. Note. *** p < .001; ** p < .01; * p < .05; ns = non-significant
Overall, the model accounted for 17% of the variance in ‘practice’ and 13% of
variance in ‘membership’, providing some support for the hypothesised impact of
the dependent variables considered here. The model also accounted for 48% of
Mindset
LGO
AGO
LoC PSSE
Memb.
Practice.42***
-.30**
.53***
0.42***
.31*
.11 ns
-.11 ns-.22*
.25*
.48***
65
the variance in PSSE with the largest path coefficient being associated with length
of membership. Meanwhile, of the variables that were set as predictors of
membership, 28% the variance on LGO and 9% on AGO were explained by the
model. Mindset, proposed as antecedent to LoC, accounted for 17% of the
variance in internality beliefs.
4.3.3 Additional analysis
Significance of LoC mediation of mindset
In view of the discussions around LoC as mediator and the support the model
received, it was appropriate to consider the magnitude and significance of the
indirect effects of mindset on goal orientations. AMOS does not – by default –
give such significance statistics but the use of bootstrapping within AMOS does
allow both significance and confidence intervals to be estimated (Arbuckle, 2007;
Cheung & Lau, 2008). With bootstrapping, data is ‘resampled’ a predetermined
number of times to build up an empirical estimation of the sampling distribution
of a statistic and this distribution provides a foundation for the construction of
confidence intervals around the indirect effect (Cheung & Lau, 2008, p.301).
However, this is not available in the presence of missing data, as is the case with
the dependent variables; therefore, only the portion of the model involving
mindset, LoC and goal orientation was tested. It is recognised that the strength of
relationships among these path coefficients would differ slightly from those in the
66
full model, so although these results are a useful test of this mediation, in the
context of the full model, these should be considered with caution.
The bootstrapping process followed Cheung and Lau (2008) and employed their
recommended use of 95% confidence intervals and specification of 1000
bootstrap samples. This analysis, using the bias corrected statistic indicated that
mindset had significant indirect effects on each of the goal orientations (AGO
standardised indirect effect = -.12, [95% CI: -.02 ~ -.27]; LGO standardised
indirect effect = .20 [95% CI: .08 ~ .35]), which further supports the model, albeit
with the aforementioned caveat. Figure 4.5 displays the direct and indirect
relationships and significance levels.
Figure 4.5: test of significance of indirect effects of mindset on learning and avoidance goal orientations. Note. Dotted lines indicate indirect effect of mindset on goal orientations via LoC; standardised path coefficients shown. ** p < .01, *** p < .001)
67
5 Discussion
5.1 Summary of analysis and results
This study aimed to test a model based upon Dweck and Leggett’s motivational
framework comprising mindset and goal orientation. It adds to the existing
literature by testing the theory-based addition of locus of control in order to shed
light on consistently weaker-than-expected relationships. The further inclusion of
self-efficacy means this model is the first to incorporate these constructs and
heeds Funder’s (2001) call for greater integration of mindset within social
cognitive theory. Furthermore, the model was tested in relation to a task which is
widely seen as anxiety provoking – public speaking – and is thus congruent with
the view that the effects of such social cognitive constructs are most salient when
facing challenging tasks or difficulties. As the study was conducted relative to a
voluntary activity, it also complements the majority of research in academic or
work settings where individuals may not have the same discretion to exercise (or
not) their preferred self-regulatory strategies. The discussion will begin with a
brief overview of findings, before discussing individual relationships in more
depth and in the order specified by the model. Specific issues are discussed
throughout, while limitations and recommendations for future research are
followed by the study implications and conclusion.
68
The final structural model indicated that LoC mediated the relationship between
mindset and goal orientations; a finding which provides a plausible extension of
Dweck and Leggett’s framework. Although it was noted that an alternative
model, more closely approximating their model, explained the same amount of
variance in both of the dependent variables, the lack of significant direct paths
from mindset to goal orientations when LoC was present, in addition to the
theoretical rationale discussed in the literature review, supported the LoC-as-
mediator model. These results also indicate that in comparison with LoC, the
effects of mindset are not as influential in the development of goal orientations as
Dweck and Leggett (1988) and numerous researchers may suggest.
This model potentially offer a resolution to previous research into the direct
effects of mindset on LGO and the generally weak relationships reported
previously. However, contrary to hypotheses, the model indicated that neither
goal orientation was related to the amount of public speaking practice undertaken
by members at their clubs in the previous six months. Nonetheless, these did
significantly predict length of membership, which perhaps suggests that goal
orientations have a greater influence on longer-term perseverance than
short/medium-term strategy. The length of membership was significantly related
to level of public speaking self-efficacy which is unsurprising and entirely in line
with research showing the benefits of practice. The model explained 13% of the
variance in membership length, while further support for the role of self-efficacy
was also provided by its significant association with amount of practice. Overall,
69
the model explained 17% of the variance in public speaking practice and provides
an interesting starting point for research incorporating these five constructs.
5.2 Discussion of results
5.2.1 Mindset and locus of control
One of the key issues the hypothesised model sought to address was the modest
relationships between mindset and goal orientations demonstrated by previous
research. A search of the literature indicated that no previous studies have
proposed and investigated the presence of LoC as a mediator. Both constructs
have control-related elements (Skinner, 1996) and correlation between mindset
and LoC were expected, though this was moderate (r = .42) and tests of
discriminant validity indicated they were clearly two different constructs, based
on the measures used here. The direct significant path between mindset and LoC
supports the first hypothesis and suggests that a perception of one’s abilities as
being amenable to change (i.e. mindset) promotes a view of having more control
over one’s own development (Dweck et al, 1995). So in response to the mixed
findings regarding smaller direct effects of mindset on goal orientations, it is
suggested that an incremental mindset alone is insufficient and needs to be
accompanied by beliefs about one’s ability to exert control over the
‘environment’, which has been strongly linked with adaptive strategies (Skinner,
1996). The significant relationships between LoC and each of the goal
orientations supports the hypothesis that LoC would mediate the effect of mindset
70
on both AGO and LGO; LoC and goal orientation relationships are discussed in
section 5.2.2. Additional support for the mediated relationship was provided by
the supplementary analysis which suggested this mediated relationship was
significant. However, due to the requirement that the analysis could not include
the two variables associated with the missing data, this must be considered
tentative rather than conclusive. Overall, this suggests the model may be useful in
interpreting previous related findings, though this does not invalidate Dweck and
Leggett’s theory and may provide a valuable explanatory extension of it.
Mindset explained 17% of the variance in LoC, so although the relationship was
significant and is comparable with other social cognitive research looking at
related relationships (e.g. Phillips and Gully, 1997), it leaves a lot of variance
unaccounted for and suggests the need to consider additional variables in future
models. It is understandable that a single construct would not be solely or
majorly responsible for the variance of a construct such as LoC which is derived
from such a wide range of sources, including ability, experience and early
developmental factors (Chorpita & Barlow, 1998; Phillips & Gully, 1997;
Skinner, 1996). Therefore, while it was asserted previously that an incremental
mindset may be a necessary, but not sufficient, precondition for action to ensue, it
is also clear that mindset alone is not sufficient to explain internality of LoC.
Nonetheless, mindset is well supported by research affirming its impact in various
contexts (e.g. Beer, 2002; Burnette, 2010; Heslin et al, 2006; Levy et al, 1998;
71
Mueller & Dweck, 1998) and there appears little doubt that it is a useful and
influential construct.
Taken at face value the results may be useful in developing our understanding of
mindset and its relationships, but there several matters which should first be
considered. Firstly, closer inspection of LoC and mindset items highlighted a
particular semantic issue. Whereas the LoC measure used the first person
singular for all items (i.e. ‘I’ or ‘my), the mindset scale uses the second person
singular or plural (i.e. ‘you’) which can refer to either the self or ‘people in
general’ (Oxford English Dictionary). This is noted by Bråten and Strømsø
(2005) in relation to the mindset measure but they conclude that it is acceptable
for their purposes. However in psychometric terms, Azjen and Fishbein (2005)
suggest this would lead some people to view items at a more global level. This
ambiguity is likely to lead mindset items being interpreted at a more general level
than LoC items, which might well have influenced the relationship between the
two constructs. For example, some people interpreting mindset questions at a
‘people in general’ level may believe that ‘[other] people’ can change their
abilities, yet not feel they themselves could; an example of such thought patterns
can be seen with low self-esteem (e.g. Judge, Erez & Bono, 1998; Marsh, 1996).
So some respondents may therefore indicate higher levels of mindset because of
its more global framing, while also indicating lower internality beliefs, which is
more specific. This would arguably reduce the strength of the relationship
between mindset and LoC and may indicate the ‘real’ relationship is actually
72
larger in magnitude. Similarly, as the goal orientation items also use the first
person (i.e. ‘I’ & ‘me’), this may also apply to these relationships and both direct
and indirect effects of mindset on goal orientations may also be stronger than
found here. As Dweck’s measures are widely used, this may also be an additional
explanation for some of the previous research findings discussed previously.
Nonetheless, further issues with the mindset measure relate to some
psychometrically questionable items. For example, one item includes the
colloquialism ‘you can’t teach an old dog, new tricks’ (item 4), which may or
may not make sense to all participants and it is unclear how it might be
interpreted. The double-barrelled nature of two of the questions also detracts
from their clarity (Bowling, 1997).
The confirmatory factor analysis of the mindset scale also suggested issues with
the way this is measured; theoretically, the two-factor mindset model does not
make sense but it did indicate a better fit. However, the subsequent parcelling
strategy appeared to ameliorate these problems and indicated no issues in the
measurement model, but they cannot be completely ignored. It is contended that
these issues, in addition to those mentioned previously are particularly important
here, because this study is examining a relationship that has not been looked at
before so there is no real empirical precedent that can be used to validate such
conclusions. Nonetheless, the theoretical underpinnings underlying the model
73
tested here suggest the relationship found is not spurious, but given the level of
research attention that mindset has received, a well-designed measure founded on
good psychometric principles should be a priority to improve inferences derived
from it.
5.2.2 Relationships from LoC to goal orientations and PSSE
The direct paths from LoC to both AGO and LGO were also supported, in line
with the mediating role of LoC; the significant positive path from LoC to PSSE
was also indicated by the model. These support the hypothesised relationships
from LoC. The latter is in line with the view that perceptions about capability to
carry out a particular action/skill are influenced by more general beliefs that
outcomes may be influenced by behaviours (Bandura, 1977). It also echoes
previous theory (e.g. Bandura, 1997; Skinner, 1996) and research (Bandura &
Wood, 1989; Phillips & Gully, 1997). However, the relationships between LoC
and goal orientations have not been as extensively studied and were based
predominantly on theory rather than research. Although Seifert and O’Keefe
(2001) do provide some evidence, they employed a series of three-item scales and
aside from reliabilities - which were reasonable - no information is provided
regarding their construction or validation. Nonetheless, the relationships
demonstrated by the final model here are supportive and with regards to the LoC
to AGO path, may also shed light on another frequently equivocal finding. As
previously stated, this study employed only the LGO and AGO dimensions of
VandeWalle’s (1997) measure, because the other, prove orientation, has so often
74
shown non-significant relationships (VandeWalle et al, 1999), appearing to have
more complex interactions with performance (Payne et al, 2007) and is not always
maladaptive (e.g. Stevens & Gist, 1997). However, as both prove and AGO have
been conflated into an overall performance goal orientation (PGO) by other
measures (e.g. Button et al, 1996; Dweck, 2000), this is likely to mask their true
effects. For example, Phillips and Gully (1997) – using Button and colleagues’
goal orientation measure - found LoC and PGO to be unrelated. The findings
here, offer further support for goal orientation measures which clearly
differentiate between these PGO dimensions.
The model accounted for 28% of the variance of LGO, suggestive that an
incremental mindset and subsequent internal locus of control are indeed
influential in the development of a learning goal orientation. However, the model
also indicates that an entity mindset and low internality do not equate to
avoidance goal orientations to the same degree, with 9% of AGO variance
explained by the model. It may also be taken as support for VandeWalle’s (1997)
model of goal orientations in that AGO does not simply appear to be the exact
opposite of learning goal orientation. A further consideration is that this study
only employed the internality subscale of Levenson’s (1981) measure and the
inclusion of the two dimensions proposed to relate explicitly to an external LoC –
chance and powerful others – should be expected to improve the amount of AGO
variance predicted. It could also be argued that other factors such as
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conscientiousness or motivations, not included here, might supersede evaluative
concerns related to AGO (e.g. Bouffard, Boisvert, Vezeau, and Larouche, 1995)
Furthermore, the internality subscale used her only approached adequate
reliability (α = .69) after the removal of three items - a similar finding to Walkey
(1979) – in addition to low convergent validity. This has clear implications when
investigating the relationships between these constructs, particularly as they are so
central to the study. There are a wide range of other LoC measures (e.g.
Duttweiler, 1984; Lefcourt, 1991; Rotter, 1966; Spector, 1988), yet together these
have often been shown to demonstrate poor convergent validity (Goodman &
Waters, 1987) and emphasises that it cannot be assumed that a single measure is
truly representative of the construct. Therefore, comparisons using different
measures would be useful and appropriate. For example, of the four dimensions
on Lefcourt’s (1991) scale, two are expressly concerned with the degree to which
one holds internal attributions of ability or effort. At face value, both of these
appear to have strong parallels with mindset and be expected to have
demonstrated even stronger relationships.
5.2.3 Goal orientations and dependent variables
The effects of goal orientations on performance tend to occur via the differing
self-regulatory and learning strategies they promote (e.g. Payne et al, 2007); this
includes maladaptive avoidance behaviours such as dropping out, in the case of
AGO (e.g. Duda, 1989). The relationships between both AGO (β = -.22; p < .01)
76
and LGO (β = .25; p < .01) to length of public speaking club membership in this
study are suggestive of a similar pattern and support both related hypotheses.
This suggests that individuals with avoidant goal orientations are simply less
likely to continue attending. Conversely, with LGO, this relationship is reversed
and higher levels linked to length of membership. As this study related to public
speaking, which is often feared precisely because of concerns about appearing
foolish to others (Leary, 1983), this is especially relevant. It is expected that
longer-term attendance and thus more practice opportunities would be linked with
improved self-efficacy- a finding supported by this study, but these results also
add to our understanding about why. This in itself is valuable to practitioners who
may be looking for ways to promote involvement with training activities or
development opportunities and emphasise the benefits of creating an environment
that encourages learning goal orientations (Utman, 1997).
Nonetheless, despite empirical support (e.g. Pintrich & de Groot, 1990;
VandeWalle et al, 1999), goal orientations did not significantly predict the
amount of public speaking practiced in the previous six-months and hypotheses
regarding these relationships were not supported. There are a number of
explanations for this though firstly, it should be noted that the relationships were
in the expected direction, but were not sufficiently strong (AGO: β = -.11, p =.33;
LGO: β = .11, p =.34). On the one hand, the sample size and a lack of statistical
power may be an issue, but regardless of significance, the relationships were
small and whether such an effect has practical use is also questionable. However,
77
from a theoretical perspective, in light of social cognitive research indicating the
importance of motivational factors such as specific task motivation or perceived
meaning (e.g. Ommundsen, 2001; Seifert & O’Keefe, 2001), the inclusion of such
a variable could conceivably have mediated this relationship (e.g. VandeWalle et
al, 1999). After all, having a general predisposition to enjoy learning new skills is
not analogous to enjoying the learning of this particular new skill. Nor should it
mean that a person with a high LGO would continue practicing a skill that has
either little interest or relevance to them.
Meanwhile, in contrast to the majority of related studies in educational or
workplace settings (e.g. Phillips & Gully, 1997; Seifert & O’Keefe, 2001) where
participation is essentially mandatory, this study looked at involvement in an
entirely voluntary endeavour. Therefore, because AGO’s are more likely to drop
out (e.g. Duda, 1989) and there is nothing to ‘prevent’ them, it is probable that the
measure of speaking practice used here is subject to a restriction of range, as those
with AGO’s are less likely to continue. This, in tandem with the decreased
likelihood of them even seeking such a development opportunity in the first
instance (Porath, Spreitzer, Gibson & Garnett, 2012) would attenuate these
relationships further and supports the view they are less likely to be represented in
the final sample. Therefore, the sample and measure of public-speaking practice
used here is likely to represent a conservative test of the hypothesis that goal
orientations are related to willingness to practice. Consequently, in this instance,
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continued membership may be a better indicator of the impact of these constructs
than amount of practice.
5.2.4 Public speaking self-efficacy and dependent variables
The relationships between PSSE and the dependent variables were as
hypothesised and in line with a huge amount of existing research regarding the
impact of self-efficacy on behaviours and the development of self-efficacy (see
Bandura, 1997; Bandura & Wood, 1989; Hardy, Jones & Gould, 1996; Wood &
Bandura, 1989). Additionally, the model accounted for 48% of the variance in
PSSE, suggesting that the factors with causal paths going towards PSSE were
good predictors. The length of membership showed a strong relationship with
PSSE (β = .48, p < .001) and although the cross-sectional nature of the study
precludes causal assertions, there is substantial evidence supporting the view that
the amount of time people spend practicing is causally related to their levels of
self-efficacy (Bandura, 1997). Moreover, it could be inferred that this
relationship between self-efficacy and practice is based upon the length of
membership - and thus cumulative amount of practice - which has additive benefit
of increasing the likelihood of practicing further. Although this is extrapolating
somewhat, it does lend further support to the bi-directional model of
performance/self-efficacy development (Bandura, 1997).
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5.3 Limitations
Naturally, there are limitations and as previously mentioned, the cross-sectional
nature of this study limits assertions of causality. While this is hardly unique to
this study, it is worth looking at precisely why this would be important here.
The interactive nature of self-efficacy and outcomes described by Bandura (1997)
means the model proposed here provides only a snapshot of a complex and
dynamic process. Without baseline measures at the very least, it makes it difficult
to tease out the true nature of these relationships. However, while this is
undoubtedly true and common to numerous other studies (e.g. Kozlowski et al,
2001; Phillips & Gully, 1997), this study has at least attempted to model their
relationships as suggested by more robust longitudinal studies (e.g. Bandura &
Wood, 1989; Wood & Bandura, 1989).
Therefore subsequent research should look to employ a longitudinal design,
recognising the reciprocal relationships between self-regulatory mechanisms
(Bandura, 1997). It is also recommended that a multi-trial methodology be
adopted (e.g. Bandura & Wood, 1989; Wood & Bandura, 1989), whereby
measures are taken at set points throughout the study in an effort to capture their
dynamic and evolving nature.
The use of data from all participants to estimate the missing data on the dependent
variables (i.e. FIML) reflected issues in the data collection; both of which must be
80
recognised as a limitation. Nonetheless, FIML - the method selected to deal with
this - has been shown to perform well in these circumstances, providing unbiased
estimates under missing-at-random (MAR) conditions (Wothke, 2000) and can
perform effectively even when data is not quite MAR (Muthén, Kaplan & Hollis,
1987). However, large proportions of missing data can reduce the power of a
model by inflating the standard error of estimates (Sternberg, 2006). Although
FIML is still superior in that regard to listwise deletion (Enders & Bandalos,
2001), in view of the proportion of missing data and inherent power limitations,
this concern does appear particularly relevant. Results concerning the dependent
variables should therefore be viewed cautiously. Nonetheless, assertions
regarding the mindset, LoC and goal orientation portion of the model are based on
the full sample.
This sample is somewhat smaller than ideally recommended for SEM (e.g. Hu &
Bentler, 1999) and the possibility these results are idiosyncratic to this small
population cannot be ruled out; particularly considering the high proportion of
university educated individuals and senior managers in the sample. This and the
data-related limitations all have implications regarding the generalisability of this
study. The missing-values-analysis also indicated significant differences
between the ‘full data’ and ‘incomplete data’ groups on four of the five
independent variables. Whilst this does not preclude valid parameter estimates
from FIML, it highlights a further reason to not overgeneralise these findings.
81
These group differences may have been interesting in themselves, if it was clear
these two groups were exclusively differentiated by their membership (or not) of
the public speaking clubs. But due to the presence of an indeterminate minority
of members in the ‘missing data’ group due to technical issues with the online
questionnaire, this would have been meaningless.
In addition to restriction of range discussed previously, the accuracy of the
‘practice’ measure is also unclear. Although measures of club involvement were
intended to be objective, they did rely on people’s recall of both their period of
club membership and the number of roles they had taken in the previous six
months. As such, it is acknowledged these are likely to be imperfect, as opposed
to more accurate records maintained by the clubs themselves, which were
unavailable for obvious confidentiality reasons. However, although they may
approximate a reasonable indicator of willingness to practice, as this is a
voluntary activity, attendance was also likely to be influenced by peoples
circumstances and availability, over and above their desire to improve their skills.
The study did not account for such mitigating factors, such as schedule conflicts
or family responsibilities, that may have reduced their amount of practice.
5.4 Future research
The point was made in the literature review that some previous models have not
including key variables in their models or have failed to account for theoretically
82
supported relationships (e.g. Phillips & Gully, 1997). Therefore, by the same
token the present study must also acknowledge the same. There are several
additional factors which could be theorised to improve the explanatory value of
this model, some of which have been mentioned previously.
The final model left a large proportion of the variance unaccounted for in both
‘length of membership’ and ‘amount of practice’. In a relatively complex model
such as this, it is not entirely surprising and in line with Bandura (2012), future
studies should look to include other variables shown to influence behavioural
outcomes. There was no objective measure of performance included in this
model, but in line with Bandura and Wood’s (1989) cyclical model of
performance and self-efficacy development, it would be expected that such a
measure would then itself influence continued membership and self-efficacy and
so on. However, factors such as goal level and other goal-setting theory variables
would be usefully incorporated into this model (Bandura & Cervone, 1983) and
would appear particularly relevant as evidence suggests these processes are more
strongly applicable to those with LGO (e.g. VandeWalle, Cron & Slocum, 2001).
This indicates that it is not as simple as either one orientation or the other leading
to either involvement or withdrawal.
Furthermore, it would also be helpful to obtain measures of public-speaking
anxiety, as well as motivation for improving their skills (i.e. task meaning), which
would provide more information on the perceived size and importance of the
83
challenge people are undertaking (e.g. Seifert & O’Keefe, 2001). Such measures
would be more informative as a baseline measure in a longitudinal study than in
the cross-sectional design used here, so their initial effects can be discerned.
It may also be fruitful to extend the call for greater integration of mindset and
social cognitive constructs to include personality traits; for example, is an entity
mindset – with its apparent links to avoidance and maladaptive strategies (Mueller
& Dweck, 1998) - predicted by neuroticism? Spinath et al (2003) appear to
suggest not, but very little research has looked at this and their measure was
domain-specific, rather than the general measure used here. Neuroticism could
also be argued to have direct effects on AGO (e.g. Wiggins, 1996). Therefore
more research looking at the personality-related correlates of this more
fundamental indicator of mindset would improve our understanding of its
antecedents.
Additionally, this study focused on individual difference and dispositional
influences on practice levels, yet clearly situational factors are important (e.g.
Ames & Archer, 1988) and not addressed here. Dispositions such as goal
orientation do not operate in a vacuum and Tabernero and Wood (1999) suggest
they are influenced by the relative importance of situational goals, and which
behaviours and outcomes are actually being rewarded (Ames & Archer, 1988).
After all, one can have an incremental mindset or learning goal orientation, but
how robust is this in the face of an extremely results-oriented culture? Future
84
research may examine this model in relation to differing psychological
organisational environments. For example, the work of Dollard and Bakker
(2010) on psychosocial safety climate – the degree of support to apply new skills
or take ‘risks’ – may complement research showing how organisational culture
and climate play an important over and above such individual differences (e.g.
Murphy & Dweck, 2010).
However, before testing more variables within the model, it is perhaps most
pressing to develop and assess robust measures of mindset, as research has tended
to be based on variations of Dweck’s (2000) measure. It has good reliability, but
that is no guarantee of quality. It has also demonstrated discriminant validity
against numerous other constructs (Dweck et al, 1995), but alongside some of the
issues described previously, a better measure would provide more confidence
regarding the construct - which appears to have genuine value – and the
inferences that may be drawn from any relationships found.
5.5 Implications
These results provide a promising initial support for this model and for the
argument that previous research has indeed missed a key variable when using
Dweck and Leggett’s framework. If these results can be replicated addressing
some of the issues discussed, it could change the way previous results are
interpreted. Aside from this theoretical implication, there may also be practical
85
ones. Research has increasingly looked at mindset in organisational contexts and
there may also be some interesting related points.
These results suggest mindset plays a role in the development of LoC, which has
been associated with a range of adaptive behaviours, such as proactivity, and
action oriented – versus helplessness – responses (Ng et al, 2006; Seligman, 1975;
Skinner, 1996). Encouragingly, research has shown that an incremental mindset
can be induced or trained (e.g. Blackwell et al, 2007; Heslin et al, 2006), so this
may be usefully explored by employers. Furthermore, in relation to the points
made regarding organisational climate, evidence also suggests that the type of
praise and feedback people get can significantly influence their self-regulatory
strategies via similar pathways indicated by the final model (e.g. Mueller &
Dweck, 1998). So careful consideration of this may also be warranted, although
this is essentially an extension of what has been known in organisational terms for
years – i.e.that people will enact behaviours that are rewarded (Schneider, White
& Paul, 1998). Praise people for effort and work, rather than just ability or results
and it can also positively influence LGO, which has situational, as well as
dispositional, antecedents (Payne et al, 2007). However, while organisations may
fear that a decreased focus on performance would reduce it should take note of
VandeWalle et al (1999). They found LGO positively associated with sales
performance, unlike PGO; furthermore - in support much of what has been argued
here – these positive outcomes were completely mediated by strategies such as
planning and effort.
86
5.6 Conclusion
One of the aims of this study was to test a model integrating a group of social
cognitive constructs, not previously tested together. It has confirmed established
relationships such as LoC and self-efficacy and also supports Bandura’s dynamic
conception of the self-efficacy with its relationships to length of membership and
amounts of recent public speaking practice. The model has also provided initial
evidence of new relationships, with the mediating role of LoC between mindset
and goal orientations. This supports the second objective, which was to
investigate this relationship and offer a plausible explanation for the previously
modest results using Dweck and Leggett’s model. Meanwhile, the study also
examined this in relation to a discretionary skill development activity and
provided partial support for the effects of these constructs on some indicators of
commitment to improve public speaking skills.
However, a number of methodological limitations and caveats were noted, and
would need to be addressed before firm conclusions are asserted; nonetheless,
some of these methodological issues may themselves add to the literature, as
issues such as the wording of Dweck’s mindset measure and its impact do not
appear to have been considered in this way previously.
Overall, this study and its results would appear to concur with Heslin’s (2010)
conclusion that mindset is not a ‘magic bullet’, but in tandem with the empirical
support for its value, they do indicate it has a role to play. Furthermore, this
87
research also provides a first look at a relationship not previously considered in
the literature and offers a useful platform for further research, pending validation
of these findings.
88
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7.0 Appendices
7.1 Appendix A: Questionnaire
Life is filled with challenges, but what is it about each of us that affects the way we approach them? This research study focuses on how beliefs about our own skills and abilities can influence the way we engage with one such challenge: public speaking.
My name is John Hudson and I am carrying out this research project as part of a Masters degree at the University of Manchester. If you would like to help with this research, you can complete this short questionnaire which should take around 5-10 minutes.
You must be 18 years or over to participate. Participation is voluntary and we do not ask for your name, contact information or any personally identifiable information, so confidentiality and anonymity are assured. Thank you for your help John
Section 1: Background details
1. Gender: female male
2. Age: __________ years
3. Please tick your highest qualification or its equivalent:
Secondary school to age 16
Non-university higher education
Postgraduate university education
Secondary school to age 18
Undergraduate university education
No formal qualifications
4. Which of the following best describes your current employment status? Employed, full time Self employed Unemployed/not currently
working
Employed, part-time Student Retired
a. If you are currently in employment, which of the following best describes your current occupation?
Administrative
Junior professional/ managerial
Semi-skilled
Service Senior professional/ managerial
Skilled
109
Public speaking and you
Are you a member of a Toastmasters public speaking club?
Yes � No � (if no, please go to section 2)
How long have you been a member of Toastmasters? _______years_______months
Please indicate the number of times you taken the following roles in the last SIX MONTHS
Prepared speech from the Toastmasters manual
Speech evaluator
Timekeeper
Grammarian
Table topics speaker
Table topics master
General evaluator
Toastmaster
110
Section 2:
The following questions require you to record the extent to which you agree or disagree with statements. Please give your honest opinion, by circling the number that applies to you, where:
1 = totally disagree through to 7 = totally agree
If you change your mind about an answer, please put a cross over your original answer and circle a new number.
1 Whether or not I get to be a leader depends mostly on my ability. 1 2 3 4 5 6 7
2 Whether or not I get into a car accident depends mostly on how good a driver I am. 1 2 3 4 5 6 7
3 When I make plans, I am almost certain to make them work. 1 2 3 4 5 6 7
4 How many friends I have depends on how nice a person I am 1 2 3 4 5 6 7
5 I can pretty much determine what will happen in my life. 1 2 3 4 5 6 7
6 I am usually able to protect my personal interests 1 2 3 4 5 6 7
7 When I get what I want, it’s usually because I worked hard for it. 1 2 3 4 5 6 7
8 My life is determined by my own actions. 1 2 3 4 5 6 7
1 = totally disagree through to 7 = totally agree
1 The kind of person you are is something very basic about you and it can’t be changed very much.
1 2 3 4 5 6 7
2 You can do things differently, but the important part of who you are can’t really be changed
1 2 3 4 5 6 7
3 You can significantly change your behaviour and characteristics 1 2 3 4 5 6 7
4 As much as I hate to admit it, you can’t teach an old dog new tricks. You can’t really change your deepest attributes
1 2 3 4 5 6 7
5 You can always substantially change the kind of person you are. 1 2 3 4 5 6 7
6 You are a certain type of person and there is not much that can be done to really change that.
1 2 3 4 5 6 7
7 No matter what kind of person you are, you can always change it a lot.
1 2 3 4 5 6 7
8 You can change even your most basic qualities. 1 2 3 4 5 6 7
1 = totally disagree through to 7 = totally agree
Public speaking
For the final section, please select your level of confidence for each of the statements that complete the sentence below: -
“When speaking in front of an audience, I am [insert level of confidence] that I can…”
1 = Not at all confident 7 = Completely confident
1 ...make an effective presentation. 1 2 3 4 5 6 7 2 ....develop a presentation with good content. 1 2 3 4 5 6 7 3 ...keep the audience engaged. 1 2 3 4 5 6 7 4 ...connect well to an audience that is familiar with the topic I
present. 1 2 3 4 5 6 7
5 ...connect well to an audience that is not familiar with the topic I present.
1 2 3 4 5 6 7
6 ...deliver a well-organized presentation. 1 2 3 4 5 6 7 7 ...design effective visual aids. 1 2 3 4 5 6 7 8 ...deliver effectively (eye contact, use of voice, movements, etc.). 1 2 3 4 5 6 7 9 ...give an effective summary of the information I presented. 1 2 3 4 5 6 7 10 ...answer questions from the audience effectively. 1 2 3 4 5 6 7
Thank you Thank you for supporting this research project. Your participation is much
appreciated
1 I am willing to select a challenging work assignment that I can learn a lot from.
1 2 3 4 5 6 7
2 I often look for opportunities to develop new skills and knowledge.
1 2 3 4 5 6 7
3 I enjoy challenging and difficult tasks at work where I’ll learn new skills.
1 2 3 4 5 6 7
4 For me, development of my ability is important enough to take risks.
1 2 3 4 5 6 7
5 I prefer to work in situations that require a high level of ability and talent.
1 2 3 4 5 6 7
6 I would avoid taking on a new task if there was a chance that I would appear rather incompetent to others.
1 2 3 4 5 6 7
7 Avoiding a show of low ability is more important to me than learning a new skill.
1 2 3 4 5 6 7
8 I’m concerned about taking on a task at work if my performance would reveal that I had low ability.
1 2 3 4 5 6 7
9 I prefer to avoid situations at work where I might perform poorly.
1 2 3 4 5 6 7
112
7.2 Appendix B: Missing value analysis and demographic details between groups with full and missing datasets
Table 7.1: comparison of demographic variables between cases with missing/non-missing data for dependent variables
Complete data n = 81
Missing data n = 80
% of complete subsample
% of missing subsample
Gender Female 44 52 54.3% 65.0% Male 37 28 45.7% 35.0%
Age
Under 20 years old 0 2 0.0% 2.5% 20 – 29 years 15 27 18.5% 33.8% 30 – 39 years 36 30 44.4% 37.5% 40 – 49 years 13 10 16.0% 12.5% 50 – 59 years 8 5 9.9% 6.3% 60 – 69 years 7 5 8.6% 6.3% 70 – 79 years 1 1 1.2% 1.3% 80+ years 1 0 1.2% 0.0%
Mean age 39.63 36.12 - -
Employment
Full time 57 34 70.3% 42.5% Part time 3 10 3.7% 12.5% Self-employed 9 12 11.1% 15.0% Student 2 19 2.5% 23.8% Unemployed 5 0 6.2% 0.0% Retired 3 3 3.7% 3.8%
Missing data 2 2 2.5% 2.5%
Occupational level
Admin 7 5 8.6% 6.25% Service 6 7 7.4% 8.8% Skilled 5 7 6.2% 8.8% Semi-skilled 2 0 3.7% 0.0% Junior mgmt. 21 6 25.9% 7.5% Senior mgmt. 26 31 32.1% 38.8%
Missing data 4 2 4.9% 2.5%
Educational Level
No qualifications 0 0 0.0% 0.0% Secondary to 16 4 3 4.9% 3.8% Secondary to 18 1 3 1.2% 3.8% Non-uni higher ed. 11 14 13.6% 17.5% Undergrad. uni ed. 29 20 35.8% 25.0% Postgrad 34 38 42.0% 47.5%
Missing data 2 2 2.5% 2.5%d
113
Table 7.2: SPSS missing value analysis showing differences in mean scores on independent variables between groups providing complete and incomplete data
Separate Variance t Testsa
Memb
period
Spk
Practice
SE_
mean LoC_mean Mind_mean LGO_mean AGO_mean
Memb
period
t . -.4 4.0 2.5 2.6 1.1 -2.4
df . 1.0 152.3 158.5 159.0 158.4 157.3
P(2-tail) . .749 .000 .012 .011 .252 .019
# Present 79 79 79 79 79 79 79
# Missing 0 2 82 82 82 82 82
Mean(Present) 47.99 17.03 54.4684 25.9045 37.2278 28.4557 12.6582
Mean(Missing) . 27.00 48.0854 24.4268 33.5610 27.6463 14.5122
Spk
practice
t . . 3.9 2.5 2.6 1.2 -2.6
df . . 148.3 157.7 158.7 159.0 158.6
P(2-tail) . . .000 .015 .011 .225 .010
# Present 79 81 81 81 81 81 81
# Missing 0 0 80 80 80 80 80
Mean(Present) 47.99 17.27 54.3580 25.8698 37.1852 28.4691 12.5926
Mean(Missing) . . 48.0375 24.4250 33.5125 27.6125 14.6250
For each quantitative variable, pairs of groups are formed by indicator variables (present, missing).
a. Indicator variables with less than 5% missing are not displayed.
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7.3 Appendix C: Confirmatory factor analyses on individual scales and parcels
Avoidance goal orientation CFA
AGO items 1 I would avoid taking on a new task if there was a chance that I would appear rather
incompetent to others. 2 Avoiding a show of low ability is more important to me than learning a new skill.
3 I’m concerned about taking on a task at work if my performance would reveal that I had low ability.
4 I prefer to avoid situations at work where I might perform poorly.
AGO parcels
AGO
.42
avoid1 e23.65 .41
avoid2 e24.64
.89
avoid3 e25.94
.72
avoid4 e26
.85
AGO
.50
AGO 1, 2 e24.71
.93
AGO 3 e25.96
.70
AGO 4 e26
.84
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Learning goal orientation CFA
LGO items 1 I am willing to select a challenging work assignment that I can learn a lot from.
2 I often look for opportunities to develop new skills and knowledge.
3 I enjoy challenging and difficult tasks at work where I’ll learn new skills.
4 For me, development of my ability is important enough to take risks. 5 I prefer to work in situations that require a high level of ability and talent.
LGO parcels
LGO
.65
learn1 e1
.81.81
learn2 e2.90
.68
learn3 e3.83
.53
learn4 e4
.73
.22
learn5 e5
.47
LGO
.74
LGO 2 e1.86
.59
LGO 3, 5 e2.76
.86
LGO 1, 4 e3
.93
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Mindset CFA
Mindset parcels
Mindset
.66
Mind1r e1
.81
.64
Mind2r e2
.80
.44
Mind4r e3
.66 .63
Mind6r e4.79
.29
mind3 e5
.54
.59
mind5 e6
.77
.57
mind7 e7
.76
.66
mind8 e8
.81
F1
.76
Mindset 1r, 3, 6r e1
.87
.81
Mindset 8, 4r, 5 e2.90
.86
Mindset 2r, 7 e3
.93
Mindset items (* items 1, 2, 4 & 6 reverse scored) 1* The kind of person you are is something very basic about you and it can’t be changed very much. 2* You can do things differently, but the important part of who you are can’t really be changed 4* As much as I hate to admit it, you can’t teach an old dog new tricks. You can’t really change your
deepest attributes 6* You are a certain type of person and there is not much that can be done to really change that. 3 You can significantly change your behaviour and characteristics 5 You can always substantially change the kind of person you are. 7 No matter what kind of person you are, you can always change it a lot.
8 You can change even your most basic qualities.
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Locus of control CFA
Locus of control parcels
.01
LoC1 e1.09
Loc2 e2.25
Loc3 e3.11
Loc4 e4.47
Loc5 e5.41
Loc6 e6.25
LoC7 e7.24
Loc8 e8
LoC
.50
.34
.68
.64
.50
.48
.30
.11
LoC
.43
LoC_6 e1.66
.40
LoC_3_8 e2.64
.61
LoC_5_7 e3
.78
LoC items (* items 1, 2, & 4 not included in parcels) 1* Whether or not I get to be a leader depends mostly on my ability. 2* Whether or not I get into a car accident depends mostly on how good a driver I am. 3 When I make plans, I am almost certain to make them work. 4* How many friends I have depends on how nice a person I am 5 I can pretty much determine what will happen in my life. 6 I am usually able to protect my personal interests 7 When I get what I want, it’s usually because I worked hard for it. 8 My life is determined by my own actions.
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Self-efficacy CFA
PSSE scale 1 ...make an effective presentation. 2 ....develop a presentation with good content. 3 ...keep the audience engaged. 4 ...connect well to an audience that is familiar with the topic I present. 5 ...connect well to an audience that is not familiar with the topic I present. 6 ...deliver a well-organized presentation. 7 ...design effective visual aids. 8 ...deliver effectively (eye contact, use of voice, movements, etc.). 9 ...give an effective summary of the information I presented. 10 ...answer questions from the audience effectively. PSSE parcels
PSSE
.73
SE1 e1
.86
.59
SE2 e2
.77
.80
SE3 e3
.90
.75
SE4 e4.87 .66
SE5 e5.81
.24
SE7 e7
.49
.67
SE8 e8
.82
.62
SE9 e9
.78
.42
SE10 e10
.65
.56
SE6 e6.75
PSSE
.87
SE 3, 7, 8, 2 e1.93
.75
SE 4, 10, 5 e2.87
.82
SE 1, 6, 9 e3
.91