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FROM GENERAL

TO STUDENT-SPECIFIC TEACHER SELF-EFFICACY

Marjolein Zee


This research project was supported by grant no. 411-12-036 of the Netherlands Organisation for Scientific Research (NWO). This dissertation was sponsored by Stichting Kohnstamm Fonds. Cover design by B. E. Hakvoort Layout by M. Zee ISBN: 978-94-028-0141-5 NUR: 841 Copyright © 2016 M. Zee


FROM GENERAL

TO STUDENT-SPECIFIC TEACHER SELF-EFFICACY

ACADEMISCH PROEFSCHRIFT

Ter verkrijging van de graad van doctor

aan de Universiteit van Amsterdam

op gezag van de Rector Magnificus

prof. dr. D. C. van den Boom

ten overstaan van een door het College voor Promoties ingestelde commissie,

in het openbaar te verdedigen in de Agnietenkapel

op dinsdag 24 mei 2016, te 14:00 uur door

MARJOLEIN ZEE

geboren te Hoorn


PROMOTIECOMMISSIE:

Promotor: Prof. dr. P. F. de Jong

Universiteit van Amsterdam

Co-promotor: Dr. H. M. Y. Koomen


Overige leden: Prof. dr. T. T. D. Peetsma


Prof. dr. F. J. Oort


Prof. dr. G. J. J. M. Stams


Prof. dr. A. E. M. G. Minnaert

Rijksuniversiteit Groningen

Prof. dr. K. Verschueren

Katholieke Universiteit Leuven

Faculteit der Maatschappij- en Gedragswetenschappen


CONTENTS

_________________________________________________________________________

CHAPTER 1 General introduction 9

CHAPTER 2 Teacher self-efficacy and its effects on classroom processes, 19

student academic adjustment and teacher well-being:

A Synthesis of 40 Years of Research

CHAPTER 3 Inter- and intra-individual differences in teachers’ self-efficacy: 79

A multilevel factor exploration

CHAPTER 4 Teachers’ self-efficacy in relation to individual students with a 115

variety of social-emotional behaviors: A multilevel investigation

CHAPTER 5 Students’ disruptive behavior and the development of teachers’ 145

self-efficacy: The role of teacher-perceived closeness and conflict

in the student–teacher relationship

CHAPTER 6 General discussion 175

Summary 191

Samenvatting (Summary in Dutch) 195

References 199

Dankwoord (Acknowledgments in Dutch) 219

About the author 223

List of publications 224


“Homework is a breeze. Cooking is a pleasant diversion. Putting up a retaining wall is a

lark. But teaching is like climbing a mountain.”

– Fawn M. Brodie


9

CHAPTER 1

GENERAL INTRODUCTION

_________________________________________________________________________

“As an elementary school teacher, I’ve come across many tough and challenging classes,

but they didn’t warn me for this one. The first school period was simply hell. Sophie has

symptoms of ADHD and ODD, and she’s just uncontrollable, constantly testing me.

She’s not the only one, though. Of the 16 students in my class, five have serious conduct

problems. There have been days that I left with a sore throat, almost feeling like a third-

year student teacher again.”

– Teacher Relationship Interview with Anna, a fourth-grade teacher

The realities of today’s elementary classrooms, where children with various backgrounds,

needs, and (dis)abilities are educated side by side, make a strong appeal to teachers’ ability to

organize and execute their daily teaching tasks. Since the inception of Dutch national policies

geared toward inclusive and appropriate education (Ministry of Education, Culture and Science,

2014)1, teachers are increasingly required to design individualized education plans to fit the

learning needs of all students, and to provide the behavioral, social, and emotional supports

that help these students participate in all aspects of school life (Derriks, Ledoux, Overmaat, &

van Eck, 2002; Schram, van der Meer, & van Os, 2012; Smeets & Rispens, 2008; van Gennip,

Marx, & Smeets, 2007). Catering for appropriate education and adequately dealing with a

diverse student body is, however, not always as straightforward as it may seem. According to

recent national reports, about half of Dutch regular elementary teachers do not believe

themselves capable of dealing with students who differ in behavior and educational needs,

despite having all kinds of valuable teaching knowledge, skills, and expertise (e.g., Smeets et al.,

2013; Smeets, Blok, & Ledoux, 2013; Smeets, Ledoux, Regtvoort, Felix, & Mol Lous, 2015).

This seeming discrepancy between teachers’ actual competencies on the one hand and their

ultimate behaviors, feelings, and actions on the other has spurred many educational

researchers, both in the Netherlands and beyond, to investigate the concept of teacher self-efficacy.

1 Appropriate Education Act, August 2014 [Wet Passend Onderwijs, augustus 2014].


CHAPTER 1

10

Teacher self-efficacy (TSE), or teachers’ beliefs in their capabilities to “organize and execute

the courses of action required to produce given attainments” (Bandura, 1997, p. 3), has

nowadays been increasingly considered as one of the central determinants of teachers’ thought

processes, motivation, affective states, and actions (Bandura, 1977, 1986, 1997; Tschannen-

Moran, Woolfolk Hoy, & Hoy, 1998; Woolfolk Hoy, Hoy, & Davis, 2009). A vast body of

evidence has suggested that highly self-efficacious teachers are generally likely to perceive

difficult students as less challenging, to take more adequate approaches to improving their

students’ behaviors and performances in class, and to bend over backwards to ensure they

succeed (e.g., Almog & Schechtman, 2007; Brownell & Pajares, 1999; Caprara, Barbaranelli,

Steca, & Malone, 2006; Dunn & Rakes, 2011; Martin & Sass, 2010). Less self-efficacious

educators, like Anna in the beginning of this chapter, have frequently been demonstrated to

impair their own functioning by magnifying the severity of possible stressors in the classroom,

avoiding difficult teaching tasks, and settling for mediocre results (Bandura, 1997; Hamre,

Pianta, Downer, & Mashburn, 2008). Unfortunately, such inefficacious trains of thought may

typically pay off in performance failures and negative changes in students’ academic adjustment

and teachers’ well-being (e.g., Brouwers, Evers, & Tomic, 2001; Caprara, Barbaranelli,

Borgogni, & Steca, 2003; Klassen & Chui, 2010; Klassen et al., 2013).

To understand why elementary teachers at times are able to translate their knowledge into

proficient action and in other cases somehow fail to orchestrate and sustain the skills,

motivation, and effort required for meeting the goals of appropriate education, it seems

important to gain insight into TSE in relation to individual students with different behaviors

and needs. Such knowledge is crucial for a comprehensive understanding of teachers’ dealings

with diversity in the classroom, yet currently lacking due to several conceptual and

methodological issues. The overarching goal of the present dissertation, therefore, is to take

stock of the current state of theory and research on teacher self-efficacy, and address several

major challenges the field of TSE is facing at present.

To set the context for this dissertation, the first section of this General Introduction provides a

brief overview of Bandura’s (1977, 1986, 1997) social-cognitive theory. This theory of human

agency has since long been considered as the dominant framework for studying teachers’ sense

of self-efficacy. Based on the basic tenets of this framework, several challenges in the field of

TSE are subsequently brought to the fore that seem to have hampered its breadth of influence

and practical usefulness to both educational researchers and practitioners alike. In closing this



11

chapter, a brief outline is provided of how these theoretical, empirical, and methodological

challenges will be addressed in the remaining chapters of this dissertation.

FOUNDATIONAL TENETS OF TEACHER SELF-EFFICACY

Teachers’ sense of self-efficacy has generally been embedded in the concepts of Bandura’s

(1977, 1986, 1997) social-cognitive theory. With this framework, Bandura has advanced a view

of human agency that accords a prominent role to both environmental events and thought

processes in human adaptation and change. Put another way, the social aspect of social-

cognitive theory adheres to the nowadays common notion that human functioning is, in

essence, deeply embedded in social conditions (cf. Bandura, 1997; Bronfenbrenner & Morris,

1998; Ryan & Deci, 2002; Sameroff & Fiese, 2000). What this idea basically indicates is that the

environments in which people operate, including family homes, schools, and workplaces, and the

persons with whom they interact on a daily basis, may offer enabling resources or impose

constraints for their behaviors and actions in given domains of functioning. Specific to the

context of teaching, for instance, studies have documented a myriad of factors positively

contributing to teachers’ classroom achievements, such as decision latitude, social support

from parents and colleagues, and students’ interest in their schoolwork (Bakker, Hakanen,

Demerouti, & Xanthopoulou, 2007; Cheung, 2008; Raudenbusch, Rowan, & Cheung, 1992;

Tschannen-Moran & Woolfolk Hoy, 2007). Other features, including changing school policies,

deficient equipment, and disruptive student behavior in class, have frequently been marked as

the sources of challenge elementary teachers usually report (e.g., Fernet, Guay, Senécal, &

Austin, 2012; Roehrig, Pressley, & Talotta, 2002; Smeets et al., 2015). In this sense, the

classroom environment may stand as teachers’ primary venue for learning about what they can

do in given teaching domains.

It is not to say, however, that teachers should be considered as simply automated conveyers of

environmental constraints or resources. Rather, they are generally believed to actively

contribute to their own development and everyday functioning by exercising some control

over their thoughts, feelings, and actions (Bandura, 1977, 1986). This is where the cognitive part

of social-cognitive theory comes in. According to Bandura (1997), all humans possess a set of

internal personal attributes, the most important of which are self-efficacy beliefs, that enable

them to choose particular courses of action from among other alternatives to attain the goals

they wish to pursue in a given domain. For example, elementary school teachers with

substantial instructional expertise may feel effective and confident in implementing


CHAPTER 1

12

differentiated instruction and act accordingly in obedient and orderly classrooms. At the same

time, however, these teachers may shy away from the same activity when they sense the

classroom environment surmounts their skills and capabilities to perform the task. By

exercising this self-influence, teachers may thus operate generatively and proactively, and not

only reactively, to chart the contours of their environment (Bandura, 2001).

Taken together, then, how teachers cognitively interpret social constraints and resources in

class may inform and alter their behaviors and actions, the results of which may subsequently

lead to changes in both the classroom environment and teachers’ self-efficacy. This is the

foundation of Bandura’s (1997) model of triadic reciprocal causation, within which personal

internal factors, behavior, and environmental influences work in concert to influence human

agency. Social-cognitive theory thus seems to adhere to and extend such other seminal

frameworks as bio-ecological theory (Bronfenbrenner & Morris, 1996), dynamic systems

theory (Sameroff & Fiese, 2000), and self-determination theory (Ryan & Deci, 2002), by

emphasizing personal cognitions, and highly particularized self-efficacy beliefs in particular, as

the core mechanisms of human agency.

Bandura’s social-cognitive model has, perhaps owing to its intuitive appeal, made increasingly

marked inroads into the literature on teacher self-efficacy. Since its inception in the late 1970s,

numerous conceptualizations of TSE have come onto the scene (cf. Dellinger, Bobbett,

Olivier, & Ellett, 2008; Gibson & Dembo, 1984; Labone, 2004; Tschannen-Moran et al., 1998),

and theoretical models incorporating a variety of antecedents and consequences have been

devised and (sometimes) tested (e.g., Tschannen-Moran et al., 1998; Woolfolk Hoy et al., 2009;

Wyatt, 2016). Evidently, these theoretical and empirical efforts have contributed a great deal to

our understanding of the potential role of teachers’ self-efficacy beliefs in shaping their

behaviors in class. Yet, some of the key conceptions behind the self-efficacy construct are far

from being fully explored, thereby potentially limiting the theoretical and practical utility of

TSE and its breadth of influence.

CHALLENGES REGARDING THE NATURE AND CONSEQUENCES OF TSE

Perhaps one of the most pressing questions that has been asked frequently since Bandura

introduced his social-cognitive model is which particular teaching behaviors, classroom

processes, and student learning outcomes may be affected by teachers’ self-efficacy beliefs.

This seemingly simple question is not an easy one to address, however, since the field of



13

teacher self-efficacy has been dominated by different research traditions that may sometimes

even be divided in itself (e.g., Henson, 2002; Tschannen-Moran & Woolfolk Hoy, 2001; Wyatt,

2014). For instance, the initial research on TSE attempted to verify that teachers’ general self-

efficacy beliefs were powerfully related to their own effectiveness and their students’

performance (e.g., Armor et al., 1977; Gibson & Dembo, 1984). Somewhat confusingly,

though, this strand of research was not only inspired by Bandura’s social-cognitive scheme, but

also by Rotter’s (1966) locus of control theory, which centers on causal beliefs about the

relationship between actions and outcomes (i.e., outcome expectations), instead of personal

capability beliefs (i.e., self-efficacy). Accordingly, a considerable amount of research that allegedly

concentrated on TSE may actually have examined a different construct, thereby confounding

the theoretical base on which Bandura’s construct is essentially built (e.g., Tschannen-Moran et

al., 1998; Wyatt, 2014).

Next to this initial research tradition, other sets of investigations rapidly began to emerge as

well. Some of these appeared to be mainly educational in nature, employing social-cognitive,

self-determination, or classroom-based frameworks to investigate associations of (domains of)

TSE with a wide range of teacher practices and classroom processes, most of which were

investigated in isolated studies (e.g., Emmer & Hickman, 1991; Goddard & Goddard, 2001;

Guo, McDonald Connor, Yang, Roehring, & Morrison, 2012; Martin & Sass, 2010). Other,

more recent studies emerged from the psychological field of stress and well-being, examining

TSE and other self-referent processes (e.g., self-esteem, self-concept, and competence) in

relation to such factors as burnout, retention and attrition, job commitment, and satisfaction

(e.g., Brouwers & Tomic, 2000; Klassen & Chiu, 2010, 2011; Skaalvik & Skaalvik, 2007, 2010).

By concentrating on such related views of TSE, these studies might have overlooked the

construct’s full complexity and context-specific nature, or referred to entirely different things

(Bandura, 1997).

In conclusion, then, the various research traditions that developed over the past forty-odd

years seem to have resulted in a massive body of work on TSE and its consequences that is

both fragmented and conceptually confused (cf. Klassen, Tze, Betts, & Gordon, 2011; Wyatt,

2014). To improve the applicability of this current literature to educational practice, an

integrative, Bandura-based framework to synthesize the literature on TSE and its associations

with a range of adjustment outcomes at different levels of classroom ecology seems, therefore,

to be needed. Without such a perspective, it seems near impossible to yield an accurate,


CHAPTER 1

14

theoretical representation of the nature of TSE, and to understand the separate findings

emerging from each prevailing research tradition.

CHALLENGES REGARDING THE MEASUREMENT OF TSE

Given the fragmented and conceptually confused foundation of the TSE literature, it is

perhaps not surprising that challenges regarding the measurement of TSE have also been at the

forefront of much of the work in the field. Markedly, this attention to measurement started as

soon as the very first teacher self-efficacy measure (Rand scale; Armor et al., 1977) came onto

the scene. This scale only consisted of two plain, rather unanalytical items that, as it turned out

later, bore a closer resemblance to Rotter’s (1966) idea of locus of control than Bandura’s self-

efficacy theory. As such, great concern began to arise about the length of self-efficacy scales,

their scale reliability and validity, and their relevance to Bandura’s social-cognitive scheme

(Tschannen-Moran & Woolfolk Hoy, 2001; Woolfolk Hoy et al., 2009). In an attempt to

improve the psychometric properties of the Rand measure and to give allegiance to Bandura’s

theoretical notions, various researchers therefore started to develop new instruments to

measure TSE. Of these instruments, the more general Teacher Efficacy Scale (TES; Gibson &

Dembo, 1984) and domain-specific Teacher Sense of Efficacy Scale (TSES; Tschannen-Moran &

Woolfolk Hoy, 2001) have, by far, been the most popular.

Regrettably, though, both instruments by no means seem to plumb the depths of the teacher

self-efficacy belief system. The TES, for instance, seems to treat teachers’ sense of self-efficacy

simply as a general construct, defined at the classroom-level of analysis, thereby obscuring potential

variation in teachers’ self-percepts of efficacy across teaching tasks and domains (Bandura,

1997; Tschannen-Moran et al., 1998; Wheatley, 2005). In addition, the TSES, despite being

“superior to previous measures of teacher efficacy in that it has a unified and stable factor

structure” (Woolfolk Hoy & Burke Spero, 2005, p. 354), has partly failed to take account of the

social part of Bandura’s social-cognitive theory. Specifically, the TSES is, in the first place,

somewhat limited with regard to the domains of teaching and learning it aims to examine. In

the standard version of this instrument, teachers are usually presented with 24 items portraying

tasks regarding instructional strategies, classroom management, and student engagement

(Tschannen-Moran & Woolfolk Hoy, 2001). Although these teaching domains are certainly

representative of teachers’ daily activities, they may not fully reflect the breadth of teachers’

activities. Indeed, other responsibilities, including teachers’ responsiveness to children’s social-

emotional needs and their regard for students’ perspectives, have also been acknowledged to



15

be crucial areas of teaching and learning (cf. Hamre, Hatfield, Pianta, & Jamil, 2014; Pianta, La

Paro, & Hamre, 2008). Such areas may be particularly important to advance understanding of

teachers’ perceived ability to deal with a diverse student population. Therefore, a good,

consensually shared conceptual analysis of what it takes for teachers to succeed in their job is

currently needed to identify additional domains of teaching and learning across which TSE can

vary (Bandura, 1997).

In the second place, the TSES tends to examine teachers’ self-percepts of efficacy at

inappropriate levels of specificity. According to Bandura (1997, 2006), the specificity of

teachers’ self-efficacy beliefs can vary on a number of different dimensions, including the

domains of functioning, the task demands, and the characteristics of the persons toward whom

teachers’ behavior is directed. Evidently, the TSES deserves credit for capturing a range of

tasks and responsibilities within different domains of teaching and learning at the classroom-

level of analysis. Yet, the persons toward whom teachers’ behaviors and actions are directed

have largely gone unheeded in this instrument. This is remarkable, given that Tschannen-

Moran and colleagues (1998) seem to be well aware of the highly context-specific nature of

TSE: “Teachers feel efficacious for teaching particular subjects to certain students in specific

settings, and they can be expected to feel more or less efficacious under different

circumstances” (pp. 227-228). Hence, major progress in understanding how TSE operates in a

model of triadic reciprocal causality can be made only if these beliefs are explicitly measured in

terms of particularized capability judgments that may vary under different levels of task

demands within given teaching domains (i.e., domain-specificity), and across different persons

toward whom teachers’ behavior is directed (i.e., student-specificity). Such measures may be more

practically relevant in that they may reveal in which teaching areas TSE may be beneficial or

problematic, and toward which particular students they feel efficacious. In addition,

instruments that are both domain- and student-specific gauge the nature of the teacher self-

efficacy construct in ways that it may better reflect Bandura’s social-cognitive frame.

CHALLENGES REGARDING THE FORMATION AND DEVELOPMENT OF TSE

One last major challenge is the general lack of understanding about the various sources of TSE

and underlying processes through which such sources may become instructive to teachers’ self-

efficacy beliefs across time. Based on Bandura’s triadic reciprocal model (1986, 1997), it is

reasonable to presume that teachers’ self-efficacy beliefs are mainly derived from rich,

reciprocal interactions with their immediate environment over extended periods of time. These


CHAPTER 1

16

teacher-environment interactions may, according to Bandura (1997), provide various types of

information that are relevant for judging personal capabilities, including classroom mastery

experiences, modeled attainments, performance feedback, and affective states. In line with this

assertion, a handful of research on the sources of TSE has provided modest evidence that

teachers are likely to build a healthy sense of general self-efficacy when they are usually

satisfied with their past classroom performances, are able to cope with psychological stressors,

and when parents and colleagues express faith in their capabilities (e.g., Bandura, 1997;

Cheung, 2008; Klassen & Chui, 2010; Ross, Cousins, & Gadalla,1996; Ruble, Usher, &

McGrew, 2011; Salanova, Llorens, & Schaufeli, 2011; Tschannen-Moran & McMaster, 2009;

Tschannen-Moran & Woolfolk Hoy, 2007). Moreover, studies on the development of TSE

(e.g., Brouwers et al., 2001) disclosed a feedback loop in which teachers’ affective state

predicted their self-efficacy beliefs for classroom management and vice versa.

Perhaps due to the above-noted lack of domain- and student-specific TSE measures, virtually

none of these investigators have yet considered teachers’ encounters with individual students

as the primary conduit through which teachers may gain access to efficacy-relevant information

and build their TSE. This lack of attention to student–teacher interactions and –relationships

in the TSE literature is noteworthy, as individual students’ idiosyncratic behaviors, feelings, and

needs in relation to their teachers may provide the most important evidence of whether

teachers can muster whatever it takes to succeed with the child (Pianta, Hamre, & Stuhlman,

2003). Empirical evidence from Spilt and Koomen (2009) has suggested, for instance, that

teachers judge themselves as angrier and less self-efficacious in relation to individual students

who display disruptive behavior in class. Other studies have spawned some evidence that poor

relationships with students may lead to increases in emotional vulnerability in teachers, and

may result in feelings of professional and personal failure (e.g., Hamre et al., 2008; Newberry &

Davis 2008; O’Connor 2008; Spilt et al., 2011; Yeo, Ang, Chong, Huan, & Quek, 2008). In

light of these findings, it seems important to update, expand, and improve the available

information on the sources of TSE, by shifting the focus to individual students’ behaviors and

characteristics, and exploring how teachers may derive their self-efficacy beliefs from their

relationships with these children over time.

ADDRESSING THE CHALLENGES OF THE TEACHER SELF-EFFICACY LITERATURE

In summary, ever since Bandura presented his seminal theory on human agency, educationists

have explored the construct of TSE in multiple ways such that we currently know much more



17

about teachers’ capability to give shape to their actions in class and motivate and regulate their

execution. Yet, the absence of a clear understanding of the nature, sources, and consequences

of TSE, and psychometrically sound instruments that give full allegiance to Bandura’s ideas

may have prevented the field from moving forward (cf. Henson, 2001; Tschannen-Moran et

al., 1998; Wheatley, 2005; Wyatt, 2014). As such, it seems difficult to identify useful research-

based insights about TSE that may help teachers better deal with a diverse student body and

meet the goals of appropriate education. The remaining chapters of the present dissertation,

therefore, aim to address the current challenges in the field of TSE in four different ways.

Starting out at the most general, classroom-level of analysis, the second chapter of this dissertation

specifically aims to address current challenges regarding the nature of TSE and its

consequences for a range of outcomes at various levels of classroom ecology. Inspired by the

realization that the field of TSE still reflects a corpus of relatively fragmented and conceptually

confused empirical work, a process-oriented model of TSE is proposed that largely resembles

the CLASS, one of today’s leading frameworks for research on classroom processes (Pianta &

Hamre, 2009; Pianta et al., 2008). The CLASS highlights three domains of student–teacher

interactions, including instructional support, classroom organization, and emotional support,

that are nowadays considered to be the most germane to teachers’ functioning and students’

development (see Downer, Sabol, & Hamre, 2010). For this reason, this triad of domains was

used heuristically to organize and synthesize the body of empirical work on TSE and its

consequences, and to suggest new directions for the field.

Based on these recommendations as well as those of Bandura (1997, 2006), the focus

subsequently shifts to the student-level of analysis in Chapter 3. Central to this chapter is the aim

to advance understanding of the multifaceted nature of teachers’ sense of self-efficacy in upper

elementary school (Grades 3 to 6). To this end, a new teacher self-efficacy scale, based on

Tschannen-Moran and Woolfolk Hoy’s (2001) TSES was developed and evaluated. This new

instrument attempted, first, to address challenges regarding the domains across which teachers’

self-efficacy beliefs may fluctuate, by adding a fourth teaching domain to the original TSES.

Second, the original TSES was adapted to the student-specific level to gain insight into

potential variations in teachers’ self-efficacy beliefs across individual students from their

classrooms. The specifics of the new domain- and student-specific measure, as well as its

association with the TSES at the general, classroom-level are also described in Chapter 3.


CHAPTER 1

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Fully refraining from teachers’ self-efficacy beliefs at the classroom-level of analysis, Chapter 4

aims to explore individual students’ background characteristics and social-emotional behaviors

as sources of upper elementary teachers’ domain- and student-specific self-efficacy beliefs. Here,

the predictive value of individual students’ internalizing, externalizing, and prosocial behaviors

are investigated, as is the moderating role of teachers’ perceived classroom misbehavior and

years of teaching experience on their student-specific self-efficacy beliefs.

The issue of the formation and development of domain- and student-specific TSE is carried

further as Chapter 5 aims to explore a theoretical model within which teachers' perceptions of

closeness and conflict in the student–teacher relationship are hypothesized to form the

intermediary mechanisms by which individual students’ disruptive behavior may affect

teachers’ student-specific self-efficacy over time. Theoretical and empirical knowledge in this

direction may help educational researchers and practitioners identify levers to increase teachers’

self-efficacy toward individual students with different behaviors and needs, and thereby

improve these students’ classroom experiences and academic adjustment.


19

CHAPTER 2 TEACHER SELF-EFFICACY AND ITS EFFECTS ON CLASSROOM PROCESSES, STUDENT

ACADEMIC ADJUSTMENT AND TEACHER WELL-BEING:

A SYNTHESIS OF 40 YEARS OF RESEARCH

_________________________________________________________________________

This study integrates 40 years of teacher self-efficacy (TSE) research to explore the

consequences of TSE for the quality of classroom processes, students’ academic adjustment,

and teachers’ psychological well-being. Via a criteria-based review approach, 165 eligible

articles were included for analysis. Results suggest that TSE shows positive links with students’

academic adjustment, patterns of teacher behavior and practices related to classroom quality,

and factors underlying teachers’ psychological well-being, including personal accomplishment,

job satisfaction, and commitment. Negative associations were found between TSE and

burnout factors. Last, a small number of studies indicated indirect effects between TSE and

academic adjustment, through instructional support, and between TSE and psychological well-

being, through classroom organization. Possible explanations for the findings and gaps in the

measurement and analysis of TSE in the educational literature are discussed.

_________________________________________________________________________ Zee, M., & Koomen, H. M. Y. (2016). Teacher self-efficacy and its effects on classroom processes, student academic adjustment and teacher well-being: A synthesis of 40 years of research. Review of Educational Research. Advance online publication. doi:10.3102/0034654315626801


CHAPTER 2

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INTRODUCTION

Ever since the seminal work of the Rand corporation in the late 1970s (Armor et al., 1976),

studies on teacher self-efficacy (TSE) have been popping up like daisies in a spring field

(Klassen, Tze, Betts, & Gordon, 2011). This increase of research can be largely ascribed to the

notion that TSE beliefs, or teachers’ self-referent judgments of capability, are relevant for a

range of adjustment outcomes at different levels of classroom ecology. Using various measures

and definitions, studies imply that teachers with an assured sense of self-efficacy set the tone

for a high-quality classroom environment by planning lessons that advance students’ abilities,

making efforts to involve them in a meaningful way, and effectively managing student

misbehavior (Chacon, 2005; Woolfolk, Rosoff, & Hoy, 1990). Next to affecting the classroom

quality, TSE has also been found to exert influence over student and teacher outcomes. On the

student side, TSE has shown some links to academic achievement, motivation, and self-

efficacy (Midgley, Feldlaufer, & Eccles, 1989; Thoonen, Sleegers, Peetsma, & Oort, 2011; Ross,

1992). On the teacher side, positive TSE beliefs have been demonstrated to result in improved

psychological well-being in terms of higher levels of job satisfaction and commitment, and

lower levels of stress and burnout (Aloe, Amo, & Shanahan, 2014; Collie, Shapka, & Perry,

2012; Klassen & Chui, 2011).

The broad range of multileveled consequences of TSE speaks to the growing complexity of

this construct since its introduction some four decades ago. Still, consensus has not yet been

reached about which particular role TSE plays at different levels of classroom ecology. Most

reviews and critiques of the TSE literature have predominantly focused on key conceptual and

methodological issues surrounding research on teachers’ capability beliefs, or have proposed

alternative paradigms and frameworks to broaden and clarify this construct (e.g., Henson,

2002; Klassen et al., 2011; Labone, 2004; Tschannen-Moran, Woolfolk Hoy, & Hoy, 1998;

Wheatley, 2005; Woolfolk Hoy, Hoy, & Davis, 2009; Wyatt, 2014). Only two authors have

extended this scope, also putting emphasis on the potential consequences of TSE (Ross, 1998;

Woolfolk Hoy et al., 2009). Although these two reviews have been extensive, they have been

either narrative in nature, or could not yet cover the substantial body of evidence on the

consequences of TSE that has been published in the last decade. Consequently, they essentially

fail to render a more systematic and updated account of TSE and its consequences. The purpose

of the present review study, therefore, is to provide an up-to-date, critical review of forty years

of research on TSE and its consequences for the quality of classroom processes, students’


NATURE AND CONSEQUENCES OF TEACHER SELF-EFFICACY

21

academic adjustment, and teachers’ psychological well-being. We aim to go beyond previous

reviews by not only focusing on outcomes related to teaching and learning, but also on

teachers’ welfare. Thereby, we provide a more detailed description of TSE and its

consequences across grades.

Before discussing the main findings and conclusions, we will first provide an overview of the

foundational tenets of TSE to elucidate the complexities surrounding its meaning and

measurement. Inspired by the seminal work of Woolfolk Hoy and colleagues (2009) and

Pianta, La Paro, and Hamre (2008), we will next describe a process-oriented model of TSE.

This model is used heuristically to synthesize empirical research to explore the consequences of

TSE for outcomes at different levels of classroom ecology, and to reveal potential gaps in our

current understanding of this complex construct.

THEORY AND MEASUREMENT OF TEACHER SELF-EFFICACY

The foundational tenets of TSE have, historically, fallen between the two stools of locus of

control (Rotter, 1966) and social-cognitive theory (Bandura, 1977). As with other social-

psychological frameworks, the emphasis in these two theories is on human agency – the idea that

individuals are able to exercise control over actions that affect their lives (e.g., Bandura, 1986,

1997). Rotter’s (1966) attribution-based theory of locus of control is probably one of the best

known examples of this viewpoint. Drawing on previous empirical work, Rotter

conceptualized locus of control as a generalized expectancy for control of reinforcement that

individuals develop in relation to their environment (e.g., Rotter, 1966). Individuals, Rotter

assumed, generally differ in their perceptions of whether outcomes are contingent upon sheer

luck, fate, or others (external control), or a result of their own actions (internal control). Such

perceptions are considered to be largely determined by person-environment transactions that

reinforce individuals’ actions, such as receiving a reward after successful task performance.

These reinforcers, in turn, may serve as (dis)incentives for particular behaviors in future

situations (Rotter, 1966). Evidently, those who believe their environment to be responsive to

their actions – and hence develop a more internal locus of control – are the most likely to

become “happy, healthy, wealthy, and wise” (Lachman, 2006, p. 283).

Over the years, Rotter’s theory has laid the groundwork for many studies and scales, including

the first measure of TSE in the 1970s (see Tschannen-Moran & Woolfolk Hoy, 2001). Using

locus of control as a conceptual base, Rand researchers (Armor et al., 1976; Berman &


CHAPTER 2

22

McLaughlin, 1977) formulated two simple items to assess teachers’ beliefs about their abilities

to bring about positive student change above the effects of child and environmental features.

Although these items only constituted a small part of the Rand studies, they have been

relatively important in laying an empirical foundation for inquiry into students’ achievement

gains. Thereby, this two-item instrument rapidly gained momentum toward new and more

comprehensive measures of TSE in relation to student outcomes (e.g., Dellinger, 2005;

Tschannen-Moran & Woolfolk Hoy, 2001). Among those instruments, Teachers’ Locus of

Control (Rose & Medway, 1981), Responsibility for Student Achievement (Guskey, 1981), and

the Webb Efficacy Scale (Ashton, Olejnik, Crocker, & McAuliffe, 1982) can be considered the

most prominent.

As refinements of the original Rotter-based construct started to appear, however, so a number

of issues regarding their relevance to TSE began to arise. These issues came barely one year

after the efforts of the Rand Corporation, with the work of Bandura (1977, 1986, 1997).

Building strongly on Rotter’s theory, Bandura argued that individuals’ behaviors are not only

influenced by generalized expectancies for control but also by these individuals’ perceived

capabilities, or self-efficacy, to perform those behaviors in particularized domains. To reinforce

this assertion, Bandura (1977) made a distinction between response-outcome expectancies and

self-efficacy expectations. Generally, response-outcome expectancies refer to individuals’

estimates “that a given behavior will lead to certain outcomes” (Bandura, 1977, p. 193). These

outcome expectancies can be assumed to be operationally equivalent to Rotter’s construct, as

they both determine whether the social environment is perceived to be reactive to personal

actions or not (see Kirsch, 1985). With self-efficacy expectations, Bandura seems to go beyond

such perceived environmental contingencies. He argued that although persons may know that

certain achievements result in desired outcomes, this information becomes virtually useless

when they lack the beliefs they have the abilities to produce such actions. For instance,

teachers’ judgment that scaffolding may increase student learning (outcome expectation) can act as

a motivator to making significant use of this teaching strategy. Yet scaffolding strategies are

unlikely to be initiated unless teachers believe they have the skills and capabilities to selectively

support their students where needed (self-efficacy). Thus, for Bandura (1997), personal self-

efficacy beliefs seem to be the most important cause of human behavior. As the predictor of

outcome expectancies, they help persons decide which courses of action they ought to pursue

and whether to persist in the face of environmental adversities. Also, they determine how

persons interpret their thoughts, actions, and emotions in given situations.



23

Bandura’s addition to Rotter’s theory has had an enormous impact on TSE research. In the

first place, most researchers have, since his writings, underscored the need to differentiate

between self-efficacy and outcome expectancies (e.g., Dellinger, Bobbett, Olivier, & Ellett,

2008; Gibson & Dembo, 1984). Of particular note are the early efforts of Gibson and Dembo

(1984), who have performed much work in this area. In an attempt to develop a new measure

of TSE, they found modest evidence for two independent factors that assumedly resembled

self-efficacy and response-outcome expectancies. These factors, which were labelled as

personal teaching efficacy (PTE) and general teaching efficacy (GTE), respectively, have been

confirmed and used by many researchers until the late 1990s (e.g., Emmer & Hickman, 1991,

Hoy & Woolfolk, 1993; Riggs & Enochs, 1990; Soodak & Podell, 1993). After that time, the

popularity of Gibson and Dembo’s Teacher Efficacy Scale (TES) has faded somewhat, due to

issues with the construct and content validity of the general teaching efficacy factor (e.g.,

Pajares, 1997; Tschannen-Moran & Woolfolk Hoy, 2001; Woolfolk & Hoy, 1990).

A second consequence of Bandura’s socio-cognitive framing is that scholars started to

conceptualize TSE as task or situation-specific rather than generalized, as Rotter does. By moving

away from the idea that self-efficacy is an omnibus trait, it is acknowledged that TSE beliefs

may vary according to different types of tasks, students, and circumstances in class (Ross,

Cousins, & Gadalla, 1996; Tschannen-Moran et al., 1998). Such particularized self-efficacy

scales have been argued to have higher predictive validity, due to the variations in TSE that

occur across different tasks and domains (Bandura, 1997). The majority of the current

instruments and conceptualizations of TSE are therefore based on the breadth of teachers’ role

in the classroom and not solely on student outcomes. In the often used Teachers’ Sense of

Efficacy Scale (TSES; Tschannen-Moran & Woolfolk Hoy, 2001), for instance, TSE is treated

as a task-specific, three-dimensional construct reflecting instructional strategies, classroom

management, and student engagement. This Bandura-based instrument – developed in reaction

to the partial invalidity of Gibson and Dembo’s TES – has been described as “superior to

previous measures of teacher efficacy in that it has a unified and stable factor structure”

(Woolfolk Hoy & Burke Spero, 2005, p. 354). Indeed, investigators using either the 24-item or

12-item TSES have reported satisfactory reliability and construct validity evidence for this

instrument, across grades and several countries (e.g., Klassen et al., 2009; Tschannen-Moran &

Woolfolk Hoy, 2001).


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In addition to the TSES-dimensions, other educational researchers have developed separate

self-efficacy scales for literacy (Tschannen-Moran & Johnson, 2011), science (Riggs & Enochs,

1990), inclusive practices (Malinen et al., 2013), technology (Sang, Valcke, van Braak, &

Tondeur, 2010), and discipline (Brouwers & Tomic, 2000), or extended the scope of TSE to

the organizational (Friedman & Kass, 2002) or cultural domain (Siwatu, 2007). Together, these

studies recognize that TSE is reflected in multiple specific components of teachers’ profession,

and that the strength of TSE can fluctuate between teaching tasks, roles, students, and over

time.

CONSEQUENCES OF TEACHER SELF-EFFICACY

Thus far, a mounting body of theoretical and empirical work has demonstrated the complex

ways in which TSE may affect outcomes at different levels of classroom ecology. In the late

1970s, student-level investigations first started to appear, focusing on TSE as a potential direct

determinant of students’ achievement and motivation. This research focus was evidently

encouraged by the Rand studies (e.g., Armor et al., 1976), which specifically hypothesized high

TSE to be beneficial for student learning. Subsequent investigators in the 1980s and beyond

have complemented this earlier work, turning their attention to direct consequences of TSE at

the teacher-level. Spurred by Bandura’s (1986) notion that self-efficacy not only affects

behaviors and actions but also thoughts and feelings, these researchers have opened new lines

of inquiry on factors associated with teachers’ psychological well-being (e.g., Klassen & Chiu,

2010, 2011; Skaalvik & Skaalvik, 2007).

Aside from investigators that posited a direction of causal influence from TSE to student-level

and teacher-level outcomes, classroom-oriented studies have suggested that TSE might rather

have an indirect effect on such outcomes. The idea behind this assumption is that TSE, as a

personal characteristic, mainly affects student and teacher outcomes through patterns of

teacher behavior and practices that define the quality of the classroom environment (Guo,

McDonald Connor, Yang, Roehring, & Morrison, 2012; Midgley et al., 1989; Woolfolk Hoy &

Davis, 2005). Gibson and Dembo (1984), for instance, underscored that highly self-efficacious

teachers “persist longer, provide a greater academic focus in the classroom, and exhibit

different types of feedback than teachers who have lower expectations concerning their ability

to influence student learning" (p. 570).



25

To add to the complex nature of TSE, other researchers additionally believe that TSE

judgments may act on raising the classroom quality by exerting reciprocal influences over

teachers’ feelings of well-being and personal accomplishment (e.g., Bandura, 1997; Brouwers,

Evers, & Tomic, 2001; Goddard, Hoy, & Woolfolk Hoy, 2004). Such personal emotions and

cognitions are believed to inform and alter future TSE beliefs and accompanying behaviors,

which, in turn, affect both the classroom environment and student performance (Goddard et

al., 2004). This system of triadic reciprocal causality (Bandura, 1986), in which the classroom

environment, teachers’ behavioral patterns, and their cognitions influence each other

dynamically, calls attention to the need for critical exploration of the role that the quality of

classroom processes plays in the relationships among TSE, students’ academic adjustment, and

teachers’ well-being.

A HEURISTIC FRAME TO LINK TSE TO OUTCOMES AT VARIOUS LEVELS OF CLASSROOM ECOLOGY

Recently, Woolfolk Hoy and colleagues (2009) developed a process-oriented framework that

may help researchers to advance understanding of the complex ways in which TSE affects

outcomes at various levels of classroom ecology. In this global framework, TSE is suggested to

have various types of consequences for a range of classroom processes at both student and

teacher levels, including instructional actions, behavioral expectations, and emotional

classroom dynamics. As such, these types of consequences resemble the theoretically driven

and empirically supported classroom quality framework of Pianta and colleagues (CLASS;

Pianta & Hamre, 2009; Pianta et al., 2008). Today, the CLASS is one of the leading frameworks

for research on the quality of classroom processes, not least because of its emphasis on teacher

supports and practices related to the well-established major domains of instructional support,

classroom organization, and emotional support.

The domain of instructional support generally reflects the degree to which teachers are able to

advance students’ meta-cognitive skills, apply their thinking to real-world situations, scaffold

for struggling students, and expand on their understanding (Hamre & Pianta, 2010; Pianta et

al., 2008). The domain of classroom organization includes teaching practices such as providing

clear directions, rules, and expectations, focusing students’ attention toward learning

objectives, and preventing instances of misconduct (Pianta et al., 2008). Emotional support,

finally, comprises such interpersonal and affective classroom dynamics as student–teacher

relationships, teachers’ sensitivity, and regard for student perspectives (Hamre & Pianta, 2010).

Over time, these efficacy-influenced processes are presumed to mainly affect students’


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26

academic adjustment (Hamre & Pianta, 2010; Woolfolk Hoy et al., 2009). Yet, given Bandura’s

model of triadic reciprocal causation, TSE may also have consequences for teachers’ well-

being, either directly or indirectly.

As the triad of domains of the CLASS-framework specifically encompass teacher supports and

practices, they may be helpful in identifying and further organizing the various classroom

processes that are at play in the association between TSE and student and teacher outcomes.

Inspired by this framework, we therefore propose a heuristic model for presenting the results

of our review study. Using this model, we will first review empirical studies on the direct

associations among TSE and the quality of classroom processes (i.e., instructional support,

classroom organization, and emotional support), students’ academic adjustment (i.e., student

motivation and academic achievement), and teachers’ well-being (e.g., burnout, stress, job

satisfaction, commitment, and attrition and retention). Second, we will explore evidence on the

mediating role of classroom processes on the association between TSE and student adjustment

and teacher well-being. Figure 1 provides an overview of these hypothesized relationships.

FIGURE 1

Heuristic Model of Teacher Self-Efficacy in Relation to Classroom Processes, Academic Adjustment, and

Teacher Well-Being

Quality of classroom processes: - Instructional support - Classroom organization - Emotional support

Teachers’ self-efficacy - Global self-efficacy - Domain-specific self-efficacy

Students’ academic adjustment: - Academic achievement - Student motivation

Teachers’ well-being: - Positive (job satisfaction, commitment, coping, retention) - Negative (burnout, stress, attrition)

Note. Solid lines symbolize expected associations that will be examined in the present review. Hypothesized reciprocal effects (dashed lines) displayed in the model will not be part of this study.



27

METHOD

LITERATURE SEARCH

To comprehensively identify relevant studies on the consequences of TSE, we used a criteria-

based review approach to search articles from 1976 to March 2014. This time span was set as

research on TSE only started to increase after the work of the Rand Corporation (e.g., Armor

et al., 1976). Potentially eligible articles were collected iteratively from the Internet databases of

PsycInfo, ERIC, and Google Scholar in three stages. During the first stage, we employed an a

priori scoping search to define separate sets of key words to locate articles referring to TSE in

relation to the dimensions of the classroom-based framework. To operationalize TSE, we only

included such subject terms as “teach* self-efficacy”, “teach* efficacy”, “academic optimism”,

“teach* sense of (self-)efficacy”. Other types of self-beliefs, including locus of control, self-

concept, self-worth, self-esteem, perceived competence, and outcome expectations, were not

entered as search terms, as these beliefs, rather than cognitive judgments of capability, reflect

affective reactions (Bandura, 1997). In the second stage, the TSE search terms were

subsequently combined with key words referring to classroom quality, students’ academic

adjustment, and teachers’ psychological well-being, using the Boolean operators “AND” and

“OR”. A search of these descriptors lead us to detect tens of thousands of journal articles,

dissertations, book chapters, and conference proceedings. Therefore, in the third stage, we

further limited the source type to empirical, English language articles published in peer-

reviewed journals. To be included, full-text versions had to be available. After adding these

restrictions, separate searches for each dimension of the framework in relation to TSE were

performed, producing 768 results for classroom processes, 910 results for students’ academic

adjustment, and 710 results for teachers’ well-being.

INCLUSION CRITERIA

There were five criteria for the inclusion of publications in our review. First, each empirical

article was required to specifically focus on preservice or inservice teachers’ individual self-efficacy. As

such, studies investigating self-efficacy beliefs of principals, teaching assistants, mentors, or

school counselors were not included in this review. Likewise, articles were excluded if they

reported solely on collective or school-level (aggregated) TSE. Second, all articles had to

address a direct or indirect relationship between TSE and at least one factor associated with

students’ academic adjustment, teachers’ well-being, or hypothesized classroom processes.

Accordingly, we only reported on direct and indirect effects of TSE, leaving out potential


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interaction terms of TSE and other variables described in the selected studies, as well as

associations in which TSE served as a mediator. Third, studies were considered relevant if they

used quantitative empirical data. Although we initially included qualitative data as well, the

small body of retrieved qualitative research appeared to be diverse in terms of study focus,

sample size, teaching context, design, and primary study outcomes. These large differences

made it difficult to compare and critically appraise these articles, and to determine their

relevance in light of the quantitative studies included in the review. Also, most qualitative

research dealt with a specific study population, thereby potentially limiting their generalizability

and influence. It is for this reason that we decided to focus on quantitative empirical research,

and excluded qualitative work from analysis. Fourth, quantitative studies were required to use

psychometrically sound, Bandura-based instruments to identify TSE. This criterion resulted,

among others, in the exclusion of articles using only Gibson and Dembo’s (1984) general

teaching efficacy scale, or other instruments measuring outcome expectancies. Last, the

samples of the studies were allowed to include special education, preschool, elementary school,

high school, and higher education teachers and/or students. Hence, no limits were set with

regard to school type.

Irrelevant papers were removed, and appropriate articles were identified based on information

provided in the abstract or, when the abstract was not available, the title. In case of doubt, the

full text was consulted. Key journals and references of articles that met our criteria were

subsequently hand-searched, to locate additional studies. The application of these criteria

ultimately resulted in 165 articles for analysis.

RESULTS

Following the heuristic model provided in Figure 1, this section provides a thematic analysis of

the literature on TSE. As the reviewed studies generally evaluated various combinations of

variables, the consequences of TSE for each of the outcomes are discussed separately. Table 1

offers a framework for these main outcome domains and their dimensions, and summarizes

the number of studies and mean sample size per dimension. First, consequences of TSE on

teacher behaviors and practices that define instructional, classroom organizational, and

emotional aspects of classroom processes are discussed. The results for each of these major

classroom domains, categorized into subthemes, are displayed in Appendix 1. Next, studies

investigating the direct and indirect links between TSE and student adjustment are reviewed,



29

including academic achievement and motivation. These studies are reported in Appendix 2.

Studies on the direct and indirect association between TSE and teachers’ well-being are

discussed last. The results for each aspect of teachers’ well-being can be found in Appendix 3.

Please note that we do not provide a standardized, statistical overview of the strength of

relationships between TSE and student and teacher outcomes in the classroom, due to the

heterogeneous nature of the reviewed studies in terms of sample sizes, analytical methods and

rigor, and instruments used.

TABLE 1

Overview of Study Outcomes, Domains, and Dimensions

Note. QCP = Quality of classroom processes; SAA = Students’ academic adjustment, TWB = Teachers’ well-being.

Main outcome Domain Dimension N studies Mdn sample size (range) QCP Instructional Overall Instructional support 8 166 (19 – 631) Support Support for literacy and math 9 94 (40 – 346) Implementation of

instructional practices 8 79 (20 – 537)

Classroom Organization

Classroom behavior management

17 182 (33 – 983)

Inclusive practices and referral decisions

14 188 (55 – 1,623)

Instructional management 26 302 (8 – 2,132) Emotional

Support Overall emotional climate 3 67 (49 – 1,043)

Student-teacher relationships 7 152 (75 – 597) Regard for student

perspectives 3 96 (75 – 336)

SAA Students’ Overall achievement 8 222 (80 – 2,184) achievement Math achievement 4 307 (19 – 1,329) Literacy achievement 6 67 (20 – 1,075) Achievement in other subjects 4 214 (18 – 450) Students’

motivation Motivational behaviors, beliefs 11 80 (58 – 1,329)

TWB Negative well-

being Teacher burnout 23 404 (49 – 2,249)

Teacher stress and coping 6 109 (30 – 479) Positive well-

being Teacher satisfaction 21 366 (30 – 1,212)

Teacher commitment 12 726 (109 – 26,257) Teacher attrition and retention 9 192 (66 – 1,214)


CHAPTER 2

30

CONSEQUENCES OF TSE FOR CLASSROOM PROCESSES – INSTRUCTIONAL SUPPORT

Scholars suggest that the instructional behaviors, practices, and strategies teachers employ to

encourage students’ cognitive development may, in part, be determined by their self-efficacy

(e.g., Tschannen-Moran & Woolfolk Hoy, 2001). This possibility was explored in 25 survey

studies (see Appendix 1). Of these studies, more than half used samples with less than 100

teachers. Moreover, around 70% of the 25 reviewed articles on instructional support relied on

simple correlations and global measures of TSE, making it difficult to determine whether

particular domains of TSE have similar patterns of effects on teachers’ instructional support.

Given these methodological choices, it is perhaps not surprising that teachers’ overall levels of

instructional support, and particularly those of preservice teachers, have not been found to be

affected by their self-efficacy (Guo, Piasta, Justice, & Kadaverek, 2010; Pakarinen et al., 2010).

Yet there are indications that inservice TSE contributes to a range of general instructional

practices. Among others, these include process-oriented instruction and differentiation, the

number of goal changes made, the ability to connect to students’ lives and employ effective

teaching strategies, and their choices of differentiated instructional strategies supporting

inclusive education (Allinder, 1995; Martin, Sass, & Schmitt, 2012; Thoonen, Sleegers, Oort,

Peetsma, & Geijsel, 2011; Wertheim & Leyser, 2002; Weshah, 2012). Rigorous structural

equation modeling (SEM) results from Geijsel, Sleegers, Stoel, and Kruger (2009), furthermore,

showed that efficacious teachers frequently engage in professional learning activities, such as

keeping up to date with the profession, trying out new approaches to improve their practices,

and changing their practice to promote process-oriented student learning.

INSTRUCTIONAL SUPPORT FOR LITERACY AND MATH

Besides overall support, nine studies (see Appendix 1) specifically considered the importance

of TSE for the employment of strategies to maximize students’ literacy and mathematics

development. Regarding math, Brown (2005) pointed out that early childhood teachers’ self-

efficacy did not result in more observed mathematics instructional practices, although self-

efficacious teachers did rate the importance of math higher than colleagues without such

beliefs. Largely similar, longitudinal results from Holzberger, Philipp, and Kunter (2013)

showed that TSE is unrelated to students’ subsequent assessment of the level of cognitive

activation or individual learning support. Instead, a reverse effect of instructional quality on

TSE was revealed, with students’ experience of cognitive activation and teachers’ ratings of

classroom management predicting subsequent TSE (ibid.). This is in line with Bandura’s notion



31

of triadic reciprocal causation, suggesting that teachers’ instructional support and TSE may

affect one another reciprocally.

The small-scale correlational studies focalizing literacy reveal that efficacious preservice teachers

generally have more knowledge of using expository text as a reading tool (Yildirim & Ates,

2012). Yet these self-efficacious preservice teachers have not been found to use more reading

strategies than less efficacious educators while teaching their students to read (Haverback,

2009). Efficacy in inservice teachers, in contrast, has been shown to contribute both to the quality

of their instructional (literacy) support, and the instructional literacy environment in preschool

(Guo, Sawyer, Justice, & Kaderavek, 2013; Justice, Mashburn, Hamre, & Pianta, 2008).

Language modeling, one key dimension of instructional support, was unrelated to early

childhood teachers’ self-efficacy (Justice et al., 2008).

In three small, cross-sectional studies, the relationship between domain-specific TSE beliefs

and the use of various language strategies in high school was investigated (Chacon, 2005;

Eslami & Fatahi, 2008; Yilmaz, 2011). In the largest study (N = 104), TSE for engagement,

classroom management, and instruction all correlated positively with communication- and

grammar-oriented strategies, but did not affect teachers’ preference for one specific type of

strategy (Chacon, 2005). A study by Yilmaz (2011; N = 54) failed to replicated these results.

Eslami and Fatahi (2008; N = 40) only found positive correlations between dimensions of TSE

and communicatively oriented language strategies. Together, these results suggest that the

consequences of TSE for teachers’ instructional (literacy) support become more evident when

teachers gain experience.

IMPLEMENTATION OF INSTRUCTIONAL PRACTICES

Despite the effectiveness of instructional practices for students’ development, not all teachers

feel capable of implementing and using such practices in class. More specifically, teachers with

high general self-efficacy have been demonstrated to perceive the implementation of new

instructional methods as more important and congruent with their own practices. They

experience less self-survival, task, and impact concerns, and more pedagogic conceptual

change, irrespective of grade (Ghaith & Shaaban, 1999; Ghaith & Yaghi, 1997; Lee, Cawthon,

& Dawson, 2013). Turning to domain-specific TSE, Dunn, Airola, Lo, and Garrison (2013)

found that TSE for data-driven decision making is positively related to collaboration concerns,

suggesting that efficacious teachers more often work with colleagues to improve and increase


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32

use of data-driven decision making in class. Even in the context of physical education, high-

school TSE in teaching daily lesson plans and student-centered teaching styles has been found

to positively affect teachers’ attitude and intention toward curriculum implementation

(Gorozidis & Papaioannou, 2011).

Next to attitudes toward implementation, results of three intervention studies show that

efficacious teachers are likely to more frequently implement and use subject-specific

instructional practices in class (Cantrell & Hughes, 2008; Eun & Heining-Boynton, 2007;

Lakshmanan, Heath, Perlmutter, & Elder, 2011). Cantrell and Hughes (2008) explored the

relationship between TSE and implementation of a content literacy approach among sixth- and

ninth-grade teachers. They found that TSE before implementation was correlated with

teachers’ observed implementation at the start of the content literacy program, but not after,

suggesting TSE to be more important during the initial implementation phase. Likewise, Eun

and Heining-Boynton (2007) were interested in the effects of an English-as-a-Second-

Language Professional Development Program on TSE and classroom practices of K-12

teachers. They revealed that teachers with high self-efficacy were more likely to use the

instructional knowledge and skills acquired from professional-development programs than

educators with low self-efficacy.

Regarding science teaching, standards-based professional development programs have been

shown to have potential to positively affect TSE and, consequently, teachers’ implementation

of reformed science teaching in upper elementary classrooms (Lakshmanan et al., 2011).

Although these studies imply that TSE may be crucial to teachers’ implementation fidelity,

some caution is warranted when making inferences from these results. Specifically, the sample

sizes of these three intervention studies were relatively small (N = 22, 79, and 90, respectively),

and control groups were not included in these investigations.

CONSEQUENCES OF TSE FOR CLASSROOM PROCESSES – CLASSROOM ORGANIZATION

Classroom organization is generally perceived as a domain of classroom processes related to

how well teachers manage students’ behavior and instructional time, and provide lessons and

materials that maximize learning opportunities (Pianta et al., 2008). Within this domain, three

particular dimensions can be distinguished that account for links between TSE and classroom

processes: behavior management, inclusive practices and referral decisions, and instructional

management. Statistical analyses in research that investigated the links between TSE and these



33

dimensions (n = 55; see Appendix 1) mostly included application of simple correlations,

regression methods, or analysis of variance (37 studies). Studies employing multilevel analysis

(4 studies), SEM (12 studies) or longitudinal analysis (2 studies) were less common. Although

some investigations focused on specific student populations (e.g., students with emotional

and/or behavioral difficulties; Almog & Shechtman, 2007; Liljequist & Renk, 2007; Yoon,

2004), most studies did not include corrections for confounding by students’ or teachers’

personal characteristics.

CLASSROOM BEHAVIOR MANAGEMENT

The notion that TSE shows links with the ability to organize and manage students’ behavior

and time is particularly consistent with small-scale, correlational research on teachers’ ability to

cope with students’ social-emotional behavior (n = 4). For instance, Lilejequist and Renk

(2007) found that preservice teachers with a high sense of personal self-efficacy report higher

levels of control over externalizing behavior, but seem more bothered by students’

internalizing behavior than teachers with a low sense of self-efficacy. Moreover, in elementary

school, self-efficacious inservice teachers have been shown to cope better with different problem

behaviors, including low achievement, social rejection, shyness, disobedience, hostility,

hyperactivity (Almog & Shechtman, 2007), and students’ bullying behavior (Yoon, 2004).

Conversely, when teachers’ efficacy is hampered by student behavior, they may develop a

critical attitude toward their own teaching abilities (Lambert, McCarthy, O'Donnell, & Wang,

2009).

Potentially, teachers’ perceived ability to cope with challenging students may partly determine

which classroom management behaviors, strategies, and styles they ultimately adopt.

Considering the preservice context, general TSE has been demonstrated to be beneficial to lesson

presenting, questioning, and classroom management behaviors (Saklofske, Michayluk, &

Randhawa, 1988). Preservice teachers with high personal and classroom management efficacy

have also been found to use more positive strategies (i.e., increasing desirable student behavior)

and external strategies (i.e., referring a disruptive student) than poorly efficacious teachers

(Emmer & Hickman, 1991). Whether or not these behavior management strategies are

effective depends on these teachers’ TSE as well (Wertheim & Leyser, 2002).

Some cross-sectional studies suggest that the positive consequences of TSE for preservice

teachers’ classroom management styles and strategies likely extend to the inservice context (e.g.,


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Dussault, 2006; Morris-Rothschild & Brassard, 2006). In elementary school and beyond, TSE

has been shown to positively contribute to teachers’ behavior, instructional, people, and overall

classroom management strategies (Abuh-Tineh, Khasawneh, & Khalaileh, 2011) and their

proactive approaches to managing student–teacher conflict. These include, among others, their

integrating, compromising, and obliging styles (Morris-Rothschild & Brassard, 2006). In high

school, TSE has furthermore been positively linked to several organizational citizenship

behaviors of teachers, such as altruism, courtesy, and conscientiousness (Bogler & Somech,

2004; Dussault, 2006; Ngidi, 2012). The only study that took a longitudinal approach to

studying TSE did not find any association between TSE and classroom management across

time, despite reporting significant correlations (Holzberger et al., 2013). Thus, these studies

generally imply that proactive behavioral management strategies are most likely to be employed

by teachers with high self-efficacy. These modest findings are consistent across grade and

context.

Complementing the corpus of research on classroom management strategies and behaviors,

primarily older research (n = 4) from the 1990s underscores that TSE might influence teachers'

beliefs about student control (Hoy & Woolfolk, 1990; Martin & Sass, 2010; Woolfolk & Hoy,

1990; Woolfolk et al., 1990). Woolfolk and Hoy (1990) investigated preservice teachers’ efficacy

in relation to their beliefs about control and motivation. Initially, their findings indicated that

TSE was unrelated to teachers’ pupil control ideology and motivational beliefs. Canonical

correlations, however, revealed that TSE was positively related to a control orientation that

rejects teacher control of students but accepts the schools' control of teachers. Two other

studies indicated that although preservice teachers tend to become more custodial and

controlling in their orientations (Hoy & Woolfolk, 1990), more experienced sixth-and seventh-

grade teachers may adopt a less custodial pupil control ideology when their self-efficacy is high

(Woolfolk et al., 1990). A more recent study of Martin and Sass (2010) discovered that teachers

who express high self-efficacy beliefs for instructional strategies, engagement, and classroom

management generally take a less directive approach in implementing tactics to manage

instruction, irrespective of grade. Probably, TSE is associated with more humanistic attitudes

about classroom control, at least for more experienced elementary school teachers.

INCLUSIVE PRACTICES AND REFERRAL DECISIONS

Several studies (n = 6; see Appendix 1) focusing on teachers’ attitudes toward inclusive practices

have revealed that elementary school teachers with resilient self-efficacy beliefs perceive



35

themselves as more successful in teaching students with disabilities (Brownell & Pajares, 1999)

and feel more comfortable in accepting responsibility for these students’ difficulties (Brady &

Woolfson, 2008). Moreover, self-efficacious teachers have been shown to be less anxious

about (Soodak, Podell, & Lehman, 1998), and to have more positive attitudes toward inclusive

education and socio-cultural diversity than inefficacious teachers (Ahsan, Sharma, & Deppeler,

2012; Gao & Mager, 2011; Malinen et al., 2012). These associations, ranging from .11 to .36

(Mdn = .20), hold across grades, and in samples of preservice and inservice teachers.

Next to the indication that self-efficacious teachers are more tolerant toward problematic

students, they may also be less likely to exclude such students from their class. Eight studies

(see Appendix 1) have identified teachers’ referral decisions as potential consequences of TSE

in elementary school, although the results are somewhat mixed. Four studies failed to provide

support for the assumption that TSE relates to decisions to refer children to special education

(Egyed & Short, 2006; Soodak & Podell, 1993; Tejeda-Delgado, 2009), or exclude them from

school (Gibbs & Powell, 2012). Results from one longitudinal study even suggested that having

low self-efficacy in the fall may result in a reduction in subsequent student referrals to the

student support team (Pas, Bradshaw, Hershfeldt, & Leaf, 2010).

Contrary to these five studies, some findings from earlier research indicated that (special

education) teachers who express high self-efficacy beliefs are less likely to perceive children as

problematic, refer them for special education placement, or seek referral or consultation

assistance (Hughes, Barker, Kemenoff, & Hart, 1993; Meijer & Foster, 1988). Teachers with

high TSE have also been found to be more likely to accept interventions suggested by

consultants (DeForest & Hughes, 1992). Note, however, that these studies used less rigorous

methods than the five studies that failed to establish relations between TSE and teachers’

referral decisions. Moreover, teachers’ decision to refer may not only denote students’

placement in special education, but also to teachers’ ability to identify special needs. Hence,

both the study’s methodological characteristics, and the potential ambiguity in the meaning of

teachers’ referral decisions may explain these mixed findings.

INSTRUCTIONAL MANAGEMENT

It is commonly believed that students learn more when teachers make use of tools that actively

involve and cognitively inspire students in class (e.g., Hamre & Pianta, 2010). Consistent with

this notion, various instructional learning formats as consequences of TSE have been


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considered in seven studies. The existing body of strictly correlational evidence suggests that

preservice teachers with high self-efficacy for engagement, instructional strategies, and

classroom management are likely to use a learner-centered and constructivist approach in their

teaching, while teachers with low efficacy prefer to use traditional learning formats (e.g., Dunn

& Rakes; 2011; Temiz & Topcu, 2013). Also, high- and low-efficacy preservice teachers have

been shown to differ in time spent in whole class versus small-group instruction and use of

criticism (Gibson & Dembo, 1984). The beneficial value of TSE for preservice teachers’

learner-centered approaches coincides with research performed in the elementary school

context and beyond, suggesting that high efficacy teachers not only hold constructivist learning

conceptions (Eren, 2009), but are also likely to more often use learner-centered, constructivist

instruction (Nie, Tan, Liau, Lau, & Chua, 2013; Ngidi, 2012) and teach more supplemental

activities (Ransford et al., 2009). Moreover, Nie and colleagues found that TSE may explain

about one third more of the variance in constructivist instruction than in didactic approaches

to teaching. Hence, it is reasonable to suggest that high TSE leads to more student-centered,

constructivist approaches to instruction among pre- and inservice teachers. These types of

learning formats are likely to make the most of students’ interest, engagement, and ability to

learn.

Classroom goal structures

TSE has been identified as a potential factor in the management of students’ behavior in

multiple lines of research (e.g., Abuh-Tineh et al., 2011; Morris-Rothschild & Brassard, 2006).

Yet, consideration has also been given to the role that self-referent thoughts play in the

management of students’ self-regulation skills. This particular strand of investigations has been

largely inspired by the theoretical underpinnings of classroom goal theory. Situated within a

socio-cognitive view of motivation, proponents of this theory posit that teachers, through their

use of instructional strategies, establish classroom goal structures that are believed to underlie

students’ own goal orientations and subsequent motivation for learning (Meece, Anderman, &

Anderman, 2006). In six studies (see Appendix 1) a case is made for considering TSE as one of

the mechanisms behind such classroom goal structures, three of which focus on the preservice

context (Capa-Aydin, Sungur, & Uzuntiryaki, 2009; Eren, 2009, 2012). Together, these

investigators have found an assortment of self-regulation strategies among teachers who

experience high self-efficacy. These include teachers’ goal setting, intrinsic interest, mastery and

performance goal orientations, self-instruction, emotional control, self-evaluation, self-reaction,

help-seeking, planned effort and persistence, leadership aspirations, and aspects of their



37

professional development. Similarly, globally efficacious inservice teachers tend to more

frequently create mastery goal structures in their classroom than less efficacious teachers, both

in the elementary and secondary grades (Cho & Shim, 2013; Deemer, 2004; Midgley,

Anderman, & Hicks, 1995).

Studies on domain-specific TSE (e.g., Rubie-Davies, Flint, & McDonald, 2011) show less

conclusive results, however. Outcomes of one study suggest that only teachers’ self-efficacy for

instructional strategies leads to a more mastery-focused classroom environment (Ciani,

Summers, & Easter, 2008). In two other studies (Rubie-Davies et al., 2011; Wolters &

Daugherty, 2007), teachers who felt efficacious in both their instructional and engaging

strategies were found to employ instructional strategies thought to foster students’ mastery

goal orientations. Unexpectedly, TSE for classroom management showed negative correlations

with mastery goal structures in both studies, although this effect was likely due to a suppressor.

Overall, TSE was not related to performance goal strategies, except for Cho and Shim’s (2013)

research, in which TSE was identified as a positive predictor of both teachers’ mastery and

performance approach goals. Thus, it can be suggested that TSE is positively associated with

mastery approaches. Whether TSE is also a stable predictor of performance approach goals

and other self-regulation strategies has yet to be clarified.

Technology use in the classroom

One specific instructional tool that helps teachers to differentiate in the classroom and has

potential to provide learning environments that relate to students’ world, is technology use

(Godfrey, 2001). Therefore, it is perhaps not surprising that in a growing corpus of research (n

= 10) the link between TSE and teachers’ technology use has been considered. The focus of

most of this research has been on preservice teachers (e.g., Sang et al., 2010; Teo, 2009). These

researchers have used large samples and structural equation models in cross-sectional designs

and their findings generally suggest that (computer) TSE may positively affect teachers’

perceived usefulness, ease of use, and attitude toward computer use (Wong, Teo, & Russo,

2012) which, in turn, may contribute to their prospective use of technology in class (Chen,

2010; Sang et al., 2010). Similarly, Teo (2009) assessed preservice teachers’ computer self-

efficacy along dimensions of basic teaching skills, advanced teaching skills, and technology for

pedagogy, and revealed that TSE for teaching skills and for technology for pedagogy served as

predictors of traditional and constructivist use of computers and technology.


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In the (upper) elementary and secondary grades, computer TSE – either general or specific –

may positively impact teachers’ attitude toward technology (Rohaan, Taconis, & Jochems,

2012), their attitude toward web-based instruction (Lee & Tsai, 2010), and their motivation to

use web-based professional development (Kao, Wu, & Tsai, 2011). In two studies (Mueller,

Wood, Willoughby, Ross, & Specht, 2008; Vannatta & Fordham, 2004) the positive

relationship between TSE and classroom technology use could not be confirmed. Last,

Ahmad, Basha, Marzuki, Hisham, and Sahari (2010) assessed direct and indirect effects of

faculty member’s computer self-efficacy on technology use. Their results indicated that

computer self-efficacy was an important determinant in affecting faculty members’ use of

computer technology. Thus, for technology and computer use in the classroom to move

forward, teachers need to perceive themselves as self-efficacious, especially in using computers

and technology.

CONSEQUENCES OF TSE FOR CLASSROOM PROCESSES – EMOTIONAL SUPPORT

Central to any conceptualization of classroom processes is teachers’ capability to establish

caring relationships with students, acknowledge their opinions and feelings, and create settings

in which students feel secure to explore and learn (Pianta et al., 2008). Although TSE may

potentially be involved in such social-emotional classroom dynamics, this possibility has only

been considered in 13 studies (see Appendix 1), focusing on overall emotional climate,

student–teacher relationship quality, or regard for student perspectives.

OVERALL EMOTIONAL CLIMATE IN THE CLASSROOM

In three large cross-sectional studies, observational methods were used to measure overall

emotional climate in preschool and Grade 5 (Guo et al., 2010; 2012; Pakarinen et al., 2010).

According to one of these studies (Guo et al., 2012), fifth-grade teachers with high self-efficacy

are more likely than poorly efficacious educators to create a supportive environment

characterized by warmth, responsiveness, enthusiasm, teacher support, and effective use of

instructional time. In the preschool context, however, consensus has not been reached on

whether highly self-efficacious teachers provide their students with higher levels of emotional

support (Guo et al., 2010; Pakarinen et al., 2010).



39

STUDENT-TEACHER RELATIONSHIP QUALITY

Turning to interpersonal student–teacher relationships, seven correlational studies have shown

mixed results as to whether high TSE increases the quality of teachers’ relationship with

individual students (e.g., de Jong et al., 2013; Mashburn, Hamre, Downer, & Pianta, 2006).

These ambiguous findings may partly be due to the studies’ selected outcome variables and

methods of data analysis. Generally, TSE did not appear to be correlated with the quality of

student–teacher relationships when total scores of teacher-perceived relationship quality were

used and the nested structure of the data was not taken into account (Chung, Marvin, &

Churchill, 2005; Hardré & Sullivan, 2008). In another Dutch study among preservice teachers, de

Jong et al. (2013) also failed to establish significant links between Tschannen-Moran and

Woolfolk Hoy’s (2001) dimensions of TSE and relationship quality, in terms of control and

affiliation. Notably, reliability coefficients of two self-efficacy dimensions in this study were

below the threshold of .70. Only in Jimmieson, Hannam, and Yeo’s (2010) study, TSE has

been shown to be a positive correlate of child-perceived student–teacher relationships in

elementary and middle school.

Researchers have also made a case for the usefulness of focusing on the unique, dyadic

student–teacher relationship dimensions of closeness and conflict. First, using a large sample

and a multilevel design, Mashburn et al. (2006) found that self-efficacious teachers were more

likely to have close, but not less conflictuous relationships with regular preschool students.

When explicitly focusing on problematic students, however, Hamre, Pianta, Downer, and

Mashburn (2008) found less efficacious preschool teachers to be experiencing higher degrees

of conflict with their students than would be expected based on their judgments of students’

problem behaviors. Last, Yoon (2002) failed to confirm that high TSE is associated with high-

quality relationships. In her research of K-5 teachers, TSE only accounted for 2% of the total

variance in student–teacher conflict and closeness.

REGARD FOR STUDENT PERSPECTIVES

Also part of teachers’ emotional support is their willingness and ability to place emphasis on

students’ interests, motivations, and points of view (Pianta et al., 2008). In two cross-sectional

studies of Hardré and Sullivan (2008, 2009), high school teachers with a healthy sense of self-

efficacy for diagnosing motivational problems were found to take such perspectives into

account by using relevance, value, and internally focused strategies that emphasize

interpersonal relatedness with their students and making the educational content more


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meaningful to them. However, teachers’ usage of autonomy supportive interpersonal styles was

not predicted by TSE. Other conclusions were reached by Leroy, Bressoux, Sarrazin, and

Trouilloud (2007), who found that fifth-grade teachers’ self-efficacy was positively related to

the creation of an autonomy supportive climate. Thus, focusing on slightly different outcome

variables, these three studies reveal that teachers’ regard for student perspectives is positively

predicted by TSE.

SYNTHESIS OF RESULTS

Taken together, results from studies on the consequences of TSE for classroom processes

indicate that high-efficacy teachers, and especially those with more experience, tend to

effectively cope with a range of problem behaviors, use proactive, student-centered classroom

behavior strategies and practices, and establish less conflictuous relationships with students.

Contrary to preservice teachers, tenured educators who believe in their capabilities also use

more diverse instructional strategies, differentiate more frequently, change their goals

according to students’ needs, and are more positive about the implementation of such

instructional strategies. Probably, more experienced educators with high self-efficacy may have

become more sensitized to students’ signals, needs, and expectations, and are thereby better

able to provide them with adequate supports in class. Yet studies revealing a curvilinear

association between TSE and experience indicated this beneficial by-effect of experience may

not last (e.g., Klassen & Chui, 2010; Wolters & Daugherty, 2007). This underscores the

importance of longitudinal studies investigating the development of TSE over teachers’

careers.

CONSEQUENCES OF TSE FOR STUDENTS’ ACADEMIC ADJUSTMENT

The most common consequence of TSE for student outcomes was based on students’

achievement, including overall school grades, literacy and math performance, and achievement

in some other subjects. Of the 23 reviewed studies (see Appendix 2), most were cross-sectional

in nature, except for four studies that relied on a longitudinal design (Caprara, Barbaranelli,

Steca, & Malone, 2006; Guo et al., 2010, 2012; Midgley et al., 1989). In another three studies,

hierarchical regression techniques were used to account for the nested data structure (Guo et

al., 2010; Jimmieson et al., 2010; Reyes et al., 2012). The sample sizes of the studies varied

extensively, ranging from less than 20 (Allinder, 1995; Ross, 1992) to more than 2,000 (Caprara

et al., 2006). Despite the idea that TSE fluctuates across teaching tasks, none of the reviewed



41

investigations used domain-specific instruments to measure the construct of teacher self-

efficacy. Given the divergences in sample size and methodological rigor, which may potentially

have an impact on the parameters estimated, the consequences of TSE for students’ academic

adjustment should be interpreted with caution.

OVERALL ACHIEVEMENT

In several studies (see Appendix 2), TSE has been assessed in relation to students’ overall

academic performance, only one of which did not find an association between TSE and

achievement, as measured by school type (Chong, Klassen, Huan, Wong, & Kates, 2010). In

the elementary school context, studies by Chang (2011) and Woolfolk Hoy, Hoy, and Kurz

(2008) explored the association between Academic Optimism – a latent variable comprising

TSE – and students’ achievement scores, and uncovered a substantial positive relationship.

Next to achievement scores, perceived achievement and academic climate have been shown to be

positively affected by TSE in middle school, although the reported coefficient was small

(Chong et al., 2010; Jimmieson et al., 2010). Consistent findings were also reported in four

studies conducted in high school, indicating that students whose teachers had higher TSE

benefited more in terms of their academic achievement than students whose teachers had a

lower sense of efficacy (Caprara et al., 2006; Hardré et al., 2006; Mohamadi & Asadzadeh,

2012; Mojavezi & Poodineh Tamiz, 2012). Caprara et al. (2006) proposed a longitudinal model

with TSE as a positive contributor to students’ achievement when previous levels of

achievement are controlled for. Hence, these studies imply that TSE may predict students’

overall performance in elementary, middle, and high school. Note, however, that coefficients

are generally modest (Mdn = .27), ranging from .02 to .78.

MATH ACHIEVEMENT

Moving to the consequences of TSE for students’ math performance, there are four empirical

studies to suggest that teachers with high self-efficacy are more likely to facilitate students to

develop their mathematical competence than teachers with low self-efficacy (Allinder, 1995;

Hines, 2008; Midgley et al., 1989; Throndsen & Thurno, 2013). When considering students’

grade level, however, these findings are less straightforward. For instance, Midgley et al. (1989)

revealed that within-year changes in students' perceived performance in mathematics during the

transition from elementary to middle school were positively predicted by TSE, although the

coefficients across years were nonsignificant. Moreover, Throndsen and Turno (2013) found a

small but positive correlation between TSE and math performance across Grades 2 and 3.


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When investigating the associations between TSE and math achievement separately for each

grade, significant correlations were only found for second-graders. Given that students’ math

achievement across years does not seem to vary as a function of TSE, it is possible that other

teacher characteristics, including knowledge, skills, and experience, may be more important to

students’ math achievement than teachers’ sense of self-efficacy.

LITERACY ACHIEVEMENT

Compared to other subjects, the associations between TSE and students’ literacy in elementary

school and beyond are relatively inconsistent in the literature (n = 6; see Appendix 2).

Coefficients range from .02 to .76, with the highest value emerging from Cantrell, Almasi,

Carter, and Rintamaa’s (2013) study. Together with Guo et al. (2012), these researchers are the

only ones to demonstrate that TSE is related to students’ literacy outcomes in Grades 5, 6 and

9. Guo et al. (2010), in a study on preschool, and Heneman, Kimball, & Milanowski (2008) and

Reyes et al. (2012) in studies on elementary teachers, failed to confirm the hypothesized

association between TSE and students’ reading and writing achievement. Moreover, teachers’

predictions of students’ success in reading has not been shown to depend on their levels of

self-efficacy in elementary and middle school (Tournaki & Podell, 2005).

It is interesting to note that children’s literacy outcomes are less often predicted by TSE than

their achievement in other subjects, and math in particular. This failure of prediction might,

amongst others, be due to the studies’ methodological characteristics. Studies focusing on

literacy have more often relied on student measures of achievement, compared to other

subjects. Thereby, they accounted for overestimation of coefficients due to shared source

variance, which probably reduced the impact of TSE on students’ literacy outcomes to non-

significance. Also, teachers’ perceptions of student achievement have been shown to be

generally more biased than test scores or grades (Kuncel, Credé, & Thomas, 2005).

Consequently, self-efficacious teachers may also judge their students’ performance to be higher

than their actual achievements, thereby biasing the results of studies in which the same

informants were used. This issue of common source variance, and the inclusion of more

reliable achievement measures, may warrant further consideration.

ACHIEVEMENT IN OTHER SUBJECTS

Four studies were identified that focused specifically on subjects other than literacy or math

(Angle & Moseley, 2010; Lumpe, Czerniak, Haney, & Beltyukova, 2012; Ross, 1992; Ross,



43

Hogaboam-Gray, & Hannay, 2001). All studies, except for the investigation of Ross et al.

(2010), were performed in the upper elementary grades and beyond, using cross-sectional

designs and revealing only small associations (Mdn = .13). Regarding students’ science

achievement, Lumpe and colleagues (2012) showed that when fourth- and sixth-grade teachers

feel more efficacious in teaching science, their students’ science scores are higher as well.

Together with outcome expectations and hours of professional development, however, TSE in

this study only explained 4% of the total variance in students’ science performance. In the

context of history, Ross (1992) examined links between TSE, contact with assigned coaches,

and students’ history achievement in a sample of Grades 7 and 8 history teachers. Teachers

who interacted more with their coaches and felt more confident about their teaching abilities

were more likely to positively affect their students’ achievement than colleagues who had less

contact with their coaches or perceived themselves as less self-efficacious. Notably, both

constructs accounted for 57% of the variance in students’ achievement. Angle and Moseley

(2010) aimed to unravel differences between the self-efficacy beliefs of high school biology

teachers whose students’ biology achievement either exceeded or fell below the state

proficiency level. Their results indicated that students’ biology achievement did not vary

according to how confident teachers were in teaching biology. One study investigating

computer literacy (Ross et al., 2001) turned its focus toward very young children. Following up

on the seminal study of Midgley et al. (1989), these authors distinguished an upward trajectory,

in which students moved from a poorly efficacious to a highly efficacious teacher, and a

downward trajectory, comprising students who shifted in exactly the opposite direction. They

found that students developed their computer skills most in the upward trajectory, when

students moved from a lower- to a higher-efficacy teacher. Also, TSE was directly related to

students’ advanced computer skills, accounting for a total of 7% of the variance in computer

skills. This handful of studies is thus indicative of associations between TSE and achievement

in subjects other than literacy and math.

MOTIVATION

In 11 studies (see Appendix 2), TSE was assessed in relation to students’ motivation, including

such aspects as student engagement, intrinsic and extrinsic motivation, academic expectations,

self-efficacy, goal orientations, and school investment. Characteristic of these studies is that

they had relatively small samples and were all cross-sectional in nature, except for the seminal

study of Midgley et al. (1989). In half of studies the authors analyzed their data in multilevel

(four studies) or SEM-designs (two studies), or used more traditional methods such as


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regression analysis. Associations between TSE and aspects of students’ motivation ranged

from .13 to .79, with the highest associations found for total motivation and attitude scores.

The existing literature suggests that students’ motivation may be more consistently predicted

by their teachers’ self-efficacy than their academic achievement (e.g., Thoonen et al., 2011b).

During the elementary school years, students have been found to be more engaged in the

classroom (Reyes et al., 2012) and academically efficacious (Ross et al., 2001) when their

teachers’ self-efficacy is high. In one rigorous Dutch study of Thoonen et al. (2011b), however,

teachers’ self-efficacy was found to be unrelated with aspects of students’ motivation, including

academic self-efficacy, mastery or performance-avoidance goals, intrinsic motivation, and

school investment. This is perhaps not surprising, given that most reliability estimates for the

motivational constructs used in this study were below the threshold of .70.

Similar to their elementary peers, middle-schoolers have been demonstrated to benefit from

self-efficacious teachers in terms of higher levels of school satisfaction and confidence in their

achievement at school, lower levels of psychological distress, and positive views about future

learning opportunities (Jimmieson et al., 2010). Additionally, the longitudinal study of Midgley

et al. (1989) lends support for the idea that TSE is associated with students’ motivation during

the transition from upper elementary to middle school. Specifically, students who shifted from

high- to low-efficacy mathematics teachers had lower expectancies and perceived

performances, and higher perceptions of task difficulty than students who were taught by

highly efficacious teachers after the transition. Furthermore, variations in TSE beliefs before

and after the transition to middle school were more important to low-achieving than to high-

achieving students' motivational beliefs.

High schoolers whose teachers feel generally self-efficacious have been shown to display

higher amounts of observed on-task behavior, increased engagement, effort and (intrinsic)

motivation for school, more positive attitudes toward learning, and better opinions about their

teacher (Hardré et al., 2006; Mojavezi & Poodineh Tamiz, 2012; Robertson & Dunsmuir,

2013). Other studies show that TSE may not only be directly related to teacher perceptions of

their students’ emotional engagement, but also indirectly affect their behavioral and emotional

engagement through influence (van Uden, Ritzen, & Pieters, 2013). Turning to domain-specific

TSE beliefs in high school, Hardré and Sullivan (2008) noted that teachers’ efficacy beliefs for

motivating and diagnosing were not associated with their perceptions of students’ academic



45

motivation and approaches to learning. However, a later, separate study (Hardré & Sullivan,

2009), indicated that TSE for motivating and diagnosing were predictive of teachers’

perceptions of student motivation, explaining almost a quarter of the variance. Thus, across

educational stages and different countries, TSE seems to be a fairly robust predictor of a

variety of motivational factors.

INDIRECT CONSEQUENCES

In only three relatively large studies did the authors look at the mediating role of classroom

processes in the association between TSE and students’ adjustment (Guo et al., 2012; Thoonen

et al., 2011b; van Uden et al., 2013). Regarding achievement, Guo and colleagues (2012)

pointed toward an indirect relationship of TSE with fifth-graders’ literacy outcomes, via

teachers’ support for learning. Focusing on aspects of motivation, TSE has been shown to

affect specific teacher practices, including interpersonal behavior, process-oriented instruction,

and cooperative learning. These practices, in turn, may raise students’ emotional and behavioral

engagement (van Uden et al., 2013), and well-being in school (Thoonen et al., 2011b). Thus,

this small research base provides initial evidence that teacher practices defining the classroom

quality, and especially those related to emotional support, may canalize the effect of TSE on

student outcomes. This finding corroborates prior evidence that points to the benefits of

emotionally supportive teacher behaviors for encouraging students’ learning, engagement, and

enjoyment in their learning tasks (Reyes et al., 2012; Rimm-Kaufman & Chui, 2007).


Overall, studies suggest that TSE is modestly associated with students’ academic adjustment in

elementary school and beyond. Yet, aspects of students’ motivation, and total motivation

scores in particular, seem to be more consistently predicted by TSE than their academic

achievement. Assumedly, students’ motivation may be partly considered a factor determining

the quality of classroom processes, and therefore, more proximal to TSE than academic

performance (e.g., Woolfolk Hoy et al., 2009). Regarding achievement, TSE appears less

important for middle and high schoolers’ achievement in various subjects than for the

attainment of elementary school children. This is perhaps not surprising, as high schoolers are

taught by many teachers and see each teacher for only a small proportion of the school day

(Midgley et al., 1989). Consequently, older students may be less affected by teachers’ efficacy

than younger children. Prior work of Guskey (1981) also revealed that secondary school


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46

teachers feel less responsible for the successes and failures of their students than do elementary

teachers. Probably, teachers’ classroom mastery experiences may affect secondary school

teachers’ self-efficacy less than elementary teachers’ capability beliefs, thereby reducing the

impact of TSE on student achievement.

CONSEQUENCES OF TSE FOR TEACHERS’ PSYCHOLOGICAL WELL-BEING

TEACHER BURNOUT

One imperative in understanding teachers’ psychological well-being is to gain insight into how

levels of burnout in teachers are buffered or exacerbated by factors such as their self-efficacy.

Indeed, almost one third of the reviewed articles (n = 22) on teachers’ well-being are

concerned with teacher burnout (see Appendix 3). Overall, the studies on this topic were

conducted in a wide variety of cultural and national settings, typically had larger sample sizes

than TSE research on classroom quality and students’ adjustment, and used more advanced

statistical methods, including multilevel and (longitudinal) SEM. These studies may extend,

therefore, the generalizability and adequacy of teachers’ self-efficacy, and advance

understanding of its causal pathways.

Associations between TSE and both overall burnout levels (range = –.17 to –.63; Mdn = –.36)

and specific dimensions of burnout (range = –.09 to –.76; Mdn = –.25 for emotional

exhaustion; range = .13 to .75; Mdn = .36 for personal accomplishment; range = –.16 to –.60;

Mdn = –.33 for depersonalization) have been fairly consistent across studies. In the preservice

context, teachers with high levels of self-efficacy for instructional strategies and classroom

management have generally been shown to be less likely to feel emotionally exhausted and to

depersonalize their students early in their student-teaching experience than less efficacious

student educators (Fives, Hamman, & Olivarez, 2007). Furthermore, self-efficacy for student

engagement and instructional strategies appears to be predictive of higher levels of personal

accomplishment over time (ibid.).

Some insights into how TSE may relate to such aspects of burnout have been provided by

Hultell, Melin, and Gustavsson (2013). Their cluster-analytic results revealed that preservice

teachers’ burnout levels were accompanied by decreases in TSE, low and stable levels of

burnout were reflected by high TSE, stable and high levels of burnout were characterized by

low TSE, and stable and moderate levels of burnout resulted from changing levels of TSE over



47

time. Although these findings imply that higher self-efficacy preservice teachers may be more

resistant to burnout, such outcomes may not necessarily extend to their first years of teaching

(Høigaard, Giske, & Sundsli, 2012).

More experienced inservice teachers with high self-efficacy, however, have been found to

report lower levels of overall burnout (Brissie, Hoover-Dempsey, & Bassler, 1988; Egyed &

Short, 2006; Friedman, 2003) and specific burnout dimensions, with negative associations

between TSE and emotional exhaustion and depersonalization, and positive associations

between TSE and personal accomplishment (Egyed & Short, 2006; Friedman, 2003).

Furthermore, the beneficial effects of these capability beliefs continue to be present in

secondary school, as indicated by rigorous, mainly Dutch research (Briones, Taberno, &

Arenas, 2010; Brouwers et al., 2001; Brudnik, 2009; Evers, Brouwers, & Tomic, 2002; Evers,

Tomic, & Brouwers, 2005). In line with Bandura’s (1997) notion of reciprocal determinism,

support has even been found for a feedback loop in which low levels of TSE reinforce

teachers’ burnout and vice versa (Brouwers et al., 2001). Focusing on specific burnout

dimensions, TSE has a longitudinal negative effect on depersonalization, and a synchronous

positive effect on personal accomplishment (Brouwers & Tomic, 2000). It should be noted,

however, that Brouwers and Tomic’s results also pointed to a reversed direction of

relationships between TSE and emotional exhaustion. This underscores the importance of

longitudinal research that advances knowledge of the definitive causation of TSE on burnout

dimensions.

Culturally diverse studies concerned with all grade levels did not deviate much from the

abovementioned pattern of results (e.g., Avanzi et al., 2013). In Norway, negative relationships

were found between TSE and overall burnout scores (Skaalvik & Skaalvik, 2007) and

emotional exhaustion and depersonalization (Skaalvik & Skaalvik, 2010). Similarly, Avanzi et al.

(2013) found that Italian teachers’ efficacy was negatively correlated with student- and work-

related burnout. Researchers from the United States suggest that teachers have a higher sense

of personal accomplishment and feel less emotionally exhausted when they have confidence in

their abilities to actively engage their students and to handle student misbehavior (Martin et al.,

2012; Shyman, 2010; Tsouloupas et al., 2010). These findings substantiate those of Schwarzer

and colleagues (Schwarzer & Hallum, 2008; Schwarzer, Schmitz, & Tang, 2000; So-kum Tang,

Au, Schwarzer, & Schmitz, 2001), who generally found the same pattern of relationships as

studies discussed above for Syrian, Chinese, and German teachers. So-kum Tang et al. (2001)


CHAPTER 2

48

furthermore found TSE to be directly and indirectly related to the mental health of teachers,

through their burnout. In one longitudinal study, it was demonstrated that changes in Canadian

teachers’ self-efficacy were negatively related to changes in their levels of emotional exhaustion

and depersonalization, and positively related to changes in their sense of personal

accomplishment (Fernet, Guay, Senecal, & Austin, 2012).

Indirect consequences of TSE on teacher burnout

In two studies (Doménech-Betoret, 2009; Martin et al., 2012) the focus has been on the

potential indirect consequences of teachers’ self-efficacy for their levels of burnout. Martin et

al. (2012), for instance, showed that TSE for student engagement relates positively to

instructional practices, which, in turn, predict the amount of student stressors in the classroom

and increases teachers’ personal accomplishment. Moreover, Doménech-Betoret (2009) found

that difficulties related to the classroom (i.e., student diversity and misbehavior) mediate the

relationship between TSE and burnout dimensions. Hence, with some exceptions, studies of

the predictive associations between TSE and teacher burnout show a quite consistent pattern

across contexts and grade levels. Practices related to instruction and behavior management

may mediate this relationship.

TEACHERS’ STRESS AND COPING

Six studies (see Appendix 3) investigated the degree to which TSE is related to teachers’ levels

of stress and coping. In these studies, correlations between TSE and stress ranged from .06 to

.50, and between TSE and coping from –.11 to .05. Across grades, the results reported indicate

that teachers with high self-efficacy experience less job-related stress (Barouch Gilbert,

Adesopea, & Schroeder, 2013; Doménech-Betoret, 2006; Robertson & Dunsmuir, 2013), and

fewer student stressors (e.g., concerns regarding student demotivation, or teaching students of

mixed ability). Such student stressors, in turn, may significantly reduce teachers’ dissatisfaction

for their job (Sass, Seal, & Martin, 2011). Next to direct associations, higher class and school

TSE also indirectly reduce the amount of teachers’ perceived tension (Helms-Lorenz, Slof,

Vermue, & Canrinus, 2012). Teachers’ active or passive coping, however, was not found to be

predicted by TSE (Chan, 2008). Instead, school coping resources and TSE have been shown to

buffer the negative effect of stressors on teachers’ burnout (Doménech-Betoret, 2006). Thus,

efficacious teachers do not necessarily seem to have more coping resources, but at least may

experience less stress in their profession. The results from these studies should be interpreted

with caution, as these studies typically included only a limited number of teachers, and, in six



49

cases, employed statistical techniques that may be unsuitable for use in smaller samples.

TEACHERS’ JOB SATISFACTION

Next to stress and burnout, few researchers have considered more positive factors underlying

teachers’ psychological well-being as outcomes of TSE (e.g., Caprara et al., 2003, 2006; Collie

et al., 2012; Helms-Lorenz et al., 2012). One of the most common outcomes studied in this

domain is teachers’ job satisfaction (n = 21). Only Avanzi et al. (2013) and Salanova et al.

(2011) have conducted longitudinal studies and can infer the causality that has been posited.

However, more than half of the reviewed investigations have gone beyond commonly used

correlational approaches, fitting complex structural equation models and revealing more

complex patterns of relationships. With some exceptions (Briones et al., 2010; Høigaard et al.,

2012; Lent et al., 2011), investigators considering this consequence of TSE have demonstrated

a stable pattern of results, with coefficients ranging from .10 to .86 (Mdn = .33). In elementary

school (Collie et al., 2012; Helms-Lorenz et al., 2012; Skaalvik & Skaalvik, 2010; Stephanou,

Gkavras, & Doulkeridou, 2013), as well as in middle and high school (Canrinus, Helms-

Lorenz, Beijaard, Buitink, & Hofman, 2010; Caprara et al., 2003, 2006; Tsigilis, Koustelios, &

Grammatikopoulos, 2010), self-efficacious teachers have been found to be more satisfied with

their job and relationships than their less efficacious counterparts. Interestingly, higher efficacy

teachers seem less satisfied with their salary, but are better able to manage this when they are at

the same time satisfied with their relationships at work (Canrinus et al., 2012).

Assessing all grade levels simultaneously, results also point to positive associations between

TSE and job satisfaction (Avanzi et al., 2013; Barouch Gilbert et al., 2013; Collie et al., 2012;

Duffy & Lent, 2009; Klassen et al., 2009; Klassen & Chiu, 2010; Moè, Pazzaglia, & Ronconi,

2010; Viel-Ruma, Houchins, Jolivette, & Benson, 2010) and (indirect) negative associations

between TSE and job discontent (Sass et al., 2011). These effects seem to hold across time,

domains of TSE, and levels of teaching experience (e.g., Blackburn & Robinson, 2008;

Canrinus et al., 2012; Salanova, Llorens, & Schaufeli, 2011; Tsigilis et al., 2010).

Indirect consequences of TSE on teachers’ job satisfaction

Probably, TSE and job satisfaction are part of a more complex dynamic. For instance, factors

such as work conditions, student stressors, personal achievement, and social support have been

shown to mediate the relationship between TSE and job satisfaction (Briones et al., 2010;

Duffy & Lent, 2009; Lent et al., 2011; Sass et al., 2011). These complex direct and indirect


CHAPTER 2

50

influences, which all encompass socioemotional aspects of teaching, evidently speak to

Bandura’s model of triadic reciprocal causation. Yet, longitudinal studies are needed to

determine the causal ordering of effects.

TEACHERS’ COMMITMENT

In total, 12 articles (see Appendix 3) have investigated the predictive associations between TSE

and teacher commitment, which range from .10 to. 36 (Mdn = .26). Large-scale correlational

studies of preservice teachers’ self-efficacy have indicated that TSE, especially in the domain of

classroom management, is a positive predictor of occupational commitment, irrespective of the

country in which teachers perform their job (Evans & Tribble, 1986; Klassen & Chui, 2011;

Klassen et al., 2013). Also in elementary, middle and high school, higher self-efficacy inservice

teachers have generally been found to feel more professionally, affectively, organizationally,

and occupationally committed than poorly efficacious educators (Barouch Gilbert et al., 2013;

Bogler & Somech, 2004; Canrinus et al., 2010; Caprara et al., 2003; Chan, Lau, Nie, Lim, &

Hogan, 2008, Coladarci, 1992; Ebmeier, 2003; Rots, Aelterman, Vlerick, & Vermeulen, 2007).

Regarding domain-specific TSE, two studies of Ware and Kitsantas (2007, 2011), using the

same vast sample of 26,257 teachers, showed that teachers with high efficacy to enlist

administrative control, make decisions, and control aspects of their classroom operations may

be more committed to teaching. In Klassen and Chui’s (2011) study, only TSE for instructional

strategies was a positive predictor of practicing teachers’ occupational commitment. Thus,

teachers feel more committed to teaching when their self-efficacy is high. These results hold

across countries and various types of commitment, including affective, organizational, and

occupational commitment (e.g., Barouch Gilbert et al., 2013; Klassen & Chui, 2011; Klassen et

al., 2013).

TEACHER ATTRITION AND RETENTION

Seven studies (see Appendix 3) sought to assess TSE in direct relation to teacher attrition and

retention. From these generally small correlational studies, it follows that only preservice

teachers with high self-efficacy intend to remain longer in the profession (Bruinsma & Jansen,

2010). Inservice TSE, irrespective of grade level, has not been found to be directly associated

with teachers’ levels of absenteeism (Imants & Van Zoelen, 1995), intention to leave (Barouch

Gilbert et al., 2013; Høigaard et al., 2012) or stay (Canrinus et al., 2010; Hughes, 2012; Malow-

Iroff, O'Connor, & Bisland, 2007).



51

Indirect consequences of TSE on teacher attrition and retention

Despite this lack of direct effects, some indirect effects of TSE on teacher attrition and

retention have been noted. Several studies (Klassen & Chui, 2011; Tsouloupas et al., 2010)

suggest that teachers with poor efficacy for classroom management and instructional strategies

may be more prone to feel emotionally exhausted and uncommitted to their occupation than

educators with high efficacy. This may cause them to leave the profession entirely. In contrast,

teachers’ responsibility to remain in their profession seems to be indirectly predicted by TSE

via their affective commitment and relationship satisfaction (Canrinus et al., 2010). Notably,

for both preservice and inservice teachers, low TSE for classroom management seems to be

the most important trigger to abandon their job. Hence, although the correlational nature of

the aforementioned literature prevents us from making causal inferences, the effect of TSE on

attrition and retention is most likely to be mediated by teachers’ job commitment and

satisfaction.


Findings pertaining to factors underlying teachers’ psychological well-being seem robust.

Irrespective of pre- or inservice context, grade level, and country, and potentially over time,

self-efficacious teachers may suffer less from stress, emotional exhaustion, depersonalization,

and overall burnout, and experience higher levels of personal accomplishment, commitment,

and job satisfaction. Moreover, emotional and organizational processes in class, such as student

misbehavior and motivation, positive affect, and instructional management, are likely to

function as catalysts in the relationship between TSE and teachers’ burnout and job

satisfaction. Following Spilt, Koomen, and Thijs (2011), this process might be attributable to

teachers’ internalization of experiences with specific students in representational models of

relationships, which not only guide teachers’ emotional responses and well-being in class, but

may also increase or compromise their self-efficacy.

TSE is not directly related to teacher attrition and retention. Rather, teachers with low self-

efficacy seem to experience higher levels of emotional exhaustion and lower levels of

satisfaction and commitment, ultimately leading them to quit their job. These indirect effects

imply that positive feelings of well-being, such as commitment and satisfaction, are the

mechanism through which TSE exerts its influence over teachers’ intention to stay or leave.


CHAPTER 2

52

Overall, positive aspects of teachers’ psychological well-being can thus be suggested to be

more mutable due to their self-efficacy than negative aspects.

DISCUSSION

The present review is probably the first to provide an up-to-date synthesis of forty years of

research on TSE and its various consequences, using a heuristic model inspired by the process-

oriented view of Woolfolk Hoy et al. (2009) and CLASS-framework of Pianta et al. (2008). In

this section, critical areas of research needed to advance TSE research across lines of inquiry

related to classroom processes, student adjustment, and teacher well-being are discussed.

Additionally, some more general methodological and theoretical challenges to the study of TSE

are considered.

CLASSROOM PROCESSES

Of all domains, aspects of classroom organization were found to be the best represented in the

literature, probably owing to the growing popularity of classroom management TSE and the

numerous attempts made to measure this construct (O’Neill & Stephenson, 2011).

Unfortunately, though, most studies in this field did not seem to inform or effectively build on

one another. Specifically, the range of efficacy-influenced teaching processes associated with

this domain was quite extensive, covering a host of different strategies, behaviors, attitudes,

and decisions in class. Moreover, each of these teacher processes appeared to be investigated

only once or twice in isolated, cross-sectional studies focusing on various student groups or

different grades, and using different measures. Such fragmentation of the field may prevent

researchers from drawing definite conclusions on the links among TSE and aspects of

classroom organization across grade level, specific types of students, and contexts. Hence, the

need for integration among those diverse literatures will continue to be an important area of

research for the years to come.

The heuristic model highlighted the current lack of research on the consequences of TSE for

emotionally supportive classroom processes. This is important, as TSE may be particularly

relevant for the interpersonal aspects of teaching (see Labone, 2004). For instance, past

research suggests that TSE may function as a protective factor against poor student–teacher

relationship quality, such that students with generally self-efficacious teachers are less

vulnerable to developing poor-quality relationships with their teacher, even if their behavior is



53

problematic (Hamre et al., 2008; Mashburn et al., 2006; Midgley et al., 1989). From a dyadic

angle, Spilt and Koomen’s (2009) results showed that teachers perceive themselves as less self-

efficacious in relation to individual, disruptive students.

Notably, the absence of the emotional support component of teaching is also evident in the

various instruments that have been developed to measure TSE and its underlying dimensions.

Possibly, the role of teachers in maintaining warm relationships with students and supporting

their basic needs is an aspect of TSE that is difficult to capture by current instruments,

especially when taking a dyadic perspective on such associations. However, as classrooms are

inherently social contexts, and teachers’ socioemotional support is one of the strongest

correlates of student adjustment (e.g., Davis, 2003), researchers should work to increase

awareness of the impact of TSE on emotional support in the classroom. Adapting TSE

instruments to include the emotional domain and broadening the construct of TSE to include

dyadic relationships may be important steps forward to further elucidate this link.

STUDENTS’ ACADEMIC ADJUSTMENT

Next to classroom processes, results bring attention to the need to better understand the role

of TSE in students’ academic adjustment. To date, many scholars have come to claim that TSE

is a particularly powerful predictor of students’ academic adjustment. However, the body of

research looking into this specific relationship is not nearly as large as can be expected from

this general assertion. Of all studies reviewed, only 27 provided insight into the links among

TSE and students’ achievement and motivation. Interestingly, these do not appear to be the

studies that are usually referred to. Specifically, most-cited articles (e.g., Tschannen-Moran et

al., 1998) seem to be theoretical in nature, only assuming possible links between TSE and

students’ academic adjustment. Additionally, the majority of scholars focusing on the link

between TSE and achievement tend to mainly discuss links between TSE and a range of

classroom processes. Although these efficacy-influenced processes are generally presumed to be

supportive of students’ achievement, empirical evidence regarding such complex, indirect links

is still lacking. This potentially suggests a bias in the interpretation of TSE research.

Another challenge in the examination of TSE in relation to students’ adjustment pertains to

the methods of data collection and analysis. Generally, the majority of reviewed studies solely

relied on teacher reports of both TSE and student adjustment, and largely overlooked the fact

that students were not sampled independently from each other. This lack of consideration for


CHAPTER 2

54

both shared method variance and the dependent nature of the data might have possibly lead to

an overstatement of statistical significance, given that students’ adjustment tends to be more

alike when they share the same teacher. As such, the validity of the results of this work may be

questionable in the absence of methodological rigor. Overall, future researchers should take

account of both teacher and student data, and use multilevel (SEM) designs to elucidate the

relationship between TSE and students’ achievement, both directly and indirectly, through

daily classroom processes.

TEACHERS’ WELL-BEING

There is an abundance of studies on the link between TSE and teachers’ psychological well-

being, but the majority of this research has concentrated on factors hampering teachers’

welfare. Perhaps, the popularity of this research focus is unsurprising, as teaching is considered

one of the most stressful occupations (Johnson et al., 2005). Still, the empirical evidence

included in this review suggests that TSE may be of higher predictive value for positive factors,

such as personal accomplishment, than for dimensions of stress and burnout (see Aloe et al.,

2014). Specifically, high TSE beliefs, and in particular those that go beyond the instructional

domain, seem to help teachers stay motivated, satisfied, and consequently on the job. Further

investigation of the complex interrelationships between TSE, positive feelings of well-being,

and teacher retention may be an important avenue of research that could be pursued.

CONCEPTUAL AND METHODOLOGICAL CHALLENGES

Both theoretically and empirically, one of the major challenges to TSE research is to realize the

full richness of the TSE construct. Theories of self-efficacy suggest that TSE should be

measured in terms of specific beliefs that fluctuate across tasks, domains, and different

contexts. Consistent with the review findings of Klassen et al. (2011), a large proportion of

empirical studies unfortunately failed to use such more complex, multidimensional measures.

Bandura (1997) has cautioned researchers attempting to measure TSE that undifferentiated

self-efficacy scales usually suffer from low predictiveness, as these scales are not distinctly

linked to what they seek to predict. This may explain, in part, why most reported coefficients,

and especially those in the domain of teaching and learning, are only small to moderate.

Studies using multidimensional measures of TSE may allow for the detection of unique self-

efficacy dimensions that may have different patterns of effects on similar outcomes. A good



55

starting point is the domain-specific Bandura-based scale of Tschannen-Moran and Woolfolk

Hoy (2001), which, coincidentally, bears a strong resemblance to the CLASS-domains of Pianta

and colleagues (2008). The identification of such dimensions of TSE has potential for

comparing, contrasting, and integrating various lines of TSE research and theory that, to date,

have occupied isolated territories.

Ideally, advancements in the sophistication of measures should be accompanied by greater

methodological and data-analytic rigor. Generally, the quality of reviewed studies varied

considerably in terms of sample size, employed measures of TSE, and complexity of statistical

analyses. Especially where students’ academic adjustment and aspects of classroom processes

were concerned, the host of research primarily relied on simple correlation techniques and total

self-efficacy scores in cross-sectional designs, thereby largely failing to take account of the

highly particularized and potentially fluctuating nature of TSE. In future studies, therefore,

greater emphasis should be placed on longitudinal analyses and (multilevel) structural equation

models, in which differing pathways of influences can be compared, and the temporal

precedence of predictors can be established.

LIMITATIONS

The current review faces some limitations. First, despite careful efforts to systematically search

the literature, it is possible that some studies have been overlooked, or their relevance

unacknowledged. This might especially be the case for unpublished work, or articles that did

not meet our inclusion and exclusion criteria. By paying attention to qualitative papers, non-

English language articles, dissertations, or book chapters, future researchers might be able to

uncover new, and more nuanced patterns of results than this review has provided, and

overcome the traditional limitation of publication bias.

A second limitation pertains to the fact that both TSE and its consequences have been

measured by a variety of different instruments, each of which focuses on slightly different

aspects. This lack of common measures not only makes it difficult to make proper distinctions

between different dimensions of TSE, but also to compare and synthesize its consequences

along the heuristic model. In some cases, this might have resulted in a lack of nuance, or even

a misclassification of effects. Related, the divergences in measurement and conceptualization

of the variables of interest in this review, as well as the sometimes largely varying sample sizes

across the included articles, have impeded comparison of the results of all studies, and


CHAPTER 2

56

especially those regarding students' academic adjustment. For this reason, we also did not

provide a detailed overview of the strength of relationships between TSE and student and

teacher outcomes in the classroom. Although full-scale meta-analyses of TSE and its

consequences are warranted to further advance the field, this is probably only (partly) possible

for research on teachers’ well-being, which generally relies on similar measures.

Third, as the bulk of selected studies was cross-sectional in nature, this review does not allow

for conclusions about definitive causation of TSE along multiple dimensions. Moreover,

following Bandura’s (1997) notions, TSE is likely to be part of a complex system of triadic

reciprocal causality, in which environmental forces, personal factors, and behaviors influence

one another bi-directionally. Consideration of alternative, potentially reciprocal pathways in

process-oriented models would probably add important insights to the discussion of these

associations. To this end, markers of change in TSE over time are needed in future research.

Fine examples in this respect come from studies on the link between TSE and burnout

(Brouwers et al., 2001; Brouwers & Tomic, 2000), suggesting feedback loops in which low

levels of TSE reinforce teachers’ burnout and vice versa. Both social-cognitive theory and

classroom-based research (e.g., Pianta et al., 2008), may provide guidelines for exploring causal

pathways among TSE, classroom processes, and student adjustment across time, and detecting

potential feedback loops. Both longitudinal and experimental studies, especially in the area of

student performance and classroom processes, may therefore be promising next steps to

considering the causal mechanisms underlying TSE and its consequences for students,

teachers, and classroom processes. Research exploring the development of different patterns

of TSE may eventually reveal warning signs early enough to allow preventative actions.

CONCLUSION

This review aimed to provide an up-to-date, critical review of forty years of research on TSE

and its direct and indirect consequences at different levels of classroom ecology. Although the

evidence tends to corroborate that TSE is relevant for the quality of classroom processes,

students’ adjustment, and teachers’ well-being, further theoretical elaboration and empirical

substantiation of the outcomes of the reviewed studies is needed to move the field forward.

Multidimensional measures, research designs that reveal more complex indirect effects and

potential feedback loops, and further integration between lines of inquiry as suggested by the

heuristic model may be helpful in achieving this goal.


AP

PEN

DIX

1

Clas

sroom

Proc

esses

Aut

hor(s

) Co

untry

Ca

tego

ry

Subt

hem

e G

rade

N

TS

E m

easu

re

Ana

lysis

Resu

lts

Abu

h-Ti

neh

et a

l.

(201

1)

Jord

an

CO

SAB

Elem

enta

ry, m

iddl

e, an

d hi

gh sc

hool

56

6 te

ache

rs

TSE

S Co

rrela

tions

TS

E→

Inst

ruct

iona

l Man

agem

ent:

r = .4

2 TS

E→

Beh

avio

r Man

agem

ent:

r = .3

6 TS

E→

Peo

ple

Man

agem

ent:

r = .3

5 TS

E→

Clas

sroo

m M

anag

emen

t Ove

rall:

r =

.47

Ahm

ad e

t al.

(201

0)

Mala

ysia

CO

Co

mpu

ter

Use

U

nive

rsity

73

1 te

ache

rs

New

ly de

velo

ped

scale

SE

M

Com

pute

r TSE→

Tec

hnol

ogy

Use

: β =

.45

Ahs

an e

t al.

(201

2)

Bang

la-de

sh

CO

Inclu

sive

Prac

tices

Pr

eser

vice

con

text

1,

623

teac

hers

TE

IP

Corr

elatio

ns

TSE

(Inc

lusiv

e Pr

actic

es)→

Con

cern

s: r =

.24

TSE

(Inc

lusiv

e Pr

actic

es)→

Atti

tude

s: r =

.20

Alli

nder

(199

5)

USA

IS

IP

E

lemen

tary

scho

ol

(Spe

cial E

duca

tion;

G

3–G

6)

19 te

ache

rs

38 st

uden

ts

TES

AN

COV

A

Hig

h vs.

low

TSE→

Goa

l Am

bitio

usne

ss: M

= 2

0.50

(S

D =

4.8

3); M

= 1

6.88

(SD

= 4

.78)

(ns)

Hig

h vs.

low

TSE→

N In

stru

ctio

nal C

hang

es: M

= .6

7 (S

D =

.26)

; M =

1.0

8 (S

D =

.49)

(ns)

Hig

h vs.

low

TSE→

N G

oal C

hang

es: M

= 1

.17

(SD

=

.67)

; M =

.54

(SD

= .4

8)

Hig

h vs.

low

TSE→

Tim

ing

of C

hang

e: M

= 2

.00

(SD

=

1.6

7); M

= 2

.23

(SD

= 1

.59)

(ns)

Alm

og &

Sh

echt

man

(200

7)

Isra

el

CO

Prob

lem

Beha

vior

s E

lemen

tary

scho

ol

(G1

– G

3)

33 te

ache

rs

TES

Corr

elatio

ns

TSE→

Low

Ach

ievem

ent:

r = .3

3 TS

E→

Shy

ness

: r =

.44

TS

E→

Diso

bedi

ence

: r =

.22

TSE→

Soc

ial R

ejec

tion:

r =

.37

TSE→

Pas

sive

Agg

ress

ion

: r =

.45

TSE→

Impu

lsive

ness

: r =

.30

TSE→

Hos

tility

and

Agg

ress

ion

: r =

.20

TSE→

Hyp

erac

tivity

: r =

.30

Bogl

er &

Som

ech

(200

4)

Isra

el

CO

SAB

Mid

dle

scho

ol

(G7–

G9)

98

3 te

ache

rs

SPE

S Re

gres

sion

TSE→

Org

aniz

atio

nal C

itize

nshi

p Be

havi

or: β

= .3

5

Brad

y &

W

oolfs

on (2

008)

UK

CO

In

clusiv

e Pr

actic

es

Prim

ary

scho

ol

118

teac

hers

TS

ES

(sho

rt)

Regr

essio

n TS

E→

Loc

us o

f Cau

salit

y: β

= .1

9 TS

E→

Con

trolla

bilit

y: β

= -.

09 (n

s) TS

E→

Sta

bilit

y: β

= .1

1 (n

s)


Brow

n (2

005)

USA

IS

IS

Mat

h/

Lite

racy

Pr

esch

ool

94 te

ache

rs

TSE

S Co

rrela

tions

TS

E (I

S, C

M, S

E, O

vera

ll)→

Mat

h Be

liefs

: r =

.23;

r =

.2

0; r

= .2

7; r

= .2

5

TSE

(Ove

rall)→

Tea

cher

Beli

efs a

bout

the

Impo

rtanc

e of

Mat

hs: r

= .1

9 (n

s) TS

E (O

vera

ll)→

Mat

h In

stru

ctio

nal P

ract

ices

: r =

.2

4(ns

) Br

owne

ll &

Pa

jares

(199

9)

USA

CO

In

clusiv

e Pr

actic

es

Elem

enta

ry sc

hool

(G

2)

200

teac

hers

N

ewly

deve

lope

d sc

ale

SEM

TS

E→

Suc

cess

in T

each

ing

Stud

ents

with

Disa

bilit

ies:

β =

.39

Capa

-Ayd

in e

t al.

(200

9)

Turk

ey

CO

Goa

l St

ruct

ures

Pr

eser

vice

con

text

1,

218

teac

hers

(T

)TSE

S Ca

noni

cal

corr

elatio

ns

TSE→

Self

-Reg

ulat

ion

Var

iables

: r =

.52

Chac

on (2

005)

Ven

ezue

la IS

IS

Mat

h/

Lite

racy

H

igh

scho

ol

104

teac

hers

TS

ES

(sho

rt)

Corr

elatio

ns

TSE

(IS,

CM

, SE

)→ C

omm

unica

tion

Stra

tegy

: r =

.32;

r =

.26;

r =

.39

TSE

(IS,

CM

, SE

)→ G

ram

mar

Stra

tegy

: r =

.24;

r =

.2

4; r

= .2

4

Cant

rell

&

Hug

hes (

2008

)

USA

IS

Im

ple-

men

tatio

n E

lemen

tary

and

m

iddl

e sc

hool

(G

6 –G

9)

22 te

ache

rs

New

ly de

velo

ped

scale

Co

rrela

tions

TS

E (f

all, s

prin

g)→

Im

plem

enta

tion

Cont

ent L

itera

cy

App

roac

h: r

= .4

8; r

= .3

1 (n

s)

Chen

(201

0)

U

SA

CO

Com

pute

r U

se

Pres

ervi

ce c

onte

xt

206

teac

hers

N

ewly

deve

lope

d sc

ale

SEM

TS

E→

Tec

hnol

ogy

Use

: β =

.45

Cho

& S

him

(2

013)

U

SA

CO

Goa

l St

ruct

ures

E

lemen

tary

, mid

dle,

and

high

scho

ol

211

teac

hers

TS

ES

(sho

rt)

HLM

TS

E→

Mas

tery

Goa

ls fo

r Tea

chin

g: β

= .3

1 TS

E→

Per

form

ance

Goa

ls fo

r Tea

chin

g: β

= .1

7

Chun

g et

al.

(200

5)

USA

E

S ST

R Pr

esch

ool

152

teac

her

608

child

ren

TBS

Regr

essio

n TS

E→

Stu

dent

-Tea

cher

Rel

atio

nshi

p: β

= .1

4 (n

s)

Cian

i et a

l. (2

008)

USA

CO

G

oal

Stru

ctur

es

Seni

or h

igh

scho

ol

156

teac

hers

TS

ES

(sho

rt)

SEM

TS

E-I

S→ M

aste

ry C

lassr

oom

Goa

l Stru

ctur

e: β

= .3

3

Dee

mer

(200

4)

U

SA

CO

Goa

l St

ruct

ures

H

igh

scho

ol

(G9–

G12

) 99

teac

hers

TE

S (a

dapt

ed)

SEM

TS

E→

Mas

tery

Inst

r. Pr

actic

es: β

= .2

6 TS

E→

Per

form

ance

Inst

r. Pr

actic

es: β

= .0

0 (n

s)

DeF

ores

t &

Hug

hes (

1992

) U

SA

CO

Refe

rral

Dec

ision

s E

lemen

tary

scho

ol

(G2–

G4)

10

2 te

ache

rs

TES

A

NO

VA

Lo

w vs

. hig

h TS

E→

Inte

rven

tion

Acc

epta

nce:

F(1,

56)

=

6.7

2

De

Jong

et a

l. (2

013)

Net

her-

lands

E

S ST

R Pr

eser

vice

con

text

12

0 te

ache

rs

TSE

S (s

hort)

H

LM

TSE

-IS→

Con

trol;

Affi

liatio

n: B

= -.

04; B

= -.

03 (n

s) TS

E-C

M→

Con

trol;

Affi

liatio

n: B

=.0

4; B

= -.

04 (n

s) TS

E-S

E→

Con

trol;

Affi

liatio

n: B

= -.

05; B

= .0

6 (n

s)


Dun

n &

Rak

es

(201

1)

USA

CO

IL

F Pr

eser

vice

con

text

18

5 te

ache

rs

TSE

S (s

hort)

Co

rrela

tions

TS

E→

Lea

rner

-Cen

tere

d Be

liefs

: r =

.32

Dun

n et

al.

(201

3)

U

SA

IS

Impl

e-m

enta

tion

Kin

derg

arte

n –

high

sc

hool

(K–1

2)

537

teac

hers

N

ewly

deve

lope

d sc

ale, T

SES

SEM

D

ata-

Driv

en D

ecisi

on M

akin

g TS

E→

Impa

ct

Colla

bora

tion

Conc

erns

: β =

.25

Dus

saul

t (20

06)

Cana

da

CO

SAB

Hig

h sc

hool

48

7 te

ache

rs

EA

EE

Co

rrela

tions

TS

E→

Altr

uism

: r =

.34

TS

E→

Cou

rtesy

: r =

.19

TS

E→

Con

scien

tious

ness

: r =

.19

TSE→

Civ

ic V

irtue

: r =

.24

Egy

ed &

Sho

rt (2

006)

U

SA

CO

Refe

rral

Dec

ision

s E

lemen

tary

scho

ol

106

teac

hers

TE

S D

iscr.

analy

sis

No r

esults

are g

iven

(insig

nific

ant)

Em

mer

&

Hick

man

(199

1)

USA

CO

SA

B Pr

eser

vice

con

text

16

1 te

ache

rs

SCM

D

Corr

elatio

ns

TSE

-CM→

Pos

itive

Stra

tegi

es: r

= .3

0 TS

E-C

M→

Red

uctiv

e St

rate

gies

: r =

.00

(ns)

TSE

-CM→

Ext

erna

l Stra

tegi

es: r

= .0

9(ns

) TS

E (P

erso

nal)→

Pos

itive

Stra

tegi

es: r

= .3

2 TS

E (P

erso

nal)→

Red

uctiv

e St

rate

gies

: r =

.11

(ns)

TSE

(Per

sona

l)→ E

xter

nal S

trate

gies

: r =

.20

Ere

n (2

009)

Turk

ey

CO

Goa

l St

ruct

ures

IL

F

Pres

ervi

ce c

onte

xt

374

teac

hers

TE

SPT

Corr

elatio

nsH

LM

Corre

lation

al res

ults:

TSE→

Per

form

ance

- App

roac

h G

oals;

: r =

.19

TSE→

Per

form

ance

- Avo

idan

ce G

oals;

: r =

-.21

TS

E→

Mas

tery

- App

roac

h G

oals;

: r =

.27

TSE→

Mas

tery

- Avo

idan

ce G

oals;

: r =

-.07

(ns)

Regre

ssion

resu

lts:

TSE→

Con

stru

ctiv

ist C

once

ptio

ns: β

= .1

2 TS

E→

Tra

ditio

nal C

once

ptio

ns: β

= -.

03(n

s)

Ere

n (2

012)

Turk

ey

CO

Goa

l St

ruct

ures

Pres

ervi

ce c

onte

xt

396

teac

hers

TS

AO

/TSE

S SE

M

AO→

Plan

ned

Effo

rt: β

= .8

3 A

O→

Plan

ned

Pers

isten

ce: β

= .

48

AO→

Pro

fess

iona

l Dev

elop

men

t Asp

iratio

ns: β

= .7

8 A

O→

Lea

ders

hip

Asp

iratio

ns: β

= .1

4

Esla

mi &

Fat

ahi

(200

9)

Iran

IS

IS

Mat

h/

Lite

racy

H

igh

scho

ol

40 te

ache

rs

New

ly de

velo

ped

scale

Co

rrela

tions

TS

E (I

S, C

M, S

E)→

Gra

mm

ar-O

rient

ed S

trate

gies

: r =

.1

9 (n

s); r

= -.

08 (n

s); r

= -.

04(n

s) TS

E (I

S, C

M, S

E)→

Com

mun

icatio

n-O

rient

ed

Stra

tegi

es: r

= .3

0; r

= .2

5 (n

s); r

= .3

7


Eun

& H

einin

g-Bo

ynto

n (2

007)

U

SA

IS

Impl

e-m

enta

tion

Kin

derg

arte

n–hi

gh

scho

ol (K

–12)

90

teac

hers

TE

S (a

dapt

ed)

Regr

essio

n TS

E→

Use

of K

now

ledge

and

Ski

lls: t

(1) =

2.4

35

Gao

& M

ager

(2

011)

USA

CO

In

clusiv

e Pr

actic

es

Pres

ervi

ce c

onte

xt

216

teac

hers

TE

S Co

rrela

tions

TS

E→

Atti

tude

s tow

ard

Child

ren

with

Aca

dem

ic D

isabi

lities

: r =

.29

TSE→

Atti

tude

s tow

ard

Child

ren

with

Phy

sical

Disa

bilit

ies: r

= .1

8 TS

E→

Atti

tude

s tow

ard

Child

ren

with

Beh

avio

ral

Disa

bilit

ies: r

= .2

0 TS

E→

Atti

tude

s tow

ard

Child

ren

with

Soc

ial

Disa

bilit

ies: r

= .2

9 TS

E→

Per

sona

l Beli

efs o

f Div

ersit

y: r =

.35

TSE→

Pro

fess

iona

l Beli

efs o

f Div

ersit

y: r =

.43

Geij

sel e

t al.

(200

9)

The

Net

her-

lands

IS

IP

Elem

enta

ry sc

hool

32

8 te

ache

rs

New

ly de

velo

ped

scale

SE

M

TSE→

Kee

ping

up-

to-d

ate: β

= .3

1 TS

E→

Ref

lectiv

e Pr

actic

e: β

= .3

0 TS

E→

Cha

nged

Pra

ctice

: β =

.25

Gha

ith &

Yag

hi

(199

7)

USA

IS

Im

ple-

men

tatio

n M

iddl

e an

d hi

gh

scho

ol

25 te

ache

rs

TES

AN

OV

A

Low

vs. H

igh

TSE→

Con

grue

nce:

M =

3.0

0 (S

D =

1.

20);

M =

4.6

0 (S

D =

.67)

Lo

w vs

. Hig

h TS

E→

Cos

t: M

= 3

.53

(SD

= 1

.13)

; M =

3.

30 (S

D =

1.4

2) (

ns)

Low

vs. H

igh

TSE→

Diff

iculty

: M =

3.3

3 (S

D =

1.1

1);

M =

2.7

0 (S

D =

1.0

6) (

ns)

Low

vs. H

igh

TSE→

Impo

rtanc

e: M

= 4

.53

(SD

= .7

4);

M =

5.2

0 (S

D =

.42)

Gha

ith &

Sha

aban

(1

999)

Leba

non

IS

Impl

e-m

enta

tion

Unk

now

n 29

2 te

ache

rs

TES

Corr

elatio

ns

TSE→

Self

-Sur

viva

l Con

cern

s: r =

-.14

TS

E→

Impa

ct C

once

rns:

r = -.

17

TSE→

Tot

al Te

ache

r Con

cern

s: r =

-.19

Gib

bs &

Pow

ell

(201

2)

UK

CO

Re

ferr

al D

ecisi

ons

Prim

ary

and

nurs

ery

scho

ols

197

teac

hers

TS

ES

(ada

pted

) Co

rrela

tions

TS

E-C

M→

Fix

ed T

erm

Exc

lusio

ns: r

= -

.14

(ns)

TSE

-IS→

Fix

ed T

erm

Exc

lusio

ns: r

= -.

10 (n

s) TS

E-S

E→

Fix

ed T

erm

Exc

lusio

ns: r

= -.

10 (n

s)

Gib

son

& D

embo

(1

984)

USA

CO

G

oal

Stru

ctur

es

ILF

Pres

ervi

ce c

onte

xt

55 te

ache

rs

(resu

lts a

re

base

d on

8

teac

hers

)

TES

Des

crip

tives

H

igh

vs. L

ow T

SE→

Am

ount

of T

ime

Spen

t in

Inst

ruct

ion:

M =

234

.0 (S

D =

.61.

9);

M =

271

.5 (S

D =

24.

9)

Hig

h vs.

Low

TSE→

Am

ount

of T

ime

Spen

t in

Non

acad

emic

Task

s: M

= 2

10.3

(SD

= 6

4.4)

; M =


172.

0 (S

D =

12.

5)

Hig

h vs.

Low

TSE→

Pra

ise p

er C

orre

ct A

nsw

er: M

=

.03

(SD

= .0

3); M

= .0

1 (S

D =

.02)

H

igh

vs. L

ow T

SE→

Crit

icism

per

Inco

rrec

t Ans

wer

: M

= .0

0 (S

D =

.00)

; M =

.04

(SD

= .0

2)

Hig

h vs.

Low

TSE→

Per

siste

nce

per I

ncor

rect

Ans

wer

: M

= .7

5 (S

D =

.37)

; M =

.66

(SD

= .3

4)

Hig

h vs.

Low

TSE→

Lac

k of

Per

siste

nce

per I

ncor

rect

A

nsw

er: M

= .3

8 (S

D =

.11)

; M =

.67

(SD

= .1

2)

Gor

ozid

is &

Pa

paio

anno

u (2

011)

Gre

ece

IS

Impl

e-m

enta

tion

Juni

or h

igh

scho

ol

130

teac

hers

N

ewly

deve

lope

d sc

ale

SEM

TS

E in

Stu

dent

-Cen

tere

d Te

achi

ng S

tyles→

Atti

tude

s to

war

d Im

plem

enta

tion:

β =

.31

TSE

in T

each

ing

Dail

y Le

sson

Plan

s→ In

tent

ion

tow

ard

Impl

emen

tatio

n: β

= .3

1

Guo

et a

l. (2

010)

USA

E

S, IS

E

C IS

Pr

esch

ool

67 te

ache

rs

328

stud

ents

TS

EQ

Co

rrela

tions

TS

E→

Em

otio

nal S

uppo

rt: r

= .1

6 (n

s) TS

E→

Inst

ruct

iona

l Sup

port:

r =

.17

(ns)

Guo

et a

l. (2

012)

USA

E

S E

C E

lemen

tary

Sch

ool

(G5)

1,

043

teac

hers

an

d th

eir

stud

ents

TSE

Q

SEM

TS

E→

Sup

port

for L

earn

ing:

β =

.19

TSE→

Sup

port

for L

earn

ing→

Gra

de 5

Rea

ding

: β =

.0

1

Guo

et a

l. (2

013)

USA

IS

IS

Mat

h/

Lite

racy

Pr

esch

ool

54 te

ache

rs

TSE

Q

Regr

essio

n TS

E→

Inst

ruct

iona

l Sup

port:

β =

.40

Ham

re e

t al.

(200

8)

USA

E

S ST

R Pr

esch

ool

597

teac

hers

2,

282

child

ren

TSE

Q (a

dapt

ed)

HLM

TS

E→

Con

flict

: B =

-.01

Har

dré

& S

ulliv

an

(200

8)

USA

E

S ST

R Pe

rsp.

H

igh

scho

ol

75 te

ache

rs

MSQ

Re

gres

sion

TSE

(Mot

ivat

ing;

Diag

nosin

g)→

Rela

tedn

ess a

nd

Em

otio

nal S

uppo

rt: β

= .2

1 (n

s); β

= .1

7(ns

) TS

E (M

otiv

atin

g; D

iagno

sing)→

Rele

vanc

e an

d V

alue:

β =

.11

(ns);

β =

.37

Har

dré

& S

ulliv

an

(200

9)

USA

E

S E

C H

igh

scho

ol

96 te

ache

rs

MSQ

Re

gres

sion

TSE

(Mot

ivat

ing;

Diag

nosin

g)→

Aut

onom

y Su

ppor

tive

Inte

rper

sona

l Sty

les:

r = -.

02 (n

s); r

= .2

1 (n

s) TS

E (M

otiv

atin

g; D

iagno

sing)→

Inte

rnal

Stra

tegi

es: β

=

.08

(ns);

β =

.59

TSE

(Mot

ivat

ing;

Diag

nosin

g)→

Can

’t In

fluen

ce: β

= -

.36;

β =

.02

(ns)


Hav

erba

ck (2

009)

USA

IS

IS

Mat

h/

Lite

racy

Pr

eser

vice

con

text

40

teac

hers

TS

ES

(read

ing)

Co

rrela

tions

TS

E (p

rete

st)→

Rea

ding

Stra

tegy

Use

: r =

.10

(ns)

TSE

(pos

ttest

)→ R

eadi

ng S

trate

gy U

se: r

= -.

12 (n

s)

Hol

zber

ger e

t al.

(201

3)

Ger

man

y CO

, IS

SAB

IS M

ath/

Li

tera

cy

Mid

dle

scho

ol (G

9)

155

teac

hers

3,

483

stud

ents

G

TSE

S (L

ongi

tudi

nal)

SEM

, co

rrela

tions

Corre

lation

al res

ults (

teach

er an

d ch

ild p

ercep

tions

): TS

E→

Cog

nitiv

e A

ctiv

atio

n: r

= .2

1 (n

s); r

= .4

5 TS

E→

Clas

sroo

m M

anag

emen

t: r =

.47;

r =

.31

TSE→

Indi

vidu

al Le

arni

ng S

uppo

rt: r

= .1

6 (n

s); r

=

.24

Long

itudin

al res

ults (

teach

er an

d ch

ild p

ercep

tions

): T1

TSE→

T2

Cogn

itive

Act

ivat

ion:

ns

T1 T

SE→

T2

Clas

sroo

m M

anag

emen

t: ns

T1

TSE→

T2

Indi

vidu

al Le

arni

ng S

uppo

rt: β

= .2

4; n

s T1

Cog

nitiv

e A

ctiv

atio

n→ T

2 TS

E: n

s; β

= .3

3 T1

Clas

sroo

m M

anag

emen

t→ T

2 TS

E: β

= .1

9; β

= .3

3

Hoy

& W

oolfo

lk

(199

0)

USA

CO

SA

B Pr

eser

vice

con

text

19

1 te

ache

rs

TES

Repe

ated

m

easu

res

AN

OV

A

Pre-

and

pos

t Pup

il Co

ntro

l Orie

ntat

ion:

M

prete

st =

50.

83; M

postt

est =

48.

50

Hug

hes e

t al.

(199

3)

USA

CO

Re

ferr

al D

ecisi

ons

Elem

enta

ry sc

hool

55

teac

hers

N

ewly

deve

lope

d sc

ale

AN

OV

A

Uni

varia

te F

for pr

oblem

beha

vior v

ignett

es ar

e not

given

.

Jimm

ieson

et a

l. (2

010)

A

ustra

lia

ES

STR

Elem

enta

ry a

nd

mid

dle

scho

ol

(G5–

G7)

170

teac

hers

3,

057

stud

ents

JE

S H

LM

TSE→

Stu

dent

-Tea

cher

Rel

atio

nshi

p : β

= .2

0

Just

ice e

t al.

(200

8)

USA

IS

IS

Mat

h/

Lite

racy

Pr

esch

ool

135

teac

hers

TS

EQ

(ada

pted

) Re

gres

sion

TSE→

Qua

lity

of L

angu

age

Mod

eling

: B

= .1

3 (n

s) TS

E→

Qua

lity

of L

itera

cy F

ocus

: B

= .6

8

Kao

et a

l. (2

011)

Taiw

an

CO

Com

pute

r U

se

Elem

enta

ry sc

hool

48

4 te

ache

rs

New

ly de

velo

ped

scale

Re

gres

sion

Resu

lts fo

r var

ious p

rofes

siona

l deve

lopme

nt ou

tcome

s: Ba

sic (I

nter

net)

TSE→

Per

sona

l Int

eres

t: β

= .1

6 A

dvan

ced

(Int

erne

t) TS

E→

Occ

upat

iona

l Pro

mot

ion:

β

= .3

2 A

dvan

ced

(Int

erne

t) TS

E→

Pra

ctic

al E

nhan

cem

ent: β

= .1

3 A

dvan

ced

(Int

erne

t) TS

E→

Soc

ial C

onta

ct: β

= .2

2 A

dvan

ced

(Int

erne

t) TS

E→

Soc

ial S

timul

atio

n: β

= .1

9

Laks

hman

an e

t al.

(201

1)

USA

IS

Im

ple-

men

tatio

n E

lemen

tary

scho

ol

(G5–

G8)

79

teac

hers

ST

EBI

G

row

th

Mod

eling

TS

E→

Tea

cher

Ref

orm

: r(ch

ange

of gro

wth)

= .3

5

Lam

bert

et a

l. U

SA

CO

Prob

lem

Elem

enta

ry sc

hool

52

1 te

ache

rs

PSE

- BM

Co

rrela

tions

TS

E-B

M→

Beh

avio

r Pro

blem

s : r

= -.

16


(200

9)

Beha

vior

s

Lee

et a

l. (2

013)

USA

IS

Im

ple-

men

tatio

n E

lemen

tary

and

se

cond

ary

scho

ol

40 te

ache

rs

TSE

S (s

hort)

Re

gres

sion

TSE

(Ove

rall)→

Con

cept

ual C

hang

e: F(

1,25

) =.3

59 (n

s) TS

E (P

rimar

y Sc

hool

)→ C

once

ptua

l Cha

nge:

F(1,

9)

=.9

9 (n

s) TS

E (S

econ

dary

Sch

ool)→

Con

cept

ual C

hang

e: F(

1,14

) =

4.7

1

Lee

& T

sai (

2010

)

Taiw

an

CO

Com

pute

r U

se

Elem

enta

ry, m

iddl

e, an

d hi

gh sc

hool

55

8 te

ache

rs

New

ly de

velo

ped

inst

rum

ent

Corr

elatio

ns

TSE

(Web

-gen

eral)→

Atti

tude

tow

ard

Web

-Bas

ed

Inst

ruct

ion:

r =

.46

TSE

(Web

-com

mun

icativ

e)→

Atti

tude

tow

ard

Web

-Ba

sed

Inst

ruct

ion:

r =

.27

TSE

(Web

-Con

tent

Kno

wled

ge)→

Atti

tude

tow

ard

Web

-Bas

ed In

stru

ctio

n: r

= .6

0 TS

E (W

eb-P

edag

ogica

l Con

tent

Kno

wled

ge)→

A

ttitu

de to

war

d W

eb-B

ased

Inst

ruct

ion:

r =

.61

Lero

y et

al.

(200

7)

Fr

ance

E

S Pe

rsp.

E

lemen

tary

scho

ol

(G5)

33

6 te

ache

rs

TES

(Fre

nch)

SE

M

TSE→

Aut

onom

y Su

ppor

tive

Clim

ate: β

= .2

1

Lilje

quist

& R

enk

(200

7)

USA

CO

Pr

oblem

Be

havi

ors

Pres

ervi

ce c

onte

xt

99 te

ache

rs

TES

SEM

TS

E→

Con

trol o

ver E

xter

naliz

ing

Prob

lems:

B =

.22

TSE→

Bot

hers

ome

of In

tern

alizi

ng P

robl

ems:

B =

.11

Mali

nen

et a

l. (2

012)

Finl

and

CO

Incl

usiv

e

Prac

tices

Pr

esch

ool t

o hi

gh

scho

ol

451

teac

hers

TE

IP

SEM

TS

E fo

r Col

labor

atio

n→ A

ttitu

de to

war

d in

clusiv

e pr

actic

es : β

= -.

03 (n

s) TS

E fo

r Inc

lusiv

e Pr

actic

es→

Atti

tude

tow

ard

Inclu

sive

Prac

tices

: β

= .3

6 TS

E fo

r Beh

avio

r Man

agem

ent→

Atti

tude

tow

ard

Inclu

sive

Prac

tices

: β

= .1

2 (n

s)

Mar

tin e

t al.

(201

2)

USA

IS

IP

E

lemen

tary

, mid

dle

and

high

scho

ol

631

teac

hers

TS

ES

SEM

TS

E-E

N→

Inst

ruct

iona

l Man

agem

ent: β

= -.

83

TSE

-EN→

Inst

r. M

an.→

Stu

dent

Beh

avio

r Stre

ssor

s:

β =

-.43

TS

E-E

N→

Inst

r. M

an.→

Per

sona

l Acc

ompl

ishm

ent:

β

= -.

63

Mar

tin &

Sas

s (2

010)

USA

CO

SA

B

Elem

enta

ry, m

iddl

e, an

d hi

gh sc

hool

55

0 te

ache

rs

TSE

S Co

rrela

tions

TS

E-C

M→

Beh

avio

r Man

agem

ent;

Inst

ruct

ion

Man

agem

ent:

r = -.

19; r

= -.

51

TSE

-IS→

Beh

avio

r Man

agem

ent;

Inst

ruct

ion

Man

agem

ent:

r = -.

02 (n

s); r

= -.

52

TSE

-SE→

Beh

avio

r Man

agem

ent;

Inst

ruct

ion


M

anag

emen

t: r =

-.12

(ns);

r =

-.65

Mas

hbur

n et

al.

(200

6)

USA

E

S ST

R Pr

esch

ool

210

teac

hers

71

1 ch

ildre

n

TSE

Q (a

dapt

ed)

HLM

TS

E→

Con

flict

: B =

-.0

1 (n

s) TS

E→

Clo

sene

ss: B

= .0

2

Meij

er &

Fos

ter

(198

8)

Net

her-

lands

CO

Re

ferr

al D

ecisi

ons

Elem

enta

ry sc

hool

(G

2)

230

teac

hers

TE

S (D

utch

ad

apte

d ve

rsio

n)

Corr

elatio

ns

TSE→

Pro

blem

Cha

nce:

r = -

.23

TSE→

Ref

erra

l Cha

nce:

r = -

.14

Mid

gley

et a

l. (1

995)

USA

CO

G

oal

Stru

ctur

es

Elem

enta

ry a

nd

mid

dle

scho

ol

(G4–

G7)

158

teac

hers

96

9 st

uden

ts

New

ly de

velo

ped

sclae

Co

rrela

tions

Re

sults

for e

lemen

tary a

nd m

iddle

schoo

l tea

chers

: TS

E→

Tas

k G

oals

for S

tude

nts:

r = .

37; r

= .2

3(ns

) TS

E→

Per

form

ance

Goa

ls fo

r Stu

dent

s: r =

.07(

ns);

r =

-.0

7(ns

) TS

E→

Inst

ruct

iona

l Pra

ctice

s-Ta

sk: r

= .

32;

r = .

21(n

s) TS

E→

Inst

ruct

iona

l Pra

ctice

s-Pe

rfor

man

ce: r

= .0

7 (n

s); r

= -.

20(n

s)

Mor

ris-R

oths

child

&

Bra

ssar

d (2

006

USA

CO

SA

B E

lemen

tary

and

se

cond

ary

scho

ol

283

teac

hers

SC

MD

Re

gres

sion

TSE→

Inte

grat

ing

Style

: β =

.33

TSE→

Obl

igin

g St

yle: β

= .2

4 TS

E→

Com

prom

ising

Sty

le: β

= .2

2 TS

E→

Dom

inat

ing

Style

: β =

.17

(ns)

TSE→

Avo

idin

g St

yle: β

= -.

05 (n

s)

Mue

ller e

t al.

(200

8)

Cana

da

CO

Com

pute

r U

se

Elem

enta

ry a

nd

seco

ndar

y sc

hool

38

9 te

ache

rs

TES

(sho

rt)

Corr

elatio

ns

TSE→

Com

pute

r Use

: r =

.01

(ns)

TSE→

Inte

grat

ion:

r =

-.01

(ns)

TSE→

Com

fort

with

Com

pute

rs: r

= .

02(n

s)

Ngi

di (2

012)

Sout

h A

frica

CO

IL

F E

lemen

tary

, mid

dle,

and

high

scho

ol

280

teac

hers

TS

ES

(sho

rt)

Corr

elatio

ns

AO→

Stu

dent

-Cen

tere

d Te

achi

ng: r

= .

32

AO→

Citi

zens

hip

Beha

vior

: r =

.29

A

O→

Hum

anist

ic M

anag

emen

t: r =

.07

(ns)

AO→

Disp

ositi

onal

Opt

imism

: r =

.30

Nie

et a

l. (2

013)

Sing

apor

e CO

IL

F E

lemen

tary

scho

ol

2,13

9 te

ache

rs

TSE

S (s

hort)

SE

M

TSE→

Con

stru

ctiv

ist In

stru

ctio

n : β

= .6

2 TS

E→

Did

actic

Inst

ruct

ion

: β =

.21

Paka

rinen

et a

l.

(201

0)

Finl

and

IS

EC

IS

Kin

derg

arte

n 49

teac

hers

TE

S (s

hort)

Co

rrela

tions

TS

E→

Em

otio

nal S

uppo

rt: r

= .

23

TSE→

Clas

sroo

m O

rgan

izat

ion:

r =

.11

(ns)

TSE→

Inst

ruct

iona

l Sup

port:

r =

.13

(ns)

Pas e

t al.

(201

0)

USA

CO

Re

ferr

al D

ecisi

ons

Elem

enta

ry sc

hool

(K

–5)

491

teac

hers

9,

795

stud

ents

PT

E

Logi

stic

regr

essio

n TS

E (f

all)→

Ref

erra

l to

stud

ent s

uppo

rt te

am (s

prin

g):

Odd

s rat

io =

.77

TS

E (f

all)→

Ref

erra

l to

spec

ial e

duca

tion

(spr

ing)

:


Odd

s rat

io =

.88

(ns)

TSE

(fall

)→ R

efer

ral t

o pr

inci

pal’s

offi

ce (s

prin

g):

Odd

s rat

io =

.94

( ns)

TSE

(fall

)→ In

- sch

ool s

uspe

nsio

n (s

prin

g): O

dds r

atio

=

.81

(ns)

TSE

(fall

)→ O

ut-o

f-sch

ool s

uspe

nsio

n (s

prin

g): O

dds

ratio

= .9

4 ( n

s)

Rans

ford

et a

l. (2

009)

U

SA

IS

ILS

Elem

enta

ry sc

hool

(K

–5)

156

teac

hers

TE

S (a

dapt

ed)

Regr

essio

n TS

E-S

E→

Impl

em..

Dos

age

(ave

rage

): β

= .1

7 (n

s) TS

E-S

E→

Impl

em. D

osag

e (s

uppl

emen

tal):

β =

.19

Roha

an e

t al.

(201

2)

The

Net

her-

lands

CO

Com

pute

r U

se

Elem

enta

ry sc

hool

(G

3–G

6)

354

teac

hers

1,

584

stud

ents

ST

EBI

SE

M

TSE

-IS→

Atti

tude

s tow

ard

Tech

olog

y: β

= .5

3

Rubi

e-D

avie

s et

al. (2

012)

New

Z

ealan

d CO

G

oal

Stru

ctur

es

Elem

enta

ry a

nd

mid

dle

scho

ol

68 te

ache

rs

TSE

S (re

adin

g)

Regr

essio

n TS

E-I

S→ M

aste

ry G

oals;

Per

form

ance

Goa

ls; C

lass-

Leve

l Exp

ecta

tion:

β =

.13

(ns);

β =

-.34

(ns);

β

= .1

1(ns

) TS

E-C

M→

Mas

tery

Goa

ls; P

erfo

rman

ce G

oals;

Clas

s-Le

vel E

xpec

tatio

n: β

= -.

41; β

= .0

1 (n

s); β

= .1

9 (n

s) TS

E-S

E→

Mas

tery

Goa

ls; P

erfo

rman

ce G

oals;

Clas

s-Le

vel E

xpec

tatio

n: β

= .7

1; β

= .1

4 (n

s); β

=- .

02 (n

s)

Sakl

ofsk

e et

al.

(198

8)

USA

CO

SA

B Pr

eser

vice

con

text

87

teac

hers

TE

S Co

rrela

tions

TS

E→

Les

son

Pres

entin

g Be

havi

ors :

r =

.26

TS

E→

Clas

sroo

m M

anag

emen

t Beh

avio

rs: r

= .

23

TSE→

Que

stio

ning

Beh

avio

rs: r

= .

22

Sang

et a

l. (2

010)

Chin

a

Com

pute

r U

se

Pres

ervi

ce c

onte

xt

727

teac

hers

TS

ES,

CSE

SE

M

TSE→

Pro

spec

tive

ICT

Use

: β =

.06

Com

pute

r TSE→

Pro

spec

tive

ICT

Use

: β =

.23

Sood

ak &

Pod

ell

(199

3)

USA

CO

Re

ferr

al D

ecisi

ons

Spec

ial e

duca

tion

167

teac

hers

TE

S (s

hort)

Re

gres

sion

TSE→

Plac

emen

t Jud

gmen

ts: F

= 4

.45

(ns)

TSE→

Ref

erra

l Jud

gmen

ts: F

= 1

.74

(ns)

Sood

ak e

t al.

(199

8)

USA

CO

In

clusiv

e Pr

actic

es

Gen

eral

educ

atio

n 18

8 te

ache

rs

TES

(ada

pted

) Re

gres

sion

TSE→

Anx

iety/

Calm

ness

: F

= 9

.19

Tejed

a-D

elgad

o (2

009)

U

SA

CO

Refe

rral

Des

icion

s E

lemen

tary

scho

ol

(G1–

G5)

16

7 te

ache

rs

TES

AN

OV

A

Zer

o vs.

1,2,

>3

refe

rrals→

TSE

: F(2

, 161

) = 1

.98

(ns)

Tem

iz &

Top

cu

(201

3)

Turk

ey

CO

ILF

Pres

ervi

ce c

onte

xt

101

teac

hers

TS

ES

Corr

elatio

ns

TSE

-IS→

Con

stru

ctiv

ist C

once

ptio

ns: r

= .

87

TSE

-CM→

Con

stru

ctiv

ist C

once

ptio

ns: r

= .

78

TSE

-SE→

Con

stru

ctiv

ist C

once

ptio

ns: r

= .

76


TS

E (O

vera

ll)→

Con

stru

ctiv

ist C

once

ptio

ns: r

= .7

8

Teo

(200

9)

Sing

apor

e CO

Co

mpu

ter

Use

Pr

eser

vice

Con

text

1,

094

teac

hers

N

ewly

deve

lope

d sc

ale

SEM

Ba

sic T

each

ing

Skill

s TSE→

Tra

ditio

nal U

se o

f Te

chno

logy

; Con

stru

ctiv

ist U

se o

f Tec

hnol

ogy: β

= .1

7;

β =

.35

Adv

ance

d Te

achi

ng S

kills

TSE→

Tra

ditio

nal U

se o

f Te

chno

logy

; Con

stru

ctiv

ist U

se o

f Tec

hnol

ogy: β

= -

.04

(ns);

β =

-.07

(ns)

Tech

nolo

gy fo

r Ped

agog

y TS

E→

Tra

ditio

nal U

se o

f Te

chno

logy

; Con

stru

ctiv

ist U

se o

f Tec

hnol

ogy: β

= .2

5;

β =

.19

Thoo

nen

et a

l. (2

011)

H

ollan

d IS

IP

E

lemen

tary

scho

ol

(G4–

G6)

62

1 te

ache

rs

3,46

2 st

uden

ts

SSE

SE

M

TSE→

Pro

cess

-Orie

nted

Inst

ruct

ion:

B =

.21

TSE→

Fit

betw

een

Scho

ol a

nd S

tude

nts’

Live

s: B

= .3

5 TS

E→

Coo

pera

tive

Lear

ning

: B =

.30

TSE→

Diff

eren

tiatio

n in

Inst

ruct

ion:

B =

.44

Van

natta

&

Ford

ham

(200

4)

USA

CO

Co

mpu

ter

Use

K

inde

rgar

ten

– hi

gh

scho

ol (K

–12)

17

7 te

ache

rs

TAS

Regr

essio

n TS

E-S

E→

Clas

sroo

m T

echn

olog

y U

se: n

s

Wer

theim

&

Leys

er (2

002)

USA

CO

SA

B IP

Pr

eser

vice

con

text

19

1 te

ache

rs

TES

Corr

elatio

ns

TSE→

Beh

avio

r Man

agem

ent (

Use

; E

ffect

iven

ess)

: r

= .3

1; r

= .2

4 TS

E→

Indi

vidu

alize

d In

stru

ctio

n (U

se;

Eff

ectiv

enes

s):

r = .

39; r

= .

25

TSE→

Diag

nost

ic Te

achi

ng (U

se;

Effe

ctiv

enes

s): r

=

.28;

r =

.21

Wes

hah

(201

2)

Jord

an

IS

IP

Pres

ervi

ce c

onte

xt

106

teac

hers

TE

S (a

dopt

ed)

Corr

elatio

ns

TSE→

Effe

ctiv

e Te

achi

ng P

ract

ices

: r =

.86

Wol

ters

&

Dau

gher

ty (2

007)

USA

CO

G

oal

Stru

ctur

es

Kin

derg

arte

n –

high

sc

hool

(K–1

2)

1,02

4 te

ache

rs

TSE

S H

LM

TSE

-IS→

Mas

tery

Stru

ctur

e; Pe

rfor

man

ce S

truct

ure: β

= .3

1; β

= .0

9 (n

s) TS

E-C

M→

Mas

tery

Stru

ctur

e; Pe

rfor

man

ce S

truct

ure:

β =

-.12

; β =

-.07

(ns)

TSE

-SE→

Mas

tery

Stru

ctur

e; Pe

rfor

man

ce S

truct

ure: β

= .2

1; β

= .0

7 (n

s)

Won

g et

al.

(201

2)

Mala

ysia

CO

Co

mpu

ter

Use

Pr

eser

vice

con

text

30

2 te

ache

rs

New

ly de

velo

ped

scale

SE

M

Com

pute

r TSE→

Per

ceiv

ed E

ase

of U

se: β

= .4

8 Co

mpu

ter T

SE→

Use

fuln

ess: β

= .2

2 Co

mpu

ter T

SE→

Att.

tow

ard

Com

pute

r Use

: β =

.38

Com

pute

r TSE

→ P

erce

ived

Use

→ B

ehav

iora

l In

tent

ion:

β =

.11


Woo

lfolk

& H

oy

(199

0)

USA

CO

SA

B Pr

eser

vice

con

text

18

2 te

ache

rs

TES

(Can

onic

al)

Corr

elatio

nsRe

gres

sion

TSE→

Pup

il Co

ntro

l Ide

olog

y: r =

-.04

(ns);

F =

.00

(ns)

TSE→

Mot

ivat

iona

l Orie

ntat

ion:

r =

.05

(ns);

F =

10.

43

TSE→

Bur

eauc

ratic

Orie

ntat

ion:

r =

.18;

F =

.28

(ns)

Woo

lfolk

et a

l. (1

990)

USA

CO

SA

B E

lemen

tary

and

m

iddl

e sc

hool

(G

6–G

7)

55 H

ebre

w

relig

ious

te

ache

rs

TES

Corr

elatio

ns

Regr

essio

n TS

E→

Pup

il Co

ntro

l Ide

olog

y: r =

-.36

; β =

-.13

(ns)

TSE→

Con

trol/

Aut

onom

y O

rient

atio

n: β

= .0

58 (n

s)

Yild

irim

& A

tes

(201

2)

Turk

ey

IS

IS M

ath/

Li

tera

cy

Pres

ervi

ce c

onte

xt

346

teac

hers

N

ewly

deve

lope

d sc

ale

Corr

elatio

ns

TSE→

Kno

wled

ge o

f Usin

g E

xpos

itory

Tex

t: r =

.14

Yilm

az (2

011)

Turk

ey

IS

IS M

ath/

Li

tera

cy

Elem

enta

ry a

nd h

igh

scho

ol

54 te

ache

rs

TSE

S (s

hort)

Co

rrela

tions

TS

E (I

S, C

M, S

E)→

Gra

mm

ar O

rient

ed S

trate

gies

: r =

.08

(ns);

r =

.07

(ns);

r =

.05

(ns)

TSE

(IS,

CM

, SE

)→ C

omm

unica

tion

Orie

nted

St

rate

gies

: r =

.22

(ns);

r =

.17

(ns);

r =

.15

(ns)

Yoo

n (2

002)

USA

E

S ST

R E

lemen

tary

scho

ol

(K–5

) 11

3 te

ache

rs

PSE

-BM

H

LM

TSE

-BM→

Goo

d Re

latio

nshi

p: R

= .1

5 (n

s) TS

E-B

M→

Neg

ativ

e Re

latio

nshi

ps: R

= .3

2 (n

s)

Yoo

n (2

004)

U

SA

CO

SAB

Elem

enta

ry sc

hool

98

teac

hers

PS

E-B

M

Regr

essio

n TS

E-B

M→

Inte

rven

tion

Bully

ing

Beha

vior

s : β

= .2

1

Note

. AO

= A

cade

mic

Opt

imism

; CO

= C

lassr

oom

Org

aniz

atio

n; C

ompu

ter

Use

= C

ompu

ter

and

tech

nolo

gy u

se in

the

class

room

; EC

= E

mot

iona

l Clim

ate;

ES

= E

mot

iona

l Sup

port;

ILF

= I

nstru

ctio

nal L

earn

ing

Form

ats;

Impl

emen

tatio

n =

Im

plem

enta

tion

of in

stru

ctio

nal p

ract

ices;

IP =

Inst

ruct

iona

l Pra

ctice

s; IS

= In

stru

ctio

nal S

uppo

rt; IS

Mat

h/Li

tera

cy =

Instr

uctio

nal s

uppo

rt fo

r mat

h an

d lit

erac

y; G

oal S

truct

ures

= T

each

ers’

class

room

goa

l stru

ctur

es; P

ersp

. = R

egar

d fo

r st

uden

t per

spec

tives

; Pro

blem

Beh

avio

rs =

Abi

lity

to c

ope

with

pro

blem

beh

avio

rs; S

AB

= C

lassr

oom

man

agem

ent s

trate

gies

, atti

tude

s, an

d be

havi

ors;

STR

= S

tude

nt–

teac

her r

elatio

nshi

p qu

ality

. TSE

-IS

= T

SE fo

r ins

truct

iona

l stra

tegi

es; T

SE-C

M =

TSE

for c

lassr

oom

man

agem

ent;

TSE

-SE

= T

SE fo

r stu

dent

eng

agem

ent.

Instr

umen

ts: C

SE; C

ompu

ter S

elf-E

ffica

cy S

cale;

EA

EE

= É

chell

e d'

Aut

o-Ef

ficac

ité d

es E

nseig

nant

s; G

TSE

S =

Gen

eral

Teac

her S

elf-E

ffica

cy S

cale;

JES

= Jo

b E

ffica

cy S

cale;

MSQ

= M

otiv

atin

g St

rate

gies

Que

stio

nnair

e; PS

E-B

M =

Per

sona

l Tea

chin

g E

ffica

cy in

Beh

avio

r Man

agem

ent;

PTE

= P

erso

nal T

each

er E

ffica

cy S

cale;

SCM

D =

Self

-effi

cacy

scale

for C

lassr

oom

Man

agem

ent a

nd D

iscip

line;

SPE

S =

Sch

ool P

artic

ipan

t Em

pow

erm

ent

Scale

; SSE

= S

ense

of S

elf-E

ffica

cy; S

TEBI

= S

cienc

e Te

achi

ng E

ffica

cy B

elief

Ins

trum

ent;

TAS

= T

each

er A

ttrib

ute

Surv

ey; T

BS =

Tea

cher

Beli

efs

Scale

; TE

IP =

Tea

cher

Eff

icacy

for I

nclu

sive

Prac

tice

scale

; TE

S =

Te

ache

r Eff

icacy

Sca

le (re

sults

are

onl

y gi

ven

for P

TE);

TESP

T =

Tea

cher

Eff

icacy

Sca

le fo

r Pro

spec

tive

Teac

hers

; TSA

O =

Tea

cher

Sen

se o

f Aca

dem

ic O

ptim

ism S

cale;

TSE

Q =

Tea

cher

Self

-Effi

cacy

Que

stio

nnair

e; TS

ES

= T

each

er S

ense

of S

elf-E

ffica

cy S

cale;

TTS

ES

= T

urki

sh T

each

ers’

Sens

e of

Eff

icacy

Sca

le.


APPE

NDI

X 2

Stud

ents’

Aca

demi

c Adju

stmen

t

Aut

hor(s

) Co

untry

Ca

tego

ry

Them

e G

rade

N

TS

E m

easu

re

Ana

lysis

Resu

lts

Alli

nder

(199

5)

USA

A

A

Mat

h E

lemen

tary

scho

ol

(Spe

cial E

duca

tion;

G

3–G

6)

19 te

ache

rs

38 st

uden

ts

TES

AN

COV

A

Hig

h vs.

low

TSE→

Mat

h A

chiev

emen

t (di

gits

): M

=

57.4

0 (S

D =

13.

06);

M =

48.

06 (S

D =

18.

08)

Hig

h vs.

low

TSE→

Mat

h A

chiev

emen

t (pr

oblem

s): M

=

21.

26 (S

D =

5.1

9); M

= 1

8.02

(SD

= 6

.14)

Ang

le an

d M

osele

y (2

010)

U

SA

AA

Bi

olog

y H

igh

Scho

ol

214

biol

ogy

teac

hers

ST

EBI

A

NO

VA

H

igh

vs. lo

w T

SE→

Bio

logy

Tes

t: M

= 5

5.59

; (SD

=

4.53

); M

= 5

5.68

(SD

= 5

5.51

) (ns

)

Capr

ara

et a

l. (2

006)

It

aly

AA

O

vera

ll Ju

nior

hig

h sc

hool

2,

184

teac

hers

N

ewly

deve

lope

d sc

ale

Long

itudi

nal

SEM

Ti

me

2 TS

E→

Tim

e 3

Aca

dem

ic A

chiev

emen

t: β

= .0

2

Cant

rell

et a

l. (2

013)

U

SA

AA

Li

tera

cy

M

iddl

e sc

hool

(G6)

(H

igh

scho

ol, G

9)

20 te

ache

rs

249

stud

ents

N

ewly

deve

lope

d sc

ale

Regr

essio

n TS

E→

Rea

ding

Ach

ievem

ent (

Gra

de 6

): β

= .7

6

Chan

g (2

011)

Ta

iwan

A

A

Ove

rall

Elem

enta

ry sc

hool

1,

003

teac

hers

E

lemen

tary

Sch

ool

Teac

her A

cade

mic

Opt

imism

Sca

le

SEM

A

cade

mic

Opt

imism

→ A

c. A

chiev

emen

t: β

= .7

8 A

cade

mic

Opt

imism

→ A

c. A

chiev

emen

t→ E

valu

atio

n Sc

ore:

β =

.20

Chon

g et

al.

(201

0)

Sing

apor

e A

A

Ove

rall

Mid

dle

scho

ol

222

teac

hers

TS

ES

(sho

rt)

Logi

stic

regr

essio

n Co

rrela

tions

TSE→

Aca

dem

ic A

chiev

emen

t: eβ

= .7

4 (n

s) TS

E-I

S→ A

cade

mic

Clim

ate:

r = .4

4 TS

E-C

M→

Aca

dem

ic Cl

imat

e: r =

.40

TSE

-SE→

Aca

dem

ic Cl

imat

e: r =

.48

Guo

et a

l. (2

010)

U

SA

AA

Li

tera

cy

Pres

choo

l 67

teac

hers

32

8 st

uden

ts

TSE

Q (a

dapt

ed)

HLM

(lon

gi-

tudi

nal)

TSE→

Lite

racy

(voc

. kno

wled

ge):

t(63)

= .7

27 (n

s);

TSE→

Lite

racy

(prin

t aw

aren

ess)

: t(6

3) =

3.4

5

Guo

et a

l. (2

012)

U

SA

AA

Li

tera

cy

Elem

enta

ry sc

hool

(G

5)

1,04

3 te

ache

rs

and

stud

ents

TS

EQ

SE

M (l

ongi

-tu

dina

l) TS

E→

Lite

racy

: β =

.04

TSE→

Sup

port

for L

earn

ing→

Rea

ding

: β =

.01

Har

dré

& S

ulliv

an

(200

8)

USA

SM

M

oti-

vatio

nal

Stra

tegi

es

Hig

h sc

hool

75

teac

hers

M

SQ

Regr

essio

n M

otiva

tion:

TSE

(Mot

ivat

ing;

Diag

nosin

g)→

Mot

ivat

ion:

β =

.2

1(ns

); β

= .1

4 (n

s) TS

E (M

otiv

atin

g; D

iagno

sing)→

Asp

iratio

ns/F

utur

es:

β =

-.22

(ns);

β =

.08

(ns)

Goa

ls an

d Perc

eived

Abil

ity:

TSE

(Mot

ivat

ing;

Diag

nosin

g)→

Lea

rnin

g G

oals


(stu

dent

s): β

= -.

22 (n

s); β

= .0

8 (n

s) TS

E (M

otiv

atin

g; D

iagno

sing)→

Fut

ure

Goa

ls (s

tude

nts)

: β =

.21

(ns);

β =

.09

(ns)

TSE

(Mot

ivat

ing;

Diag

nosin

g)→

Per

ceiv

ed A

bilit

y: β

=

-.29

(ns);

β =

-.03

(ns)

Moti

vatio

nal S

trateg

ies:

TSE

(Mot

ivat

ing;

Diag

nosin

g )→

Rele

vanc

e an

d V

alue:

β =

.11

(ns);

β =

.37

TSE

(Mot

ivat

ing;

Diag

nosin

g)→

Asp

iratio

ns: β

= .1

5 (n

s); β

= .4

0 TS

E (M

otiv

atin

g; D

iagno

sing)→

Can

’t In

fluen

ce: β

= -

.47;

β =

.16(

ns)

TSE

(Mot

ivat

ing;

Diag

nosin

g)→

Rela

tedn

ess a

nd

Em

otio

nal S

uppo

rt: β

= .2

1 (n

s); β

= .1

7(ns

) TS

E (M

otiv

atin

g; D

iagno

sing)→

Ack

now

ledge

Pee

r Pr

essu

re: β

= .1

9 (n

s); β

= .3

1

Har

dré

& S

ulliv

an

(200

9)

USA

SM

M

oti-

vatio

nal

Stra

tegi

es

Hig

h sc

hool

96

teac

hers

M

SQ

Regr

essio

n M

otiva

tion:

TSE

(Mot

ivat

ing;

Diag

nosin

g)→

Mot

ivat

ion:

β =

.52;

β

= -.

03 (n

s) M

otiva

tiona

l Stra

tegies

: TS

E (M

otiv

atin

g; D

iagno

sing)→

Inte

rnal

stra

tegi

es: β

=

.08

(ns);

β =

.59

TSE

(Mot

ivat

ing;

Diag

nosin

g)→

Ack

now

ledge

Pee

r Pr

essu

re: β

= .0

3 (n

s); β

= .3

0 TS

E (M

otiv

atin

g; D

iagno

sing)→

Can

’t In

fluen

ce: β

= -

.36;

β =

.02

(ns)

Har

dré

et a

l. (2

006)

Ta

iwan

A

A, S

M

Ove

rall

(Tas

k)

Mot

i-va

tion

and

Eng

age-

men

t

Hig

h sc

hool

40

4 te

ache

rs

TES

Corr

elatio

ns

TSE→

Stu

dent

Abi

lity:

r =

.27

TSE→

Stu

dent

Eng

agem

ent a

nd E

ffort:

r =

.29

TSE→

Stu

dent

Mot

ivat

ion:

r =

.31

TSE→

Lea

rnin

g G

oals:

r =

.23

TSE→

Per

form

ance

App

roac

h G

oals:

r =

-.10

(ns)

TSE→

Per

form

ance

Avo

idan

ce G

oals:

r =

.09

(ns)

Hen

eman

et a

l. (2

008)

U

SA

AA

Li

tera

cy

Elem

enta

ry sc

hool

1,

075

teac

hers

TSE

S (s

hort)

SE

M

TSE→

Rea

ding

Ach

ievem

ent: β

= .0

2 (n

s)

Hin

es (2

008)

U

SA

AA

M

ath

Mid

dle

scho

ol (G

7)

307

stud

ents

an

d th

eir

TSE

Q (a

dapt

ed)

AN

OV

A

Hig

h vs.

low

TSE→

Mat

h A

chiev

emen

t (te

st 1

): M

=

81.2

3; (S

D =

7.4

2); M

= 7

1.47

(SD

= 9

.43)


teac

her

Hig

h vs.

low

TSE→

Mat

h A

chiev

emen

t (te

st 2

): M

=

78.9

0; (S

D =

9.2

9); M

= 7

1.46

(SD

= 8

.59)

H

igh

vs. lo

w T

SE→

Mat

h A

chiev

emen

t (te

st 3

): M

=

82.0

9; (S

D =

10.

11);

M =

69.

01 (S

D =

11.

42)

Jimm

ieson

et a

l. (2

010)

A

ustra

lia

AA

, SM

O

vera

ll W

ell-b

eing

Elem

enta

ry a

nd

mid

dle

scho

ol

(G5–

G7)

170

teac

hers

3,

057

stud

ents

JE

S H

LM

TSE→

Gen

eral

Satis

fact

ion

(stu

dent

s) : β

= .1

7 TS

E→

Psy

chol

ogic

al D

istre

ss (s

tude

nts)

: β

= -.

12

TSE→

Ach

ievem

ent :

β =

.08

TSE→

Opp

ortu

nity

(stu

dent

s) : β

= .1

3

Lum

pe e

t al.

(201

2)

USA

A

A

Scien

ce

Elem

enta

ry sc

hool

(G

4, G

6)

450

teac

hers

ST

EBI

Re

gres

sion

TSE→

Scie

nce

Ach

ievem

ent :

β =

.13

Mid

gley

et a

l. (1

989)

U

SA

AA

, SM

M

ath

Exp

ec-

tanc

ies

Elem

enta

ry a

nd

mid

dle

scho

ol

(G5–

G6)

1,32

9 st

uden

ts

and

their

te

ache

r

New

ly de

velo

ped

scale

Re

gres

sion

Resu

lts ac

ross

two t

ime p

oints:

TS

E→

Exp

ecta

ncie

s: β

= .0

4 (n

s); β

= .0

6;

TSE→

Mat

h Pe

rfor

man

ce: β

= .0

8; β

= .0

7 TS

E→

Tas

k D

iffic

ulty

: β =

-.05

(ns);

β =

-.10

Moh

amad

i &

Asa

dzad

eh (2

012)

Ir

an

AA

O

vera

ll H

igh

scho

ol

284

teac

hers

TS

ES

(sho

rt)

SEM

TS

E→

Aca

dem

ic A

chiev

emen

t: β

= .4

0

Moj

avez

i &

Pood

ineh

Tam

iz

(201

2)

Iran

A

A, S

M

Ove

rall

Atti

tude

s an

d M

oti-

vatio

n

Seni

or h

igh

scho

ol

80 te

ache

rs

120

stud

ents

TS

ES

(long

) Co

rrela

tions

A

NO

VA

Co

rrelat

ional

result

s (M

otiva

tiona

l var

iables

): TS

E→

Mot

ivat

ion

(tota

l): r

= .4

5;

TSE→

Intri

nsic

Mot

ivat

ion:

r =

.39

TSE→

Ext

rinsic

Mot

ivat

ion:

r =

-.09

(ns)

TSE→

Atti

tude

: r =

.79

TSE→

Opi

nion

: r =

.24

AN

OV

A re

sults

(Aca

demi

c ach

ievem

ent):

F(

2,77

) = 8

.40,

p <

.001

.

Reye

s et a

l. (2

012)

U

SA

AA

, SM

Li

tera

cy

Eng

age-

men

t

Elem

enta

ry sc

hool

(G

5–G

6)

63 te

ache

rs

1,39

9 st

uden

ts

AE

S H

LM

TSE→

Eng

lish

Lang

uage

gra

des)

: γ =

.48

(ns)

TSE→

Rea

ding

Ach

ievem

ent (

Eng

agem

ent):

γ =

.22

Robe

rtson

&

Dun

smui

r (20

13)

UK

SM

O

n-Ta

sk

Beha

vior

Se

cond

ary

scho

ol

58 te

ache

rs

TSE

S (s

hort)

H

LM

TSE→

On-

Task

Pup

il Be

havi

or: β

= .4

4

Ross

(199

2)

Cana

da

AA

H

istor

y M

iddl

e sc

hool

(G

7–G

8)

18 te

ache

rs

TES

Regr

essio

n TS

E→

Hist

ory

Ach

ieve

men

t : m

ultipl

e R =

.80

Ro

ss e

t al.

(200

1)

Cana

da

AA

, SM

Co

mp.

Li

tera

cy

Self-

Elem

enta

ry sc

hool

(K

–3)

387

stud

ents

an

d te

ache

rs

New

ly de

velo

ped

scale

Re

gres

sion

Corre

lation

s for

two t

ime p

oints:

TS

E (C

ompu

ter U

se)→

Bas

ic Sk

ills:

r =

-.10

(ns);

r =

-.08

(ns)


effic

acy

TSE

(Adv

. Com

pute

r Use

)→ B

asic

Ski

lls:

r = -.

03 (n

s); r

= .2

8 TS

E (C

ompu

ter U

se)→

Adv

ance

d Sk

ills:

r =

.05

(ns);

r =

.22

TSE

(Adv

. Com

pute

r Use

)→ A

dvan

ced

Skill

s:

r = .1

0 (n

s); r

= .2

4 TS

E (C

ompu

ter U

se)→

Self

-eff

icacy

: r =

.15;

r =

.13

TSE

(Adv

. Com

pute

r Use

)→ S

elf-e

ffica

cy: r

= .2

0; .2

8

Thoo

nen

et a

l. (2

011)

H

ollan

d SM

Ta

sk

Mot

iatio

n E

lemen

tary

scho

ol

(G4–

G6)

62

1 te

ache

rs

3,46

2 st

uden

ts

SSE

SE

M

TSE→

Well

-bein

g (c

lass,

scho

ol): β

= .0

2; β

= .0

3 (n

s) TS

E→

Aca

dem

ic E

ffica

cy: β

= -.

01(n

s) TS

E→

Int

rinsic

Mot

ivat

ion:

β =

.00

(ns)

TSE→

Mas

tery

Goa

ls: β

= -.

01 (n

s) TS

E→

Per

form

ance

Avo

idan

ce: β

= -.

01 (n

s) TS

E→

Sch

ool I

nves

tmen

t: β

= -.

01 (n

s)

Thro

ndse

n &

Tu

rmo

(201

3)

Nor

way

A

A

Mat

h E

lemen

tary

scho

ol

(G2–

G3)

52

1 te

ache

rs

9,98

0 st

uden

ts

PALS

(ada

pted

) Co

rrela

tions

TS

E→

Mat

h A

chiev

emen

t (to

tal):

r =

.11;

TS

E→

Mat

h A

chiev

emen

t (G

rade

2):

r = .1

5;

TSE→

Mat

h A

chiev

emen

t (G

rade

3):

r = .0

7 (n

s).

Tour

naki

& P

odell

(2

005)

U

SA

AA

Li

tera

cy

Elem

enta

ry a

nd

mid

dle

scho

ol

384

teac

hers

TE

S A

NO

VA

N

on-si

gnifi

cant

resu

lts w

ere n

ot rep

orted

.

Van

Ude

n et

al.

(201

3)

Hol

land

SM

Eng

age-

men

t Se

cond

ary

scho

ol

((pre

)voc

atio

nal

educ

atio

n)

195

teac

hers

TS

EQ

(ada

pted

) Re

gres

sion

SEM

Re

gressi

on re

sults

: TS

E→

Beh

avio

ral E

ngag

emen

t: β

= .1

3 (n

s) TS

E→

Em

otio

nal E

ngag

emen

t: β

= -.

15

SEM

resu

lts:

TSE→

Influ

ence→

Beh

. Eng

agem

ent: β

= .1

1 TS

E→

Influ

ence→

Em

. Eng

agem

ent: β

= .1

1

Woo

lfolk

Hoy

et

al. (2

008)

U

SA

AA

O

vera

ll E

lemen

tary

scho

ol

(G3–

G4)

18

7 te

ache

rs

TSE

S (s

hort)

Co

rrela

tions

A

cade

mic

Opt

imism

→ A

chiev

emen

t: r =

.24

Note

. Bio

logy

= b

iolo

gy p

erfo

rman

ce; C

omp.

lite

racy

= c

ompu

ter l

itera

cy; H

istor

y =

hist

ory

perf

orm

ance

; Lite

racy

= li

tera

cy p

erfo

rman

ce; M

ath

= m

ath

perf

orm

ance

; Ove

rall

= s

tude

nts’

over

all p

erfo

rman

ce; S

cienc

e =

sc

ience

per

form

ance

; SM

= st

uden

ts’ m

otiv

atio

n (e

.g.,

enga

gem

ent,

on-ta

sk b

ehav

ior,

goal

orien

tatio

ns, s

elf-e

ffica

cy);

TSE

-IS

= T

SE fo

r ins

truct

iona

l stra

tegi

es; T

SE-C

M =

TSE

for c

lassr

oom

man

agem

ent;

TSE

-SE

= T

SE

for s

tude

nt e

ngag

emen

t. In

strum

ents:

AE

S =

Ada

ptiv

e E

ffica

cy S

cale;

JES

= Jo

b E

ffica

cy S

cale;

MSQ

= M

otiv

atin

g St

rate

gies

Que

stion

naire

; PA

LS =

Pat

tern

s of A

dapt

ive

Lear

ning

Sur

vey;

SSE

= S

ense

of S

elf-E

ffica

cy;

STE

BI =

Scie

nce

Teac

hing

Effi

cacy

Beli

ef In

stru

men

t; TE

S =

Tea

cher

Effi

cacy

Sca

le; T

SEQ

= T

each

er S

elf-E

ffica

cy Q

uest

ionn

aire;

TSE

S =

Tea

cher

Sen

se o

f Self

-Effi

cacy

Sca

le.


APPE

NDI

X 3

Teac

hers’

Psyc

holog

ical W

ell-B

eing

Aut

hor(s

) Co

untry

Th

eme

Gra

de

N

TSE

mea

sure

A

nalys

is O

utco

mes

A

vanz

i et a

l.(20

13)

Italy

(N

orw

ay)

Burn

out

Satis

f E

lemen

tary

, mid

dle

and

high

scho

ol

(G1–

G10

)

348

Itali

an

teac

hers

; 558

N

orw

egian

te

ache

rs

NTS

ES

SEM

TS

E→

Job

Satis

fact

ion:

r =

.36

TSE→

Bur

nout

(wor

k-re

lated

): r

= -.

26

TSE→

Bur

nout

(stu

dent

-relat

ed):

r =

-.32

Baro

uch

Gilb

ert e

t al.

(201

3)

Dom

i- ni

can

Repu

blic

Stre

ss

Sats

if Co

mm

it A

ttriti

on

Pres

choo

l–hi

gh

scho

ol

109

teac

hers

TS

ES

(sho

rt)

Corr

elatio

ns

Eng

lish

and S

pani

sh-m

ediu

m con

tent t

each

ers:

TSE→

Job

Satis

fact

ion:

r =

.29;

r =

.00

(ns)

TSE→

Job

Stre

ss: r

= -.

40; r

= -.

06

TSE→

Com

mitm

ent:

r = .3

6; r

= .3

2 TS

E→

Inte

ntio

n to

qui

t: r

= -.

18 (n

s); r

= -2

8 (n

s)

Blac

kbur

n &

Rob

inso

n (2

008)

U

SA

Satis

f A

gricu

lture

ed

ucat

ion

80 a

gricu

lture

te

ache

rs

TSE

S (lo

ng)

Corr

elatio

ns

Low,

mod

erate,

and h

igh te

ache

r exp

erien

ce:

TSE

-SE→

Job

Satis

fact

ion:

r =

.54;

r =

.56;

r =

.12

TSE

-IS→

Job

Satis

fact

ion:

r =

-.12

; r =

.84;

r =

.10

TSE

-CM→

Job

Satis

fact

ion:

r =

.57;

r =

.68;

r =

-.52

Bogl

er &

Som

ech

(200

4)

Isra

el

Com

mit

Mid

dle

scho

ol

(G7–

G9)

98

3 te

ache

rs

SPE

S Re

gres

sion

TSE→

Org

aniz

atio

nal C

omm

itmen

t: β

= .1

5 (n

s) TS

E→

Pro

fess

iona

l Com

mitm

ent: β

= .2

9

Brio

nes e

t al.

(201

0)

Spain

Bu

rnou

t Sa

tisf

Seco

ndar

y sc

hool

68

teac

hers

TI

SES

SEM

TS

E→

Em

otio

nal E

xhau

stio

n: β

= -.

20

TSE→

Per

sona

l Ach

ieve

men

t: β

= .4

0 TS

E→

Ach

ievem

ent→

Job

Satis

fact

ion:

β =

.16

TSE→

Sup

port→

Job

Satis

fact

ion:

β =

.08

Briss

ie et

al.

(198

8)

USA

Bu

rnou

t E

lemen

tary

scho

ol

1,21

3 te

ache

rs

TOQ

Re

gres

sion

TSE→

Bur

nout

: β =

-.17

Brou

wer

s & T

omic

(200

0)

Net

her-

lands

Bu

rnou

t Se

cond

ary

scho

ol

558

teac

hers

SC

MD

Lo

ngitu

dina

l SE

M

Onl

y fit

indi

ces a

re g

iven

.

Brou

wer

s et a

l. (2

001)

N

ethe

r-lan

ds

Burn

out

Seco

ndar

y Sc

hool

27

7 te

ache

rs

TISE

S SE

M

Feed

back

loop

: TS

E→

Em

otio

nal E

xhau

stio

n: β

= -.

45 →

D

eper

sona

lizat

ion:

β =

.60 →

Per

sona

l Acc

ompl

ishm

ent:

β =

-.41→

TSE

: β =

.18

Brud

nik

(200

9)

Polan

d Bu

rnou

t Se

cond

ary

Scho

ol

404

teac

hers

G

TSE

S Co

rrela

tions

Co

rrelat

ions a

cross

subje

cts ta

ught

: TS

E→

Em

otio

nal E

xhau

stio

n: r

= -.

23 –

-.63

.


TSE→

Neg

. Per

sona

l Acc

ompl

ishm

ent:

r = -.

28 –

-.66

TS

E→

Dep

erso

naliz

atio

n: r

= -.

18 –

-.53

Brui

nsm

a &

Jans

en

(201

0)

Net

her-

lands

Re

tent

ion

Pres

ervi

ce c

onte

xt

198

teac

hers

TS

ES

(ada

pted

) SE

M

TSE→

Inte

ntio

n to

Sta

y: β

= .1

7

Canr

inus

et a

l. (2

012)

N

ethe

r-lan

ds

Satis

f Co

mm

it Re

tent

ion

Seco

ndar

y sc

hool

1,

214

teac

hers

CS

C SE

M

Clas

sroo

m T

SE→

Affe

ctiv

e Co

mm

itmen

t: β

= .1

4 (d

irect)

; β

= .1

2 (in

direct

) Cl

assr

oom

TSE→

Sala

ry S

atisf

actio

n: β

= -.

20 (d

irect)

; β =

.0

7 (in

direct

) Cl

assr

oom

TSE→

Rela

tions

hip

Satis

fact

ion:

β =

.18

Clas

sroo

m T

SE→

Cha

nge

in M

otiv

atio

n: β

= .2

0 (d

irect)

; β

= .1

3 (in

direct

) Cl

assr

oom

TSE→

Res

p. to

Rem

ain: β

= .0

2 (in

direct

) Cl

assr

oom

TSE→

Job

Satis

fact

ion:

β =

.74

Capr

ara

et a

l. (2

003)

It

aly

Com

mit

Satis

f Ju

nior

hig

h sc

hool

72

6 te

ache

rs

New

ly de

velo

ped

scale

SE

M

TSE→

Job

Satis

fact

ion:

β =

.28

TSE→

Org

aniz

atio

nal C

omm

itmen

t: β

= .2

1 (d

irect)

; β =

.0

5 (in

direct

thro

ugh

collec

tive e

ffica

cy)

Capr

ara

et a

l. (2

006)

It

aly

Satis

f Ju

nior

Hig

h Sc

hool

75

teac

hers

N

ewly

deve

lope

d sc

ale

SEM

TS

E→

Job

Satis

fact

ion:

β =

.74

Chan

(200

8)

Chin

a Co

ping

Pr

eser

vive

and

in

serr

vice

con

text

27

3 te

ache

rs

GTS

ES

Regr

essio

n TS

E→

Act

ive

Copi

ng: β

= .0

5 (n

s) TS

E→

Pas

sive

Copi

ng: β

= -.

11 (n

s)

Chan

et a

l. (2

008)

Si

ngap

ore

Com

mit

Prim

ary

and

seco

ndar

y sc

hool

2,

130

prim

ary

1,58

7 se

cond

ary

TSE

S SE

M

TSE→

Com

mitm

ent (

prim

ary

scho

ol): β

= .2

6 TS

E→

Com

mitm

ent (

seco

ndar

y sc

hool

): β

= .2

2

Colad

arci

(199

2)

USA

Co

mm

it E

lemen

tary

and

m

iddl

e sc

hool

(K

–8)

170

teac

hers

TE

S Re

gres

sion

TSE→

Com

mitm

ent: β

= .1

9

Colli

e et

al.

(201

2)

Cana

da

Satis

f Pr

imar

y an

d se

cond

ary

scho

ol

664

teac

hers

TS

ES

(sho

rt)

SEM

TS

E→

Job

Satis

fact

ion:

β =

.33

Dom

énec

h-Be

tore

t (2

006)

Sp

ain

Stre

ss

Seco

ndar

y sc

hool

24

7 te

ache

rs

New

ly de

velo

ped

scale

M

AN

OV

A

HLM

H

igh

vs. L

ow T

SE→

cop

ing:

M =

74.

57 (

SD =

12.

77);

M

= 7

0.35

(SD

= 1

4.28

)

Dom

énec

h-Be

tore

t (2

009)

Sp

ain

Burn

out

Prim

ary

and

seco

ndar

y sc

hool

72

4 te

ache

rs

Teac

her p

erce

ived

te

achi

ng se

lf-ef

ficac

y sc

ale

SEM

Pr

imar

y sch

ool:

TS

E→

Stre

ssor

s→ E

mot

iona

l Exh

aust

ion:

β =

-.24

TS

E→

Stre

ssor

s→ D

eper

sona

lizat

ion:

β =

-.10

TS

E→

Stre

ssor

s→ R

educ

ed A

ccom

plish

men

t: ns


Secon

dary

schoo

l: TS

E→

Stre

ssor

s→ B

urno

ut: β

= -.

21

Duf

fy &

Len

t (20

09)

USA

Sa

tisf

Elem

enta

ry, m

iddl

e an

d hi

gh sc

hool

36

6 te

ache

rs

TSE

S (s

hort)

; PE

BS;

new

ly de

velo

ped

scale

SE

M

TSE→

Sat

isfac

tion:

β =

.17

TSE→

Goa

l Pro

gres

s: β

= .3

7 TS

E→

Wor

k Co

nditi

ons →

Sat

isfac

tion:

β =

.09

Ebm

eier (

2003

) U

SA

Com

mit

Elem

enta

ry, m

iddl

e, an

d hi

gh sc

hool

55

4 te

ache

rs

TES

SEM

TS

E→

Com

mitm

ent: β

= .3

8

Egy

ed &

Sho

rt (2

006)

U

SA

Burn

out

Elem

enta

ry sc

hool

10

6 te

ache

rs

TES

Corr

elatio

ns

TSE→

Bur

nout

(tot

al): r

= -.

23

TSE→

Em

otio

nal E

xhau

stio

n: r

= -.

12 (n

s) TS

E→

Dep

erso

naliz

atio

n: r

= -.

26

TSE→

Per

sona

l Acc

ompl

ishm

ent:

r = .2

8 E

vans

& T

ribbl

e (1

986)

U

SA

Com

mit

Pres

ervi

ce c

onte

xt

179

teac

hers

TE

S Co

rrela

tions

TS

E→

Ove

rall

Prob

lem S

core

s: r =

.07

(ns)

TSE→

Com

mitm

ent:

r = .2

3

Eve

rs e

t al.

(200

2)

Net

her-

lands

Bu

rnou

t Se

cond

ary

scho

ol

490

teac

hers

N

ewly

deve

lope

d m

easu

re

HLM

TS

E T

owar

d G

uidi

ng G

roup

s→ E

E, D

P, P

A: β

= -.

03

(ns);

-.16

; .32

, res

pect

ively

. TS

E T

owar

d U

sing

Task

s→ E

E, D

P, P

A: β

= .0

8 (n

s); -

.02

(ns);

.13,

resp

ectiv

ely.

TSE

Tow

ard

Usin

g In

nova

tions→

EE

, DP,

PA

: β =

-.60

; -.3

4; .3

3, re

spec

tively

.

Eve

rs e

t al.

(200

5)

Net

her-

lands

Bu

rnou

t Se

cond

ary

scho

ol

545

teac

hers

G

SES

(Dut

ch)

HLM

TS

E→

Em

otio

nal E

xhau

stio

n: β

= -.

27

TSE→

Dep

erso

naliz

atio

n: β

= -.

30

TSE→

Per

sona

l Acc

ompl

ishm

ent: β

= .4

4

Fern

et e

t al.

(201

2)

Cana

da

Burn

out

Elem

enta

ry, m

iddl

e an

d hi

gh sc

hool

(G

1–G

11)

806

teac

hers

CS

C (lo

ngitu

dina

l) SE

M

ΔTSE→

ΔE

mot

iona

l Exh

aust

ion:

β =

-.37

ΔT

SE→

ΔD

eper

sona

lizat

ion:

β =

-.38

ΔT

SE→

ΔPe

rson

al A

ccom

plish

men

t: β

= .6

3

Five

s et a

l. (2

007)

U

SA

Burn

out

Pres

ervi

ce c

onte

xt

49 te

ache

rs

TSE

S

Corr

elatio

ns

Resu

lts ac

ross

two t

ime p

oints:

TS

E-S

E→

EE

, DP,

PA

.: r =

-.13

(ns);

-.20

(ns);

.38

(tim

e 1)

and

r =

-.59

; -.5

9; .3

4 (ti

me

2), r

espe

ctiv

ely.

TSE

-IS→

EE

, DP,

PA

.: r =

-.21

(ns);

-.30

; .38

(tim

e 1)

an

d r =

-.54

; -.5

4; .3

4 (ti

me

2), r

espe

ctiv

ely.

TSE

-CM→

EE

, DP,

PA

.: r =

-.19

(ns);

-.32

; .36

(tim

e 1)

an

d r =

-.36

; -.3

3; .2

4 (n

s) (ti

me

2), r

espe

ctiv

ely.

Fried

man

(200

3)

Isra

el

Burn

out

Elem

enta

ry sc

hool

32

2 te

ache

rs

New

ly de

velo

ped

scale

Re

gres

sion

TSE

(Inf

luen

ce, C

onsid

erat

ion)→

Bur

nout

(tot

al): β

= -


.20;

β =

-.25

; TS

E (I

nflu

ence

, Con

sider

atio

n)→

Em

otio

nal

Exh

aust

ion:

β =

-.20

; β =

-14

; TS

E (I

nflu

ence

, Con

sider

atio

n)→

Una

ccom

plish

men

t: β

= -.

20; β

= -

.15;

TS

E (C

onsid

erat

ion)→

Dep

erso

naliz

atio

n: β

= -.

36

Helm

s-Lo

renz

et a

l. (2

012)

N

ethe

r-lan

ds

Stre

ss

Diss

atisf

Se

cond

ary

scho

ol

30 b

egin

ning

te

ache

rs

CSC

Corr

elatio

ns

Clas

sroo

m T

SE→

Ten

sion:

β =

-.28

(ns)

Clas

sroo

m T

SE→

Job

Disc

onte

nt: β

= -2

3 (n

s) Sc

hool

TSE→

Ten

sion:

β =

-.23

(ns)

Scho

ol T

SE→

Job

Disc

onte

nt: β

= -.

56

(indir

ect ef

fects

are n

ot dis

played

)

Høi

gaar

d et

al.

(201

2)

Nor

way

Bu

rnou

t Sa

tisf

Attr

ition

Unk

now

n 19

2 be

ginn

ing

teac

hers

PT

E

Regr

essio

n TS

E→

Job

Satis

fact

ion:

β =

-.04

(ns)

TSE→

Wor

k Bu

rnou

t: β

= -.

04 (n

s) TS

E→

Inte

ntio

n to

Qui

t: β

= -.

02 (n

s)

Hug

hes (

2012

) U

SA

Rete

ntio

n E

lemen

tary

, mid

dle,

and

high

scho

ol

(K–1

2)

782

teac

hers

N

ewly

deve

lope

d sc

ale

Logi

stic

Regr

essio

n TS

E-C

M→

Ret

entio

n: β

= -.

16 (n

s) TS

E-I

S→ R

eten

tion:

β =

.28

(ns)

TSE

-Stu

dent

Mot

ivat

ion

Ret

entio

n: β

= -.

20 (n

s) TS

E-T

echn

olog

y→ R

eten

tion:

β =

-.32

Hul

tell

et a

l. (2

013)

Sw

eden

Bu

rnou

t Pr

eser

vice

con

text

81

6 te

ache

rs

TSE

S (s

hort)

Cl

uste

r an

alysis

χ2

= 2

0.15

, p =

.003

Iman

ts &

Van

Zoe

len

(199

5)

Net

her-

lands

A

ttriti

on

Elem

enta

ry sc

hool

66

teac

hers

TP

SES

MA

NO

VA

N

o resu

lts ar

e give

n.

Klas

sen

& C

hui (

2010

) Ca

nada

Sa

tisf

Pres

ervi

ce c

onte

xt,

elem

enta

ry, m

iddl

e, an

d hi

gh sc

hool

1,43

0 te

ache

rs

TSE

S (s

hort)

SE

M

TSE

-IS→

Job

Satis

fact

ion:

β =

.29

TSE

-CM→

Job

Satis

fact

ion:

β =

.26

TSE

-SE→

Job

Satis

fact

ion:

ns

Klas

sen

& C

hiu

(201

1)

Cana

da

Qui

t Co

mm

it

Pres

ervi

ce c

onte

xt,

elem

enta

ry, m

iddl

e an

d hi

gh sc

hool

379

pres

ervi

ce

teac

hers

43

4 in

serv

ice

teac

hers

TSE

S (s

hort)

SE

M

Prac

tising

teac

hers:

TS

E-I

S→ C

omm

itmen

t: β

= .2

6 TS

E-I

S→ C

omm

itmen

t→ In

tent

ion

to Q

uit: β

= -.

19

Pres-

servic

e tea

chers

: TS

E-C

M→

Com

mitm

ent: β

= .3

2 TS

E-C

M→

Com

mitm

ent→

Inte

ntio

n to

Qui

t: β

= -.

58

Klas

sen

et a

l. (2

009)

Ca

nada

, Cy

prus

, Sa

tisf

Elem

enta

ry, m

iddl

e, an

d se

cond

ary

1,21

2 te

ache

rs

TSE

S (s

hort)

Co

rrela

tions

Re

sults

acro

ss fiv

e cou

ntrie

s: TS

E (o

vera

ll) →

Job

Satis

fact

ion:

β =

.33

– .4

8


Kor

ea,

Sing

apor

e, an

d U

SA

scho

ol

TSE

-IS→

Job

Satis

fact

ion:

β =

.17

– .4

5 TS

E-C

M →

Job

Satis

fact

ion:

β =

.19

– .4

4 TS

E-S

E →

Job

Satis

fact

ion:

β =

.34

– .4

4

Klas

sen

et a

l. (2

013)

Ca

nada

, H

ong

Kon

g,

UK

, and

Th

ailan

d

Com

mit

Pres

ervi

ce c

onte

xt

1,18

7 te

ache

rs

TSE

S (s

hort)

Bo

otst

rapp

ing

analy

sis

(regr

essio

n)

TSE→

Com

mitm

ent (

acro

ss 4

cou

ntrie

s): B

= .1

0 –

.32

Stud

ent B

ehav

ior S

tress→

TSE→

Com

mitm

ent (

acro

ss 4

co

untri

es):

B =

-.03

– -.

13

Wor

k St

ress→

TSE→

Com

mitm

ent (

acro

ss 4

cou

ntrie

s):

B =

-.04

(ns)–

.05

Lent

et a

l. (2

011)

It

aly

Satis

f M

iddl

e an

d hi

gh

scho

ol

235

teac

hers

TS

ES

(sho

rt)

SEM

TS

E→

Goa

l Pro

gres

s: β

= .3

8 TS

E→

Job

Satis

fact

ion:

β =

.09

(ns)

TSE→

Wor

k co

nditi

ons: β

= .1

6 TS

E→

Wor

k co

nditi

ons→

Job

Sat

isfac

tion:

β =

.06

TSE→

Goa

l Pro

gres

s→ L

ife S

atisf

actio

n: β

= .0

5

Malo

w-I

roff

et a

l. (2

007)

U

SA

Rete

ntio

n E

lemen

tary

scho

ol

(K–6

) 68

teac

hers

TE

S Re

gres

sion

TSE→

Dec

ision

to S

tay: β

= .0

6 (n

s)

Mar

tin e

t al.

(201

2)

USA

Bu

rnou

t

Elem

enta

ry, m

iddl

e an

d hi

gh sc

hool

63

1 te

ache

rs

TSE

S SE

M

TSE

-SE→

Inst

r. M

an.→

Stu

dent

Stre

ssor

s: β

= -.

43

TSE

-EN→

Inst

r. M

an.→

Per

s. A

ccom

p.: β

= -.

63

Moè

et a

l. (2

010)

It

aly

Satis

f E

lemen

tary

, mid

dle

and

high

scho

ol

399

teac

hers

TS

ES

SE

M

TSE→

Job

Satis

fact

ion:

β =

.32

Robe

rtson

&

Dun

smui

r (20

13)

UK

St

ress

Se

cond

ary

scho

ol

58 te

ache

rs

TSE

S (s

hort)

H

LM

TSE→

Tea

cher

Stre

ss: β

= -.

31

Rots

et a

l. (2

007)

Be

lgiu

m

Com

mit

Seco

ndar

y sc

hool

20

9 te

ache

rs

TSE

S (s

hort)

SE

M

TSE→

Tea

chin

g Co

mm

itmen

t: β

= .2

9

Salan

ova

et a

l. (2

011)

Sp

ain

Satis

f Se

cond

ary

scho

ol

483

teac

hers

G

TSE

S Lo

ngitu

dina

l SE

M

Resu

lts fo

r two

time

poin

ts:

TSE→

Ent

hous

iasm

: β =

.50;

β =

.34

TSE→

Sat

isfac

tion:

β =

.38;

β =

.36

TSE→

Com

fort:

β =

.41;

β =

.27

TSE→

Eng

agem

ent: β

= .2

2; β

= .2

3

Sass

et a

l. (2

011)

U

SA

Stre

ss

Diss

atisf

E

lemen

tary

, mid

dle,

and

high

scho

ol

479

teac

hers

TS

ES

SEM

TS

E-E

N→

Stu

dent

stre

ssor

s: β

= -.

50 .

TSE

-EN→

Stu

dent

stre

ssor

s→ Jo

b D

issat

isf.: β

= -.

04

Schw

arze

r & H

allum

(2

008)

Sy

ria a

nd

Ger

man

y Bu

rnou

t U

nkno

wn

1,20

3 te

ache

rs

458

teac

hers

G

TSE

S Co

rrela

tions

Re

sults

for S

yrian

and G

erman

teac

hers:

TS

E→

Em

otio

nal E

xhau

stio

n: r

= -.

17; r

= -.

48

TSE→

Dep

erso

naliz

atio

n: r

= -.

24; r

= -.

56


TSE→

Red

uced

Acc

ompl

ishm

ent:

r = -.

66; r

= -.

75

Schw

arze

r et a

l. (2

000)

H

ong

Kon

g,

Ger

man

y

Burn

out

Unk

now

n 54

2 te

ache

rs

GTS

ES

Corr

elatio

ns

Resu

lts fo

r Chin

ese an

d Germ

an su

bsam

ples:

TSE→

Em

otio

nal E

xhau

stio

n: r

= -.

36;

r = -.

50

TSE→

Dep

erso

naliz

atio

n: r

= -.

26; r

= -.

38

TSE→

Per

sona

l Acc

ompl

ishm

ent

r = .3

1; r

= .5

8

Shym

an (2

010)

U

SA

Burn

out

Unk

now

n

100

para

-ed

ucat

ors

TSE

S (s

hort)

H

LM

TSE→

Em

otio

nal E

xhau

stio

n: β

= .0

1

Skaa

lvik

& S

kaalv

ik

(200

7)

Nor

way

Bu

rnou

t E

lemen

tary

–Hig

h sc

hool

(G1–

G10

) 24

4 te

ache

rs

NTS

ES

SE

M

TSE→

Tea

cher

Bur

nout

: β =

-.76

Skaa

lvik

& S

kaalv

ik

(201

0)

Nor

way

Bu

rnou

t Sa

tisf

Elem

enta

ry–H

igh

scho

ol (G

1–G

10)

2,24

9 te

ache

rs

NTS

ES

SE

M

TSE→

Em

otio

nal E

xhau

stio

n: r

= -.

29

TSE→

Dep

erso

naliz

atio

n: r

= -.

41

TSE→

Job

Satis

fact

ion:

β =

.17

So-k

um T

ang

et a

l. (2

001)

H

ong

Kon

g Bu

rnou

t E

lemen

tary

, mid

dle,

and

high

scho

ol

269

teac

hers

G

TSE

S SE

M

TSE→

Bur

nout

(tot

al) β

= -.

53

TSE→

Neg

ativ

e M

enta

l hea

lth: β

= -.

19

TSE→

Bur

nout→

Neg

ativ

e M

enta

l hea

lth: β

= -.

24

Step

hano

u et

al.

(201

3)

Gre

ece

Sats

if E

lemen

tary

scho

ol

268

teac

hers

N

ewly

deve

lope

d sc

ale

HLM

TS

E→

Job

Satis

fact

ion:

r =

.77

Tsig

ilis e

t al.

(201

0)

Gre

ece

Satis

f Se

cond

ary

scho

ol

405

teac

hers

TS

ES

Co

rrela

tions

TS

E-I

S→ Jo

b Sa

tisfa

ctio

n: r

= .3

0 TS

E-C

M→

Job

Satis

fact

ion:

r =

.39

TSE

-EN→

Job

Satis

fact

ion:

r =

.40

Tsou

lopa

s et a

l. (2

010)

U

SA

Burn

out

Qui

t E

lemen

tary

, mid

dle

and

high

scho

ol

610

teac

hers

PS

ECM

SE

M

TSE→

Em

otio

nal E

xhau

stio

n: β

= -.

09

TSE→

Em

otio

nal E

xhau

stio

n→ A

ttriti

on: β

= -.

05

TSE→

Em

otio

nal E

xhau

stio

n→ M

igra

tion:

β =

-.05

Viel

-Rum

a et

al.

(201

0)

USA

Sa

tisf

Elem

enta

ry, m

iddl

e an

d hi

gh sc

hool

70

Spe

cial

educ

ator

s TE

S Co

rrela

tions

TS

E→

Job

Satis

fact

ion:

r =

.29

W

are

& K

itsan

tas

(200

7, 2

011)

U

SA

Com

mit

Publ

ic sc

hool

26

,257

teac

hers

SA

SS

Regr

essio

n H

LM

TSE

to E

nlist

Adm

in. D

irect

ion→

Com

mitm

ent: β

= .2

9 TS

E-C

M→

Com

mitm

ent: β

= -.

14

Note

. Bur

nout

= te

ache

r bur

nout

; Com

mit

= te

ache

r com

mitm

ent;

Qui

t = te

ache

r ret

entio

n an

d at

tritio

n; S

atisf

= te

ache

r job

satis

fact

ion;

Stre

ss =

teac

her s

tress

and

cop

ing;

TSE

-IS

= T

SE fo

r ins

truct

iona

l stra

tegi

es; T

SE-

CM =

TSE

for c

lassr

oom

man

agem

ent;

TSE

-SE

= T

SE fo

r stu

dent

eng

agem

ent.

Instr

umen

ts: C

SC =

Clas

sroo

m a

nd S

choo

l Con

text

Tea

cher

Self

-Eff

icacy

Sca

le; G

SES

= G

ener

al Se

lf-Ef

ficac

y Sc

ale; G

TSES

= G

ener

al Te

ache

r Se

lf-Ef

ficac

y Sc

ale; (

N)T

SES

= N

orw

egian

Tea

cher

Self

-Eff

icacy

Sca

le;

PEBS

= P

erso

nal E

ffica

cy B

elief

s Sc

ale; P

SECM

= P

erce

ived

Self

-Eff

icacy

in C

lassr

oom

Man

agem

ent

ques

tionn

aire;

PTE

= P

erso

nal T

each

er E

ffica

cy S

cale;

SA

SS =

SA

SS T

each

er; S

CMD

= S

elf-e

ffica

cy s

cale

for

Clas

sroo

m M

anag

emen

t and

Disc

iplin

e; SP

ES

= S

choo

l Par

ticip

ant E

mpo

wer

men

t Sca

le; T

ES

= T

each

er E

ffica

cy S

cale;

TSE

Q =

Tea

cher

Self

-Effi

cacy

Que

stio

nnair

e; TS

ES

= T

each

er S

ense

of S

elf-E

ffica

cy S

cale.

TIS

ES

= T

each

er In

terp

erso

nal S

elf-E

ffica

cy S

cale;

TO

Q =

Tea

cher

Opi

nion

Que

stio

nnair

e; TP

SES

= T

he T

each

ers’

and

Prin

cipals

’ Sen

se o

f Eff

icacy

Sca

le.


79

CHAPTER 3 INTER- AND INTRA-INDIVIDUAL DIFFERENCES IN TEACHERS’ SELF-EFFICACY:

A MULTILEVEL FACTOR EXPLORATION

_________________________________________________________________________

This study explored inter- and intra-individual differences in teachers’ self-efficacy (TSE) by

adapting Tschannen-Moran and Woolfolk Hoy’s (2001) Teachers’ Sense of Efficacy Scale

(TSES) to the domain- and student-specific level. Multilevel structural equation modeling was

used to evaluate the factor structure underlying this adapted instrument, and to test for

violations of measurement invariance over clusters. Results from 841 third- to sixth-grade

students and their 107 teachers supported the existence of one higher-order factor (Overall

TSE) and four lower-order factors (Instructional Strategies, Behavior Management, Student

Engagement, and Emotional Support) at both the between- and within-teacher level. In this

factor model, intra-individual differences in TSE were generally larger than inter-individual

differences. Additionally, the presence of cluster bias in 18 of 24 items suggested that the

unique domains of student-specific TSE at the between-teacher level cannot merely be

perceived as the within-teacher level factors’ aggregates. These findings underscore the

importance of further investigating TSE in relation to teacher, student, and classroom

characteristics.

_________________________________________________________________________ Zee, M., Koomen, H. M. Y., Jellesma, F. C., Geerlings, J., & de Jong, P. F. (2016). Inter- and intra-individual differences in teachers’ self-efficacy: A multilevel factor exploration. Journal of School Psychology, 55, 39–56. doi:10.1016/j.jsp.2015.12.003


CHAPTER 3

80

INTRODUCTION

The last decades have witnessed the growth of teacher self-efficacy (TSE) studies from a small

side-branch of school effectiveness research to a major area of educational psychology

(Klassen, Tze, Betts, & Gordon, 2011). One of the triggers for this progress is the belief that

generalized TSE, or the self-confidence with which teachers approach and bring about their

daily teaching tasks, is a central determinant of teachers’ behaviors and actions in the

classroom (Bandura, 1997; Tschannen-Moran & Woolfolk Hoy, 2001). Both theoretical and

empirical sources have surfaced the tacit notion that teachers high in self-efficacy are more

likely than poorly efficacious educators to set high goals for themselves, to activate adequate

effort to perform specific teaching tasks, and to persist when the goings get tough (e.g.,

Bandura, 1997, 2000; Gibson & Dembo, 1984; Tschannen-Moran & Woolfolk Hoy, 2001).

Moreover, there is evidence to suggest that teachers with a resilient sense of self-efficacy are

generally effective in providing the instructional and affective supports that match their

students’ needs and lead to positive learning outcomes (e.g., Guo, McDonald Connor, Yang,

Roehring, & Morrison, 2012; Justice, Mashburn, Hamre, & Pianta, 2008; Leroy, Bressoux,

Sarrazin, & Trouilloud, 2007).

To date, empirical research has predominantly concentrated on measuring between-teacher

differences in TSE and its outcomes (cf. Ross, 1994). As such, most studies have implicitly

assumed TSE to be a relatively stable, almost trait-like teacher characteristic which, at best, may

fluctuate across various teaching tasks and domains (Raudenbusch, Rowan, & Cheong, 1992;

Tschannen-Moran & Woolfolk Hoy, 2001). Apart from its static aspects, however, TSE has

also been perceived as an inherently mutable state within teachers, which largely depends on

challenges presented by different types of students in class (Raudenbusch et al., 1992; Ross,

Cousins, & Gadalla, 1996; Tschannen-Moran, Woolfolk Hoy, & Hoy, 1998). Unfortunately,

though, the examination of intra-individual variability in teachers’ self-efficacy has largely gone

unheeded by educational research, as its measurement and analysis have generally been

presumed to be relatively complex. In the present study, therefore, we aimed to advance

understanding of the multifaceted nature of teachers’ sense of self-efficacy by exploring this

construct across various domains of teaching and learning and particular students.

Distinguishing inter- and intra-individual differences in TSE may be important for determining

how these capability beliefs are shaped and what their effects are on individual students’

academic adjustment in the classroom.


MEASUREMENT OF TEACHER SELF-EFFICACY

81

A SOCIAL-COGNITIVE PERSPECTIVE ON TEACHER SELF-EFFICACY

Empirical research on TSE has predominantly been grounded in Bandura’s (1977, 1986, 1997)

social-cognitive framework. Central to this framework is the idea that people are not merely

nudged by the whims of their environment or biological makeup, but rather operate within a

system of triadic reciprocal causation. This complex system indicates that environmental

constraints or resources are likely to operate through such important personal cognitions as

self-efficacy, which organize and produce actions for given purposes (Bandura, 1997, 2006;

Pajares, 1997). According to Bandura (1997), these capability beliefs provide the power to act

differently from what specific contextual forces dictate, by activating and sustaining the skills,

motivation, and effort required for desired achievements to be realized. Educational

researchers have, for instance, highlighted the importance of TSE for teachers’ ability to

manage and motivate difficult students, and their level of effort and persistence in getting these

students to study (e.g., Almog & Shechtman, 2007; Bandura, 1997; Lambert, McCarthy,

O'Donnell, & Wang, 2009; Tschannen-Moran & Woolfolk Hoy, 2001). Accordingly, teachers’

self-efficacy has generally been considered a vital predictor of behavior and action in the

domain of teaching and learning.

The basic tenets of the social-cognitive paradigm have offered some useful insights into how

self-efficacy could be best approached. Among those guiding principles is the recognition of

the “person-in-context” in capturing the construct of self-efficacy. For the domain of teaching

and learning, this emphasis on environment implies that the degree of specificity of teaching

tasks and domains has to be adequately identified (Bandura, 1997, 2006; Tschannen-Moran &

Woolfolk Hoy, 2001). Moreover, it underscores the importance of considering environmental

obstacles that embody gradations of challenge to which teachers can adjudge their sense of

self-efficacy.

DEGREE OF DOMAIN SPECIFICITY OF TSE

Teachers’ sense of self-efficacy has been generally conceptualized at various levels of

specificity. As such, this construct can be perceived to reside along a continuum from domain

generality at one end to increasingly advanced specificity levels at the other (Lent & Brown,

2006). At the most universal level, TSE has been regarded as a single-level, trait-like construct,

reflecting generalized capability beliefs that fluctuate between teachers (e.g., Schwarzer, 1992;

Schwarzer & Jerusalem, 1995). Investigators taking such a theoretical stance habitually


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decontextualize TSE from a wider scope of tasks and domains in the classroom, resulting in

one-dimensional, all-purpose measures that are widely applicable to a range of outcomes (e.g.,

Bandura, 1997; Pajares, 1996). Moreover, they commonly treat within-teacher variations in

TSE as error variance, as these variations only represent deviations from teachers’ baseline

level of self-efficacy.

Generalized measures that capture between-teacher differences in TSE appear, by far, to be the

most frequently used in studies conducted from 1998 to 2009 (Klassen et al., 2011). Indicative

of such between-teacher tests are the oft-cited Teacher Efficacy Scale (TES; Gibson &

Dembo, 1984) and Schwarzer and Jerusalem’s (1995) General Efficacy Scale (GES). Despite

their popularity, however, these measures have been criticized for being invalid and lacking

predictive relevance (Bandura, 1997, 2006; Kagan, 1990; Pajares, 1996). For instance, domain-

general scales have been suggested to be problematically ambiguous in the sense that teachers

are forced to guess what the unspecified contextual details of individual items might be

(Bandura, 1997; Wheatley, 2005). Items such as “I know that I can motivate my students to

participate in innovative projects” (Schwarzer, Schmitz, & Daytner, 1999) may place a burden

on teachers to comprehend what is being asked of them, as it leaves unspecified what

“innovative projects” are. Moreover, it is likely that global measures fail to adequately match

with the particular outcomes in the classroom that are of interest to the researcher (Bandura,

1997, 2006). Those potential misalliances between predictor and outcome may come at the

expense of the explanatory and predictive merit of general TSE measures (Pajares, 1996).

Recognizing that further specification of TSE is required to elucidate the self-efficacy

regulation of teachers’ behaviors in the classroom, more recent scholars have shifted focus to

subject-, task-, or domain-specific conceptualizations of TSE (Brouwers & Tomic, 2000;

Dellinger, Bobbett, Olivier, & Ellett, 2008; Friedman & Kass, 2002; Riggs & Enochs, 1990;

Siwatu, 2007, 2011; Tschannen-Moran & Woolfolk Hoy, 2001; Tschannen-Moran & Johnson,

2011; Tsouloupas, Carson, Matthews, Grawitch, & Barber, 2010). One of the most celebrated

attempts at this domain-level of specificity comes from Tschannen-Moran and Woolfolk Hoy

(2001). In a seminar on efficacy in teaching and learning, these researchers pooled and

discussed both new and existing items to construct a TSE scale that assumedly considers the

full range of teaching tasks and responsibilities. This measure, which is generally known as the

Teachers’ Sense of Self-Efficacy Scale (TSES), holds promise as a flexible research tool that

can be used across grades (Tschannen-Moran & Woolfolk Hoy, 2001), subjects (Tschannen-



83

Moran & Woolfolk Hoy, 2007), and teaching contexts (Klassen et al., 2009; Woolfolk Hoy &

Burke Spero, 2005). Moreover, its factorial, convergent, and concurrent validity has been

demonstrated in several empirical studies (e.g., Heneman, Kimball, & Milanowski, 2006;

Klassen et al., 2009; Tschannen-Moran & Woolfolk Hoy, 2001; Wolters & Daugherty, 2007).

The TSES has taken account of three unique teaching domains that appear to be the most

germane to teachers’ daily activities and students’ academic adjustment. Tschannen-Moran and

Woolfolk Hoy (2001) labeled these domains as TSE for instructional strategies, student

engagement, and classroom management, with the first two domains usually being the most

highly correlated (e.g., Tsigilis, Koustelios, & Grammatikopoulos, 2010). Recently, attention

has also been drawn to another domain that may be relevant to teachers’ self-efficacy for

teaching and learning. This domain of emotional support involves tasks and responsibilities

related to how well teachers can establish caring relationships with students, acknowledge

students’ opinions and feelings, and create settings in which students feel secure to explore and

learn (e.g., Pianta, La Paro, & Hamre, 2008). A rich body of empirical research has indicated

that emotionally supportive teacher behaviors, next to instructional, motivational, and

organizational aspects of teaching and learning, are one of the strongest correlates of students’

achievement, engagement, and enjoyment during learning tasks (e.g., Crosnoe, Johnson, &

Elder, 2004; Hamre et al., 2014; Reyes, Brackett, Rivers, White, & Salovey, 2012; Rimm-

Kaufman & Chui, 2007; Rimm-Kaufman, La Paro, Downer, & Pianta, 2005; Roorda, Koomen,

Spilt, & Oort, 2011). Therefore, at the domain-level of specificity, adding the emotional

support domain may provide, above and beyond the domains of instructional strategies,

classroom management, and student engagement, relevant insights into the multifaceted nature

of TSE and its outcomes in the classroom.

Investigators have increasingly supported the need to use measures of TSE in specific domains

of teaching and learning (e.g., Bandura, 1997; Brouwers & Tomic, 2000; Dellinger et al., 2008;

Tschannen-Moran et al., 1998; Tsouloupas et al., 2010). Tschannen-Moran et al. (1998, p. 227-

228), for instance, are adamant of the idea that “teachers feel efficacious for teaching particular

subjects to certain students in specific settings, and [that] they can be expected to feel more or

less efficacious under different circumstances”. Consistent with this Bandurian notion, a

modest body of empirical research on within-teacher variations in TSE (Raudenbusch et al.,

1992; Ross et al., 1996) has furthermore indicated that teachers’ sense of self-efficacy may be

significantly affected by contextual factors, such as subject matter, student behavior, and the


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type of students they teach. Apart from between-teacher differences, this within-person

variation in TSE is important to recognize, as it may advance understanding of the changing

states of teachers’ self-efficacy beliefs across domains and particular students. Unfortunately,

though, research on TSE toward particular children under different domains of functioning

seems to be more the exception than the rule. For this reason, we will not only consider the

degree of domain specificity of teachers’ self-efficacy, but the level of student specificity as

well.

DEGREE OF STUDENT SPECIFICITY OF TSE

Taking teachers’ self-efficacy to both the domain- and student-specific level without becoming

too specific is no easy matter. Similar to global capability beliefs, overly particularized self-

efficacy judgments have been criticized by prior research (e.g., Bandura, 1997; Pajares, 1996;

Tschannen-Moran et al., 1998) for their potential lack of external validity and practical

relevance to the field of education. Tschannen-Moran and Woolfolk Hoy (2001, p. 795), for

instance, strikingly illustrate how such microscopically operationalized self-efficacy items as “I

am confident I can teach simple subtraction to middle-income second graders in a rural setting

who do not have specific learning disabilities, as long as my class is smaller than 22 students

and good manipulatives are available” may lose both predictive power for other teaching

contexts and students, as well as practical utility. Potentially, such issues may be circumvented

by allowing the level of domain specificity to depend on obstacles against which teachers can

adjudge their self-efficacy (cf. Pajares, 1996). Assumedly, the behaviors and characteristics that

students bring to the classroom may function as such obstacles, determining the strength of

teachers’ self-efficacy across various domains of teaching and learning.

Past research on self-efficacy has since long acknowledged the importance of viewing teachers’

self-efficacy in light of various environmental obstacles (Bandura, 1997, 2006; Coladarci &

Breton, 1997; Pajares, 1996; Wheatley, 2005; Wyatt, 2014). Without such obstacles, the

interpretation of TSE may be ambiguous, as teachers are likely to base their responses on

imagined students or situations. For instance, teachers may respond confidently to such TSES-

items as “How much can you do to get children to follow classroom rules?” (Tschannen-

Moran & Woolfolk Hoy, 2001), but may reply far less self-confident when the question is

“How much can you do to get disruptive children in your classroom to follow classroom

rules?”. Hence, obstacles, such as disruptive children in this case, may avoid teachers to



85

become naïvely optimistic about their self-efficacy beliefs, and may ameliorate the predictive

validity of TSE (Bandura, 1997, 2006; Wheatley, 2005; Wyatt, 2014).

Defining obstacles may present, as Tschannen-Moran and Woolfolk Hoy (2001, p. 794) aptly

point out, “thorny issues”, as they may substantially increase the complexity of each individual

item. In existing measures of TSE, most of the items usually lack such clear obstacles. Yet,

some attempts have been made to include them in a handful of TSES-items. All these

embedded challenges extend to student characteristics or behaviors, including ‘very capable

students’, ‘problem students’, ‘students who show low interest in schoolwork’, and ‘students

who are failing’ (Tschannen-Moran & Woolfolk Hoy, 2001). Thus, the gradations of challenge

to teachers’ performance are likely to be mainly determined by individual students’ behaviors

and actions.

The idea that obstacles are predominantly reflected in student characteristics fits fairly well

with the assumption that TSE may vary across different students. Moreover, with this assertion

comes a way to resolve the persisting issue of how situational impediments should be defined.

By letting teachers report on their self-efficacy for individual students, it becomes possible to

specify the forms the impediments take, without unnecessarily complicating individual self-

efficacy items, or limiting the generalizability of the TSES or other domain-specific self-

efficacy instruments. In addition, through this particular manner of specifying obstacles,

teachers may be less likely to respond in a socially desirable direction, as they may rather

ascribe their low self-efficacy to characteristics of particular students, than to their incompetent

self.

PRESENT STUDY

From the social-cognitive paradigm, it follows that TSE is best approached by capturing the

teaching domains and students that generate inter- and intra-individual differences in teachers’

capability beliefs. Such domain-linked and student-specific self-efficacy beliefs may generally be

more predictive of specific teacher behaviors and actions, due to the variations in self-efficacy

percepts that occur across different task domains and specific students. Unfortunately,

however, conceptual and methodological issues have largely prevented researchers to take such

conditional self-efficacy states into consideration. This study, therefore, set out to explore

teachers’ sense of self-efficacy across various domains of teaching and learning and particular

students. To this end, we took Tschannen-Moran and Woolfolk Hoy’s (2001) original TSES to


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the domain- and student-specific level by making its individual items student-specific and

including the domain of emotional support.

Initially, we examined the multilevel factor structure of the adapted instrument to explore

inter-individual (trait) differences in TSE at the between-teacher level and intra-individual

(state) differences at the within-teacher level. Largely consistent with the original TSES, we

expected to find empirical support for a four-factor multilevel model, representing the TSE

domains of Instructional Strategies, Behavior Management, Student Engagement, and

Emotional Support. To meaningfully compare domain- and student-specific TSE across

teachers, we subsequently tested for violations of measurement invariance over clusters, or

cluster bias (Jak, Oort, & Dolan, 2013, 2014). In the present study, the absence of cluster bias

would indicate that teachers’ self-efficacy reports are likely to measure the same constructs

across educators, and that its hypothesized domains at the between-teacher level can be

perceived as the aggregate of the within-teacher level dimensions (Jak et al., 2014). As such, it

can also be expected that our adapted instrument is likely to show moderate to strong

correspondence with the original TSES, providing evidence for the concurrent validity of this

measure.

METHOD

PARTICIPANTS

The participants in the present study included regular elementary school teachers and their

students drawn from third- to sixth-grade classrooms in the Netherlands. After ethical

approval from the Ethics Review Board of the Faculty of Social and Behavioral Sciences,

University of Amsterdam, was granted (project no. 2013-CDE-3188), approximately 700

schools across the Netherlands were drawn from the total pool of 6800 regular Dutch

elementary schools. To promote the sample’s representativeness with respect to the variables

measured in our study, we aimed at selecting a wide range of schools that were

demographically diverse in terms of geographical spread, denomination, school size, urbanicity,

and characteristics of the student population.

Of the schools that were initially invited, 42 ultimately agreed to take part in the study. This

sample of schools appeared to represent a relatively balanced cross-section of the larger

population of schools in the Netherlands (see Table 1). Non-participation was mainly due to



87

schools’ already full agendas, or their involvement in other research studies. After schools

agreed to participate, information letters about the nature and purposes of the study were sent

to all teachers who taught in the upper elementary grades, soliciting their voluntary

participation in the study. On average, three teachers per participating school (range = 1 – 8

teachers; participation rate = 70.8%) expressed their interest in participation, resulting in an

original sample of 113 teachers. Teachers who refrained from participation generally were

substitute teachers and educators with additional tasks and responsibilities, including

mentoring, coordinating, or remedial teaching tasks. Of the original sample of 113 teachers, six

(5.3%) additionally failed to complete all questionnaires due to long-term absence, sickness, or

strenuous workloads. Given that these data were not missing completely at random, we

decided to exclude those cases from analyses.

TABLE 1

Demographic Characteristics of Participating Schools

Total Sample Total Population N Percentage Percentage Geographical region

North East

South West

6

12 10 14

14.3% 28.6% 23.8% 33.3%

10.1% 22.5% 19.8% 47.6%

Denomination Public school

Protestant Christian school Catholic school

Other

19 10 10 3

45.3% 23.8% 23.8% 7.1%

33.0% 30.0% 29.0% 8.0%

School size < 101 students

101-201 students 201-501 students

> 501 students

5

16 17 4

11.9% 38.1% 40.5% 9.5%

18.9% 31.7% 44.9% 4.5%

Urbanicity Urban

Peri-Urban Rural

16 15 11

38.1% 35.7% 26.2%

– – –

Note. Demographic data for the total population of Dutch elementary schools are retrieved from CBS Statline (2015b).


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TEACHER SAMPLE

Complete data were available for 107 teachers (73.5% females), ranging in age from 20 to 63

years (M = 42.02, SD = 12.36). On average, teachers had 16.58 years (SD = 11.58) of

professional teaching experience, with the least experienced teacher working only half a year in

primary education, and the most experienced teacher having a 44-year teaching career. These

demographic characteristics are comparable to those of the larger population of Dutch

teachers, who generally have a mean age of 43.25 years (range = 19 – 67 years), and are

typically female (84%; DUO, 2014).

Some past empirical research has suggested that teachers’ years of professional experience may

positively add to their sense of self-efficacy (e.g., Klassen & Chui, 2010; Morris-Rothschild &

Brassard, 2006). Other studies, however, have indicated that TSE is likely to decrease over time

(Cantrell et al., 2003) or may not at all be associated with teaching experience (e.g., Gaith &

Yaghi, 1997; Soodak & Podell, 1996). In the present sample, analyses of variance showed that

teachers with little experience (<5 years), average experience (5 – 10 years), or high experience

(>10 years) did not differ in their domain- and student-specific self-efficacy beliefs, p > .05.

STUDENT SAMPLE

For the student sample, both the first and fourth authors randomly selected four boys and four

girls from each teacher’s classroom. This sample contained children from grades 3 (n = 54), 4

(n = 262), 5 (n = 270) and 6 (n = 255), respectively. The students ranged from 7 to 13 years of

age (M = 10.83, SD = 1.04) and the gender composition was evenly distributed with 420 boys

(49.9%) and 421 girls (50.1%). Most students had a Dutch origin (73%), with the remaining

27% of students representing other ethnic backgrounds. Based on teacher reports of parents’

working status and educational level, most students were considered to have an average to high

socioeconomic status. Both parents were employed in 65.9% of the families, 27.5% had at least

one employed parent, and only 4.9% of the families included two unemployed parents. In

addition, teachers indicated the majority of the parents to have finished senior vocational

education (48.8%) or higher education (39.3%), leaving 9% of the parents to only have finished

primary education. For less than 3% of the students, teachers failed to provide information on

parents’ working status and educational background.

The student sample appeared to be relatively similar to the larger population of third- to sixth-

graders in the Netherlands in terms of gender (50.5% male students) and ethnicity (15% non-



89

Dutch origin; CBS Statline, 2015a, 2015b). Moreover, previous studies using nationally

representative elementary school samples (e.g., Hornstra, van der Veen, Peetsma, & Volman,

2013; Zee, Koomen, & van der Veen, 2013) reported demographic characteristics for third-

and sixth-graders that resemble those of the students included in the present study. Hence,

although the participating schools, teachers, and students cannot be considered to be fully

representative in this study, they seem to reasonably approximate the larger population.

INSTRUMENTS

OVERALL TEACHER SELF-EFFICACY

Teachers’ perceptions of their overall level of self-efficacy were measured using a short, 12-

item version of Teachers’ Sense of Efficacy Scale (TSES; Tschannen-Moran & Woolfolk Hoy,

2001). The TSES is specifically designed to evaluate teachers’ perceptions of their competence

across a variety of important teaching tasks. Analogous to the original 24-item instrument, the

short TSES has been evidenced to comprise three interrelated dimensions of teacher self-

efficacy, which are labeled Instructional Strategies (IS), Classroom Management (CM), and

Student Engagement (SE). The domain of IS (4 items) measures the extent to which teachers

feel able to use various instructional methods that enable and enhance student learning. The

CM domain (4 items) taps teachers’ perceptions of their ability to organize and guide students’

behavior. TSE for SE (4 items) captures teachers’ perceived ability to activate students’ interest

in their schoolwork. Example items for each of these domains of TSE include “To what extent

can you provide an alternative explanation or example when students are confused?”, “How

much can you do to get children to follow classroom rules?”, and “How much can you do to

help your students value learning?”, respectively. Although the TSES is usually measured on a

9-point rating scale, teachers in the present study responded on a 7-point rating scale, ranging

from 1 (nothing) to 7 (a great deal). Reason to deviate was that prior research (e.g., Diefenbach,

Weinstein, & O’Reilly, 1993) has indicated that 7-point scales generally outperform 2-, 5-, 9-,

11-, 12-, and 100-point scales on accuracy, perceived ease of use, and agreement of scale-

derived ranks with direct rankings.

The psychometric properties of the short form of the TSES have been shown to be adequate

and largely comparable to those of the long form (e.g., Tschannen-Moran & Woolfolk Hoy,

2001). In prior research, alpha coefficients ranged between .71 and .87 for IS, .83 and .94 for

CM, and .74 and .88 for SE, respectively (e.g., Klassen et al., 2009; Tschannen-Moran &


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Woolfolk Hoy, 2001). In addition to these adequate alpha coefficients, Klassen and colleagues

(2009) found evidence of strong structural and measurement invariance in groups of teachers

who differed on language, cultural practices and beliefs, teaching environment, and school

level. Correlations between the TSES dimensions and adjacent constructs, including personal

efficacy, teaching efficacy and job satisfaction, also lend credence to the convergent and

concurrent validity of the short TSES (Klassen et al., 2009; Tschannen-Moran & Woolfolk

Hoy, 2001). Together, these reliability and validity assessments seem to support the

appropriateness of the short TSES for use in different contexts.

To evaluate the reliability and factorial validity of the short TSES in the present study, we

performed a confirmatory factor analysis for complex survey data, using robust maximum

likelihood estimation (MLR) in Mplus 7.11 (Muthén & Muthén, 1998-2012). This method takes

the non-independence of data due to clustering into account, and provides a mean-adjusted

chi-square test and standard errors that are robust for non-normality (Muthén & Muthén,

1998-2012; Yuan & Bentler, 2000). Both a three-factor solution (χ2(50) = 93.20, p < .001,

RMSEA = .033 (90% CI [.022, .043]), CFI = .89, SRMR = .076) and a one-factor solution

(χ2(43) = 77.12, p < .001, RMSEA = .031 (90% CI [.020, .043]), CFI = .89, SRMR = .066)

yielded a reasonable fit, after adding a theoretically plausible correlation residual to both

models. Although the CFI was below the conventional threshold of .90 for satisfactory fit, the

model showed quite sound goodness of fit according to established cutoff values of .08 for the

RMSEA and SRMR (Bentler, 1992; Browne & Cudeck, 1993; Hu & Bentler, 1999; Kline,

2011). Factor loadings ranged between .47 and .85 in the three-factor model, and between .37

and .79 in the one-factor solution. Alpha coefficients were .84 for Overall TSE, .71 for IS, .76

for CM and .77 for SE, respectively.

DOMAIN- AND STUDENT-SPECIFIC TEACHER SELF-EFFICACY

To measure teachers’ self-efficacy toward particular children in different domains of

functioning, we developed a new instrument, based on the original, 24-item TSES of

Tschannen-Moran & Woolfok Hoy (2001). The adaptation process began with the adjustment

of the original TSES items to the student-specific level (see Appendix 1). For instance, the item

“How much can you do to get children to follow classroom rules?” was changed into “How

much can you do to get this student to follow classroom rules?”. Classroom Management items

12 (“How well can you establish a classroom management system with each group of

students?”) and 16 (“How well can you establish routines to keep activities running



91

smoothly?”) of the original scale were omitted, as they could not be accurately made specific to

the level of individual students. Notably, whereas all other adapted items of this scale

concentrated on teachers’ perceived ability to manage the behavior of individual students,

items 12 and 16 mainly focused on aspects of classroom management. As such, these two

items also reflected a slightly different construct. In addition, several original TSES items

(items 8, 13, 19, and 21) included embedded obstacles that embody gradations of challenge to

teachers’ tasks in a given teaching domain. Examples of such obstacles are “very capable

students” (item 8), “problem students” (item 13), “students who show low interest in

schoolwork” (item 19), and “students who are failing” (item 21). By evaluating teachers’ self-

efficacy beliefs in relation to individual students, however, it becomes possible to specify the

forms the impediments take in all TSES items, without unnecessarily complicating these items.

In the process of making the original TSES items student-specific, we therefore consistently

removed all embedded obstacles from items 8, 13, 19, and 21.

After adjusting the original TSES items to be student-specific, we further shortened the

original TSES by removing four less relevant items. The first item (“To what extent can you

use a variety of assessment strategies?”) was discarded because this item was not representative

of the regular teaching tasks of Dutch elementary school teachers. Furthermore, this item

appeared to have one of the poorest factor loadings in samples of elementary school teachers

(e.g., Heneman et al., 2006; Klassen et al., 2009), suggesting that this item may be more

relevant for secondary school teachers. The main reason to remove the fifth item (“How well

can you respond to difficult questions from your students?”) involved the ambiguous nature of

this item. Specifically, this item might either relate to students’ difficulties regarding instruction

or learning tasks, or refer to issues of a more personal nature, such as family problems.

Probably, this ambiguity is also reflected in the relatively low factor loading of this item in

previous research (e.g., Wolters & Daugherty, 2007; Tschannen-Moran & Woolfolk Hoy,

2001). This may explain why this item is not part of the short form of the original TSES.

Additionally, after adjusting the level of specificity of item 14 (“How well can you respond to

defiant students?”), a substantial overlap between this question and other Classroom

Management items was recognized. Therefore, this item was removed as well. Given that the

reported factor loadings of Classroom Management items are usually quite substantial (> .70),

and of roughly equal magnitude (e.g., Heneman et al., 2006; Klassen et al., 2009; Wolters &

Daugherty, 2007), the removal of this item did probably not affect the consistency of this scale.


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Lastly, item 20 (“How much can you assist families in helping their children do well in

school?”) was considered to have too little in common with the domain of Student

Engagement. Moreover, prior research reporting on the factor structure of the TSES

consistently showed that this item is least stable and has the poorest factor loading in general

(Heneman et al., 2006; Klassen et al., 2009; Wolters & Daugherty, 2007). The removal of six

items in total (in stage one and two) resulted in a total of 18 adapted items (6 IS, 5 CM, and 7

SE items) that were retained in the new instrument.

Subsequent to adapting the three original TSES domains, we used the CLASS framework (for

an overview, see Hamre et al., 2013 and Pianta et al., 2008) to construct seven new items that

aimed to cover the domain of Emotional Support. These items were based on the common

metric used to describe positive dimensions of the CLASS-domain of Emotional Support,

including Positive Climate, Teacher Sensitivity, and Regard for Student Perspectives (Pianta et

al., 2008). Three items concerned teachers’ perception of their ability to establish a warm

connection with individual students (Positive Climate). Two other pairs of items measured

teachers’ perceived ability to be aware of, and responsive to individual students’ academic and

emotional needs (Teacher Sensitivity) and to emphasize students’ viewpoints and interest

(Regard for Student Perspectives). The addition of these items resulted in a 25-item

instrument, reflecting the domains of Instructional Strategies (IS; 6 items), Behavior

Management (BM; 5 items), Student Engagement (SE; 7 items), and Emotional Support (ES; 7

items), respectively. Largely similar to the original TSES, responses to each of these items were

given on a seven-point rating scale, ranging from 1 (nothing) to 7 (a great deal).

The translation of the TSES, lastly, was performed using a standard forward-backward

procedure, involving two forward translators and one backward translator. In the first step of

the translation process, the first and second author, both native Dutch speakers, independently

translated the original English version of the TSES into the Dutch language. After the

translations were completed, they compared all items, and critically evaluated them on

parameters like difficulties in translation, doublets of items, and relevance for the Dutch school

context. Any discrepancies between the two translations were solved by consensus with the

other authors. This process resulted in a single conditional forward translation of the student-

specific TSES, which offered some alternative wordings for (parts of) items that appeared to

be difficult to translate, and included the seven new items on Emotional Support. This



93

provisional version was back-translated by a native English speaker from Dutch into English,

and checked by the first author.

In the second step of the translation process, the student-specific TSES was pilot tested with

six elementary school teachers, who reviewed the items for content validity, clarity of wording,

and relevance of the response scale. Based on their analysis, the first two authors slightly

reworded one adapted TSES-item (item 1) that was deemed too complex, without altering its

meaning.

PROCEDURE

Data for this study were collected between January and March 2014. Prior to data collection,

participating schools were asked to distribute a letter to students’ parents, explaining the nature

and purposes of the study and providing a form to refuse permission, which could be returned

to school. All parents voluntarily gave their consent to their child’s participation in this study.

Participating teachers signed a written informed consent form at the start of data collection.

To avoid common method variance, teacher survey data were collected in two parts. The first,

written part of the survey was administered during a planned school visit, and consisted of

demographic items and the short TSES, respectively. Teachers who were not present at the

time of data collection could return the survey by regular mail. The second part of the survey

was distributed directly after the school visit, by sending an e-mail invitation that contained an

anonymous survey link. This digital survey, which was completed for eight randomly selected

students from teachers’ classrooms, had a forced response format and involved the newly

developed student-specific TSES, and some general questions regarding parents’

socioeconomic status. Teachers were asked to return the digital survey within two weeks after

the invitation was sent. To improve the participation rate, reminders were sent to non-

responding teachers. Ultimately, six teachers failed to fill out the survey and another four

teachers completed the survey for less than eight students, due to time constraints. This

resulted in a total response rate of 94.6%.

DATA ANALYSIS

We used multilevel confirmatory factor analysis (MCFA) to test the factor structure of the

student-specific TSES. With this analytic technique, model fit and parameter estimate biases


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can be avoided by decomposing the total sample covariance matrix into a pooled within-group

(ΣWITHIN) and a between-group (ΣBETWEEN) covariance matrix (Muthén, 1994). In addition, MCFA

is well suited to detect violations of measurement invariance across clusters, or cluster bias, in

multilevel data (Jak et al., 2013, 2014). This relatively new technique is particularly useful when

collecting the same measure from qualitatively different groups or individuals operating in

distinct contexts, as it aims to take differences in response processes into account (Muthén &

Asparouhov, 2013; Ryu, 2014). Generally, cluster bias indicates that teachers might answer

differently on the self-efficacy items, despite having similar beliefs in their capability. These

systematic differences in observed self-efficacy scores seem to occur when contextual factors

or personal teacher characteristics implicitly affect teachers’ interpretation of self-efficacy

items. Thus, in this study, the presence of cluster bias would indicate that the dimensions of

the student-specific TSES do not measure the same constructs over teachers, and that part of

the variance in teachers’ student-specific self-efficacy beliefs may be attributed to teacher

and/or classroom characteristics.

MODELING PROCEDURE

In line with Jak and colleagues’ (2014) strategies for the investigation of cluster bias, we

followed four analytical steps. First, to determine whether multilevel modeling was required,

we calculated the intraclass correlation coefficients (ICC) for each of the model’s indicators

and tested whether the between-teacher level variance and covariance deviated significantly

from zero. To this end, we fitted a Null Model (ΣBETWEEN = 0, ΣWITHIN = free) and an

Independence Model (ΣBETWEEN = diagonal, ΣWITHIN = free) to the data (Jak et al., 2013, 2014;

Muthén, 1994). Generally, poor fit of these models are indicative of meaningful between-

teacher level variance and covariance (Hox, 2002).

In step two, we first conducted a confirmatory factor analysis on the sample pooled-within

covariance matrix to determine the factor structure at the within-group level only (Dyer,

Hanges, & Hall, 2005; Hox, 2002; Muthén, 1994). Apart from the proposed four-factor model,

we also considered several alternative models, including one-factor and three-factor solutions,

and Tschannen-Moran and Woolfolk Hoy’s (2001) original three-factor and higher-order

factor models, to determine potential sources of model misspecification.

In the third step, we used the measurement model that was established in step two to

investigate cluster bias. We started with a fully constrained model, in which all factor loadings



95

were constrained to be equal across the within- and between-teacher level, and residual

variances at the between-teacher level were fixed at zero. To test whether strong factorial

invariance held across clusters, we sequentially allowed the between-teacher level residual

variances to be freely estimated. Generally, residual variances greater than zero are indicative of

cluster bias in their corresponding indicators (Jak et al., 2013, 2014). Subsequently, we

evaluated whether factor loadings could be considered equal across clusters. Unequal factor

loadings indicate that the unique domains of student-specific TSE at the between-teacher level

cannot merely be assumed to be the within-teacher level factor’s aggregates.

Finally, in the fourth step, we fitted a restricted factor analysis (RFA; Oort, 1992) to investigate

the concurrence between Tschannen-Moran and Woolfolk Hoy’s (2001) original TSES and the

student-specific TSES. To this end, we extended the multilevel measurement model to include

correlations between the generalized TSES and the student-specific TSES at the between-level

of measurement. Depending on the presence of cluster bias, we expected a moderate to strong

correspondence between the original and student-specific TSES.

MODEL GOODNESS-OF-FIT

Multilevel models were fitted in Mplus 7.11, using robust maximum likelihood estimation

(MLR; Muthén & Muthén, 1998-2012). This method of estimation offers a mean-adjusted χ2,

which is asymptotically equivalent to Yuan and Bentler’s (2000) T2-test statistic and generates

adjusted standard error estimates that are robust for non-normality (Muthén & Muthén, 1998-

2012). Generally, the adjusted χ2 test statistic indicates a good overall model fit when it does

not reach the significance threshold. However, as even trivial discrepancies between the

expected and the observed model may lead to the model’s rejection (Chen, 2007), other criteria

in evaluating fit were used as well. These included the root mean square of approximation

(RMSEA) and standardized root mean square residual (SRMR), with values ≤.05 reflecting a

close fit, and ≤.08 a satisfactory fit (Browne & Cudeck, 1993; Hu & Bentler, 1999; Kline,

2011), and the comparative fit index (CFI), with values ≥.95 indicating close fit, and values

≥.90 indicating acceptable fit (Bentler, 1992). To compare alternative models, we employed the

(Satorra–Bentler scaled) chi-square difference test (TRd; Satorra, 2000; Satorra & Bentler,

2010), with non-significant chi-squares indicating equivalent fit, and the CFI-difference, with

CFI changes ≥.02 being indicative of model nonequivalence (Cheung & Rensvold, 2002).


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RESULTS

DATA SCREENING AND DESCRIPTIVE STATISTICS

Inspection of the distributional properties of both the total score and the three subscales of the

original, overall TSES-domains revealed no serious departures from normality and linearity.

Skewness levels were −0.20 for Overall TSE, −0.54 for IS, −0.78 for CM, and −0.30 for SE,

and kurtosis values −0.42 for Overall TSE, −0.01 for IS, 0.71 for CM, and 0.22 for SE,

respectively. Teachers’ mean responses on the original TSES, reported on a 7-point scale, were

lowest for SE (M = 5.46, SD = 0.69), followed by IS (M = 5.67, SD = 0.65), and CM (M =

5.90, SD = 0.67). The mean total score of teachers’ Generalized Self-Efficacy was 5.70 (SD =

0.52). These relatively high means and small standard deviations are consistent with previous

findings (e.g., Heneman et al., 2006; Tschannen-Moran & Woolfolk Hoy, 2001).

Teachers’ responses on the Student-Specific TSES domains of IS and SE were approximately

normally distributed. In these domains, most items did not reach the skewness threshold of ±

1.00 (range = −0.63 to −1.07 for IS and −0.64 to −1.19 for SE). Moreover, kurtosis values

ranged from −0.17 to 1.62 for items comprising the IS domain, and from 0.00 to −1.16 for

SE-items. Items appeared to be highly skewed, however, in the domains of BM (range = 1.16

to −1.84) and ES (range = −0.75 to −1.41), and were characterized by high kurtosis (range =

1.16 to 3.82 for BM and 0.35 to 2.32 for ES). To deal with these high skewness levels, we used

robust maximum likelihood estimation to obtain parameter estimates (Muthén & Muthén,

1998-2012), as this estimator is robust to non-normality and enables the adjustment of

standard errors.

Table 2 displays the means, within-teacher standard deviations, and between-teacher standard

deviations of the Student-Specific TSES items. The descriptive statistics indicate that all item

means were relatively high and largely comparable with the averages found for the original

TSES domains. Notably, the highest item means were found for items comprising the BM and

ES domains of Student-Specific Self-Efficacy. Inspection of the partitioned standard

deviations, which provide an indication of Self-Efficacy differences within and between

teachers, furthermore shows that there is more variability within teachers than between

teachers. This is in line with Bandura’s (1997) premise that self-efficacy is more likely to reflect

a dynamic state, than a relatively stable trait.



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TABLE 2

Item Means and Standard Deviations of the Student-Specific TSES

Item M SDwithin SDbetween ICC TSE for Instructional Strategies

IS1 5.87 0.83 0.54 .30 IS2 5.46 1.11 0.61 .23 IS3 5.43 1.05 0.60 .25 IS4 5.71 0.87 0.55 .29 IS5 5.83 0.96 0.53 .24 IS6 5.43 1.01 0.59 .25

TSE for Behavior Management BM1 6.07 1.17 0.36 .09 BM2 6.13 1.11 0.39 .11 BM3 6.18 1.08 0.37 .11 BM4 6.15 1.08 0.41 .13 BM5 6.30 0.84 0.44 .21

TSE for Student Engagement SE1 5.87 0.93 0.50 .23 SE2 5.72 1.19 0.49 .15 SE3 5.72 1.21 0.52 .16 SE4 5.67 1.11 0.51 .18 SE5 5.46 1.11 0.63 .24 SE6 5.26 1.05 0.69 .31 SE7 5.81 1.10 0.45 .14

TSE for Emotional Support ES1 6.30 0.81 0.38 .19 ES2 6.20 0.77 0.44 .25 ES3 6.12 0.79 0.49 .28 ES4 5.81 0.92 0.63 .32 ES5 5.63 0.90 0.62 .32 ES6 5.65 0.92 0.68 .36 ES7 5.43 0.98 0.64 .30

Note. Item means are reported on a 7-point scale. TSE = teacher self-efficacy.

MULTILEVEL CONFIRMATORY FACTOR ANALYSIS OF THE STUDENT-SPECIFIC TSES

STEP 1: EVALUATING BETWEEN-TEACHER LEVEL VARIANCE AND COVARIANCE

The intraclass correlations (ICCs) for the Student-Specific TSES items (see Table 2) ranged

between .09 (item 7) and .36 (item 24), with a mean ICC of .23. Fit indices of the Null Model,

χ2(302) = 3244.27, RMSEA = .108, CFI = .77, SRMRWITHIN = .10, SRMRBETWEEN = .65, and the


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Independence Model, χ2(278) = 1898.58, RMSEA = .083, CFI = .88, SRMRWITHIN = .09,

SRMRBETWEEN = .65, suggested that there is meaningful between-teacher level variance and

covariance. Hence, these clustering effects were substantial enough to warrant the use of

MCFA.

STEP 2: EVALUATING THE MEASUREMENT MODEL AT THE WITHIN-TEACHER LEVEL

Using the sample pooled-within covariance matrix, we examined the hypothesized four-factor

model. The overall fit of the model was reasonable, with RMSEA and SRMR values below .08

and a CFI greater than .90, χ2(269) = 1484.15, p < .001, RMSEA = .073 (90% CI [.070–.077]),

CFI = .91, SRMR = .05. To diagnose systematic patterns of misfit, we inspected the model’s

modification indices. These indices suggested model improvement by adding a correlation

between the residuals of SE-items 13 (“To what extent can you help this student to value

learning?”) and 14 (“To what extent can you motivate this student for his/her schoolwork?”).

These two items showed a considerable conceptual overlap, both focusing on teachers’

perceived capability to motivate individual students for their schoolwork. Following

Tabachnick and Fidell’s (2007) cut-off criteria, we additionally removed item 22 (“To what

extent can you timely recognize that this student does not feel well?”), which loaded poorly on

its corresponding factor (<.40). These alterations resulted in a satisfactory fit to the data:

χ2(245) = 1229.13, p < .001, RMSEA = .069 (90% CI [.065–.073]), CFI = .93, SRMR = .05.

Alternative models

Although the fit of the hypothesized model was acceptable, there might be alternative models

that generate roughly similar, or even better predicted covariances (Kline, 2011). To justify the

appropriateness of the hypothesized model, we therefore examined a series of theoretically

plausible competing models, including one-factor, three-factor, and higher-order factor

models.

The first two competing models tested were a one-factor model and a three-factor model, in

which the SE and ES dimensions were combined to create a single Engaging Strategies factor.

Comparison of the four-factor model with these one-factor, Δχ2(30) = 2407.90, p < .001, ΔCFI

= .17, and three-factor alternatives, Δχ2(27) = 345.72, p < .001, ΔCFI = .02, indicated that

both alternative models had a poorer fit to the data, and had slightly worse structural parameter

estimates. These results lend credence to the proposed four-factor structure of the student-

specific TSES.



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Secondly, we evaluated whether the original factor structure proposed by Tschannen-Moran

and Woolfolk Hoy (2001) held in the present sample. To this end, we fitted a three-factor

model in which all Emotional Support items were omitted. This model obtained an acceptable

fit, χ2(132) = 708.87, p < .001, RMSEA = .072 (90% CI [.067–.077]), CFI = .95, SRMR = .04.

The results of this model suggest that the additional domain of Emotional Support can be

distinguished from the original TSES-domains and may provide information about teachers’

perceived capabilities that goes above and beyond their Self-Efficacy for Instructional

Strategies, Behavior Management, and Student Engagement.

Thirdly, and largely consonant with Tschannen-Moran and Woolfolk Hoy’s findings, we

considered a hierarchical factor model, in which one second-order factor of teachers’ General

Self-Efficacy beliefs toward particular students was hypothesized to underlie the four proposed

TSE domains of teaching and learning. Such higher-order models are particularly relevant

when hypothesizing general constructs that comprise several closely related domains (Chen,

West, & Sousa, 2006). Although this model fitted the data reasonably well, the χ2 difference test

statistic suggested that the hypothesized four-factor model is to be preferred over its higher-

order equivalent, Δχ2(2) = 107.54, p < .001, ΔCFI = .01. Based on these comparisons, we

gleaned that the proposed four-factor model is most likely the preferred solution.

STEP 3: DETECTING VIOLATIONS OF MEASUREMENT INVARIANCE ACROSS CLUSTERS

In the third step, we established a measurement model at both the within-teacher (state) and

between-teacher (trait) level, resulting in a poor overall fit, χ2(540) = 2817.20, p < .001,

RMSEA = .071, CFI = .83, SRMRWITHIN = .075, SRMRBETWEEN = .290. Similar to the within-

teacher level model, this baseline model appeared to poorly explain the observed correlation

between items 13 and 14, indicating that a correlation between the residuals of these items may

be required. In tests of cluster bias, however, residual variances on the between-teacher level

have to be fixed at zero, while constraining the factor loadings at the within- and between-

teacher level to be equal (Jak et al., 2014). To obtain an estimate of this residual covariance, we

therefore re-parameterized the measurement model by allowing items 13 and 14 to load on an

additional factor, which is uncorrelated to the four Student-Specific Self-Efficacy domains.

Moreover, we fixed the factor loading of these two items at one, such that the obtained factor

variance equals the estimate of the residual covariance (Jak, 2014).


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Although the re-parameterized, fully constrained four-factor model significantly improved on

the baseline model, TRd(2) = 307.09, ΔCFI = .02, it did not converge to an admissible solution

and yielded an unacceptable fit, χ2(538) = 2559.23, p < .001, RMSEA = .069, CFI = .85,

SRMRWITHIN = .075, SRMRBETWEEN = .283. Generally, the pattern of discrepancies between the

model and the data indicated that strong factorial invariance across teachers does not hold.

Moreover, the substantial factor correlations (see Table 3) suggested that models with fewer

latent factors might provide a more plausible alternative. Based on the model’s parameters and

theory, we therefore successively fitted a one-factor model, a three-factor model in which the

IS and SE domains were combined, and a three-factor model in which the ES and SE domains

were combined. Neither the one-factor solution, TRd(12) = 2255.24, ΔCFI = .20, nor both

three-factor alternatives, TRd(6) = 74.66, ΔCFI = .01; TRd (6) = 94.72, ΔCFI = .01,

significantly improved the model’s fit.

TABLE 3

Estimated Correlations for the Latent Factors

1 2 3 4 5 1. TSE for Instructional Strategies 1.00 2. TSE for Behavior Management .59 1.00 3. TSE for Student Engagement .98 .60 1.00 4. TSE for Emotional Support .95 .57 .95 1.00 5. General TSE .99 .60 .99 .96 1.00

Note. All correlations are statistically significant (p < .001). TSE = teacher self-efficacy.

Given that TSE likely resides along a continuum from domain generality to domain- and

student specificity, we explored whether the four specified domains of teachers’ Self-Efficacy

toward particular students may be accounted for by one common underlying higher-order

construct of General Self-Efficacy. This model with four first-order factors and one second-

order factor showed no convergence problems and had a slightly better fit than the one-factor,

TRd(5) = 2740.44, ΔCFI = .00, and three-factor alternatives, TRd(1) = 12.44, ΔCFI = .00;

TRd(1) = 41.80, ΔCFI = .00.

Taking the model with four first-order factors and one second-order factor as a baseline, we

subsequently tested the significance of the between-teacher level residual variances. Based on

the modification indices, we successively freed 18 of 24 residual variances, resulting in a



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statistically significant improvement of model fit, TRd(18) = 2818.49, ΔCFI = .05. Further

improvement of fit was established by allowing the factor loadings of nine items (4, 7, 11, 13,

14, 15, 17, 18, 19) to be freely estimated across teachers. These factor loadings were all more

indicative of higher Student-Specific TSE at the between-teacher level, suggesting that the

domains of TSE, and especially Student Engagement, do not have the same interpretation

across teachers. Hence, these violations of measurement invariance across clusters suggest that

the domains of Student-Specific TSE at the between-teacher level cannot merely be assumed

to be the within-teacher level factor’s aggregates. The final, partially constrained model, had an

acceptable fit to the data, χ2(518) = 1864.92, p < .001, RMSEA = .056, CFI = .90, SRMRWITHIN

= .068, SRMRBETWEEN = .152. The standardized factor loadings of the final model are depicted in

Figure 1.

STEP 4: EVALUATING THE CONCURRENCE BETWEEN THE GENERALIZED AND STUDENT-SPECIFIC TSES

To investigate the concurrence between the Generalized and Student-Specific TSES, we

allowed the total score of the Generalized TSES to correlate with the second-order common

Self-Efficacy factor at the between-teacher level of the final model from step 3 (see Figure 1).

Addition of this correlation resulted in a satisfactory model fit, χ2(541) = 1941.95, p < .001,

RMSEA = .055, CFI = .90; SRMRWITHIN = .068, SRMRBETWEEN = .151. Although the chi-square

value of this model indicated a statistically significant lack of fit, the CFI of .90 was reasonable,

and the RMSEA of .055 and SRMRWITHIN of .068 were smaller than Hu and Bentler’s (1999)

cutoff value of .08, suggesting acceptable fit. The SRMRBETWEEN value of .151 indicated that the

component fit of the between part was slightly worse than the within part of the model. This

poorer fit at the between-level has been noted by previous research as well (cf. Dyer et al.,

2005). Assessment of the correlation coefficient pointed to a statistically association between

generalized TSE and teachers’ student-specific TSE, r = .59, p < .001). This association

suggests a moderate correspondence between the original TSES and the adapted, student-

specific TSES.


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DISCUSSION

Since long, empirical studies have mainly equated teachers’ sense of self-efficacy with a

relatively stable omnibus trait that generates inter-individual differences between teachers.

Following the basic tenets of social-cognitive theory, however, TSE could also be considered

to embody domain-linked cognitive states that depend on challenges presented by particular

students (e.g., Bandura, 1997; Tschannen-Moran et al., 1998). As such, a premium has been

placed on the effort to disentangle within-teacher fluctuations in TSE across various teaching

tasks, domains, and students (Raudenbusch et al., 1992; Ross et al., 1996; Tschannen-Moran et

al., 1998). The present study is one of the first to come to grips with trait- and state-variability

in TSE, by evaluating these capability beliefs in relation to particular students, and across

various domains of teaching and learning. Recognizing the existence of both inter- and intra-

individual differences in TSE has important theoretical and practical implications for the

investigation of TSE.

DOMAIN SPECIFICATION OF TSE

In line with prior theory and research (e.g., Bandura, 1997; Lent & Brown, 2006; Tschannen-

Moran et al., 1998; Tschannen-Moran & Woolfolk Hoy, 2001), we hypothesized teachers’ self-

efficacy beliefs to reside along a continuum from domain generality to domain specificity. The

present study’s findings generally afforded credence to this idea. Initially, evidence was found

for the presence of a single, higher-order construct that potentially reflects teachers’

generalized sense of self-efficacy. This common factor of general TSE took the commonality

among the lower-order domains of self-efficacy into consideration, thereby providing a strong

rationale for the unidimensional total score of these capability beliefs. In their seminal study,

Tschannen-Moran and Woofolk Hoy (2001) have also found evidence for such a second-order

construct of teacher self-efficacy, which accounted for 75% of the variance and showed a high

internal consistency (α = .94).

In our study, the substantial factor loadings of the generalized teacher self-efficacy factor

indicated that between 21% and 98% of the variance is shared between the TSE domains at

the lower level of the structural hierarchy. Still, the markedly poorer fit of a first-order single

factor solution, as well as several other plausible alternatives, suggested that specific

dimensions of TSE can be distinguished. Markedly, the strongest support was found for the

unique domain of behavior management, which evaluates the extent to which teachers feel able


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to promote positive behavior in a particular child. The interrelationships between this factor

and other domains of self-efficacy were moderate, suggesting that tasks and capabilities related

to behavior management may be relatively distinct from other core responsibilities, such as

providing the instructional, motivational, and emotional supports that generate gains in

learning. As such, these results substantiate previous findings from related studies, in which the

classroom management domain was also found to be the most distinctive (e.g., Fives,

Hamman, & Olivarez, 2007; Tschannen-Moran & Woolfolk Hoy, 2001). The potential

uniqueness of the behavior management factor may explain, in part, why this domain of TSE

has increasingly gained popularity among educational researchers as a separate field of study

(cf. Emmer & Hickman, 1991; O’Neill & Stephenson, 2011).

The final second-order model’s factor structure also provided evidence for the existence of the

unique TSE domains of instructional strategies and student engagement. These domains tap

into teachers’ perceived capability to use various instructional methods that enable and

enhance individual students’ learning, and activate their interest in their schoolwork. Notably,

the inter-factor correlations between TSE for instructional strategies and student engagement

appeared to be the highest, which is consistent with previous empirical findings (e.g.,

Tschannen-Moran & Woolfolk Hoy, 2001; Tsigilis et al., 2010). Following classroom-based

research (e.g., Hamre & Pianta, 2005), these strong links may be explained in terms of the

important role teachers’ instructional strategies play in making content relevant, meaningful,

and enjoyable to their students. Thereby, such skills and capabilities may set the stage for

students’ motivation and engagement in schoolwork, and may play a key role in enhancing

students’ knowledge and skills (Hamre & Pianta, 2005; Hamre et al., 2013; Hardré & Sullivan,

2009).

From a methodological viewpoint, the high correspondence among the instructional strategies

and student engagement domains can also be explicated by the less stable structure of the SE

factor in prior studies. The factor analytic results from Wolters and Daugherty (2007), for

instance, revealed a pattern of cross-loadings of items related to the student engagement

domain that was indicative of poor discriminant validity between the instructional strategies

and student engagement subscales of the original TSES. Moreover, Henson (2002) noted that

caution should be exercised when using scores from the student engagement subscale, as the

evidence for the existence of the third domain of the original TSES is far from conclusive.



105

Further large-scale research using the student-specific TSES is therefore needed to verify the

uniqueness of the self-efficacy domains of instructional strategies and student engagement.

Apart from the three domains proposed by Tschannen-Moran and Woolfolk Hoy (2001), the

student-specific TSES also appeared to be targeted to teachers’ emotional support.

Comparison of the four-factor solution with the original three-factor model suggested that

teachers’ self-efficacy for emotional support can be distinguished separately from other

domains of TSE. Yet, this dimension of self-efficacy also corresponded highly with domains of

instructional strategies and student engagement. It might well be that teachers with a strong

sense of self-efficacy for emotional support are generally better attuned and responsive to

individual students’ needs, ideas, and thoughts. Theoretical and empirical work from Hamre

and colleagues (2013, 2014) substantiates this notion, suggesting that the strategies teachers use

to foster students’ learning and engagement in the classroom are likely to be based on

individual students’ basic, affective needs for relatedness, autonomy, and competence. This

sensitivity to students’ perspectives might explain why these considerable associations were

found in the present study.

Adding the emotional support dimension to the extant domains of TSE may be of particular

importance for studies investigating outcomes related to teaching and learning. A sizeable

literature has provided evidence, both theoretically and empirically, that sensitive and

emotionally supportive teachers may provide students with experiences that foster their

motivation and learning outcomes in the classroom (Crosnoe et al., 2004; Hamre et al., 2014;

Pianta, La Paro, Payne, Cox, & Bradley, 2002; Roeser, Eccles, & Sameroff, 2000). Moreover,

teachers’ emotional support has frequently been shown to reduce the risk of low-quality

student–teacher relationships, especially for students who display uncontrollable or disruptive

behavior (Ahnert, Pinquart, & Lamb, 2006; Buyse, Verschueren, Doumen, Van Damme, &

Maes, 2008; Hamre & Pianta, 2005; La Paro, Pianta, & Stuhlman, 2004). Building self-efficacy

around the domain of emotional support may therefore advance further understanding of the

multifaceted ways in which teachers’ self-percepts of efficacy function.

Taken together, the overall, higher-order factor of TSE seems to account for substantial

amounts of variance shared by the four hypothesized domains of self-efficacy beliefs. As such,

it may be compelling to expand the original structure of the TSES by adding one higher-order

dimension, without losing sight of the relevance and potential independence of the four TSE


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domains of teaching and learning. These domains remain essential, both theoretically and

practically, yet their commonality is not negligible. Thus, adapting the hierarchical structure of

the student-specific TSES, which suggests a continuum from domain generality to domain and

student specificity, may potentially advance our understanding of the nature of teachers’ sense

of efficacy.

INTER- AND INTRA-INDIVIDUAL DIFFERENCES IN TEACHERS’ SELF-EFFICACY

Results of this study indicated that the adapted, student-specific TSES may be suitable for

capturing both inter- and intra-individual differences in TSE. Generally, there was significant

state and trait variability for each of the model’s items. Intraclass correlations showed that the

variability at the state (within-teacher) level was larger than at the trait (between-teacher) level.

These larger within-teacher differences mirror the social-cognitive view that teachers’ self-

efficacy beliefs, despite reflecting some degree of trait variability, may vary across realms of

activity, situational demands, and characteristics of the students toward whom their behaviors

and actions are directed (Bandura, 1997; Tschannen-Moran et al., 1998).

Notably, the within-teacher variability seemed to be the largest in teachers’ student-specific

self-efficacy for behavior management. There might be several reasons for the smaller amount

of variation in TSE for behavior management at the between-teacher level. First, this lack of

variability might in part be attributable to the process of revising the original TSES. Three out

of eight items related to classroom management were removed from the adapted instrument,

as these could not be accurately made specific to the level of individual students, or overlapped

too substantially with other items. As a consequence, the domain of behavior management

seemed to reflect a greater focus on student behavior issues, thereby concentrating less on

classroom routines and organization of time and resources (e.g., Emmer & Stough, 2001;

O’Neill & Stephenson, 2009). Second, teachers’ beliefs about their capability to deal with

individual students’ classroom behaviors may depend more heavily than other TSE beliefs on

interpersonal aspects of teaching. Prior research suggests that teachers tend to appraise

individual students’ behavior on the basis of relationship beliefs, feelings, and expectations,

which usually stem from teachers’ previous affective experiences and day-to-day interactions

with the child (Bandura, 1997; Spilt & Koomen, 2009; Stuhlman & Pianta, 2002). Whereas

positive appraisals may lead teachers to believe in their capabilities to positively affect the

child’s behavior, negative appraisals may thwart teachers’ self-efficacy for behavior

management and subsequent behavior toward this child (ibid.). Arguably, teachers who doubt



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their ability to effectively deal with individual students’ behaviors may unintentionally convey

poor expectations and ideals, thereby potentially further stimulating undesirable behavior and

attributes in the child and confirming their already poor efficacy beliefs. Hence, further

research on the reciprocal relationships between teachers’ appraisals of individual students’

behavior and relationship representations is needed to explain fluctuations in teachers’ self-

efficacy across individual students.

To meaningfully compare variations in domain- and student-specific TSE between teachers,

we tested for violations of measurement invariance over clusters. Recent research (e.g., Jak et

al., 2013, 2014; Muthén & Asparouhov, 2013; Ryu, 2014) underscored the necessity of using

this relatively new, but complex technique, as it attempts to take account of differences in

response processes that may result from personal and contextual characteristics, while still

allowing for comparisons of groups on similar latent variables.

In the present study, cluster bias was detected in 18 of 24 items, with the lowest amount of

bias found in the behavior management items and, after that, the emotional support items. The

partial absence of cluster bias in those items might be due to the smaller amount of variance in

these items across teachers. Furthermore, nine additional factor loadings could not be

considered equal across educators. These factor loadings were all indicative of higher TSE at

the between-teacher level, especially with respect to the domain of student engagement. Hence,

(the domains of) TSE at the between-teacher level cannot be merely perceived as the aggregate

of within-teacher level self-efficacy beliefs (Jak et al., 2013, 2014), which is also reflected in the

moderate correlation between the total score of the original TSES and the second-order

common self-efficacy factor at the between-teacher level.

Importantly, the presence of cluster bias in teachers’ self-efficacy underscores the complexity

of purely estimating these elusive capability beliefs. Both teachers’ and students’ idiosyncratic

characteristics and behaviors are likely to shape a unique classroom environment that

ultimately affects how teachers judge and interpret their own sense of efficacy. There is some

literature to suggest, for instance, that teachers’ knowledge and provision of instructional

strategies may be dependent on prior education, years of teaching experience, and satisfaction

with past performance (Tschannen-Moran & Woolfolk Hoy, 2001, 2007). Contextual factors,

including school and classroom climate, principal leadership, student behavior, available

teaching materials, and collective efficacy have also been proposed as sources of teachers’ self-


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efficacy (Goddard & Goddard, 2001; Hipp & Bredeson, 1995; Moore & Esselman, 1992;

Tschannen-Moran & Woolfolk Hoy, 2001). Variability in such distinct features may potentially

lead to inconsistencies in self-efficacy reports across teachers, which are not accounted for by

the common factor structure. This indicates that two teachers with equal values on the latent

domain(s) of self-efficacy but from different classrooms are likely to vary in their expected

observed test score (Jak et al., 2013, 2014).

LIMITATIONS

The present study’s results should be interpreted in light of several limitations. First, the

generalizability of our findings remains to be established across teachers and classrooms.

Although our sample appeared to be largely comparable to the larger population of Dutch

schools, teachers, and students, it primarily consisted of female teachers with relatively high

levels of teaching experience. Moreover, a small amount of participating teachers (5.3%)

dropped out before data collection as a result of long-term sickness, strenuous workloads, or

burnout. This dropout might have given rise to both non-response bias and bias across

clusters, suggesting that teachers may report different self-efficacy scores, despite having

similar beliefs in their capability. Following Bandura’s (1997) notions of triadic reciprocal

causality, it may be reasonable to assume that teachers’ responses to individual items do not

only rely on their self-efficacy beliefs for a specific domain and/or student, but also on

personal characteristics and the context in which teachers operate. Yet, such biases across

teachers might have implications for the psychometric quality of self-efficacy measures, as well

as the interpretation of inter- and intra-individual differences in TSE. An important next step

for future research, therefore, is to explore the explaining factors underlying the cluster bias by

including features of both teachers and the classroom as potentially biasing attributes.

Moreover, additional tests for measurement invariance across (subgroups of) teachers may

warrant consideration in future research, to establish whether observed differences in teachers’

reports reflect systematic response biases across teachers, or substantive differences in TSE per

se.

Second, and in a related vein, participating students in this study were predominantly Dutch,

and had relatively high socioeconomic backgrounds. Probably, the nature of the student

sample might have affected teachers’ responses on the student-specific TSES. Indeed, previous

research has suggested that teachers may hold different self-efficacy beliefs in relation to

different students, depending on students’ demographic backgrounds and behaviors (e.g.,



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Raudenbusch et al., 1992; Ross et al., 1996; Spilt & Koomen, 2009; Spilt, Koomen, & Thijs,

2011). In any attempt to replicate the results, it is therefore recommended that future

researchers consider individual student characteristics as covariates of teachers’ sense of

student-specific self-efficacy.

Third, it should be noted that the response rate among schools invited to participate was very

low. This low response rate may have biased the present study’s results, since schools with self-

efficacious teachers and an open mind to research were probably more likely to take part than

schools with already full agendas or strenuous workloads. Nonetheless, a sincere attempt was

made to increase the response rate among teachers within the participating schools, by

rewarding participation with school reports containing a conceptual overview of the study’s

results and gift vouchers. As a result, more than 70% of the teachers was willing to participate,

which may to some extent compensate for the low participation rate among schools.

Fourth, analytic techniques such as multilevel structural equation modeling are subject to

assumptions of multivariate normality of continuous data. In the present study, several

student-specific TSES items were found to be skewed, indicating potential non-normality of

the data. Essentially, violations of assumptions of multivariate normality may result in bias in

the model’s parameter estimates and fit indices. However, the size of our sample was

substantial, and robust maximum likelihood was used to deal with the non-normality of some

student-specific TSES items.

Fifth, the student-specific TSES was filled out by participating teachers for a limited number of

randomly selected students, thereby possibly raising questions of selective bias. It should be

noted, however, that Snijders and Bosker (1999) have demonstrated that inclusion of all

students from each classroom is insensible and needless when the cluster size of the sample is

sufficient, as is the case in the present study. Moreover, including the full amount of students

per class would have made the data gathering process excessively time-consuming and

burdensome for teachers.

IMPLICATIONS FOR RESEARCH AND PRACTICE

Despite these shortcomings, the present study may provide some promising avenues for

further research and practice in the field of teaching and learning. First, the present study

generally maintains the view that unique TSE domains of teachers’ functioning can be


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distinguished, but may also converge to an overarching construct of general teacher self-

efficacy. Although more research is evidently needed to refine and further confirm the

instrument’s distinct dimensions, the adapted TSES may be already relevant for educational

researchers and practitioners alike. Specifically, our new instrument might provide meaningful

and relevant profiles of teachers’ self-efficacy judgments across various domains of

functioning, each of which require specific knowledge, skills, and competencies (cf. Bandura,

1997). Uncovering such distinctive patterns of TSE across teaching tasks and domains may

help school psychologists in their quest to develop intervention strategies for a myriad of

sources that may influence teachers’ sense of efficacy and associated performance in

instructional, affective, and behavioral teaching domains. For instance, helping teachers to

selectively focus on their performance attainments and to monitor their physiological reactions

to inefficacious control of difficult students or challenging teaching tasks may raise teachers’

self-efficacy for teaching domains in which they feel less confident (Bandura, 1997).

Second, the student-specific TSES is one of the first measures to empirically support the

social-cognitive view that TSE is a multifaceted phenomenon that fluctuates over teaching

domains and particular students (Bandura, 1997; Tschannen-Moran et al., 1998). Compared to

the original TSES, which exclusively focuses on inter-individual differences in TSE across

domains, our instrument seems well suited to grasp teachers’ unique sense of self-efficacy in

relation to different students as well. This interpersonal view on TSE may be relevant for

understanding teachers’ differential treatment of particular students in class, and fluctuations in

the affective quality of dyadic student–teacher relationships. Existing research has increasingly

encouraged such an interpersonal focus of analysis to comprehend the mechanisms behind

teachers’ and students’ behaviors and actions in the classroom (Pianta, Hamre, & Stuhlman,

2003; Spilt et al., 2011). In light of this recommendation, the unique domain of emotional

support seems a meaningful addition to the construct of teacher self-efficacy. Insights into

teachers’ sense of self-efficacy toward individual children in this particular domain may help

school psychologists to coach teachers in emotionally connecting with, and getting through to

particular students in class.

Third, and in a related vein, the present study’s results seem to underscore the importance of

investigating teachers’ self-efficacy in relation to the particular context in which they perform

their daily job. Although there are some relatively stable, trait aspects of TSE, these capability

beliefs cannot be merely perceived as context-free attributes of a teacher. Rather, features of



111

the classroom context, individual students, and teachers themselves may play an important role

in producing fluctuations in teachers’ capability beliefs. For future researchers, it may be a

challenge to uncover important variables that may further explain variations in self-efficacy

between and within teachers.

Lastly, self-efficacy measures that are tailored to various teaching domains and specific

students may increase the predictive power of the self-efficacy construct, and potentially afford

better explanation of teachers’ supportive behaviors and students’ school adjustment in the

classroom (Bandura, 1986, 1997). To date, evidence regarding the consequences of TSE for

students’ and teachers’ classroom performances seems far from conclusive (e.g., Klassen et al.,

2011). Probably, the lack of particularized instruments has, in large part, prevented

improvement in interpretations of these complex relationships. Measuring teachers’ self-

efficacy in relation to individual students may provide a rich context for understanding and

interpreting teachers’ differential treatment of, and day-to-day interactions with particular

students in the classroom. Educational researchers and practitioners such as school

psychologists may use these insights to develop training programs and interventions targeting

teacher’ student-specific efficacy beliefs as a means of improving students’ outcomes, and

especially those at risk of academic failure.


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APPENDIX 1

Original and Student-Specific TSES Items Domain Item Original TSES Domain Item Student-Specific TSES IS 1 To what extent can you use a

variety of assessment strategies?

-

IS 2 To what extent can you provide an alternative explanation or example when students are confused?

IS 1 To what extent can you provide an alternative explanation or example when this student is confused?

IS 3 To what extent can you craft good questions for your students?

IS 2 To what extent can you craft stimulating questions for this student?

IS 4 How well can you implement alternative strategies in your classroom?

IS 3 How well can you let this student apply alternative problem solving strategies?

IS 5 How well can you respond to difficult questions from your students?

-

IS 6 How much can you do to adjust your lessons to the proper level for individual students?

IS 4 How well can you adjust your lessons to the proper level for this student?

IS 7 To what extent can you gauge student comprehension of what you have taught?

IS 5 To what extent can you gauge this student’s comprehension of what you have taught?

IS 8 How well can you provide appropriate challenges for very capable students?

IS 6 How well can you provide appropriate challenges for this student?

CM 9 How much can you do to control disruptive behavior in the classroom?

BM 7 How well can you control disruptive behavior in this student?

CM 10 How much can you do to get children to follow classroom rules?

BM 8 How well can you get this student to follow classroom rules?

CM 11 How much can you do to calm a student who is disruptive or noisy?

BM 9 How well can you calm this student when he/she is disruptive or noisy?

CM 12 How well can you establish a classroom management system with each group of students?

-

CM 13 How well can you keep a few problem students from ruining an entire lesson?

BM 10 How well can you prevent this student from negatively affecting the classroom atmosphere?

CM 14 How well can you respond to defiant students?

-

CM 15 To what extent can you make your expectation clear about student behavior?

BM 11 To what extent can you make your behavioral expectations clear to this student?

CM 16 How well can you establish routines to keep activities running smoothly?

-

SE 17 How much can you do to get students to believe they can do well in schoolwork?

SE 12 How well can you get this student to believe he/she can do well in schoolwork?

SE 18 How much can you do to help your students value learning?

SE 13 To what extent can you help this student to value learning?



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SE 19 How much can you do to motivate students who show low interest in schoolwork?

SE 14 To what extent can you motivate this student for his/her schoolwork?

SE 20 How much can you assist families in helping their children do well in school?

-

SE 21 How much can you do to improve the understanding of a student who is failing?

SE 15 How well can you help this student to understand the learning content?

SE 22 How much can you do to help your students think critically?

SE 16 How well can you help this student to think critically?

SE 23 How much can you do to foster student creativity?

SE 17 To what extent can you help this student to explore new things?

SE 24 How much can you do to get through to the most difficult students?

SE 18 How well can you get through to this student?

ES 19 How well can you respond positively and sincerely to this student in the classroom?

ES 20 To what extent can you provide positive feedback to this student?

ES 21 How well can you provide a safe and secure environment for this student?

ES 22 To what extent can you timely recognize that this student does not feel well?

ES 23 How well can you timely provide support to this student?

ES 24 To what extent can you provide this student with the space to make his/her own choices?

ES 25 To what extent can you adjust learning tasks to this student’s needs and interests?


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CHAPTER 4 TEACHERS’ SELF-EFFICACY IN RELATION TO INDIVIDUAL STUDENTS WITH A

VARIETY OF SOCIAL–EMOTIONAL BEHAVIORS: A MULTILEVEL INVESTIGATION

_________________________________________________________________________

The present study examined teachers’ domain-specific self-efficacy (TSE) in relation to

individual students with a variety of social–emotional behaviors in class. Using a sample of 526

third-to-sixth grade students and 69 teachers, multilevel modeling was conducted to examine

students’ externalizing, internalizing, and prosocial behaviors as predictors of TSE toward

individual students, and the potential moderating roles of teaching experience and teachers’

perceived amount of classroom misbehavior. Results showed that most of the variance in TSE

occurred within teachers. Students’ externalizing behavior was negatively associated with TSE

for instructional strategies, behavior management, student engagement, and emotional support.

In contrast, teachers reported higher levels of self-efficacy toward students with high levels of

prosocial behavior, irrespective of teaching domain. Students’ internalizing behavior predicted

lower levels of TSE for instructional strategies and emotional support, and higher levels of

TSE for behavior management. Lastly, teachers’ perceived levels of classroom misbehavior

exacerbated the negative association between externalizing student behavior and TSE for

behavior management. These findings illustrate the importance of viewing TSE from a dyadic

perspective.

_________________________________________________________________________ Zee, M., de Jong, P. F., & Koomen, H. M. Y. (2016). Teachers’ self-efficacy in relation to individual students with a variety of social–emotional behaviors: A multilevel investigation. Journal of Educational Psychology. Advance online publication. doi:10.1037/edu0000106


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INTRODUCTION

Challenging students bring many behaviors and qualities to the classroom that may seriously

hamper teachers’ ability to execute their daily teaching tasks (Westling, 2010). Studies have

indicated that behaviorally or emotionally disturbed students unnecessarily take time away

from instruction, try teachers’ patience, fail to comply with classroom rules, and consequently,

may hinder teachers’ efforts to sustain a positive learning climate (Bru, 2009; Clunies-Ross,

Little, & Kienhuis, 2008; Putnam, Luiselli, Handler, & Jefferson, 2003). Undoubtedly, some

teachers may experience little trouble nipping such behaviors in the bud. For many others,

however, students’ challenging behavior frequently marks the beginning of a vicious cycle of

stress and burnout (e.g., Brouwers & Tomic, 2000; Fernet, Guay, Senécal, & Austin, 2012;

Friedman, 2006), which may eventually lead these teachers to leave the profession entirely

(Tsouloupas, Carson, Matthews, Grawitch, & Barber, 2010).

Scholars have laid claim to a number of factors that potentially discriminate teachers who cope

effectively from those who are commonly struggling to manage challenging behavior. Of these

factors, teachers’ self-efficacy (TSE) beliefs, or self-referent judgments of operative capability,

are probably one of the most pervasive (Bandura, 1997; Tschannen-Moran & Woolfolk Hoy,

2001). Past empirical evidence suggests that when educators have a resilient sense of self-

efficacy, they are more likely to successfully deal with challenging student behavior and to

persist longer than teachers who lack such beliefs (e.g., Almog & Shechtman, 2007; Lambert,

McCarthy, O'Donnell, & Wang, 2009). On a more theoretical note, self-efficacious teachers are

also presumed to be steadily capable of motivating challenging students, to believe in their

improvability, and to rely on intrinsic inducements to get these students to study (Bandura,

1997; Tschannen-Moran & Woolfolk Hoy, 2001).

To date, the significance of self-percepts of efficacy for teachers’ dealings with students at the

classroom level of analysis is fairly well-established in various teaching domains (Woolfolk Hoy,

Hoy, & Davis, 2009). There is, however, a dearth of studies considering TSE toward individual

students. This lack of research is disadvantageous, as efficacy judgments related to various

teaching domains and individual students may more reliably predict teachers’ behaviors toward

specific children, as well as the effort and persistence teachers put in teaching them (Bandura,

1997; Tschannen-Moran, Woolfolk Hoy, & Hoy, 1998). For a comprehensive understanding of

teachers’ ability to manage particular students, and targeting interventions for handling a


SOURCES OF TEACHER SELF-EFFICACY

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variety of social–emotional student behaviors, knowledge of both domain- and student-specific

TSE may therefore be vital. To add to this knowledge, the present study aims to examine TSE

in relation to individual students with a variety of social–emotional behaviors (i.e.,

externalizing, internalizing, and prosocial behavior) in the classroom.

CONCEPTUALIZATION OF TEACHERS’ SELF-EFFICACY

Teachers’ self-percepts of efficacy have long been considered a vital cognitive resource for

teachers, with clear contributions to their performances and sense of well-being in the

classroom (Klassen & Tze, 2014; Tschannen-Moran & Woolfolk Hoy, 2001; Woolfolk Hoy et

al., 2009). When teachers generally perceive themselves as highly efficacious, they are more

likely to use differentiated instructional methods, employ emotionally supportive behaviors

that increase students’ confidence, and adopt proactive approaches to managing student–

teacher conflict (Andreou & Rapti, 2010; Hoy & Woolfolk, 1990; Martin & Sass, 2010; Morris-

Rothschild & Brassard, 2006; Thoonen, Sleegers, Oort, Peetsma, & Geijsel, 2011; Wertheim &

Leyser, 2002). Teachers with a robust sense of general, classroom-level self-efficacy have

furthermore been found to be more satisfied with their job and to suffer less from burnout

symptoms than less efficacious educators (Brouwers, Evers, & Tomic, 2001; Caprara,

Barbaranelli, Borgogni, & Steca, 2003; Friedman, 2003; Klassen & Chui, 2010; Skaalvik &

Skaalvik, 2010). These outcomes resonate well with the social-cognitive view that self-efficacy

is a potent force in affecting the motivational, affective, cognitive, and selective processes

needed for desired goals to be realized (Bandura, 1986, 1997).

Scholars have keenly been on the lookout for relevant dimensions in teachers’ sense of self-

efficacy (Tschannen-Moran & Woolfolk Hoy, 2001). Over the years, various

conceptualizations and measures of TSE have come onto the scene, from global TSE scales

based on locus of control theory (Gibson & Dembo, 1984; Guskey, 1981; Rose & Medway,

1981) to subject-, task-, or domain-specific measures that consider the contextualized,

multifaceted nature of TSE (e.g., Brouwers & Tomic, 2000; Friedman & Kass, 2002;

Tschannen-Moran & Johnson, 2011; Tsouloupas et al., 2010). Since the studies of Tschannen-

Moran and colleagues (Tschannen-Moran et al., 1998; Tschannen-Moran & Woolfolk Hoy,

2001), however, the well-validated three-factor model of TSE for instructional strategies,

classroom management, and student engagement has dominated the field. The domains of

TSE for instructional strategies and student engagement mainly focus on aspects of

instructional delivery. Generally, the instructional strategies domain attempts to capture


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teachers’ perceived capability in using various instructional methods that enable and enhance

student learning. Teachers’ self-efficacy for student engagement is useful in measuring the

extent to which teachers feel able to activate students’ interest in their schoolwork. In addition

to the instructional aspects of teaching and learning, TSE for classroom management

encompasses teachers’ judgments of their ability to organize students’ time, behavior, and

attention (cf. Emmer & Stough, 1991). Although moderate to strong correlations among the

three domains of TSE exist, there is empirical evidence to suggest that each construct assesses

unique aspects of teachers’ sense of self-efficacy (e.g., Heneman, Kimball, & Milanowski, 2006;

Tschannen-Moran & Woolfolk Hoy, 2001). Thereby, Tschannen-Moran and Woolfolk Hoy’s

model substantiates the social-cognitive premise that TSE is specific to different tasks and

domains of teachers’ functioning (Bandura, 1997; Tschannen-Moran et al., 1998).

Despite general consensus on the highly context-specific nature of TSE, most research has

been conducted at the classroom-level of analysis, focusing on teachers’ general beliefs of

capability toward the class they currently teach. As such, these studies could be considered to

be subject to the ecological fallacy (Piantadosi, Byar, & Green, 1988) that teachers’ self-

percepts of efficacy also hold for individual students. Assumedly, students all bring idiosyncratic

behaviors and characteristics to the classroom that may more or less impact teachers’ self-

efficacy beliefs across different domains of teaching and learning. Whereas obliging and hard-

working students will most likely raise teachers’ self-efficacy, instances of misconduct may

seriously undermine teachers’ student-specific capability beliefs. Two multilevel studies

(Raudenbusch, Rowan, & Cheong, 1992; Ross, Cousins, & Gadalla, 1996), based on a single-

item measure to evaluate TSE at the classroom-level, indicated that between 13% and 44% of

the variance in TSE can be explained by such within-class variables as students’ grade, academic

level, and interest in their schoolwork. In addition, empirical research and theorizing from Spilt

and colleagues (Spilt & Koomen, 2009; Spilt, Koomen, & Thijs, 2011) suggested that individual

students who display behavioral problems are more likely to weaken teachers’ self-efficacy

beliefs and to evoke feelings of helplessness than students without such problems. These

findings suggest that teachers may significantly vary in their self-efficacy toward particular

students.

STUDENTS’ SOCIAL-EMOTIONAL BEHAVIORS AS PREDICTORS OF TSE

Social-cognitive theorists have generally asserted that self-percepts of efficacy are shaped, in

large part, by specific events and experiences linked to distinct realms of functioning (Bandura,



119

1997). For teachers, such experiences typically derive from authentic educational endeavors

with students. Indeed, a sparse amount of existing research (Bandura, 1997; Tschannen-Moran

et al., 1998; Tschannen-Moran & Woolfolk Hoy, 2007) has theorized that successful

experiences with instructing, engaging, and managing students may significantly add to a

healthy sense of TSE. In contrast, unsuccessful dealings with individual students, and

particularly those who display challenging behavior, have been empirically evidenced to elicit

negative emotions that lead teachers to lose faith in their capabilities and collapse under the

burden of everyday stress (Emmer & Stough, 2001; Spilt & Koomen, 2009; Spilt, Koomen, &

Thijs, 2011; Tsouloupas et al., 2010). Accordingly, teachers’ classroom experiences and

subsequent feelings of self-efficacy may be heavily influenced by a variety of social–emotional

student behaviors in the classroom. In line with prior research on students’ social–emotional

adjustment (e.g., Roorda, Verschueren, Vancraeyveldt, van Craeyevelt, & Colpin, 2014), we

consider students’ externalizing, internalizing, and prosocial behaviors as sources of TSE

toward individual students.

EXTERNALIZING BEHAVIOR

Past empirical research has repeatedly pinpointed externalizing student behavior, including

aggression, hyperactivity, and antisocial behavior, to be at the core of the challenges most

teachers face on a daily basis (Brouwers & Tomic, 2000; Evers, Tomic, & Brouwers, 2004;

Hastings & Bham, 2003; Kokkinos, Panayiotou, & Davazoglou, 2004, 2005; Kyriacou, 2001;

Roehrig, Pressley, & Talotta, 2002). These disruptive behaviors may ripple through the entire

classroom and have been suggested to cause elevated levels of stress and emotional exhaustion

in teachers (Clunies-Ross et al., 2008; Kokkinos et al., 2004; Spilt & Koomen, 2009;

Tsouloupas et al., 2010). Evidently, individual students’ externalizing behavior patterns may

color teachers’ initial experiences and enduring beliefs of capability to effectively deal with

them. The correlational results of Lambert and colleagues (2009), for instance, put forward that

highly overactive and distractible students may generally hamper US teachers’ attitude toward

their teaching abilities, and their sense of self-efficacy in dealing with, and establishing positive

relationships with challenging students. Also focusing on US teachers’ self-efficacy for

classroom management, Tsouloupas et al. (2010) demonstrated that high levels of teacher-

perceived misbehavior in the classroom may negatively affect TSE in dealing with disruptive

behavior and stressful situations, which, in turn, may cause them to feel emotionally exhausted.

Other empirical research from Cyprus (e.g., Kokkinos et al., 2004, 2005) and the United States

(Roehrig et al., 2002) has indicated that behaviors of an externalizing nature, including conduct


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problems, hyperactivity, anger, and disrespectfulness, generally yield the most negative

impressions on teachers and may lead them to feel helpless and inefficacious.

Additional to the literature linking students’ externalizing behavior to general or domain-

specific (classroom management) TSE at the classroom-level, a modest body of primarily

American research has also begun to explore within-person variability in teacher cognitions. For

instance, several scholars (e.g., Abidin & Robinson, 2002; Greene, Abidin, & Kmetz, 1997;

Greene, Beszterczey, Katzenstein, Park, & Goring, 2002) have highlighted teachers’ cognitions

and judgments of individual student behavior as crucial contributors to their differential

treatment of particular students in class. In line with this assertion, Spilt and Koomen (2009)

used Pianta’s (1999) Teacher Relationship Interview and associated coding system to assess

strengths and difficulties in teachers’ beliefs and feelings in relationships with specific,

disruptive students in the Netherlands. They revealed that teachers perceive themselves as

angrier and less self-efficacious in relation to individual students who display disruptive

behavior in the classroom. These outcomes are consistent with the idea that TSE may be

highly individualized in nature and might depend on how teachers appraise individual students’

disruptive, externalizing behaviors.

Notably, negative personal feelings, cognitions, and efficacy beliefs seem to be particularly

echoed in inexperienced teachers’ reports of their students’ behaviors (cf. Emmer & Stough,

2001). Using a grounded theory approach to study US teachers’ perceptions of student needs,

Feuerborn and Chinn (2012) revealed that novice teachers may express more emotionally-

laden reactions in relation to externalizing behavior than their experienced coworkers, and

seem more afflicted by the instructional disruptions these behaviors cause. These qualitative

findings stretch across empirical studies from Europe as well. Results from Kokkinos and

colleagues (Kokkinos et al., 2004, 2005) suggested that more experienced teachers generally

perceive disruptive student behavior as less challenging and more controllable in the

classroom. From this line of evidence, it can be hypothesized that increases in teachers’

experience may potentially buffer the negative association between teacher-perceived

externalizing student behavior and student-specific TSE.

INTERNALIZING BEHAVIOR

Counter to externalizing behavior, students with symptoms of internalizing behavior, including

shyness, verbal inhibition, anxiety, or social withdrawal (Coplan, 2000; Gazelle & Ladd, 2003;



121

Merrell, 1999), have been suggested to evoke less challenging experiences or negative thoughts

in their teachers (Rubin & Coplan, 2004). These internalizing difficulties may be more subtle

than manifestations of externalizing conduct and usually tend to reflect more appropriate

classroom behavior and decorum (e.g., Coplan, 2000; Gresham & Kern, 2004; Kokkinos et al.,

2004; Rubin & Coplan, 2004). As such, internalizers are more likely to go undetected or

ignored by their teachers than students with externalizing conduct (Coplan & Prakash, 2003)

and may have little, if any, influence on teachers’ self-efficacy judgments toward them in

different teaching domains.

Yet, there might be some reason to believe that behaviors of a more internalizing nature may

still be bothersome to the teacher and contribute to their self-percepts of efficacy (e.g., Olson

& Cooper, 2001; Westling, 2010). Notably, the one empirical study to examine US teachers’

self-efficacy at the classroom-level in relation to internalizing student behavior indicated that

highly self-efficacious teachers may be more bothered by students’ internalizing behavior than

those who are less confident in their personal teaching effectiveness (Liljequist & Renk, 2007).

One of the scenarios that may account for this finding is that a healthy sense of TSE frequently

coincides with increases in teaching experience (e.g., Klassen & Chui, 2010). Empirical studies

of Kokkinos and colleagues (2004, 2005) pointed out that this growth in experience is essential

for gaining knowledge of, and becoming sensitized to internalizers’ more subtle behavioral and

affective cues. Without such vital knowledge and experience, teachers may feel less worried

about and less responsible for students’ internalizing behavior patterns, and thereby, less

hindered in their self-efficacy to deal with them (cf. Liljequist & Renk, 2007). In contrast, when

teachers consciously experience that their instructional initiatives are unsuccessful in

establishing reciprocal interchanges with a student who displays internalizing behavior, a

lowered sense of TSE toward this child is likely to arise. Hence, counter to the protective

effect of teaching experience on the negative association between externalizing behavior on

TSE, increases in teaching experience might serve as an additional risk factor for teachers’ self-

efficacy toward students with internalizing symptoms. Unless teachers believe they can gather

up the resources to successfully deal with individual students with internalizing symptoms, they

will probably dwell on their actions, exercise inadequate effort, and may consequently

experience failure.


CHAPTER 4

122

PROSOCIAL BEHAVIOR

Most of the previous work on teacher self-efficacy has predominantly attempted to study

challenging student behavior as antecedents of these capability beliefs (e.g., Lambert et al.,

2009; Liljequist & Renk, 2007; Tsouloupas et al., 2010). It is likely, however, that students’

propensity to act prosocially may also contribute to teachers’ self-efficaciousness toward

individual children, but in a more favorable sense. Generally, prosocial behaviors are

implicated with various voluntary acts intended to benefit others, including helping, sharing,

comforting, and cooperating (Dunfield & Kulhmeier, 2013; Dunfield, Kuhlmeier, O’Connell,

& Kelley, 2011; Eisenberg, 1982). Such prosocial tendencies have frequently been linked to key

classroom outcomes such as academic achievement (e.g., Caprara, Barbaranelli, Pastorelli,

Bandura, & Zimbardo, 2000; Malecki & Elliott, 2002; Wentzel, 1993), engagement (Coolahan,

Fantuzzo, Mendez, & McDermott, 2000), and the quality of students’ relationships with

teachers and peers (Birch & Ladd, 1998; Henricsson & Rydell, 2004; Zimmer-Gembeck,

Geiger, & Crick, 2005). Assumedly, these agreeable behaviors and performances may provide

teachers with the classroom mastery experiences that reinforce a healthy sense of self-efficacy

(Goddard & Goddard, 2001; Goddard, Hoy, & Woolfolk Hoy, 2004). Therefore, teachers may

feel more self-efficacious when dealing with students who generally display prosocial behavior

in the classroom, irrespective of teachers’ domain of functioning.

TEACHERS’ PERCEIVED AMOUNT OF MISBEHAVIOR IN THE CLASSROOM

A number of empirical investigations from the United States have demonstrated that

classrooms with many aggressive students may have a negative impact on the behaviors of its

individual members. For instance, Werthamer-Larsson, Kellem, and Wheeler (1991) found that

regular students from poorly behaving classrooms were more often perceived as shy by their

teacher, which can be perceived as an aspect of internalizing behavior (e.g., Letcher, Smart,

Sanson, & Toumbourou, 2009). Several longitudinal studies have also indicated that students

who are enrolled in classrooms with many aggressive students are likely to gradually become

more aggressive themselves (e.g., Kellam, Ling, Merisca, Brown, & Ialongo, 1998; Thomas &

Bierman, 2006; Thornberry & Krohn, 1997). Evidently, such trends may place an additional

burden on teachers’ ability to control these students’ behaviors, and to maintain positive

relationships with them (Brophy, 1996; Doumen et al., 2008; Roorda et al., 2014). Hence, as

classmates may contribute to escalating trends in students’ challenging behaviors, teachers’

perceived negative classroom dynamics may be hypothesized to exacerbate the relationship

between individual students’ externalizing or internalizing behavior and TSE.



123

PRESENT STUDY

The present study aimed to extend the current literature by exploring a variety of social–

emotional behaviors as predictors of teachers’ domain- and student-specific self-efficacy

beliefs. Although the consequences of classroom-level TSE for teachers’ dealings with student

behavior have been fairly well established (Woolfolk Hoy et al., 2009), empirical work on TSE

seems to have stopped short of considering how students’ social–emotional behaviors are

associated with TSE across various teaching domains (e.g., instructional strategies or classroom

management) and toward individual students (cf. Klassen, Tze, Betts, & Gordon, 2011).

Moreover, the handful of studies (e.g., Lambert et al., 2009; Tsouloupas et al., 2010; Spilt &

Koomen, 2009) that have specifically looked into these effects tend to focus solely on patterns

of externalizing behavior, thereby largely neglecting internalizing and prosocial behaviors as

correlates of TSE. Building an understanding of how teachers’ sense of self-efficacy is shaped

by individual students’ various behaviors in different domains of teaching and learning may

provide a vital foundation for interventions targeted to teachers’ dealings with challenging

students.

Based on the body of evidence on teachers’ classroom-level self-efficacy, several hypotheses

were formulated. First, we expected teachers to report lower levels of self-efficacy toward

individual students with externalizing and internalizing problems, and higher levels of self-

efficacy toward students who display prosocial behavior, irrespective of teachers’ domain of

functioning. Given the more subtle nature of students’ internalizing behavior, we expected the

link between this student behavior and student-specific TSE across domains of teaching and

learning to be weaker than the associations between students’ externalizing and prosocial

behavior and student-specific TSE. Secondly, we hypothesized that relatively high levels of

teachers’ perceived classroom misbehavior and a lack of teacher experience may further

worsen the negative association of individual students’ externalizing and internalizing behavior

with student-specific TSE.

METHOD

PARTICIPANTS

Data for the current study were collected from 69 regular Dutch elementary school teachers

and 526 third-to-sixth grade students. The schools from which the sample was drawn were

recruited via telephone and e-mail, after ethical approval was granted by the Ethics Review


CHAPTER 4

124

Board of the Faculty of Social and Behavioral Sciences, University of Amsterdam (project no.

2013-CDE-3188). Of the 350 schools that were initially invited, 24 (6.9%) from both rural and

urban areas across the Netherlands ultimately agreed to take part in this study. Non-

participation was mainly due to the school’s already full agenda, or their involvement in other

research studies.

Participating teachers (72.6% females) had a mean age of 41.42 years (SD = 12.34, range = 23

to 63 years). The professional teaching experience of these educators in primary education

ranged from 1.5 to 44 years, with a mean of 16.67 years (SD = 11.87). Four teachers did not

provide complete demographic information. For the student sample, eight students (four boys

and four girls) were randomly selected from the pool of students from each teacher’s

classroom whose parents had initially provided informed consent. These students were

distributed across grades 3 (n = 54), 4 (n = 157), 5 (n = 165), and 6 (n = 150), respectively. At

recruitment, the sampled children ranged from 7.71 to 13.04 years of age (M = 10.57, SD =

1.11), and the gender composition was evenly distributed with 263 boys (50.0%) and 263 girls

(50.0%). Based on students’ self-reports, the study sample appeared to be 85.2% Dutch, and

12.3% non-Dutch. In 2.5% of the cases, students failed to provide information regarding their

ethnicity. Based on employment statistics and parents’ education, most students could be

considered to have an average to high socioeconomic status. Teachers reported both parents

of participating students to be employed in 76.8% of the families. In 20.4% of the cases, at

least one parent appeared to be employed, and only 2.5% of the families included two

unemployed parents. In addition, teachers indicated the majority of the parents to have

finished senior vocational education (49.0%) or higher education (46.2%), leaving less than 5%

of the parents to only have finished primary education.

INSTRUMENTS

STUDENTS’ SOCIAL–EMOTIONAL BEHAVIORS

Teachers were asked to complete the Dutch version of the Strengths and Difficulties

Questionnaire (SDQ; van Widenfelt, Goedhart, Treffers, & Goodman, 2003) to evaluate a

variety of students’ social-emotional behaviors. The SDQ is a brief 25-item behavioral

screening questionnaire that measures students’ adjustment and psychopathology in the

classroom. The scale originally consists of positive and negative student attributes that together

represent five factors reflecting strengths (Prosocial Behavior) and difficulties (Emotional



125

Symptoms, Conduct Problems, Hyperactivity-Inattention, and Peer Problems). In the present

study, however, use was made of the more general Internalizing, Externalizing, and Prosocial

Behavior subscales, which generally are preferred over the original SDQ factors in low-risk

samples (Goodman, Lamping, & Ploubidis, 2010). The Externalizing Behavior dimension (10

items) combines the subscales of Hyperactivity-Inattention and Conduct Problems, with items

such as “Restless, hyperactive, cannot sit still for long” and “Often has temper tantrums or hot

tempers”. Additionally, the Internalizing Behavior subscale (8 items) comprises all items from

the Emotional Symptoms factor, and three items from the Peer Problems factor (i.e., “Rather

solitary, tends to play alone”, “Gets on better with adults than with other children” and

“Picked on or bullied by other children”). The 7-item Prosocial Behavior scale, lastly, reflects

all five items from the Prosocial scale, and two items from the Peer Problems scale (i.e.,

“Generally liked by other children” and “Has at least one good friend”). Teachers responded

on the 25 items on a 5-point Likert scale, ranging from 1 (not true) to 5 (certainly true).

The psychometric properties of the three-factor SDQ model have been demonstrated to be

especially suited for use in non-risk samples (Dickey & Blumberg, 2004; Goodman et al., 2010;

van Leeuwen, Meerschaert, Bosmans, de Medts, & Braet, 2006). To evaluate whether the

SDQ’s three-factor solution also held in the present study, we performed a confirmatory factor

analysis (CFA), using maximum likelihood estimation with robust standard errors and a mean-

adjusted chi-square test statistic (MLR; Muthén &Muthén, 1998-2012). Guided by the residual

covariance matrix and modification indices, we added four theoretically plausible correlated

residuals to the baseline model. Two of those correlated residuals were indicative of aspects of

students’ externalizing behavior. Specifically, the residuals of items 2 and 10 both reflected

students’ hyperactivity, and the residuals of items 15 and 25 primarily evaluated students’

attention span. Also correlated were the residuals of prosocial items 9 and 20, which indicated

students’ willingness to help others. Lastly, the residuals of internalizing items 16 and 24 were

allowed to correlate, as they were both symptomatic of students’ nervousness and anxiety.

Despite a relatively low comparative fit index (CFI), this revised model yielded an acceptable fit

according to established cutoff values of .08 for the root-mean-square error of approximation

(RMSEA) and standardized root-mean-square residual (SRMR; Browne & Cudeck, 1993; Hu &

Bentler, 1999; Kline, 2011), χ2(268) = 890.04, p < .001, RMSEA = .067 (90% CI [.062, .072]),

CFI = .84, SRMR = .074. These fit indices are consistent with previous research (Goodman et

al., 2010; van Leeuwen et al., 2006), reporting acceptable RMSEA and SRMR values for the


CHAPTER 4

126

three-factor solution, but CFIs below the conventional threshold of .90 for satisfactory fit (e.g.,

Bentler, 1990, 1992; Little, 2013). Recommendations for cutoff values for various fit indices

have previously been called into question, however, given that the mean value and the

distribution of most fit indices are likely to change with sample size, the distribution of the

data, and the chosen test statistic (e.g., Yuan, 2005). The factor loadings of the SDQ subscales

in the present study were adequate, ranging from .42 to .73 for Externalizing Behavior, from

.41 to .80 for Internalizing Behavior, and from .50 to .82 for Prosocial Behavior, respectively.

Cronbach’s alphas were .81 for Internalizing Behavior, .87 for Externalizing Behavior, and .86

for Prosocial Behavior, respectively.

CLASSROOM MISBEHAVIOR

A short, three-item scale developed by Tsouloupas et al. (2010) was used to measure teachers’

perceived amount of student behavior problems in their classroom. Items that made up this

instrument included “How frequently do you experience negative interactions with students?”,

“How often do you deal with student discipline problems?” and “On average, how emotionally

intense are your dealings with student discipline problems?”. All items were scored on a 5-

point Likert-type scale, ranging from 1 (almost never occurs) to 5 (occurs very frequently). In the

present sample, Cronbach's alpha for this measure was .83.


Teachers’ perceptions of their self-efficacy toward individual students across various teaching

domains were estimated using the Student-Specific Teacher Self-Efficacy Scale (Zee &

Koomen, 2015). This instrument, which is adapted from the Teachers’ Sense of Efficacy Scale

(TSES; Tschannen-Moran & Woolfolk Hoy, 2001), is specifically designed to evaluate teachers’

student-specific capability beliefs across various domains of teaching and learning. Largely

similar to the original TSES, this instrument represents the three domains of Instructional

Strategies (IS; 6 items), Behavior Management (BM; 5 items), and Student Engagement (SE; 6

items). The domain of IS measures the extent to which teachers feel able to use various

instructional methods that enable and enhance individual students’ learning, with items such as

“How well can you respond to difficult questions from this student?”. Slightly different from

the original Classroom Management dimension is the BM domain, which no longer taps

aspects of classroom organization, but rather concentrates on teachers’ perceptions of their

ability to organize and guide the behaviors of a particular student. A sample item of this

subscale includes “How much can you do to get this child to follow classroom rules?”.



127

Teachers’ self-efficacy for SE captures teachers’ perceived ability to activate the interest of a

particular student in his or her schoolwork. This domain of TSE includes items such as “How

much can you do to get this student to believe he/she can do well in schoolwork?”.

Next to the three broad domains proposed by Tschannen-Moran and Woolfolk Hoy (2001),

the student-specific TSES is also targeted to the domain of Emotional Support (ES; 7 items).

This additional domain involves tasks and responsibilities related to how well teachers can

establish caring relationships with students, acknowledge students’ opinions and feelings, and

create settings in which students feel free to explore and learn. One example item of this

subscale includes “How well can you establish a safe and secure environment for this

student?”.

All items that made up this measure were rated by teachers on a seven-point Likert-type scale,

ranging from 1 (nothing) to 7 (a great deal). A CFA using MLR (Muthén & Muthén, 1998-2012)

provided sufficient fit to the present study’s data, after adding correlations between the

residuals of items 13 and 14, and 19 and 20, χ2(244) = 810.36, p < .001, RMSEA = .067 (90%

CI [.062, .072]), CFI = .91, SRMR = .073. Both correlated residuals seemed theoretically

plausible. Specifically, SE-items 13 and 14 focused on teachers’ perceived capability to

motivate individual students for their schoolwork. Items 19 and 20, in addition, concentrated

on the extent to which the teacher felt capable of responding positively and sincerely to a

particular student. All standardized factor loadings were considered high in this model ( >.55),

thereby supporting the factorial validity of the student-specific TSES. Internal consistency

scores of the student-specific TSES domains were .89 for IS, .94 for BM, .90 for SE, and .85

for ES, respectively.

PROCEDURE

During recruitment, either school principals or participating teachers distributed information

letters and consent forms to parents of all students from teachers’ classrooms. On average,

parental consent rates per classroom ranged between 46% and 100%. From all consents

received, we randomly selected eight students from participating teachers’ classrooms and

subsequently let these teachers know which eight students to report on. Students were asked to

fill out several questions about their background characteristics, including students’ age,

gender, and ethnicity, during a planned school visit. Teacher-reported questionnaires assessing

students’ social–emotional behavior at school and teachers’ self-efficacy in relation to


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128

individual students were collected via an individually-addressed digital survey link that was

distributed by e-mail. Teachers filled out these questionnaires for each of the eight selected

students from their classroom. Participating educators additionally reported on some general

questions regarding their background characteristics. The total survey took approximately one

hour to complete. Teachers were asked to return the digital survey within two weeks after the

survey link was sent. To improve the participation rate, reminders were sent to nonresponding

teachers, resulting in a total response rate of 93.9%. Nonparticipation was due to long-term

sickness absence or teachers’ busy schedule. After participation, all teachers received a gift

voucher of €20,00.

DATA ANALYSIS

To examine the contribution of teachers’ and students’ background characteristics and a variety

of student behaviors in predicting teachers’ sense of self-efficacy toward individual students,

we fitted a series of multivariate hierarchical linear models using Mplus 7.11 (Muthén &

Muthén, 1998-2012). This analytical technique is quite flexible in that it corrects for nested data

structures, and avoids aggregation bias and underestimation of standard errors that sometimes

compromise the outcomes of Ordinary Least Squares-analyses of multilevel data (Snijders &

Bosker, 1999). All fixed and random effects parameters in these models were based on

maximum likelihood estimation with robust standard errors and a mean-adjusted chi-square

test statistic (MLR). Predictors were centered around the grand mean to ease their

interpretation.

Scale scores, represented by teachers’ mean response to relevant items, were used to reflect the

main constructs of interest. Several empirical sources (e.g., Allen & Seaman, 2007; Kislenko &

Grevholm, 2008; Leung, 2011; Parker, McDaniel, & Crumpton-Young, 2002) have indicated

that scale scores may be treated as interval-level measures, as long as the psychometric

properties of the scale are sufficient. Generally, such scale scores have been shown to be

largely insensitive to the violation of the interval assumption at the item-level (e.g., Leung,

2011; Parker et al., 2002).

In accordance with the methods proposed by Raudenbusch and Bryk (2002), we adopted a

stepwise sequential modeling strategy, reflecting an increasing complexity with each successive

model. In the first step, we estimated an unconditional means model without predictors to

partition the variance of teachers’ student-specific self-efficacy at the within- and between-



129

teacher level. This preliminary model was used as a baseline for subsequent model

comparisons. In the second step, we added students’ background characteristics, and their

externalizing, internalizing, and prosocial behaviors as within-level (fixed) effects of teachers’

student-specific self-efficacy. After these individual student characteristics were accounted for,

we added between-teacher covariates to the equation to explain variance at the between-

teacher level. Lastly, to examine the existence of cross-level interactions of students’ behaviors

and teaching experience with teachers’ perceived classroom misbehavior, we allowed potential

random slopes to vary across teachers. If a particular association between students’ behaviors

and teachers’ student-specific self-efficacy significantly varied across teachers, cross-level

interactions were added.

RESULTS

DESCRIPTIVE STATISTICS

Table 1 presents descriptive statistics, including zero-order correlations, means, and standard

deviations of the variables. Consistent with expectations, moderate to strong negative

correlations were found between students’ Externalizing Behavior and dimensions of teachers’

Student-Specific Self-Efficacy. Notably, the association between Externalizing Behavior and

TSE for BM appeared to be the strongest, suggesting that teachers felt the least confident in

dealing with disruptive students in the domain of Behavior Management. Somewhat smaller

negative correlations were found between students’ Internalizing Behavior and teachers’

Student-Specific self-percepts of Efficacy. These behaviors seemed to have a slightly higher

association with teachers’ belief in their capability to provide individual students with adequate

emotional support and security. The positive correlations between students’ Prosocial Behavior

and TSE in relation to individual students were also in line with hypotheses. Teachers who

generally perceived their students to act prosocially in the classroom seemed to experience

higher levels of Self-Efficacy toward these students in all domains of teaching and learning.

Teachers’ perceptions of the amount of misbehavior in the classroom were not associated with

any of the domains of Student-Specific TSE. Interestingly, though, teachers who reported a

large amount of Student Misbehavior in the classroom did not appear to judge the

externalizing behaviors of individual students to be higher than those who reported a smaller

amount of Classroom Misbehavior. In contrast, a negative association was noted between

teachers’ perceived Classroom Misbehavior and individual students’ Internalizing Behavior.


TA

BLE

1

Desc

riptiv

e Sta

tistic

s and

Corr

elatio

ns

Var

iable

1

2 3

4 5

6 7

8 9

10

11

12

1. T

each

er G

ende

r 1.

00

2. T

each

er E

xper

ienc

e –.

28**

1.

00

3. S

tude

nt G

ende

r .0

3 –.

03

1.00

4. S

tude

nt A

ge

.05

–.08

–.

11**

1.

00

5. E

xter

naliz

ing

Beha

vior

–.

08

–.03

–.

26**

.1

0*

1.00

6. In

tern

aliz

ing

Beha

vior

–.

18**

.1

3**

–.03

.0

8 .4

2**

1.00

7. P

roso

cial

Beha

vior

.0

2 .0

3 .3

1**

–.12

**

–.55

**

–.41

**

1.00

8. C

lassr

oom

Beh

avio

r Pro

blem

s .0

8 –.

02

.03

–.02

–.

07

–.12

**

.06

1.00

9. S

tude

nt–S

peci

fic T

SE fo

r IS

–.04

.1

5**

.15*

* –.

13**

–.

46**

–.

27**

.4

5**

.07

1.00

10. S

tude

nt–S

peci

fic T

SE fo

r BM

.0

0 .1

1*

.27*

* –.

08

–.73

**

–.28

**

.59*

* .0

2 .5

0**

1.00

11. S

tude

nt–S

peci

fic T

SE fo

r SE

–.

04

.18*

* .1

8**

–.17

**

–.57

**

–.31

**

.54*

* .0

8 .8

8**

.59*

* 1.

00

12. S

tude

nt–S

peci

fic T

SE fo

r ES

.02

.16*

* .2

4**

–.17

**

–.56

**

–.35

**

.56*

* .0

8 .8

0**

.65*

* .8

4**

1.00

M

– 16

.67

– 10

.57

1.96

2.

03

4.07

2.

48

5.53

6.

14

5.60

5.

82

SD

– 11

.87

– 1.

11

0.81

0.

78

0.74

0.

78

0.91

0.

99

0.96

0.

77

* p

< .0

5; *

* p

< .0

1. G

ende

r: 0

= b

oys/

male

teac

hers

, 1 =

girl

s/fe

male

teac

hers

. TSE

= T

each

ers’

self–

effic

acy;

IS =

Inst

ruct

iona

l stra

tegi

es; B

M =

Beh

avio

r man

agem

ent;

SE =

St

uden

t eng

agem

ent;

ES

= E

mot

iona

l sup

port.



131

Last, the correlations among students’ and teachers’ background characteristics, students’

behaviors, and Student-Specific TSE revealed, first, that male teachers and more experienced

educators generally reported their students to display higher levels of Internalizing Behavior.

Teaching Experience also seemed to be positively linked to all domains of Student-Specific

TSE, indicating that more experienced teachers perceive themselves as more efficacious than

their less experienced counterparts. In addition, teachers were likely to report higher levels of

Externalizing Behavior and lower levels of Prosocial Behavior for boys and older students, and

felt the least efficacious when dealing with these particular students. Lastly, it is interesting to

note that students’ Internalizing and Externalizing Behavior were moderately correlated with

each other, potentially suggesting comorbidity between behaviors in the externalizing and

internalizing spectrum (cf. Keiley, Lofthouse, Bates, Dodge, & Pettit, 2003). In the present

study, the focus was on the unique associations between students’ social–emotional behaviors

and teachers’ self-efficacy beliefs across domains and individual students.

UNCONDITIONAL MEANS MODEL

In the first step of the analyses, we fitted an unconditional means model, only containing the

four outcome variables (teachers’ Student-Specific Self-Efficacy for IS, BM, SE, and ES), and

no predictors other than the intercept. Intraclass correlations in this model indicated that

14.8% to 30.7% of the variance in teachers’ self-efficacy toward individual students occurred

between teachers. Generally, less than 5% of the variance in the domains of Student-Specific

TSE, however, was found to be associated with the school-level of hierarchy, implying that

teachers’ Student-Specific capability beliefs did not vary much across schools. Given the

substantial variance accounted for at the within- and between-teacher level, it can be concluded

that the data require a model that addresses the nesting of students within teachers.

STUDENT PREDICTORS OF TEACHERS’ STUDENT-SPECIFIC SELF-EFFICACY

Fixed effects of students’ background characteristics (Age and Gender) and behaviors

(Internalizing, Externalizing, and Prosocial Behavior) were modeled to allow the identification

of variables that were uniquely related to variation among dimensions of Student-Specific TSE.

This first model (see Table 2) significantly improved the prediction of teachers’ Student-

Specific Self-Efficacy beliefs, TRd(6) = 826.82, p < .001. Assessment of unstandardized

coefficients pointed to statistically significant negative associations between students’

Externalizing Behavior and teachers’ Student-Specific Self-Efficacy for IS (B = –.38, p < .001),


CHAPTER 4

132

BM (B = –.73, p < .01), SE (B = –.55, p < .001), and ES (B = –.27, p < .001). This indicates

that with each scale point higher on students’ Externalizing Behavior, teachers’ Student-

Specific Self-Efficacy across domains is expected to decrease between –.27 and –.73 scale

points (Hox, 2002). In addition, students’ Internalizing Behavior was only uniquely and

positively associated with Student-Specific TSE for BM (B = .13, p < .001), and negatively

associated with Student-Specific TSE for ES (B = –.08, p < .05). After accounting for

Externalizing and Internalizing Behaviors, students’ Prosocial Behavior yielded statistically

significant positive results for all dimensions of Student-Specific TSE (IS: B = .28, p < .001;

BM: B = .40, p < .001, SE: B = .34, p < .001; ES: B = .41, p < .001). Regarding students’

background characteristics, only students’ Age appeared to be negatively associated with

Student-Specific TSE for SE (B = –.11, p < .01) and ES (B = –.06, p < .05), indicating that

teachers generally feel less self-efficacious in providing emotional support and promoting

students’ engagement when dealing with older students.

TEACHER PREDICTORS OF TEACHERS’ STUDENT-SPECIFIC SELF-EFFICACY

After the effects of students’ background characteristics and behaviors were accounted for at

the within-teacher level, we subsequently added teachers’ Gender, Teaching Experience, and

perceived Classroom Misbehavior to the model to explain variance at the between-teacher

level. Table 2 presents the results of these fixed and random effects of the analysis (Model 2).

Compared to Model 1, we generally found no significant changes in the variables at the within-

teacher level. After addition of the teacher variables, however, the association between

students’ Internalizing Problems and Student-Specific TSE for IS became statistically

significant in Model 2 (B = –.13, p < .01), suggesting that teachers’ appraisals of students’

Internalizing Behavior may be affected by features inherent to the teacher. Yet, the significant

link between students’ Age and TSE for ES failed to reach the significance threshold in this

second model.


TABL

E 2

Fixe

d an

d Ran

dom

Esti

mates

for P

redict

ors of

Tea

chers

’ Dom

ain- a

nd S

tude

nt–S

pecif

ic Se

lf–E

ffica

cy

* p

< .0

5; *

* p <

.01.

Gen

der:

0 =

boy

s/m

ale te

ache

rs, 1

= g

irls/

fem

ale te

ache

rs; T

SE =

Tea

cher

s’ se

lf–ef

ficac

y; IS

= In

stru

ctio

nal s

trate

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6


CHAPTER 4

134

Regarding the teacher-level variables, only statistically significant associations were noted

between Teacher Experience and teachers’ sense of Student-Specific Self-Efficacy for SE (B =

.01, p < .01) and ES (B = .01, p < .05). The relationships of teachers’ Gender and perceived

Classroom Misbehavior with the dimensions of Self-Efficacy toward particular students were

not statistically significant. Overall, student variables accounted for 40% of the within-teacher

variance in Student-Specific TSE for IS, 65% in TSE for BM, 46% in TSE for SE, and 56% in

TSE for ES, respectively. At the between-teacher level, 14%, 9%, 25%, and 16% of the

variance in the respective Student-Specific TSE domains for IS, BM, SE, and ES was explained

by the student- and teacher-level predictors.

CROSS-LEVEL INTERACTIONS

To evaluate whether Teacher Experience and perceived Classroom Misbehavior interacted in

the prediction of Student-Specific TSE, the slopes of the student predictors were first allowed

to vary across teachers. The random slope coefficients of the association between students’

Externalizing Behavior and Student-Specific TSE for BM (σ2 = .08, p < .01), and between

Prosocial Behavior and Student-Specific TSE for BM (σ2 = .09, p < .01) and ES (σ2 = .02 p <

.05) were significantly different from zero, indicating that these parameters varied across

teachers. Consequently, cross-level interactions between the teacher variables (i.e., Teacher

Experience and perceived Classroom Misbehavior) and these student predictors were added

stepwise to the model. Adding these cross-level interactions did not affect the significance of

the parameter estimates of Model 2. None of these cross-level interactions reached the

significance threshold, except for the negative effect of teachers’ perceptions of Classroom

Behavior Problems on the association between students’ Externalizing Behavior and Student-

Specific TSE for BM (B = –.19, p < .01). This finding indicates that teachers feel less

efficacious in managing individual students’ externalizing behavior when they perceive high

amounts of misbehavior in the classroom.

DISCUSSION

This study investigated the associations between a variety of social–emotional student

behaviors and teachers’ self-efficacy beliefs toward individual students in various teaching

domains. In addition, the moderating role of teachers’ professional experience and perceived

classroom misbehavior was examined. Results from this study offer new insights into the ways



135

in which students’ externalizing, internalizing, and prosocial behaviors may hamper or support

teachers’ self-efficacy beliefs across teaching domains at a dyadic level.

TEACHERS’ SELF-EFFICACY IN RELATION TO EXTERNALIZING BEHAVIOR

Consistent with expectations, teachers perceived themselves as less self-efficacious in relation

to students who exhibited externalizing behavior in class, after controlling for students’ and

teachers’ background characteristics. This is in support of previous research on teachers’

classroom-level self-efficacy (e.g., Lambert et al., 2009; Tsouloupas et al., 2010), indicating that

disruptive children may hamper teachers’ self-efficacy in dealing with challenging behavior and

stressful situations in the classroom. However, whereas past studies have almost solely

concentrated on total efficacy scores or domain-specific TSE for behavior management, our

results additionally show that these under-controlled behaviors are consistently linked to various

domains of self-efficacy for teaching and learning. Accordingly, unsuccessful encounters with

students who display externalizing conduct are likely to undermine teachers’ perceived

capability to effectively instruct, motivate, manage, and emotionally support individual students.

Such poorer self-efficacy beliefs, in turn, may also bring about more disruptive student

behavior in new situations (e.g., Bandura, 1997).

Not surprisingly, the association between externalizing student behavior and teachers’

perceived capability in deploying effective methods to prevent and redirect instances of student

misbehavior appeared to be the largest. Possibly, these patterns of externalizing misconduct

reflect a poorer fit with teachers’ expectations for appropriate behavior in the classroom than

other challenging student behaviors (Gresham & Kern, 2004). Such behavioral mismatches

may trigger a pattern of disturbed student–teacher interactions, which potentially undermine

teachers’ feelings of efficacy and satisfaction in teaching (cf. Koomen & Spilt, 2011). This is

alarming, given that an unhealthy sense of self-efficacy for behavior management may

encourage teachers’ use of ineffective conflict management styles, which may exacerbate

students’ disruptive behavior and potentially advance the erosion of teachers’ already feeble

capability beliefs (e.g., Goddard et al., 2004; Jennings & Greenberg, 2008; Morris-Rothschild &

Brassard, 2006).

Perhaps of a more interesting note is the finding that symptoms of externalizing student

behavior may also come at the expense of teachers’ student-specific self-efficacy beliefs in the

instructional domain. There are some studies to support this finding, indicating that teachers


CHAPTER 4

136

generally feel less confident and effective in proactively involving disruptive students in high-

quality instructional interactions and activities, and consequently resort to controlling and

punitive behaviors toward these students (e.g., Arbeau & Coplan, 2007; Sutherland & Oswald,

2005; Wehby, Symons, Canale, & Go, 1998). Probably, such a lack of efficacy in instructing

and motivating challenging students may further reinforce these children’s expressions of anger

and frustration toward the teacher, as well as increase their off-task behavior and

maladjustment in class (Arnold, 1997; Stipek & Miles, 2008). Thereby, a vicious cycle may be

set into motion in which teachers’ student-specific self-efficacy percepts and instructional

actions, and students’ subsequent social–emotional and task behaviors in class may influence

each other in a reciprocal manner (cf. Bandura, 1997; Stipek & Miles, 2008). Hence, given that

externalizing student behaviors may hamper student-specific TSE in both instructional and

social–emotional domains, it seems essential to provide educators with the knowledge and

skills necessary for teaching disruptive students self-regulation strategies that improve their

classroom adjustment (cf. Koomen & Spilt, 2011).

TEACHERS’ SELF-EFFICACY IN RELATION TO INTERNALIZING BEHAVIOR

Consistent with expectations, internalizing behaviors seemed to be less of a factor than

externalizing student behavior in explaining variations in teachers’ self-percepts of student-

specific self-efficacy. This finding resonates well with those of past research (e.g., Coplan &

Prakash, 2003; Gresham & Kern, 2004; Kokkinos et al., 2004), suggesting that students’

internalizing symptoms might go undetected by their teachers, or are merely perceived as less

serious. Accordingly, it is possible that teachers may display a greater zeal and persistence in

educating internalizing children than externalizing children.

As yet, our results give reason to believe that behaviors in the internalizing spectrum may

contribute to some aspects of teachers’ sense of student-specific self-efficacy. Specifically,

teachers’ student-specific self-efficacy for emotional support seemed to be predicted best by

students’ internalizing behaviors, after accounting for students’ and teachers’ background

features. One possibility that may explain this negative association is that internalizers feel

more wary and anxious in the face of social stimuli and consequently tend to refrain from daily

interactions with their teacher (e.g., Arbeau, Coplan, & Weeks, 2010; Coplan & Prakash, 2003;

Rudasill, 2011). Such socially withdrawn behaviors may result in a student–teacher relationship

pattern characterized by lower levels of closeness and higher levels of dependency (e.g., Arbeau

et al., 2010; Henricsson & Rydell, 2004; Roorda et al., 2014). When teachers recurrently fail to



137

connect and get through to these internalizing children, poorer self-efficacy beliefs toward

these particular children may be prompted (e.g., Bandura, 1997). This may explain why

teachers usually fall back into regulatory and dominant behaviors toward students with

internalizing behavior (Roorda, Koomen, Spilt, Thijs, & Oort, 2013).

Somewhat surprisingly, teachers also reported slightly elevated levels of self-efficacy in the

domain of behavior management toward students with internalizing behavior. One mainly

methodological explanation for this finding may be that internalizing student behavior merely

functioned as a suppressor for predicting the fairly stronger, unique association among

students’ externalizing behavior and TSE for behavior management. According to Maassen

and Bakker (2001), this phenomenon may occur when a predictor is positively correlated with

another independent variable, but not with the criterion. In the present study, suppression may

indicate that internalizing student behavior has more in common with externalizing conduct

than with teachers’ student-specific self-efficacy for behavior management, and thereby

improved externalizing behavior as a predictor of TSE for behavior management. This

potential suppressor effect mirrors previous empirical research, suggesting that comorbid

externalizing and internalizing symptoms may occur more frequently than single-form

behaviors, and should therefore be interpreted in combination with each other, rather than

separately (e.g., Keiley et al., 2003). Another, more theoretical justification for this effect is that

students with anxious and withdrawn patterns of behavior (without potentially co-occurring

externalizing symptoms) usually do not disturb their peers or challenge their teachers’

authority. Thereby, these students seem to meet teachers’ behavioral values and expectations in

the classroom (Gresham & Kern, 2004). As such, it is possible that teachers might actually feel

quite self-efficacious in managing these students’ behaviors.

Lastly, internalizing student behavior did not seem to seriously upset their teachers’ self-

efficacy for tasks related to motivation and instructional delivery. The lack of association

between students’ internalization and student-specific TSE for student engagement was, for

instance, at odds with our expectation that teachers may feel less efficacious in activating their

students’ interest in schoolwork when dealing with emotionally disturbed students. Moreover,

the negative association between students’ internalizing behavior and TSE for instructional

strategies only reached the significance threshold after accounting for teachers’ gender,

experience, and perceived classroom misbehavior. It may be that educators’ recognition of, and

responsiveness to internalizers’ subtle cues are more likely to be affected by factors inherent or


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contextual to the teacher than their preoccupation with externalizers’ more blatant signs.

Research of Kokkinos and colleagues (Kokkinos et al., 2005; Kokkinos & Kargiotidis, 2014),

for instance, put forth that teachers’ ability to recognize the needs and behaviors of students

with internalizing problems increases as they have more teaching experience, and may depend

on their own interpersonal sensitivity and gender. Correlational patterns between students’

social–emotional behaviors and teacher-level variables in the present study, including teaching

experience and gender, largely substantiate this assumption. Also, there is a strong possibility

that students with internalizing symptoms, due to their subdued behaviors, generally provoke

less negative thoughts about instruction or feelings of inefficacy in their teachers, as it is more

difficult for teachers to gauge these students’ comprehension of what they have taught (e.g.,

Rubin & Coplan, 2004).

TEACHERS’ SELF-EFFICACY IN RELATION TO PROSOCIAL BEHAVIOR

In line with expectations, teachers consistently reported higher levels of self-efficacy in relation

to students who exhibit high levels of prosocial behavior. Again, stronger associations were

noted for teachers’ self-efficacy toward emotional and behavioral domains of teaching and

learning, than for instruction-related tasks. This is perhaps not surprising, as the domains of

behavior management and emotional support are, in large part, concerned with how well

teachers relate to, and interact with their students. Several empirical sources have shown that

patterns of prosocial student behavior may pave the way for higher quality relationships with

their teachers (Birch & Ladd, 1998; Henricsson & Rydell, 2004; Roorda et al., 2014). Such

enactive mastery experiences may raise teachers’ beliefs in their self-efficacy (Bandura, 1997;

Goddard et al., 2004), potentially further stimulating individual students’ prosocial behaviors in

the classroom.

Despite teachers’ higher self-efficacy beliefs in relation to students who display relatively high

levels of prosocial behavior, teachers have repeatedly been shown to spend less time with

prosocial students, and regularly fail to give them credit for their positive behavior, especially

when they get older (e.g., Arbeau & Coplan, 2007; Nesdale & Pickering, 2006). To maintain

and further encourage prosocial behavior in their students, teachers should recognize the need

to praise and respond to students’ appropriate behaviors in class. In doing so, teachers may

further enhance their feelings of self-efficacy toward these individual children.



139

TEACHERS’ SELF-EFFICACY IN RELATION TO STUDENT AND TEACHER CHARACTERISTICS

In investigating students’ background characteristics, we only found students’ age to be

negatively associated with teachers’ student-specific self-efficacy for student engagement. This

finding is supported by prior research (Wolters & Daugherty, 2007), noting that teachers, when

dealing with older children, tend to report less confidence in their ability to keep students

engaged. This intriguing finding seems to complement those of studies on student motivation

(e.g., Fredricks & Eccles, 2009), which demonstrated a downward spiral in students’

competence-related behaviors and motivation during their transition to middle school. Future

research should take the complex interplay between teachers’ self-efficacy, students’ age, and

motivation into account.

Although bivariate correlations suggested a potential association between professional teaching

experience and dimensions of TSE toward individual students, multilevel analyses indicated

that teaching experience only added to the prediction of student-specific TSE for student

engagement and emotional support. This finding suggests that educators’ teaching experience

particularly ameliorates their self-efficacy in the affective domain of teaching, including such

tasks as providing emotional support and increasing individual students’ interest in

schoolwork. Previous studies have supported this slight increase in more experienced teachers’

self-efficacy, both for affective domains as well as other areas of teaching and learning (Klassen

& Chui, 2010; Ross et al., 1996; Wolters & Daugherty, 2007). The potential value of teachers’

experience for their self-efficacy might explain, in part, why experienced teachers seem to be

more effective in managing students’ behaviors and addressing their needs than inexperienced

teachers (Kokkinos et al., 2004).

THE MODERATING ROLE OF TEACHING EXPERIENCE AND PERCEIVED CLASSROOM MISBEHAVIOR

In seeking to discern the moderating role of teachers’ experience and perceived classroom

misbehavior, we noted that years of experience did not buffer or exacerbate the association

between students’ social-emotional behaviors and teachers’ self-efficacy toward individual

students. This is unlike the findings of Kokkinos et al. (2004), which seemed to suggest that

teachers’ experience-induced behavioral knowledge, skills and awareness may buffer or

exacerbate the potential negative relationship between challenging student behavior and TSE.

However, results did point to a moderation effect of teachers’ perceptions of classroom

misbehavior. Specifically, teachers in poorly behaving classrooms experienced lower levels of


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140

self-efficacy in managing the behavior of individual students with externalizing conduct than in

classrooms with fewer instances of misbehavior. This finding substantiates prior research (e.g.,

Bynes, 1994), indicating that teachers may develop increasingly negative attitudes toward their

students in classrooms with many challenging students. Importantly, however, the moderating

role of teacher-perceived amounts of classroom misbehavior could not be ascribed to teachers’

appraisals of individual students’ externalizing behavior. In the present study, the zero-order

correlation between misbehavior in class and ratings of externalizing student behavior was not

significant. Hence, these findings underline the relevance of considering characteristics of the

classroom when investigating teachers’ beliefs of self-efficacy.

LIMITATIONS

The present study’s findings need to be interpreted in the context of several limitations. First,

the correlational and cross-sectional nature of the study precludes any speculation on causal

relations. Although our results provide preliminary support of the potential relationships

between students’ behavior and TSE, it may well be that the nature of these associations are

reciprocal. Indeed, Bandura’s (1997) model of triadic reciprocal causation asserts that teachers’

personal factors, their behaviors, and aspects of the classroom context may function as

interacting factors that influence one another bi-directionally. Longitudinal, cross-lagged

designs could advance our understanding of how individual students’ behaviors and teachers’

self-efficacy toward these students in various domains of teaching and learning influence one

another across time.

In relation to this issue, some caution is warranted when generalizing the results of this study

to other populations and settings. Specifically, this study relied on a sample of primarily

experienced, female teachers who generally taught students with mid- to high socioeconomic

backgrounds. These teachers, by virtue of their experience and more advantaged student

population, may have felt more efficacious and better prepared to deal with their students

across teaching domains. Including teachers from a wider range of backgrounds may result in a

more reliable and generalizable picture of teachers’ self-efficacy in relation to particular

students in different spheres of functioning.

Third, teachers not only reported about their sense of self-efficacy toward individual students,

but also about these students’ behaviors. As such, this study might have been threatened by

shared source variance, resulting in an overestimation of the strength of associations. However,



141

teachers’ self-efficacy is most likely constructed from information conveyed by experienced

events in the classroom (Bandura, 1997). Given that teachers’ own experiences and self-

knowledge are crucial sources of their self-efficacy, teacher reports may seem an adequate

method of measuring students’ classroom behaviors. Still, it would be useful for future

research to employ multiple methods, including interviews and observations, to further

elucidate the present study’s findings.

Fourth, although we made use of multilevel analysis to handle the clustering of students within

teachers, we did not address the nesting of classrooms within schools. One reason for

choosing to ignore a third level of nesting is that we generally found less than 5% of the

variance in TSE to be associated with the school-level of hierarchy, suggesting that teachers’

capability beliefs did not vary much across schools. Probably, this lack of variation might be

explained by the fact that the 69 teachers who participated in this study were relatively evenly

distributed across the 24 schools. Indeed, only two to three teachers per school decided to take

part. Nevertheless, a number of studies on the sources of TSE has indicated that teachers’ self-

efficacy may depend, in part, on aspects such as school atmosphere, principal leadership, and

social support provided by parents and colleagues (e.g., Cheung, 2008; Lee, Dedrick, & Smith,

1991; Moore & Esselman, 1992; Tschannen-Moran & Woolfolk Hoy, 2007). With this in mind,

it may be important to include such school contextual influences at the school-level of analysis

when investigating teachers’ self-efficacy beliefs.

Fifth, it is possible that the relations discovered in this study emanate from a common relation

with contextual or structural features of the classroom context. Although we were able to

account for differences between teachers in their gender, years of experience, and perceived

classroom misbehavior, there might have been other important between-teacher factors that

we did not include in this study. For instance, teachers’ collegial support (Brownell & Pajares,

1999; Ciani, Summers, & Easter, 2008), instructional quality and classroom management

(Holzberger, Philipp, & Kunter, 2013), and perceived work pressure (Leroy, Bressoux,

Sarrazin, & Trouilloud, 2007) have been shown to be associated with teachers’ sense of self-

efficacy. Thus, in any attempt to replicate the results, it is recommended that future researchers

should take account of classroom and teacher characteristics to explain between-teacher

differences in TSE.


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142

Lastly, teachers’ perceptions of self-efficacy were characterized by relatively high means and

small standard deviations, suggesting the existence of social desirability bias. Generally, social

desirability has been presumed to generate more flattering reports about the self and a limited

range of answers (Goffin & Gellatly, 2001). This potential bias in teachers’ responses have also

been noted in prior research on teachers’ domain-specific self-efficacy at the classroom-level

(e.g., Heneman, Kimball, & Milanowski, 2006), and might have weakened the associations with

students’ behaviors in this study.

CONCLUSION

Despite its limitations, the present study has demonstrated the theoretical and practical

relevance of studying TSE in relation to individual students’ social–emotional behaviors across

various domains of teachers’ functioning. Teachers’ self-efficacy has long been conceptualized

as a relatively stable teacher characteristic which, at best, may be dependent upon particular

teaching tasks and domains (Raudenbusch et al., 1992; Tschannen-Moran & Woolfolk Hoy,

2001). Our results show, however, that most of the variance in TSE occurred within teachers,

suggesting that these capability beliefs may also vary over the particular students they teach.

Central contributors to such self-efficacy fluctuations seem to be both prosocial and

challenging student behaviors, and externalizing behavior in particular. Notably, these

behaviors not only appear to relate to teachers’ perceived effectiveness in providing behavioral

and affective support during reciprocal student–teacher interchanges, but their TSE in

delivering instruction as well. This is an important finding, given that teachers’ dealings with

individual students’ misbehavior are likely to come at the expense of high-quality instructional

activities and student–teacher interactions (e.g., Arbeau & Coplan, 2007; Sutherland & Oswald,

2005).

The results of the present study, if they are replicated in future studies, may have several

implications for educational researchers and practitioners alike. First, the ways teachers

appraise and integrate individual students’ behavior into student-specific self-efficacy

judgments may play an important role in teachers’ preparedness and motivation to deal with a

particular child (Bandura, 1997). Assumedly, educators who perceive themselves as unable to

teach and affectively support a child have a tendency to shy away from these children or

slacken their efforts when the goings get tough. Teachers must be made aware that such

behaviors and actions may have serious implications for the academic and social–emotional



143

adjustment of challenging students, and externalizing children in particular. Specifically,

children with externalizing problems may become easily frustrated or unhappy about their

teachers’ lack of instructional or emotional support, and may express these feelings by acting

more aggressively toward the teacher in future situations (cf. Stipek & Miles, 2008). As such,

teachers’ self-efficacy beliefs toward disruptive students and associated behavior and actions

may serve as an additional risk factor for poor quality student–teacher relationships and

students’ social-emotional and academic maladjustment in school. Yet, the importance of

teachers’ confidence in their ability to provide internalizing students with adequate emotional

support should also not be underestimated. These capability beliefs may serve as important

tools for helping students with internalizing symptoms to come out of their shell and to

navigate the social world. Thus, helping teachers to reflect on the effects of their cognitions

about externalizing and internalizing children may be vital to improving the quality of students’

and teachers’ shared interactions and experiences.

Second, the dynamic interplay between students’ disruptive behaviors and TSE may not only

hamper students’ academic adjustment, but may also result in increased levels of emotional

labor, daily stress, and burnout in teachers (e.g., Chang, 2013; Hargreaves, 1998; Spilt et al.,

2011). This suggests that teacher training and development programs must incorporate

strategies that teachers might use to bolster their self-efficacy in relation to individual

(disruptive) students, including goal setting, behavior management, and providing emotional

support. These activities may allow teachers to gain more pleasant emotional experiences with,

and social feedback from their students, resulting in less stress and higher TSE (Spilt et al.,

2011).

In conclusion, it behooves educational researchers and practitioners alike to further investigate

the complex ways in which teachers’ self-efficacy in relation to individual students with

externalizing and internalizing symptoms and their subsequent behaviors and actions toward

them affect students’ motivation, conduct, and achievement in the classroom. Viewing

teachers’ self-efficacy from a dyadic perspective may be a first step forward.


145

CHAPTER 5 STUDENTS’ DISRUPTIVE BEHAVIOR AND THE DEVELOPMENT OF TEACHERS’ SELF-

EFFICACY: THE ROLE OF TEACHER-PERCEIVED CLOSENESS AND CONFLICT IN THE

STUDENT–TEACHER RELATIONSHIP

_________________________________________________________________________

Data gathered from a short-term longitudinal study within regular upper elementary schools

were used to evaluate a theoretical model within which teachers’ perceptions of conflict and

closeness in the student–teacher relationship were considered as the intermediary mechanisms

by which individual students’ disruptive behavior may generate changes in teachers’ student-

specific self-efficacy beliefs (TSE) across teaching domains (instructional strategies, behavior

management, student engagement, and emotional support). Surveys were administered among

a Dutch sample of 524 third-to-sixth graders and their 69 teachers. Longitudinal mediation

models indicated that individual students’ disruptive behavior generally predicted higher levels

of teacher-perceived conflict which, in turn, resulted in lower student-specific TSE across

teaching domains. Teacher-perceived closeness, however, was not found to mediate the link

between disruptive student behavior and student-specific TSE. Instead, support was found for

an alternative model representing the hypothesis that TSE, irrespective of teaching domain,

mediated behavior-related changes in teachers’ perceptions of closeness in the student–teacher

relationship.

________________________________________________________________________________________ Zee, M., de Jong, P. F., & Koomen, H. M. Y. (2015). Students’ disruptive behavior and the development of teachers’ self-efficacy: The role of teacher-perceived closeness and conflict in the student–teacher relationship. Manuscript submitted for publication.


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146

INTRODUCTION

Teachers’ self-efficacy beliefs (TSE) have been widely acknowledged to be one of the most

basic, yet potent psychological resources of teachers’ functioning in the classroom (Bandura,

1997; Klassen, Tze, Betts, & Gordon, 2011; Tschannen-Moran & Woolfolk Hoy, 2001).

Accumulating evidence has indicated that teachers with a firm belief in their capabilities may

translate their knowledge and abilities into proficient action, whereas those who lack such

beliefs will probably not attempt to make things happen in class (e.g., Bandura, 1997; Klassen

& Tze, 2014). When teachers live up to their generalized sense of self-efficacy, they are more

likely to provide high-quality instruction, adopt proactive approaches to managing disruptive

behavior, and convey supports that activate students’ motivation and engagement in class (e.g.,

Dunn & Rakes, 2011; Martin & Sass, 2010; Morris-Rothschild & Brassard, 2006; Reyes et al.,

2012; Wertheim & Leyser, 2002). Given the important role TSE might play in students’

socioemotional and academic development, it is critical to explore the factors and processes

that may account for these beliefs.

One potentially compelling contribution to the corpus of evidence on the sources of TSE has

recently been provided by cross-sectional investigations focusing on teachers’ sense of self-

efficacy in relation to individual students (e.g., Zee & Koomen, 2015; Zee, Koomen, Jellesma,

Geerlings, & de Jong, 2016). These studies have suggested that teachers are likely to develop

differentiated sets of self-beliefs about their ability to deal with individual children in distinct

teaching domains, depending on these students’ disruptive, or externalizing behaviors in the

classroom (ibid.). Relatively little information has been generated, however, about the

mechanisms by which individual students’ disruptive behavior may generate changes in these

student-specific TSE beliefs. Following Bandura (1997), there is a need for research to move

away from cross-sectional examinations of TSE and its underlying sources, and explore the

role of potential mediating processes through which sources of self-efficacy may become

instructive to teachers’ self-efficacy beliefs across time. In the present study, therefore, we seek

to expand the available information on the sources of student-specific TSE, by evaluating an

interpersonal social-cognitive model within which teachers’ perceptions of closeness and

conflict in the relationships with individual students are hypothesized to form the intermediary

mechanisms by which individual students’ disruptive behavior may affect teachers’ student-

specific self-efficacy over time. Theoretical and empirical knowledge in this direction may help

educational researchers and practitioners identify levers to increase teachers’ self-efficacy


PROCESSES UNDERLYING TEACHER SELF-EFFICACY

147

toward disruptive students, and thereby improve these students’ classroom experiences and

academic adjustment.

AN INTERPERSONAL SOCIAL-COGNITIVE MODEL OF TEACHERS’ SELF-EFFICACY

In this study, we extended Bandura’s (1997) social-cognitive assumptions about self-efficacy by

embedding them within an interpersonal framework of student–teacher relationships (Pianta,

1999; Pianta, Hamre, & Stuhlman, 2003). With this integrated model, we aimed to subscribe to

the longstanding notion that TSE, rather than being a single-level, trait-like construct, is

intrinsically related to the specific students with whom they interact in distinct realms of

activity (Tschannen-Moran, Woolfolk Hoy, & Hoy, 1998; Tschannen-Moran & Woolfolk Hoy,

2001; Zee et al., 2016). This conception of TSE as being both student- and domain-specific

maintains, generally, that features of individual students, such as their background

characteristics and behaviors, may serve as key sources of information about whether teachers

can muster whatever it takes to adequately instruct, manage, motivate, and emotionally support

a particular student (Bandura, 1997; Pianta et al., 2003; Zee et al., 2016). Consistent with this

view, a modest body of work on within-teacher predictors of TSE has spawned some evidence

that teachers’ general efficaciousness can rise or fall according to their students’ level of

engagement and achievement in class (e.g. Raudenbusch, Rowan, & Cheong, 1992; Ross,

Cousins, & Gadalla, 1996). Other studies have marked students’ disruptive behaviors as the

type of information teachers attend to and use as direct sources of their self-efficacy in

domains of behavior management, relationship building, instructional strategies, and student

motivation (e.g., Lambert, McCarthy, O'Donnell, & Wang, 2009; Spilt & Koomen, 2009;

Tsouloupas, Carson, Matthews, Grawitch, & Barber, 2010; Zee, de Jong, & Koomen, 2016).

Relative to other sources of self-efficacy, these disruptive student behaviors have thus far been

demonstrated to achieve the highest explanatory and predictive power for both classroom-level

and student-specific TSE (ibid.).

Further broadening beyond the original social-cognitive paradigm, our framework adheres to

the notion that disruptive student behaviors, as sources of student-specific TSE, may not per

se be enlightening to the formation of these beliefs (cf. Bandura, 1982, 1997). Rather,

individual students’ conduct can be presumed to become instructive to TSE only through

teachers’ subjective evaluations of these behaviors in the context of their daily interactions with

individual students. Theory and research on student–teacher interactions (e.g., Pianta et al.,

2003; Spilt & Koomen, 2009; Spilt, Koomen, & Thijs, 2011; Stuhlman & Pianta, 2002) has


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indicated that teachers’ evaluations of student behavior may derive, in part, from previous

experiences with individual students, stored in underlying representational models of

relationships with these students. This idea is premised on the attachment-based assumption

that relationship representations may yield internalized and relatively stable patterns of beliefs,

feelings, and expectations about the self as a teacher and the student in the relationship (Pianta

et al., 2003; Spilt & Koomen, 2009). Such belief systems can be primarily positive, reflecting

experiences of close student–relationships, or predominantly negative, incorporating a history of

conflict in the relationship with a particular child (e.g., Verschueren & Koomen, 2012).

Accordingly, teachers’ representations, or perceptions, of closeness and conflict in the student–

teacher relationship can be considered powerful cognitive tools, as they largely guide their

interpretations of individual students’ underlying intentions, behaviors, and actions in the

relationship, and provide teachers with vital information about their capability to deal with the

child (Howes, Hamilton, & Matheson, 1994; Pianta, 1999; Pianta et al., 2003; Spilt et al., 2011).

Guided by the interpersonal social-cognitive principles proposed above, we aim to explore a

model (see Figure 1a) in which teachers’ perceptions of closeness and conflict in the student–

teacher relationship are considered to be the intermediary mechanisms that could explain why

teachers may develop a positive or negative sense of self-efficacy toward individual disruptive

children. Theoretical and empirical justification for the sequence of linkages delineated by our

hypotheses are provided in the next sections.

DISRUPTIVE STUDENT BEHAVIOR AND TEACHERS’ RELATIONSHIP PERCEPTIONS

Multiple sources of evidence have increasingly indicated that disruptive student behavior

matters for teachers’ perceptions of conflict and closeness in the student–teacher relationship

(e.g., Mejia & Hoglund, 2016; Roorda, Verschueren, Vancraeyveldt, van Craeyevelt, & Colpin,

2014). Drawing on both attachment and developmental systems frameworks, these studies

have postulated that teachers generally have more difficulty forming relationships with

disruptive students that are marked by warmth, trust, and affection (i.e., closeness), and instead

develop relationships that reflect high levels of negativity, discordance, and distrust (i.e., conflict;

Pianta, 1999). In line with this assumption, both cross-sectional and longitudinal studies have

convincingly disclosed the negative effect of disruptive, aggressive, or antisocial student

behavior on teachers’ experiences of student–teacher conflict (Birch & Ladd, 1998; Henricsson

& Rydell, 2004; Jerome et al., 2009; Ladd & Burgess, 1999; Murray & Murray, 2004; Murray &

Zvoch, 2011; O’Connor, 2010).



149

FIGURE 1A Hypothesized Mediational Model

Note. IS = Instructional Strategies, BM = Behavior Management, SE = Student Engagement, ES = Emotional Support. Coefficients a and b reflect associations between predictor and mediators, and mediators and outcome measures, respectively. The product ab reflects the hypothesized indirect effect of individual students’ disruptive behavior on domains of student-specific self-efficacy, through teachers’ perceptions of conflict and closeness. Coefficient c reflects the direct association between predictor (disruptive student behavior) and outcome variables (domains of student-specific self-efficacy).

FIGURE 1B Alternative Mediational Model

Note. IS = Instructional Strategies, BM = Behavior Management, SE = Student Engagement, ES = Emotional Support. Coefficients a and b reflect associations between predictor and mediators, and mediators and outcome measures, respectively. The product ab reflects the hypothesized indirect effect of individual students’ disruptive behavior on teachers’ perceptions of conflict and closeness, through domains of student-specific self-efficacy. Coefficient c reflects the direct association between predictor (disruptive student behavior) and outcome variables (teachers’ perceptions of conflict and closeness).

Individual Students’ Disruptive Behavior

Teachers’ Perceptions of Conflict and Closeness in the Student–

Teacher Relationship

Student-Specific Self Efficacy for IS, BM, SE and ES

c

Individual Students’ Disruptive Behavior

Student-Specific Self-Efficacy for IS, BM, SE, and ES

Teachers’ Perceptions of Conflict and Closeness in the Student–

Teacher Relationship

c


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Furthermore, some studies (Doumen et al., 2008; Roorda et al., 2014) have even acknowledged

that students’ displays of externalizing behavior may be sufficient to commence a vicious cycle

of disharmonious relationships and escalating problem behaviors. These outcomes are

consistent with the idea that student behavior and teachers’ perceptions of the student–teacher

relationship are reciprocally related to one another. Hence, it can be suggested that disruptive

student behavior may generate negative changes in teachers’ perceptions of conflict in the

student–teacher relationship.

Far less consistent are the findings regarding the linkage between disruptive student behavior

and teachers’ perceptions of closeness in the student–teacher relationship. Specifically, several

primarily cross-sectional studies have identified disruptive student behavior as a negative predictor

of teachers’ perceptions of relational closeness (Birch & Ladd, 1998; Buyse et al., 2008; Mejia

& Hoglund, 2016; Thijs, Westhof, & Koomen, 2012). Following these investigations, teachers

may thus experience lower, concurrent levels of closeness in the relationship with students

who display disruptive behavior in the classroom. The handful of prior longitudinal studies, in

addition, has generally indicated that the modest association between individual students’

disruptive behaviors and teacher-reported degrees of closeness may remain relatively stable

over time (Baker, Grant, & Morlock, 2008; Henricsson & Rydell, 2004; Jerome, Hamre, &

Pianta, 2009; Mejia & Hoglund, 2016; Roorda et al., 2014; Zhang & Sun, 2011). For example,

some cross-lagged panel studies (Mejia & Hoglund, 2016; Roorda et al., 2014) have revealed

significant within-time correlations between individual students’ disruptive behavior and

teacher-reported closeness, but no additional effects of these behaviors on prospective levels

of relational closeness, after accounting for the stability in both constructs. Whether the link

between students’ disruptive behavior and teachers’ subsequent student-specific self-efficacy

beliefs can be explained by changes in teachers’ perceptions of closeness thus remains to be

explored.

TEACHERS’ RELATIONSHIP PERCEPTIONS AND SELF-EFFICACY BELIEFS

To date, only a scant amount of literature has provided empirical illustrations of our hypothesis

that teachers’ experiences in relationships with individual students may generate changes in

their student-specific self-efficacy beliefs. In part, this lack of research may stem from the fact

that TSE, in contrast to the dyadic constructs of closeness and conflict, is usually defined at the

classroom-level of analysis, thereby reflecting the collective valence of teachers’ sense of self-

efficacy toward their students in the classroom. Yet, the results of these studies seem to yield a



151

fairly consistent picture across dimensions of the student–teacher relationship, pointing to

student–teacher conflict as the strongest predictor of general, classroom-level TSE (e.g.,

O’Connor 2008; Spilt et al., 2011). In a sample of secondary school teachers, for instance, Yeo,

Ang, Chong, Huan, and Quek (2008) indicated that teachers who experience high levels of

conflict in the relationships with their students are likely to develop unhealthy, classroom-level

self-efficacy beliefs in the teaching domains of classroom management and instructional

strategies. Other research explicates that poor relationships with students may lead to increases

in emotional vulnerability in teachers, and result in feelings of professional and personal failure

(Hargreaves 1998, 2000; Newberry & Davis, 2008; O’Connor, 2008; Spilt et al., 2011).

Together, these findings lend credence to the idea that teachers, through their perceptions of

conflict in the student–teacher relationship, come to see the task of teaching disruptive

students as more difficult and consequently adjust their self-percepts of self-efficacy toward

these students downward.

Counter to student–teacher conflict, high levels of relational closeness can be assumed to

provide teachers with the affective cues, performance successes, and persuasive boosts that

convince them they have whatever it takes to succeed with a child. In the study of Yeo et al.

(2008), however, this hypothesized association could not be confirmed. Their findings revealed

that positive aspects of student–teacher relationships, including teachers’ instrumental help and

satisfaction, were not associated with teachers’ general sense of self-efficacy for instructional

strategies, classroom management, and student engagement. Patterns of bivariate correlations

from a study of Spilt, Koomen, Thijs, and van der Leij (2012) largely mirror these findings.

Their results indicated that the linkage between teachers’ reports of closeness in the

relationships with disruptive kindergartners and general TSE was not significant.

Several empirical studies have also spawned some evidence for the alternative hypothesis that

teachers’ self-efficacy beliefs may affect their perceptions of student–teacher relationships,

although the results are a bit mixed (e.g., Chung, Marvin, & Churchill, 2005; Spilt et al., 2011;

Yoon, 2002). Specifically, Mashburn, Hamre, Downer, and Pianta (2006) indicated that

generally self-efficacious teachers were likely to experience more close, but not less

conflictuous relationships with individual, regular preschool students. When explicitly focusing

on problematic students, Hamre, Pianta, Downer, and Mashburn (2008) even found that

preschool teachers with generally low self-efficacy judgments at the classroom-level tended to

experience higher degrees of conflict with individual students than would be expected based


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on their judgments of these students’ problem behaviors. For this reason, we also aimed to

explore an alternative model, in which teachers’ sense of self-efficacy in relation to individual

students’ disruptive behavior may feedback on their perceptions of the student–teacher

relationship in confirming or disconfirming ways.

PRESENT STUDY

The present study aims to broaden the purview of primarily cross-sectional research on

teachers’ general sense of self-efficacy at the classroom-level by testing a theoretical model

describing teachers’ student–teacher relationship perceptions (i.e., closeness and conflict) as the

processes through which individual students’ disruptive behavior may contribute to teachers’

subsequent student-specific self-efficacy beliefs across domains of teaching and learning (i.e.,

instructional strategies, behavior management, student engagement, and emotional support).

Guided by our interpersonal social-cognitive model, we first examined whether teachers’

perceived levels of closeness and conflict in the student–teacher relationship mediates the

longitudinal association between individual students’ disruptive behavior and student-specific

TSE in various domains of teaching and learning (see Figure 1a). Based on the idea that

disruptive behavior is more likely to be perceived as a threat to TSE when teachers have

internalized negative feelings about the student–teacher relationship, we expected teacher-

perceived conflict to mediate the negative association between disruptive student behavior and

student-specific TSE. In addition, due to mixed results in previous studies, we did not have

clear expectations about the mediating role of closeness in the association between disruptive

student behavior and student-specific TSE.

As an additional test of validity for the hypothesized model, we secondly tested an alternative

model in which student-specific TSE mediates the association between disruptive behavior and

teachers’ perceptions of closeness and conflict in the student–teacher relationship (see Figure

1b). Support for these alternative models would consist of evidence indicating that individual

students’ disruptive behavior leads to changes in teachers’ sense of self-efficacy in relation to

individual students across teaching domains, which, in turn, leads to changes in their

perceptions of conflict and closeness.



153

METHOD

PARTICIPANTS

The present study contained Dutch elementary school teachers and third- to sixth-grade

students who participated in a short-term, two-wave longitudinal study on teachers’ dealings

with diversity. Sample selection proceeded in three phases. First, 350 randomly selected

schools across the Netherlands were contacted by telephone and e-mail, after obtaining ethical

approval from the Ethics Review Board of the Faculty of Social and Behavioral Sciences,

University of Amsterdam (project no. 2013-CDE-3188). Of these schools, 24 were inclined to

participate in the study. Second, all upper elementary teachers from participating schools

received a letter about the study’s purposes and an informed consent form, which was

ultimately signed by 70 teachers. Information letters describing the nature and purposes of the

research project were also sent to students’ homes. After parental consent was obtained, we

randomly selected four boys and four girls from participating teachers’ classrooms in the last

phase, resulting in an initial sample of 550 students.

Within the dataset, however, data were both missing cross-sectionally and longitudinally due to

teacher and student non-response, long-term absence or sickness during data collection, or

students moving to another school. Of all teachers assessed, 4.4% had missing data during the

first wave, and 10.6% during the second wave. Whereas cases with incomplete data for the

main study variables at both waves were excluded, we decided to retain participants with

incomplete data at only one time point. These missing data were treated using full information

maximum likelihood estimation. This resulted in a final sample of 69 teachers in relation to 524

students.

Participating teachers were predominantly female (72.6%), having a mean age of 41.42 years

(SD = 12.34, range = 23 – 63 years). Most teachers could be considered veteran teachers, with

an average professional teaching experience of 16.67 years (SD = 11.87, range = 1.5 – 44

years). The average tenure in teachers’ current job ranged from only half a year to 36 years (M

= 10.64, SD = 9.09). For four teachers, demographic data were not available.

At the time of data collection, students attended third (n = 53), fourth (n = 157), fifth (n =

165), and sixth grade (n = 149), respectively. Children ranged from 7.71 to 13.04 years of age

(M = 10.57, SD = 1.11) and the gender composition was evenly distributed with 262 boys


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(50.0%) and 262 girls (50.0%). Based on their parents’ working status and educational level, the

vast majority of students were considered to have an average to high socioeconomic status:

both parents were employed in 76.8% of the families, 20.4% had at least one employed parent,

and 2.5% of the families included two unemployed parents. Additionally, teachers indicated the

majority of the parents to have finished senior vocational education (49.0%) or higher

education (46.2%), leaving less than 5% of the parents to have finished only primary education.

INSTRUMENTS

TEACHERS’ PERCEPTIONS OF THE STUDENT–TEACHER RELATIONSHIP

Teachers' perceptions of the quality of their relationships with individual students were

measured using a short form of the authorized translated Dutch version of the Student–

Teacher Relationship Scale (STRS; Koomen, Verschueren, van Schooten, Jak, & Pianta, 2012;

Koomen, Verschueren, & Pianta, 2007; Pianta, 2001). Similar to the original STRS, the short

form estimates specific, teacher-perceived student–teacher relationship patterns of Closeness,

Conflict, and Dependency, using a 5-point Likert-type scale (1 = definitely does not apply; 5 =

definitely applies). In the present study, we made use of the Closeness and Conflict dimensions of

the STRS. The Closeness dimension (5 items) evaluates the extent to which teachers perceive

the student–teacher relationship to be warm, open, and secure, with items such as “I share an

affectionate and warm relationship with this child”. The Conflict dimension (5 items) generally

concentrates on negative aspects of the student–teacher relationship, including tension, anger,

and mistrust in the relationship. An example item is “This child and I always seem to be

struggling”. In a previous study, the psychometric properties of the short form of the STRS

have been demonstrated to be adequate (Zee, Koomen, & van der Veen, 2013). In the present

investigation, alpha coefficients at the first and second wave of measurement were satisfactory,

.85 and .86 for Closeness, and .89 and .88 for Conflict, respectively.

DISRUPTIVE STUDENT BEHAVIOR

Teachers completed the Dutch version of the Strengths and Difficulties Questionnaire (SDQ;

van Widenfelt, Goedhart, Treffers, & Goodman, 2003) to judge selected students’ disruptive

behaviors in the classroom. This behavioral screening questionnaire originally yields positive

and negative student attributes that together represent five factors reflecting strengths

(Prosocial Behavior) and difficulties (Emotional Symptoms, Conduct Problems, Hyperactivity-

Inattention, and Peer Problems). For purposes of the present study, however, we only used the



155

broader Externalizing Behavior domain proposed by Goodman, Lamping, and Ploubidis

(2010), which combines the Conduct Problems (5 items) and Hyperactivity-Inattention (5

items) subscales. This more comprehensive domain has been shown to have more adequate

psychometric properties than the original SDQ factors in low-risk samples (Dickey &

Blumberg, 2004; Goodman et al., 2010; van Leeuwen, Meerschaert, Bosmans, de Medts, &

Braet, 2006). Teachers responded on the SDQ-items on a 5-point Likert scale, ranging from 1

(not true) to 5 (certainly true). Example items are “Restless, hyperactive, cannot sit still for long”

and “Often has temper tantrums or hot tempers”. The internal consistency of the

Externalizing Subscale of the SDQ was satisfactory, α = .87. Moreover, several researchers

(Goodman et al., 2010; van Leeuwen et al., 2006) have provided sufficient evidence for the

construct validity of the scale.


Teachers rated their self-efficacy beliefs toward each of the selected students using the

Student-Specific Teacher Self-Efficacy Scale (Zee & Koomen, 2015; Zee et al., 2016). This 24-

item self-report instrument, adapted from Tschannen-Moran and Woolfolk Hoy’s (2001)

original measure, has been shown to represent teachers’ capability beliefs in relation to

individual students across four comprehensive domains of teaching and learning, including

Instructional Strategies (IS), Student Engagement (SE), Behavior Management (BM), and

Emotional Support (ES). Of these domains, the former two mainly focus on aspects of

instructional delivery. The IS subscale (6 items) captures the extent to which teachers feel

capable of using various instructional methods that enable and enhance individual students’

learning, including items such as “How well can you respond to difficult questions from this

student?”. The SE domain (6 items), in addition, reflects items that tap into teachers’ perceived

ability to activate the interest of a particular student in his or her schoolwork. A sample item of

this domain is “How much can you do to get this student to believe he/she can do well in

schoolwork?”. Next to these instruction-oriented subscales, the BM domain (5 items)

encompasses teachers’ judgments of their ability to organize and guide the behaviors of a

particular student, with items such as “How much can you do to get this child to follow

classroom rules?”. Lastly, inspired by the CLASS-framework (for an overview, see Hamre et

al., 2013 and Pianta, La Paro, & Hamre, 2008), the domain of ES (7 items) is related to how

well teachers can establish caring relationships with students, acknowledge students’ opinions

and feelings, and create settings in which students feel free to explore and learn (e.g., “How

well can you establish a safe and secure environment for this student?”).


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All items were rated by teachers on a seven-point Likert-type scale, ranging from 1 (nothing) to

7 (a great deal). Support for the construct validity of the student-specific TSES has been

provided by Zee and colleagues (Zee et al., 2016; Zee & Koomen, 2015). Internal consistency

scores of the student-specific TSES domains across waves were .89 and .92 for IS, .94 and .94

for BM, .90 and .92 for SE, and .85 and .86 for ES, respectively.

PROCEDURE

Data were collected from teachers in two waves (January-March and May-July) with a three-

month time interval. During each wave, teachers completed a two-part survey on demographic

background factors, the quality of the student–teacher relationship, and their sense of student-

specific self-efficacy for the eight selected students from their classroom. Teachers were asked

to fill out the first, written part of the survey during two planned school visits. This part

contained items regarding teachers’ perceived quality of their relationships with the eight

selected students and students’ and teachers’ background characteristics, which served as

covariates in this study. Directly after the school visits, teachers received an e-mail invitation

with a personal link to the second part of the survey that contained, among others, items

regarding disruptive student behavior and the student-specific self-efficacy questionnaire about

the eight selected students. Teachers were requested to return this digital survey within two

weeks after the invitation was sent. To improve the participation rate, regular reminders were

sent to non-responding teachers.

Once all surveys were collated, the cover sheet of the written part of the survey (containing the

name of the participants) was discarded and all completed surveys were assigned a unique

identification number that could be used to identify responses for matching T1 and T2 data in

longitudinal analyses. This unique identifier was also used to assure anonymity and

confidentiality for all participants.

DATA ANALYSIS

Given that mediation, by its very definition, infers change over time, we specified a series of

two-wave longitudinal mediation models (see Figure 2) in Mplus 7.11, adjusting for the nested

nature of the data (Muthén & Muthén, 1998-2012). Although models with at least three time-

points would essentially be required to establish a true indirect pathway across time, two-wave

models have previously been recognized as a relatively valid method to test for mediation (Cole



157

& Maxwell, 2003; Little, 2013). Specifically, similar to full-longitudinal models with three

waves, two-wave mediation models rely on the assumptions that the causal parameters are

constant over time (i.e., stationarity), and that the relationships among the predictors (X),

mediators (M), and outcome variables (Y) are unchanging in terms of their variances and

covariances (i.e., equilibrium; Cole & Maxwell, 2003; Little, 2013). Under these two assumptions,

the hypothesized associations between the mediators at Wave 1 and outcome variables at Wave

2 (mediation parameters a and b, and the direct effect c in Figure 2) can be expected to be equal

to the same associations measured at later time-points, and estimates of the effects of X on Y

through M in the two-wave model can be expected to be the same as in the three-wave model.

Additionally, two-wave models allow the modeling of prior levels of M and Y to isolate the

amount of change variance in these variables (Little, 2013). As such, these models can generally

be considered as superior to cross-sectional research on mediation, in which this change

information is not an explicit part of the design (ibid.).

FIGURE 2A

The Hypothesized Longitudinal Mediation Model

Note. X1 = Predictor at Wave 1; M1, M2 = Mediators at Waves 1 and 2; Y1, Y2 = Outcome variables at Wave 1 and 2. Coefficients a and b reflect associations between predictor and mediators, and mediators and outcome measures, respectively. The product ab reflects the indirect effect of individual students’ disruptive behavior on domains of student-specific self-efficacy, through teachers’ perceptions of conflict and closeness. Coefficient c reflects the direct association between predictor (students’ disruptive behavior) and outcome variables (domains of student-specific self-efficacy). Dashed lines represent autoregressive paths.

Students’ Disruptive Behavior (X1)

Conflict or Closeness (M1)

Domain- and Student-Specific TSE (Y1)

Conflict or Closeness (M2)

Domain- and Student-Specific TSE (Y2)

E

E


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FIGURE 2B

The Alternative Longitudinal Mediation Model

Note. X1 = Predictor at Wave 1; M1, M2 = Mediators at Waves 1 and 2; Y1, Y2 = Outcome variables at Wave 1 and 2. Coefficients a and b reflect associations between predictor and mediators, and mediators and outcome measures, respectively. The product ab reflects the indirect effect of individual students’ disruptive behavior on teachers’ perceptions of conflict and closenesss, through domains of student-specific self-efficacy. Coefficient c reflects the direct association between predictor (students’ disruptive behavior) and outcome variables (teacher-perceived conflict and closeness). Dashed lines represent autoregressive paths.

MODELING PROCEDURE

To ensure adequate statistical power, separate two-wave longitudinal mediation models were

fitted for each of the relationship dimensions (i.e., closeness and conflict) in relation to each of

the domains of student-specific TSE (i.e., instructional strategies, behavior management,

student engagement, and emotional support), resulting in eight different models. Based on

Cole and Maxwell’s (2003) recommendations, all eight models were specified in three steps (see

Figure 2). First, we estimated path a in the regression of M2 (student–teacher conflict or

closeness at Wave 2) onto X1 (individual students’ disruptive behavior at Wave 1), controlling

for the effects of M1 and path b in the regression of Y2 (student-specific TSE domain at Wave

2), after taking prior levels of Y1 into account (Cole & Maxwell, 2003; Little, 2013). The

product of paths a and b then offered an estimate of the mediation effect of X on Y via M.

This first model pertained to the hypothesized full-mediation model depicted in Figure 1a.

E

Students’ Disruptive Behavior (X1)

Domain- and Student-Specific TSE (M1)

Conflict or Closeness (Y1)

Domain- and Student-Specific TSE (M2)

Conflict or Closeness (Y2)

E



159

Second, we performed follow-up tests to explore the possible existence of direct effects of

individual students’ disruptive behavior on domains of student-specific TSE over time. To this

end, we estimated path c in the regression of Y2 onto X1. The statistical significance of this

direct path would indicate that teachers’ perceptions of closeness and conflict in the student–

teacher relationship only partially mediate the longitudinal association between disruptive

student behavior and domains of student-specific TSE. Third, as an additional test of validity

for the hypothesized model, we tested the alternative proposition that student-specific TSE has

a mediational effect on the association between disruptive student behavior and teachers’

student–teacher relationship perceptions (see Figure 1b).

After estimating all models, we employed the Monte Carlo simulation approach developed by

MacKinnon, Lockwood, and Williams (2004; see also Preacher & Selig, 2012) to formally test

the statistical significance of the mediation effects. This method involves directly spawning

sample statistics based on the joint asymptotic distribution of the component statistics to

obtain multiple estimates of the mediating pathway (Little, 2013). As such, the Monte Carlo

method largely resembles other recommended approaches for testing the significance of the

indirect effects, including bootstrap estimation (Preacher & Hayes, 2008). In line with our

hypotheses, we reported 90% confidence intervals for all Conflict models, and 95% confidence

intervals for the Closeness models, based on 5000 simulated draws for the indirect effects. If

the confidence interval around the point estimate of the indirect effect covers zero, this effect

is considered to be non-significant.

MODEL GOODNESS-OF-FIT

Overall fit of each of the specified models was gauged by using a number of absolute and

relative fit indices. Absolute fit was evaluated with the (mean-adjusted) model χ2. Generally,

non-significant χ2 tests are considered indicative of good model fit, implying that the

reproduced variance-covariance matrices are statistically equal to the observed matrices (Kline,

2011; Little, 2013). However, as even trivial discrepancies between the expected and the

observed model may lead to the model’s rejection (Chen, 2007), other fit indices were

consulted as well. Among those were the root mean square error of approximation (RMSEA),

the standardized root mean square residual (SRMR), and the comparative fit index (CFI). The

RMSEA and SRMR are absolute fit indices of the degree of misfit in the model, with values

≤.05 reflecting a close fit, and ≤.08 a satisfactory fit (Browne & Cudeck, 1993; Hu & Bentler,

1999; Kline, 2011). The CFI essentially reflects the ratio of misfit of the specified model, with


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values ≥.95 indicating close fit, and values ≥.90 indicating acceptable fit (Bentler, 1992; Little,

2013). Based on these model fit criteria, modification indices, and theoretical considerations,

the most parsimonious and best fitting models were chosen as final models.

RESULTS

DATA SCREENING AND DESCRIPTIVE STATISTICS

Prior to main analysis, all variables used in this study were examined for conformity to

multivariate regression assumptions. Means, standard deviations, and bivariate correlations (see

Table 1) were inspected to determine whether the main constructs correlated in the expected

directions. The correlation coefficients supported a priori expectations. Specifically, both

teachers’ Student-Specific Self-Efficacy percepts and Student–Teacher Relationship judgments

appeared to be relatively stable over time, with correlations between time-adjacent variables

ranging from .65 (Student-Specific TSE for IS) to .81 (Conflict). Individual students’

Disruptive Behavior was negatively associated with Closeness, and positively associated with

Conflict, both concurrently and predictively. Moreover, statistically significant negative

correlations were documented between students’ Disruptive Behavior and all domains of

Student-Specific TSE, and the domain of BM in particular.

Associations among Closeness and Conflict and Student-Specific TSE were also in the

expected direction. Whereas Closeness was associated with stronger Self-Efficacy toward

individual students in all teaching domains, Conflict was found to be negatively correlated with

these capability beliefs. Notably, the highest correlations were noted between Student-Specific

TSE for Behavior Management and Conflict. Lastly, the correlations among domains of

Student-Specific TSE were all moderate to high, suggesting potential multicollinearity among

dimensions of teachers’ Student-Specific Self-Efficacy. To circumvent issues related to

multicollinearity, we estimated separate models for each of the Student-Specific TSE domains.

Students’ Age and Gender, and teachers’ years of Teaching Experience and Gender served as

the study’s covariates. Correlations showed that teachers were likely to report higher levels of

Closeness and Student-Specific TSE domains in relation to girls and younger students, and

higher levels of Conflict and Disruptive Behavior in relation to boys. Teaching Experience,

lastly, was positively associated both with teacher-perceived Closeness, and teachers’ sense of

Student-Specific TSE, irrespective of teaching domain.


TABL

E 1

Desc

riptiv

e Sta

tistic

s and

Corr

elatio

ns

1.

2.

3.

4.

5.

6.

7.

8.

9.

10

. 11

. 12

. 13

. 14

. 15

. 16

. 17

. St

uden

t Beh

avior

1. D

isrup

tive

Beha

vior

1.

00

Stud

ent–

Teac

her R

elatio

nship

2. C

lose

ness

T1

-.25*

* 1.

00

3.

Con

flict

T1

.69*

* -.3

8**

1.00

4.

Clo

sene

ss T

2 -.2

3**

.70*

* -.3

5**

1.00

5. C

onfli

ct T

2 .6

4**

-.25*

* .8

1**

-.36*

* 1.

00

Stud

ent-S

pecif

ic TS

E

6.

TSE

for I

S T1

-.4

6**

.31*

* -.4

1**

.34*

* -.3

5**

1.00

7. T

SE fo

r BM

T1

-.74*

* .3

2**

-.73*

* .3

3**

-.66*

* .5

0**

1.00

8.

TSE

for S

E T

1 -.5

7**

.34*

* -.4

8**

.36*

* -.4

2**

.87*

* .5

6**

1.00

9. T

SE fo

r ES

T1

-.55*

* .4

6**

-.51*

* .4

8**

-.47*

* .8

0**

.66*

* .8

1**

1.00

10

. TSE

for I

S T2

-.3

9**

.27*

* -.3

3**

.38*

* -.3

2**

.65*

* .3

4**

.65*

* .5

7**

1.00

11. T

SE fo

r BM

T2

-.60*

* .2

2**

-.58*

* .2

8**

-.59*

* .3

7**

.66*

* .4

2**

.50*

* .5

2**

1.00

12

. TSE

for S

E T

2 -.4

9**

.27*

* -.3

9**

.35*

* -.3

9**

.62*

* .4

0**

.70*

* .5

9**

.90*

* .6

0**

1.00

13. T

SE fo

r ES

T2

-.46*

* .3

8**

-.39*

* .4

8**

-.40*

* .5

6**

.47*

* .5

9**

.66*

* .8

2**

.69*

* .8

3**

1.00

Cova

riates

14. T

each

er G

ende

r -.0

8 .0

8 -.0

4 .1

3**

-.09*

-.0

4 .0

0 -.0

4 .0

1 .0

2 .0

6 -.0

0 .0

42

1.00

15. T

each

ing

Exp

erie

nce

-.04

.09*

-.0

5 .1

3**

-.02

.15*

* .1

1*

.18*

* .1

7**

.10

.14*

* .1

3**

.13*

* -.2

8**

1.00

16

. Stu

dent

Gen

der

-.26*

* .3

0**

-.17*

* .3

0**

-.19*

* .1

5**

.27*

* .1

6**

.26*

* .1

1*

.22*

* .1

3**

.20*

* .0

3 -.0

2 1.

00

17

. Stu

dent

Age

-.0

3 -.1

5**

.02

-.14*

* .0

5 -.1

8**

-.05

-.15*

* -.1

7**

-.13*

* -.0

2 -.1

3**

-.11*

-.2

8**

.05

-.07

1.00

Desc

riptiv

e Sta

tistic

s

Mea

n 1.

96

3.91

1.

55

4.00

1.

58

5.53

6.

14

5.57

5.

81

5.56

6.

16

5.56

5.

85

- 16

.67

- 10

.57

Stan

dard

Dev

iatio

n 0.

82

0.81

0.

88

0.78

0.

87

0.91

0.

99

1.00

0.

78

0.94

0.

95

1.00

0.

79

- 11

.87

- 1.

11

Note

. * p

< .0

5; **

p <

.01.

Gen

der:

0 =

boy

s/m

ale te

ache

rs, 1

= g

irls/

fem

ale te

ache

rs. T

SE =

Tea

cher

s’ st

uden

t-spe

cific

self–

effic

acy;

IS =

Inst

ruct

iona

l stra

tegi

es; B

M =

Be

havi

or m

anag

emen

t; SE

= S

tude

nt e

ngag

emen

t; E

S =

Em

otio

nal s

uppo

rt.


CHAPTER 5

162

LONGITUDINAL MEDIATION MODELS

Model fit indices and parameter estimates of the Hypothesized and/or Alternative Mediation

Models per domain of Student-Specific TSE and dimension of the Student–Teacher

Relationship are provided in the following sections. In all models, teachers’ Gender and

Teaching Experience, and students’ Gender and Age were entered first into the regression

equation to accurately gauge the unique effect of the model’s predictors and mediators on the

outcome variables. Notably, though, none of these covariates appeared to be statistically

significant, nor did these variables alter the direction and magnitude of the coefficients in our

models. For reasons of parsimony, all models were therefore reported without covariates.

INDIRECT EFFECTS OF DISRUPTIVE BEHAVIOR ON STUDENT-SPECIFIC TSE THROUGH CONFLICT

Student-specific TSE for instructional strategies

The Hypothesized Mediation Model showed quite sound goodness of fit, χ2(2) = 3.34, p = .19,

RMSEA = .036 (90% CI [.000–.101]), CFI = .996, SRMR = .014. Freely estimating the direct

path from Disruptive Student Behavior to Student-Specific TSE for IS in the second step

could not further improve this already well-fitting model. Moreover, the Alternative Model,

placing Student-Specific TSE for IS in a mediational role between Disruptive Student Behavior

and teachers’ perceptions of Conflict, appeared to reflect a poorer fit of the data than the

Hypothesized Mediation Model, χ2(2) = 20.62, p < .001, RMSEA = .133 (90% CI [.085–.188]),

CFI = .941, SRMR = .021. Accordingly, we retained the Hypothesized Full-Mediation Model.

Table 2 presents the standardized path estimates for the final model. After accounting for prior

levels of teacher-perceived Conflict, individual students’ Disruptive Behavior predicted more

subsequent Conflict (β = .18, p < .001). Perceptions of Conflict, in turn, predicted lower levels

of Student-Specific TSE for IS over time (β = –.11, p < .05). The estimate of the indirect effect

of individual students’ Disruptive Behavior on their teachers’ Student-Specific TSE in the

domain of IS was –.025 (Monte Carlo 90% CI [–.045 –.004]), suggesting a statistically

significant mediation effect.


TABL

E 2

Fina

l Lon

gitud

inal M

odels

with

Med

iatin

g Effe

cts A

mong

Disr

uptiv

e Stu

dent

Beh

avior

, Con

flict,

and

Stu

dent

-Spe

cific

Self-

Effi

cacy

N

ote. *

p <

.05;

** p

< .0

1. S

tand

ardi

zed

regr

essio

n co

effic

ient

s (β)

are

repo

rted.

Poi

nt e

stim

ates

of t

he in

dire

ct e

ffec

ts a

re u

nsta

ndar

dize

d.

M

odel

1

Stud

ent-S

peci

fic T

SE fo

r IS

M

odel

2

Stud

ent-S

peci

fic T

SE fo

r BM

Mod

el 3

St

uden

t-Spe

cific

TSE

for S

E

M

odel

4

Stud

ent-S

peci

fic T

SE fo

r ES

Co

nflic

t T2

TS

E fo

r IS

T2

Co

nflic

t T2

TS

E fo

r BM

T2

Conf

lict

T2

TSE

for S

E

T2

Co

nflic

t T2

TS

E fo

r ES

T2

Dire

ct E

ffects

Disr

uptiv

e St

uden

t Beh

avio

r T1

.18*

* –

.1

2*

–.25

**

.1

8**

–

.18*

* –

Conf

lict T

1 .6

9**

–.11

*

.61*

* –

.7

0**

–.10

*

.70*

* –.

11*

Stud

ent-S

peci

fic T

SE fo

r IS

T1

– .6

1**

–

–

– –

–

– St

uden

t-Spe

cific

TSE

for B

M T

1 –

–

–.16

**

.49*

*

– –

–

– St

uden

t-Spe

cific

TSE

for S

E T

1 –

–

– –

–

.67*

*

– –

Stud

ent-S

peci

fic T

SE fo

r ES

T1

– –

–

–

– –

–

.61*

*

In

direct

Effe

cts

Th

roug

h Co

nflic

t –

–.03

*

– –

–

–.02

*

– .0

2*

Thro

ugh

TSE

for B

M

– –

.0

4*

–

– –

–

–

R2

statis

tics

.68*

* .4

4**

.6

9**

.48*

*

.68*

* .5

2**

.6

9**

.47*

*


CHAPTER 5

164

Student-specific TSE for behavior management

The Hypothesized Mediation Model for Conflict did not reach a satisfactory fit to the data,


Although slight improvement in model fit was achieved by adding a direct path from students’

Disruptive Behavior to Student-Specific TSE for BM, follow-up analyses indicated that the

Alternative Model with Student-Specific TSE for BM as the mediator produced better

parameter estimates and yielded a slightly better fit than the Hypothesized Model, χ2(2) = 8.70,

p < .01, RMSEA= .080 (90% CI [.031–.138]), CFI = .971, SRMR = .024. Modification indices

suggested some further improvement by adding the direct path from Disruptive Student

Behavior to Conflict. This resulted in a well-fitting final model, χ2(1) = 3.05, p = .08, RMSEA

= .063 (90% CI [.000–.148]), CFI = .991, SRMR = .019.

Table 2 displays the standardized coefficients for the final Alternative Partial-Mediation Model.

Teachers were found to experience lower TSE for BM (β = –.25, p < .01) and more Conflict (β

= .12, p < .05) in relation to individual students with Disruptive Behavior, when controlling for

initial levels of Student-Specific TSE. In addition, teachers’ Student-Specific capability beliefs

for BM predicted less subsequent Conflict in the student–teacher relationship (β = –.16, p <

.01). The Monte Carlo confidence limits suggested that the indirect effect of Disruptive

Student Behavior on Conflict through Student-Specific TSE for BM is statistically significant,

(point estimate = .044, Monte Carlo 90% CI [.012–.076]). Hence, contrary to expectations,

Student-Specific TSE for BM partially mediated the association between Disruptive Student

Behavior and teachers’ perceptions of Conflict in the relationship.

Student-specific TSE for student engagement

The model fit of the Hypothesized Mediation Model was satisfactory, χ2(2) = 4.14, p = .13,

RMSEA = .045 (90% CI [.000–.108]), CFI = .994, SRMR = .014. In the second step, we added

the direct path between individual students’ Disruptive Behavior and Student-Specific TSE for

SE. This path did not reach the significance threshold and could not further improve the fit of

this model. Moreover, the Alternative Model clearly reflected an inferior summary of the data,

χ2(2) = 16.89, p < .001, RMSEA = .119 (90% CI [.071–.175]), CFI = .955, SRMR = .019. For

this reason, the Hypothesized Full-Mediation Model was chosen as the most parsimonious and

best fitting model.



165

The standardized coefficients for the final model for Student-Specific TSE for SE are depicted

in Table 2. Assessment of path coefficients pointed to a small, though statistically significant

association between individual students’ Disruptive Behavior and teachers’ succeeding

perceptions of Conflict in the relationship (β = .18, p < .001), after initial levels of Conflict

were taken into account. In turn, teachers who perceived the relationship with the child to be

marked by Conflict were likely to feel less efficacious in relation to the child in the domain of

SE (β = –.10, p < .05). Using the Monte Carlo simulation approach, the estimate of the indirect

effect was –.023 (Monte Carlo 90% CI [–.044 – –.003]). As the confidence interval did not

cover zero, the indirect effect of Disruptive Student Behavior on Student-Specific TSE for SE

through Conflict can be assumed to be statistically significant.

Student-specific TSE for emotional support

Fit indices suggested that the Hypothesized Mediation Model fitted the sample reasonably well,


Examination of the modification indices, as well as the parameters estimates and their standard

errors, indicated that no further estimates would improve the model’s fit. Hence, the

Hypothesized Full-Mediation Model appeared to be a reasonable approximation of the data,

and fitted slightly better than the Alternative Model, χ2(2) = 12.43, p < .01, RMSEA = .100

(90% CI [.052–.156]), CFI = .967, SRMR = .018.

The final model (see Table 2) generally reflected the hypothesized indirect effects of students'

Disruptive Behavior on the Student-Specific TSE for ES, through Conflict. Specifically,

Disruptive Student Behavior led to significant changes in teachers–perceived Conflict (β = .18,

p < .001), after controlling for preceding levels of Conflict. Additionally, teachers’ perceptions

of Conflict resulted in lower levels of Student-Specific TSE for ES (β = –.11, p < .05). The

point estimate of the indirect effect (.021, Monte Carlo 90% CI [.002–.041]) deviated

significantly from zero, suggesting that Conflict mediates the association between Disruptive

Student Behavior and Student-Specific TSE for ES.

INDIRECT EFFECTS OF DISRUPTIVE BEHAVIOR ON STUDENT-SPECIFIC TSE THROUGH CLOSENESS

Student-specific TSE for instructional strategies

Contrary to the Conflict Model, the Hypothesized Model that placed Closeness in a mediating

role between Disruptive Student Behavior and Student-Specific TSE for IS had a poor fit to


CHAPTER 5

166

the data, χ2(2) = 19.80, p < .001, RMSEA = .130 (90% CI [.082–.185]), CFI = .942, SRMR =

.052. To identify possible sources of misfit, we examined the model’s modification indices,

parameter estimates, and standard errors. These provided clear evidence in favor of the

Alternative Model, specifying an indirect effect of Disruptive Student Behavior on Closeness

through Student-Specific TSE for IS. This Alternative Model indeed approximated the data

well, χ2(2) = 3.91, p < .001, RMSEA = .043 (90% CI [.000–.106]), CFI = .994, SRMR = .021,

and could not be further improved by adding a direct path between Disruptive Student

Behavior and Closeness.

As displayed in Table 3, students’ Disruptive Behavior, while controlling for initial levels of

Student-Specific TSE for IS, was significantly related to subsequently lower levels of these

capability beliefs (β = –.13, p < .05). In turn, teachers’ Student-Specific Self-Efficacy beliefs for

IS predicted higher levels of Closeness (β = .19, p < .001), after accounting for the stability in

these positive relationship perceptions. Using the Monte Carlo simulation approach, the

estimate of the mediating pathway proved to be statistically significant, –.024 (Monte Carlo

95% CI [–.047– –.000]).

Student-specific TSE for behavior management

The Hypothesized Mediation Model reflected a far poorer fit to the data (χ2(2) = 19.04, p <

.001, RMSEA = .127 (90% CI [.079–.183]), CFI = .931, SRMR = .037) than the Alternative

Mediation Model (χ2(2) = 0.64, p = .72, RMSEA = .000 (90% CI [.000–.062]), CFI = 1.00,

SRMR = .010). Moreover, follow-up tests provided no evidence for the existence of direct

effects of students’ Disruptive Behavior on teachers’ perceptions of Closeness in the Student–

Teacher Relationship. Therefore, the Alternative Model was chosen as the final model.

The standardized path estimates for this model are presented in Table 3. Even after controlling

for cross-wave stability, the negative association between Disruptive Student Behavior and

teachers’ subsequent levels of Student-Specific Self-Efficacy for BM was statistically significant

(β = –.25, p < .001). In turn, Student-Specific TSE for BM predicted higher levels of teacher-

perceived Closeness in the relationship (β = .16, p < .001). The indirect effect of Disruptive

Student Behavior on Closeness via Student-Specific TSE was also statistically significant, point

estimate = –.040, Monte Carlo 95% CI [–.067– –.013].


TABL

E 3

Fina

l Lon

gitud

inal M

odels

with

Med

iatin

g Effe

cts A

mong

Disr

uptiv

e Stu

dent

Beh

avior

, Clos

eness

, and

Stu

dent

-Spe

cific

Self-

Effi

cacy

Note

. * p

< .0

5; **

p <

.01.

Sta

ndar

dize

d re

gres

sion

coef

ficie

nts (β)

are

repo

rted.

Poi

nt e

stim

ates

of t

he in

dire

ct e

ffec

ts a

re u

nsta

ndar

dize

d.

M

odel

1

Stud

ent-S

peci

fic T

SE fo

r IS

M

odel

2

Stud

ent-S

peci

fic T

SE fo

r BM

Mod

el 3

St

uden

t-Spe

cific

TSE

for S

E

M

odel

4

Stud

ent-S

peci

fic T

SE fo

r ES

Cl

osen

ess T

2 TS

E fo

r IS

T2

Cl

osen

ess

T2

TSE

for B

M T

2

Clos

enes

s T2

TS

E fo

r SE

T2

Cl

osen

ess T

2 TS

E fo

r ES

T2

Dire

ct E

ffects

Disr

uptiv

e St

uden

t Beh

avio

r T1

– –.

13*

–

–.25

**

–

–.14

*

– –.

15**

Cl

osen

ess T

1 .6

5**

–

.65*

* –

.6

4**

–

.58*

* –

Stud

ent-S

peci

fic T

SE fo

r IS

T1

.19*

* .6

0**

–

–

– –

–

– St

uden

t-Spe

cific

TSE

for B

M T

1 –

–

.16*

* .4

9**

–

–

– –

Stud

ent-S

peci

fic T

SE fo

r SE

T1

– –

–

–

.18*

* .6

4**

–

– St

uden

t-Spe

cific

TSE

for E

S T1

–

–

– –

–

–

.26*

* .5

9**

Indir

ect E

ffects

Thro

ugh

TSE

for I

S –.

02*

–

– –

–

–

– –

Thro

ugh

TSE

for B

M

– –

–.

04*

–

– –

–

– Th

roug

h TS

E fo

r SE

–

–

– –

–.

03*

–

– –

Thro

ugh

TSE

for E

S –

–

– –

–

–

–.04

* –

R2 sta

tistic

s .5

2**

.45*

*

.52*

* .4

8**

.5

2**

.53*

*

.54*

* .4

7**


CHAPTER 5

168

Student-specific TSE for student engagement

The fit of the Hypothesized Mediation Model for Student-Specific TSE for ES was poor, χ2(2)

= 18.44, p < .05, RMSEA = .125 (90% CI [.077–.180]), CFI = .950, SRMR = .043. Inspection

of parameter estimates and modification indices suggested that Student-Specific TSE for SE,

rather than teachers’ perceptions of Closeness, is more likely to serve as a mediator. This

assumption was indeed substantiated by the Alternative Model’s fit criteria, χ2(2) = 0.66, p =

.72, RMSEA = .000 (90% CI [.000–.062]), CFI = 1.00, SRMR = .009. Because the direct path

in the regression of teacher-perceived Closeness at Wave 2 onto Disruptive Student Behavior

at Wave 1 was not statistically significant, we retained the Alternative Full-Mediation Model as

the final model.

Table 3 presents the standardized path estimates for the Alternative Model testing the indirect

effect of Disruptive Student Behavior on teacher-perceived Closeness via Student-Specific

TSE for SE. Inspection of these estimates suggest that, after accounting for the stability in

Student-Specific TSE for SE, teachers are likely to experience lower subsequent levels of self-

efficacy for SE in relation to individual students who display Disruptive Behavior (β = –.14, p

< .05). In turn, Student-Specific TSE for SE appeared to be a statistically significant positive

predictor of teachers’ perceived levels of Closeness over time (β = .18, p < .001). The product

of these two pathways was also significant, –.026 (Monte Carlo 95% CI [–.050 – –.001]),

thereby providing support for the indirect effect of Disruptive Student Behavior on Closeness

through Student-Specific TSE for SE.

Student-specific TSE for emotional support

The Hypothesized Mediation Model appeared to be a far worse representation of the data

(χ2(2) = 35.89, p < .001, RMSEA = .180 (90% CI [.131–.234]), CFI = .901, SRMR = .070) than

the Alternative Mediation Model (χ2(2) = 4.33, p = .11, RMSEA = .047 (90% CI [.000 –.109]),

CFI = .993, SRMR = .024). Moreover, the direct path from Disruptive Student behavior to

teachers’ perceptions of Closeness in the relationship did not significantly add to prediction

and consequently deteriorated the model's initial fit. Therefore, the Alternative Full-Mediation

Model was chosen as the final model (see Table 3).

After accounting for initial levels of Student–Specific TSE for ES, teachers’ reported lower

subsequent levels of self-efficacy for Emotional Support in relation to children with Disruptive

Behavior (β = –.15, p < .01). Also, teachers’ sense of Student-Specific Self-Efficacy for ES,



169

when controlling for initial levels of Closeness, was significantly and positively associated with

their subsequent experiences of relational Closeness (β = .26, p < .001). Using the Monte Carlo

simulation approach, the coefficient of the indirect effect is estimated as –.039 (Monte Carlo

95% CI [–.073 – –.006]). Hence, these results suggest that Student-Specific TSE for ES

significantly mediates the association between Disruptive Student Behavior and teachers’

perceptions of Closeness in the student–teacher relationship.

DISCUSSION

Following Bandura’s (1986, 1997) social-cognitive principles, we aimed to explore a model

within which teachers’ perceptions of closeness and conflict in the student–teacher relationship

acted as the intermediary mechanisms by which individual students’ disruptive behavior may

affect teachers’ student-specific self-efficacy over time. Our approach departed from previous

work on the sources of TSE in three essential ways. First, we adhered to and extended

Bandura’s original conceptualization of self-efficacy by embedding TSE in an interpersonal

social-cognitive framework and measuring this complex construct both at the student- and

domain-specific level. Second, rather than focusing on direct sources of TSE, we were explicitly

interested in specifying mediating processes through which disruptive student behavior, as a

source of self-efficacy information, may become instructive to teachers’ student-specific

capability beliefs. Lastly, given that mediation essentially is a statement of change (Little, 2013),

we used a longitudinal design to evaluate hypothesized and alternative models, controlling for

prior levels of teachers’ perceptions of student–teacher closeness and conflict, and judgments

of student-specific TSE.

LINKAGES BETWEEN DISRUPTIVE BEHAVIOR, CONFLICT, AND STUDENT-SPECIFIC TSE

Generally, the results of our study provide a first indication that teachers’ perceptions of

relational conflict may function as the mediating or explaining mechanism whereby individual

students’ disruptive behavior leads to changes in student- and domain-specific TSE. To be

specific, teachers seemed to experience slightly higher subsequent levels of conflict in

relationships with individual students who initially displayed disruptive behavior in class which,

in turn, translated into lower levels of self-efficacy toward these students in various teaching

domains. These associations held even after taking relatively stable prior levels of student–

teacher conflict and student-specific teacher self-efficacy into account. Previous longitudinal

studies with younger elementary school children (e.g., Mejia & Hoglund, 2016; Roorda et al.,


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2014; Zhang & Sun, 2011) are largely in line with our findings, reporting cross-lagged paths

between externalizing student behavior and teachers’ perceptions of conflict that were similar

in magnitude to the coefficients reported in the present study. However, no empirical studies

have yet uncovered that deleterious judgments of the student–teacher relationship quality may

also serve as a go-between, passing on efficacy-relevant sources of information from individual

students to the teacher. By unveiling these complex processes, our study gently corroborates

and extends Bandura’s (1997) longstanding belief that teachers not only have to manage

various sources of self-efficacy during their interactions with students, but also weigh and

integrate this information via such common judgmental processes as their representations of

relational conflict.

Interestingly, what our models seem to emphasize is that the role of teachers’ percepts of

conflict may vary across different domains of teaching and learning. More precisely, it appears

that the associations between individual students’ behavior and the more instructional and

affective domains of TSE are primarily mediated by teacher-perceived conflict. Through their

perceptions of conflict, teachers may thus come to see the task of teaching, engaging, and

emotionally supporting disruptive students as more problematic and may subsequently adjust

their self-efficacy toward these students downward. This finding accords well with prior

notions that, for most teachers, it is probably a major and time-consuming challenge simply to

get disruptive students with whom they entertain conflictuous relationships to learn and pay

attention in class (e.g., Arbeau & Coplan, 2007; Sutherland & Oswald, 2005; Yeo et al., 2008).

The sequels of such challenges evidently are that teachers, despite their sustained efforts, feel

less effective in teaching and motivating disruptive students, thereby stimulating student–

teacher interactions marked by even more anger, conflict, and disruptive student behavior over

time (e.g., Emmer & Stough, 2001; Pianta, 2001; Spilt et al., 2011; Yeo et al., 2008). This is

alarming, given that challenging students, and especially those with conflictuous student–

teacher relationships, have repeatedly been shown to be at risk for social and academic

adjustment problems (e.g., Hamre & Pianta, 2001; Roorda, Koomen, Spilt, & Oort, 2011).

Markedly, relational conflict did not appear to act as a mediator in the association between

disruptive student behavior and student-specific TSE for behavior management. Rather,

individual students who displayed disruptive behavior first seemed to hamper teachers’ efforts

to adequately manage these students’ behavior in class which, in turn, resulted in higher levels

of conflict in the student–teacher relationship. This relatively unexpected finding corroborates



171

the idea that teaching tasks related to behavior management may be relatively distinct from

other core responsibilities, such as providing the instructional, motivational, and emotional

supports that generate gains in learning (cf. Tschannen-Moran & Woolfolk Hoy, 2001). An

explanation for this contrasting result that aligns with prior empirical work (e.g., Tsouloupas et

al., 2010; Zee et al., 2016) is that student-specific TSE for behavior management may serve as a

strong and direct proxy for teachers’ inability to deal with disruptive students’ behavior.

Thereby, these student-specific capability beliefs for behavior management may, more than any

other domain of TSE, be more contiguous with students’ disruptive behavior than perceptions

of conflict. This is a notable outcome, given that students’ disruptive behavior, among other

child-level correlates, have previously been found to be most predictive of teachers’

experiences of relational conflict, and may even promote vicious cycles of disharmonious

relationships and escalating problem behaviors (e.g., Doumen et al., 2008; Hamre et al., 2008;

Murray & Murray, 2004; Roorda et al., 2014).

Although the sequence of linkages described in the present study are only preliminary in nature

and not fully consistent, they generally seem to suggest that teachers’ student-specific capability

beliefs are inextricably intertwined with their experiences of conflict in relationships with

disruptive students. Helping teachers to reflect on their actions and behaviors toward

disruptive students, and associated emotions and cognitions during daily interactions with

these children, may be a step forward to break negative relationship patterns between teachers

and behaviorally at-risk elementary students (e.g., Spilt et al., 2012).

LINKAGES BETWEEN DISRUPTIVE BEHAVIOR, CLOSENESS, AND STUDENT-SPECIFIC TSE

Initial evidence from this study corroborates the alternative premise that the association

between individual students’ disruptive behavior and teachers’ perceptions of closeness in the

relationship is mediated, or explained, by student-specific TSE. Counter to the mixed findings

in prior cross-sectional and longitudinal work (e.g., Roorda et al., 2014; Thijs et al., 2012),

teachers were consistently found to develop less healthy self-efficacy beliefs toward disruptive

students in all teaching domains, and consequently, to experience less closeness in the dyadic

relationship with these students. The theoretical significance of these findings is substantial,

given that there is a general shortage of evidence on how features of teachers may impact on

the formation of their relationships with individual students (Pianta et al., 2003). Moreover, the

observed differences between closeness and conflict in the sequence of associations appear to


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underscore that these constructs reflect two distinct qualities of the relationship, as opposed to

falling along an underlying continuum.

We can only make a well-educated guess about why closeness and conflict play different roles

in the development of teachers’ sense of efficacy toward individual disruptive students. For

instance, sources of self-efficacy, including students’ behaviors and characteristics, can be

suggested to significantly vary in the degree of information they provide to teachers (cf.

Bandura, 1997). Probably, disruptive student behaviors are stronger and more reliable

indicators of student–teacher conflict than closeness, and may thereby contribute less

information to teachers’ representations of relational closeness and subsequent self-efficacy

beliefs. Indeed, prior research (Hamre et al., 2008; Jerome et al., 2009) has indicated that

conflict may depend more on stable student attributes (e.g., disruptive behavior), whereas

closeness seems to be more proximal to dynamic teacher characteristics (e.g., student-specific

TSE). This may explain why teachers’ sense of student-specific self-efficacy may better account

for the association between disruptive student behavior and closeness in the student–teacher

relationship than closeness for the association between those challenging behaviors and

student-specific TSE.

One other compelling proposition of Bandura (1997) is that the route to low-quality student–

teacher relationships may go through teachers’ perceived (social) inefficacy to develop affective

relationships with students who bring stress to teachers’ job. Presumably, when teachers

believe they cannot muster whatever it takes to support and deal with a disruptive child, they

are apt to slacken their teaching efforts, avoid warm and open communications with the child,

and settle for mediocre results or controlling actions (ibid.). This presumption fits reasonably

well with our findings that individual disruptive students may particularly hamper teachers’

perceptions of relational closeness through their student-specific self-efficacy for emotional

support and behavior management. Thus, teachers’ lack of self-efficacy may ultimately come at

the expense of trust, warmth and affect between teachers and disruptive children.

Overall, the model evaluations in the current study seem to be in line with the social-cognitive

and dynamic systems models advanced by Bandura (1997) and Pianta et al. (2003), suggesting

that teachers’ and students’ personal characteristics and behaviors, as well as their daily

interactions, may influence one another in a complex, reciprocal way. Future, longitudinal

research in which multiple methods and data sources are integrated is needed to spur further



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understanding of the complex relationships between disruptive student behavior, student–

teacher conflict and closeness, and student- and domain-specific TSE.

LIMITATIONS AND FUTURE DIRECTIONS

The methodology and design of the present investigation brought several limitations that

require further attention in future studies. First, analytic techniques such as longitudinal

(multilevel) structural equation modeling are also bound by several specific assumptions,

including multicollinearity, stationarity, and equilibrium (Cole & Maxwell, 2003; Sobel, 1990).

Although we circumvented the issue of multicollinearity by evaluating separate models for the

two student–teacher relationship qualities and the four domains of student-specific TSE, we

cannot be sure whether the stationarity and equilibrium assumptions held. To be specific, with

only two waves of data, it was virtually impossible to test whether the measured variables are

invariant over time (i.e., stationarity), and whether the relationships among those variables are

unchanging in terms of their variances and covariances (i.e., equilibrium; Cole & Maxwell, 2003;

Little, 2013). Fortunately, however, several authors have argued that violating those two

assumptions of mediation testing does not necessarily invalidate evidence of statistically

significant mediation effects (ibid.). Nevertheless, future studies that incorporate analyses of

stationarity and equilibrium over at least three time intervals could provide a stronger basis

from which to discuss the complex, mediating processes proposed in the present study.

Related to this, the lags for the measurement occasions might not have been optimal for

detecting changes in teachers’ judgments of student-specific self-efficacy and experiences of

closeness and conflict. Empirical research from Roorda and colleagues (2014) has indicated,

for instance, that students’ disruptive behavior and teachers’ relationship perceptions mainly

affect one another during the first couple of months of the school year, when relationships

between teachers and students have yet to be crystalized. Possibly, teachers’ relationships with

individual students and their student-specific self-efficacy beliefs in the present study were

already stabilized at the time of data collection (middle and end of the school year), making it

more difficult to detect changes in teacher-perceived closeness and conflict, and student-

specific TSE. Therefore, longitudinal data on changes in teachers’ perceptions of the student–

teacher relationships and student-specific self-efficacy beliefs from the beginning to the end of

the school year would probably provide a more fine-grained picture of the processes by which

individual students’ disruptive behavior may exert pressure to change teachers’ self-efficacy

beliefs toward these children in different domains of teaching and learning.


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Last, this study concentrated only on teachers’ perceptions of relational closeness and conflict

as mediators of the association between disruptive student behavior and student-specific TSE.

It is likely, however, that the mediation processes presented in the current study may be far

more complex, and that other cognitive or motivational factors or processes are responsible

for changes in teachers’ sense of self-efficacy in relation to particular students with disruptive

behavior. Examples of such factors may be teachers’ beliefs about student control, their

motivation to engage in high-quality interactions with the child, their (perceived) skill level, and

their classroom goal orientations (cf. Bandura, 1997; Cho & Shim, 2013; Deemer, 2004; Pianta

et al., 2003; Tschannen-Moran et al., 1998; Woolfolk & Hoy, 1990). These and other

potentially relevant factors and processes, measured either at a more general level or the dyadic

level, may warrant consideration in future longitudinal studies.

CONCLUSION

In summary, we sought to expand the available evidence on the sources of student-specific

TSE by evaluating an interpersonal social-cognitive model in which teachers’ perceptions of

the student–teacher relationship quality were assumed to account for the association between

disruptive student behavior and student-specific TSE. Generally, data from this investigation

provided initial support for the idea that teacher-perceived conflict may function as one

explaining mechanism through which individual students’ disruptive behavior results in

changes in student- specific TSE across domains. Interestingly, though, teachers’ experiences

of closeness in their relationship with individual students did not mediate the association

between students’ disruptive behavior and student-specific TSE. Instead, convincing evidence

was found for the alternative premise that teachers, through their poorer self-percepts of

domain- and student-specific efficacy, are less capable of teaching and helping behaviorally

disruptive students in ways that lead to closeness in the student–teacher relationship. These

findings clearly suggest that student–teacher conflict and closeness each may play a different

role in the development of teachers’ sense of self-efficacy toward disruptive students in various

teaching domains. For the development of empirically-based intervention programs for

teachers, it is therefore essential to spur further understanding of the complex

interrelationships among individual students’ disruptive behavior, the student–teacher

relationship quality, and teachers’ student-specific self-efficacy across domains of teaching and

learning.


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CHAPTER 6

GENERAL DISCUSSION

_________________________________________________________________________

This dissertation aimed to take stock of where the field of teacher self-efficacy has been and

the steps needed to be taken to move current theory and research on TSE forward. First off,

Chapter 1 outlined several critical challenges in the field of TSE that must be accounted for to

advance understanding of the self-efficacy construct and identify useful research-based insights

about TSE that may help teachers better deal with diversity in class. Motivated by Bandura’s

(1977, 1986, 1997) social-cognitive principles, these theoretical and methodological issues were

subsequently explored in Chapters 2 to 5, within which the focus gradually shifted from

teachers’ general, classroom-level self-efficacy beliefs to TSE at the student-specific level. The

General Discussion now seeks a return to the challenges raised in Chapter 1. In this last

chapter, thought is given to the extent to which these issues have been addressed by the four

studies in this dissertation, to noteworthy or contradictory empirical findings across these

studies, and to new challenges that seem to have emerged out of this work. In addition,

implications of the results of the studies for educational research and practice are discussed in

the closing section of this chapter.

ADDRESSING CHALLENGES REGARDING THE NATURE AND CONSEQUENCES OF TSE

The last forty-odd years have seen a rise in research focusing on the effects of teacher self-

efficacy on teachers’ behaviors, feelings, and actions, and their students’ learning outcomes in

class. What initially started out as a modest side-branch of school effectiveness research now

seems to have blossomed into a massive body of work reflecting different traditions and

theories, various definitions, and innumerable outcomes of TSE related to the quality of

classroom processes, students’ academic adjustment, and teachers’ well-being (e.g., Klassen &

Chiu, 2010, 2011; Klassen, Tze, Betts, & Gordon, 2011; Skaalvik & Skaalvik, 2007, 2010;

Tschannen-Moran & Woolfolk Hoy, 2001). To a considerable extent, these different and

sometimes isolated traditions have each provided important pockets of insight into teachers’

sense of self-efficacy. However, one remaining challenge identified in Chapter 1 is to bring

these existing fields of study together and afford a clear, integrative perspective on the


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construct of TSE and its consequences (cf. Henson, 2001; Wheatley, 2005; Wyatt, 2016). In

Chapter 2, we took up this challenge by proposing a process-oriented framework that allowed

us to integrate and synthesize the current body of work on TSE and its consequences. Of note,

this model seems to complement and extend other seminal frameworks of teacher self-efficacy

(e.g., Tschannen-Moran, Woolfolk Hoy, & Hoy, 1998; Woolfolk Hoy, Hoy, & Davis, 2009) in

two essential ways.

THE NATURE OF TSE

Unlike prior conceptual models (Tschannen-Moran et al., 1998), the process-oriented

framework attempted, first, to distinguish Bandura’s ideas from other potentially relevant

theories and constructs, including locus of control, self-concept, competence, and self-esteem.

According to Bandura (1997), these theories and concepts fail to do justice to the highly

particularized and multifaceted nature of teacher self-efficacy, and usually tend to measure a

different construct. For that reason, we took a first theoretical step in elucidating and

reinstating the social-cognitive foundation of TSE, which has every so often been obscured by

Rotter’s (1966) conceptual scheme (cf. Henson, 2001; Tschannen-Moran & Woolfolk Hoy,

2001; Wheatley, 2005; Wyatt, 2014). In view of the results of Chapter 2, this step did not seem

an unnecessary exercise. Largely consistent with the review of Klassen et al. (2011), we noted

that only a quarter of the 165 reviewed studies tended to treat TSE as a multifaceted construct

involving various domains of activity. Of this quarter of studies, some defined the self-efficacy

construct at a highly specific level (e.g., TSE for data-driven decision making or TSE in

student-centered teaching styles), whereas others operationalized teachers’ self-efficacy as a

construct that may also be generalizable to other contexts (e.g., computer self-efficacy).

Additionally, a considerable number of studies, despite employing Tschannen-Moran and

Woolfolk Hoy’s (2001) multifaceted measure, tended to calculate total scores of TSE, instead

of gauging the three intended domains of instructional strategies, classroom management, and

student engagement. Hence, the current field of work on TSE seems to have fallen short in

defining the construct of TSE in the way Bandura initially intended, thereby hindering the

process of seeking consensus on TSE and its consequences.

It is an interesting question why the large majority of studies may still resort to the “one

measure fits all” (Bandura, 2006, p. 307) approach in studying teachers’ sense of self-efficacy.

Indeed, in Chapter 3, it was argued that domain-general operationalizations of TSE, though

conveniently serving diverse purposes, may be problematic in the sense that they leave much


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ambiguity about what is being measured and which specific tasks teachers have to accomplish

(cf. Bandura, 1997, 2006). Also, teachers’ general capability beliefs have previously been

claimed to inadequately match with the particular outcomes that are being measured, thereby

potentially losing their explanatory and predictive merit (ibid.). Of note, these issues were, in

part, also evident in Chapter 2, where teachers’ general self-efficacy beliefs appeared to be

somewhat poorer predictors of a range of adjustment outcomes in class than domain-specific

capability beliefs.

Beyond the “thorny issues” that Tschannen-Moran and Woolfolk Hoy (2001, p. 794) describe

in their paper on the meaning and measurement of TSE, there may be some other reasonable

explanations for the current lack of domain-specific self-efficacy research. One justification

that can be extrapolated from Chapter 2 is that many empirical studies, probably due to

pragmatic reasons, tend to investigate samples involving teachers from multiple grades and

across different subjects. In such cases, it may be particularly difficult to tailor measures of

TSE to relevant realms of activity. First-grade teachers who usually have one class and teach

several subjects may have to deal, for instance, with far different task and situational demands

than high-school teachers, who typically specialize in one subject area and may see various

classes a day. General measures of TSE, then, may have more practical relevance in that they

are designed to serve various samples and teaching contexts. The potential benefits of these

general measures may, however, be bought at the price of the predictive power of TSE. Efforts

to discover how teachers’ self-efficacy beliefs may contribute to their functioning in different

domains of activity should therefore ideally focus on particular grades or school periods (e.g.,

preschool, the elementary years, or the high-school period). This narrower focus may help

future researchers disclose areas of teaching and learning within which teachers feel less

capable and need additional training or intervention. Results from the studies described in

Chapters 3 to 5, for instance, underscore the need to provide upper elementary teachers with

some additional help in domains of behavior management and emotional support.

Another reason for falling back on teachers’ more general self-efficacy beliefs is that the

outcome domains of interest may, somewhat paradoxically, be too specific as well. To be more

precise, some of the outcome variables of the reviewed studies in Chapter 2, including

teachers’ attitudes toward web-based instruction or their self-survival concerns, require self-

efficacy measures that are also cast in a very specific form (Bandura, 1997, 2006). Yet, there

may be a danger of developing items that are so detailed that they prevent researchers from


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drawing conclusions about TSE that, to some extent, are generalizable across outcome

variables and teaching domains (e.g., Pajares, 1996; Tschannen-Moran & Woolfolk Hoy, 2001).

To avoid such issues, relying on teachers’ general sense of efficacy may be a somewhat safer

option. Studies concentrating on very specific outcomes thus serve as a useful reminder that, to

understand TSE and its consequences, the selection of and generalizability across outcome

variables of interest are just as important as the adequate measurement of TSE itself.

THE CONSEQUENCES OF TSE

A second distinction from prior conceptual models of TSE (Tschannen-Moran et al., 1998) is

that the process-oriented framework yielded a set of specific parameters for meaningfully

differentiating between the various consequences of teacher self-efficacy. By carefully sorting

through the latest theory and evidence on TSE, and using both the frameworks of Pianta et al.

(2008) and Woolfolk Hoy et al. (2009), it was possible to identify three major outcome

domains of TSE, including the quality of classroom processes, students’ academic adjustment,

and teachers’ sense of well-being. These domains, as well as their underlying dimensions,

helped in the quest to interpret the findings emerging from each separate field of study and

provided directions for future research.

The quality of classroom processes

Findings related to instructional, organizational, and emotional aspects of classroom processes

generally appeared to lack a clear focus. Overall, studies in this field covered a wide range of

teaching strategies, behaviors, attitudes, and decisions, each of which tended to be explored

only once or twice in isolated, cross-sectional studies focusing on different student groups

and/or grades. As a result, it appeared to be difficult to seek consensus on the direct links

among TSE and the quality of classroom processes. Across these studies, two other interesting

outcome patterns did emerge, though. First, a clear lack of research was noticed that

specifically concentrated on teachers’ ability to establish a warm connection with their students

and to be responsive to their social, emotional, and developmental needs (cf. Hamre, Hatfield,

Pianta, & Jamil, 2014; Pianta et al., 2008). Importantly, subsequent chapters show that this

domain of emotional support can be distinguished separately from other important domains of

TSE (Chapter 3), and may provide new insights into teachers’ ability to deal with individual

students with a variety of social-emotional behaviors (Chapters 4 and 5). As such, teachers’

sense of self-efficacy in the domain of emotional support may advance further understanding

of the multifaceted ways in which teachers’ self-percepts of efficacy function.


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Second, in a number of the reviewed studies, teachers’ years of experience appeared to play a

role in the association between TSE and teacher practices that define the classroom quality.

For instance, tenured educators with high levels of general self-efficacy were found to be likely

to use more diverse instructional strategies, to differentiate more frequently, to change their

goals according to students’ needs, and to be more positive about the implementation of such

instructional strategies than less experienced teachers (Allinder, 1995; Martin, Sass, & Schmitt,

2012; Wertheim & Leyser, 2002; Weshah, 2012). Other empirical research (e.g., Klassen &

Chui, 2010; Morris-Rothschild & Brassard, 2006) indicated that TSE may increase with

experience as teachers become better able to effectively instruct, manage, and motivate the

children in their classrooms. The conclusion that teaching experience may have a potentially

beneficial (by)effect on TSE did not hold across the empirical studies on student-specific TSE,

though. Chapter 3 demonstrated, for instance, that teachers with little (<5 years), average (5–

10 years), or high experience (>10 years) did not significantly differ in their sense of self-

efficacy across domains and individual students. Using a smaller, longitudinal sample in

Chapter 5, we additionally failed to establish the presumed associations between teaching

experience and domain- and student-specific TSE over time. Only in Chapter 4 did teachers’

years of experience concurrently add to the prediction of student-specific TSE, but only in

domains of student engagement and emotional support.

There may be several reasons for these contradictory findings. On the theoretical front, it can

be suggested that teachers’ years of experience are most likely to help them become sensitized

to students’ signals, emotional needs, and expectations in class (e.g., Kokkinos, Panayiotou, &

Davazoglou, 2005). Compared to instructional and classroom organizational skills, such more

soft competencies do usually not form a major part of their training and may therefore be best

learned and developed on the job (Hargreaves, 1998). This might explain why the studies

repeatedly failed to establish significant associations between teaching experience and student-

specific TSE for instructional strategies and behavior management. Another possibility is that

(some) student-specific capability beliefs, more than generalized, classroom-level TSE, tend to

depend on characteristics of individual students, rather than such teacher features as

experience. Indeed, results from both Chapter 3 and 4 demonstrated that most of the

variability in TSE occurred within teachers, mirroring the social-cognitive view that TSE,

despite reflecting some degree of trait variability, is most likely to vary across teaching domains

and the students toward whom their behaviors are directed. Lastly, it should be noted that the

samples used in Chapters 3 to 5 included teachers who worked, on average, more than 16 years


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in primary education. This is perhaps not surprising, given that more than one third of all

elementary school teachers in the Netherlands are over 50 (DUO, 2014). Yet, this relative lack

of beginning teachers, whose beliefs in their capability have yet to be established, might have

biased our results. To avoid such potential biases, teacher self-efficacy research incorporating a

larger number of beginning teachers is definitely warranted. Such research may also help

explain the complex interrelationships between TSE, teachers’ years of experience, and their

decision to leave or stay in the profession.

Students’ academic adjustment

The studies reviewed in Chapter 2 generally imply that teachers’ sense of self-efficacy is a

positive predictor of students’ academic performance in various subjects, including math,

history, biology, and, to a lesser extent, reading and writing. In addition, aspects of students’

motivation, including student engagement, intrinsic and extrinsic motivation, academic

expectations, self-efficacy, and goal orientations, appeared to be predicted by their teachers’

general self-efficacy beliefs. It should be kept in mind, however, that small samples, generalized

instruments, poor methodologies, and cross-sectional designs were fairly common in this

literature. Particularly those studies focusing on student achievement appeared to lack

methodological rigor and to reveal small associations between TSE and students’ academic

performance. Following Bandura (1997, 2006), upcoming research on links between TSE and

students’ academic adjustment would profit from self-efficacy measures that are tailored to

tasks and domains that best reflect teachers’ capability to increase individual students’

motivation and academic achievement. Compared to generalized instruments, such highly

particularized scales may have more predictive power, as they measure the type of TSE beliefs

that determine which activities teachers embark on and how well they perform those activities

in relation to a particular child (Bandura, 1997). In this sense, the student-specific TSES, as

described in Chapter 3, may be a useful tool to better understand the role of teachers’ self-

efficacy in students’ academic adjustment.

Also noteworthy is that potential indirect associations between TSE and students’ academic

adjustment have been investigated in only three of the 165 reviewed studies. This is despite the

nowadays common belief that TSE, as a personal characteristic, mainly affects student and

teacher outcomes through patterns of teacher behavior and practices that define the quality of

the classroom environment (Guo, McDonald Connor, Yang, Roehring, & Morrison, 2012;

Midgley, Feldlaufer, & Eccles, 1989; Woolfolk Hoy & Davis, 2005). Perhaps, the general lack


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of rigorous longitudinal (structural equation modeling) techniques and small samples used in

the reviewed studies may explain why researchers have fallen short in investigating the

hypothesized indirect links between TSE and students’ academic adjustment. The field of TSE,

therefore, would probably profit from research using models in which mediational

relationships can be established and differing pathways of influences can be compared.

Teachers’ well-being

In contrast to the quality of classroom processes and students’ adjustment, the literature on

TSE and its consequences for teachers’ well-being yielded far more reliable and consistent

results. In total, 71 relatively well-executed studies provided support for the contention that

generally self-efficacious teachers may suffer less from stress and burnout symptoms, and

experience higher levels of personal commitment and job satisfaction. Perhaps even more

compelling are the handful of studies positing that classroom processes and experiences related

to student misbehavior and positive affect may function as a go-between in the relationship

between teachers’ self-efficacy and their subsequent sense of well-being (e.g., Briones,

Tabernero, & Arenas 2010; Doménech-Betoret, 2009; Duffy & Lent, 2009; Lent et al., 2011;

Sass, Seal, & Martin, 2011). These investigations seem to accord relatively well with evidence

from Chapter 5, in which teachers’ student-specific capability beliefs were found to be

inextricably intertwined with their experiences of conflict and closeness in relationships with

disruptive students. Therefore, it seems to be relevant for future researchers to combine

unique elements of self-efficacy research on classroom processes and teacher well-being in a single,

longitudinal model. This may yield a fuller portrait of teachers’ sense of self-efficacy and its

consequences over time than presented by any one existing strand of research, and provide

much needed guidelines for educational researchers and practitioners alike.

ADDRESSING CHALLENGES REGARDING THE MEASUREMENT OF TSE

In Chapter 1, several important issues regarding the measurement of TSE were highlighted

that may warrant further attention in future educational research. Most of those, including

adequate domain specification and the inclusion of environmental obstacles against which

teachers can judge their self-efficacy, are as old as the work of Tschannen-Moran and

colleagues (1998, 2001). Yet, despite giving full attention to these issues (e.g., Henson, 2002;

Klassen et al., 2011; Wheatley, 2005; Wyatt, 2014) none of the current studies have, to our

knowledge, come up with a single measure that may adequately address them. Using


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Tschannen-Moran and Woolfolk Hoy’s (2001) Teacher Sense of Efficacy Scale (TSES) as a

baseline, a new measure was therefore developed and evaluated in Chapter 3 that may gauge

the teacher self-efficacy construct in ways that it more reliably reflects the social-cognitive

foundation of self-efficacy.

DOMAIN SPECIFICATION OF TSE

The search for methods to empirically capture the meaning of TSE began by clarifying what it

takes for elementary school teachers to succeed in given domains of teaching and learning. To

this end, we largely drew on the review results from Chapter 2 and the CLASS-framework of

Pianta et al. (2008) and discovered that the TSES, next to its original domains of instructional

strategies, classroom/behavior management, and student engagement, might require an

additional domain of emotional support. Similar to the CLASS-dimensions of positive climate,

student sensitivity, and regard for student perspectives, this additional domain was specified to

involve tasks and responsibilities related to how well teachers can establish caring relationships

with students, acknowledge students’ opinions and feelings, and create settings in which

students feel secure to explore and learn. Of note, some other domains that have previously

been suggested by Bandura (undated) were not included in the student-specific TSES. These

domains, comprising tasks and responsibilities related to teachers’ ability to influence decision

making, school resources, and community involvement, have already been discarded by

Tschannen-Moran and Woolfolk Hoy (2001) as being not representative of the kinds of

teaching tasks that typically make up elementary teachers’ daily activities. Moreover, such

domains, albeit part of teachers’ job, did not seem relevant to study teachers’ sense of self-

efficacy for teaching and learning in relation to individual students in the classroom.

The final confirmatory factor model in Chapter 3 suggested that the four domains of the

student-specific TSES can be distinguished separately from one another. At the same time,

though, the results pointed to a high degree of correspondence among domains of

instructional strategies, student engagement, and emotional support. Evidently, these results

may raise issues with respect to construct and discriminant validity. Yet, there may be some

conditions that may explain this unexpectedly high level of covariance. First, large associations

among teachers’ self-efficacy beliefs in various domains of teaching and learning may occur

when these domains influence one another in a reciprocal way. For instance, it is likely that

teachers’ emotional connection and positive communications with individual students (emotional

support) may help these students become motivated for their schoolwork (student engagement), and


GENERAL DISCUSSION

183

see the relevance and meaningfulness of their teachers’ instruction (instructional support; cf.

Hamre & Pianta, 2005; Pianta et al., 2008). In a related vein, it is possible that teachers

experience roughly the same student-specific self-efficacy beliefs across teaching domains

because they may develop different instructional, affective, and classroom organizational skills

simultaneously around the behaviors and needs of individual children (Bandura, 1997, 2006;

Hamre et al., 2013, 2014). It is likely that this process of concurrent skill development in

dissimilar teaching areas, in which individual children serve as the common denominator, may

result in student-specific abilities that are somewhat different from teachers’ more general teaching

skills. Accordingly, it might explain why the domains of student-specific TSE show a higher

level of correspondence with one another than self-efficacy domains measured at the

classroom-level (e.g., Tschannen-Moran & Woolfolk Hoy, 2001; Wolters & Daugherty, 2007).

Lastly, Bandura (1997, 2006) has pointed to powerful experiences of mastery or failure with

individual children that may yield a transformational reorganization of TSE, manifested across

various domains of teaching and learning. To some extent, results from Chapter 5 seem to

substantiate this notion, suggesting that teachers unfortunately experience higher levels of

conflict in the relationship with disruptive children, which may subsequently transform into

lower levels of self-efficacy toward these individual students across teaching domains.

Taken together, the substantial correlations among the domains of the student-specific TSES

give some reason to believe that teachers, next to more generic teaching skills, tend to

concurrently develop and orchestrate highly overlapping cognitive, social, emotional, and

behavior skills to deal with a particular child. The presence of such student-specific skills may

explain why only moderate correlations between the original, classroom-level TSES and the

student-specific TSES at the (aggregated) between-teacher level were established in Chapter 3.

Evidently, replication of the results in Chapter 3 is needed to firmly establish the validity of the

student-specific TSES, both in comparable, but larger samples, as well as across grades,

countries, types of education, and different children. Yet, it may be safe to argue that teachers’

functioning in distinct areas of teaching may be better observed in relation to the particular

children toward whom their actions are directed than the classroom as a whole.

DEFINING OBSTACLES

In response to Bandura’s (1997, 2006) claim that self-efficacy beliefs must be measured in light

of environmental obstacles, the original TSES was further adapted by making its individual

items student-specific. By letting teachers report on their self-efficacy for randomly selected


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students, it became possible to specify gradations of challenge to which teachers could adjudge

their capability beliefs, without further complicating the items of the original TSES. The

decision to take the TSES to the student-specific level was based on the idea that obstacles to

TSE tend to be mainly reflected in the behaviors, needs, and characteristics of individual

students in class. In the conceptual model of Tschannen-Moran and colleagues (1998), for

instance, teachers’ assessment of what will be required of them in the anticipated teaching

situation has been hypothesized to be affected by micro-contextual factors, including students’

abilities and motivation. To some extent, this idea is also mirrored in their Teacher Sense of

Efficacy Scale, where some items include such gradations of challenge as ‘very capable

students’, ‘problem students’, and ‘students who are failing’ (Tschannen-Moran & Woolfolk

Hoy, 2001). The results from Chapter 3 provided supporting evidence for the ideas of

Tschannen-Moran et al. (1998), indicating that the variability at the state (within-teacher) level

was larger than at the trait (between-teacher) level. This indicates that teachers’ sense of self-

efficacy may not only fluctuate across teaching tasks and domains, but may also differ in

relation to individual students.

Teachers’ personal self-efficacy beliefs in relation to individual students appeared to vary most

in the domain of behavior management, suggesting that this domain may be most dependent

on such obstacles as individual students’ behaviors and characteristics. This finding accords

well with evidence from Chapter 4, in which individual students’ prosocial, internalizing, and

particularly externalizing behavior predicted more variance in teachers’ self-efficacy for

behavior management than any other teaching domain. Other research (e.g., Emmer &

Hickman, 1991; Kyriacou, 2001; Roehrig, Pressley, & Talotta, 2002) has also suggested that

issues such as spending too much time on discipline, not knowing when and how to punish a

student, and dealing with students who are behaviorally challenging, are usually the most

problematic for teachers. This may explicate, in part, why teachers’ sense of self-efficacy for

classroom management and/or discipline appeared to be most frequently investigated in the

literature (see Chapter 2), either as a single teaching area (e.g., Brouwers & Tomic, 2000;

Emmer & Hickman, 1991; Yoon, 2004) or as a sub-domain of TSE (e.g., Skaalvik & Skaalvik,

2007; Tschannen-Moran & Woolfolk Hoy, 2001; Woolfson & Brady, 2009). Overall, both

prior research and current findings seem to suggest that teachers may gain the most from

tailored advice on managing individual students’ behaviors, and especially those of an

externalizing nature.


GENERAL DISCUSSION

185

TEACHERS’ INDIVIDUAL INTERPRETATIONS OF THE STUDENT-SPECIFIC TSES

It should be noted that the examination of teachers’ student-specific self-efficacy beliefs may

present difficulties with respect to measurement non-invariance across teachers, or cluster bias.

Specifically, Chapter 3 demonstrated that teachers are likely to differ in their individual

interpretation of the student-specific TSES-items, and particularly those that tapped into their

sense of student-specific self-efficacy for instructional strategies and student engagement.

Given that these self-efficacy domains primarily make an appeal to teachers’ cognitive and

affective skills, our findings may raise the question of which other internal personal factors may

cause differences in response processes. Following Bandura’s (1997) model of triadic reciprocal

causation, such internal personal factors, including cognitive and affective events, may act as

interacting determinants that influence teachers’ perceptions of environmental events, their

self-efficacy, and their behaviors. Accordingly, it is possible that personal factors, such as

teachers’ affective state, their knowledge and skill level, or their personality, may affect the way

teachers ultimately interpret the individual items of the student-specific TSES, thereby causing

potential differences in response processes. Exploration of personal internal factors, as well as

other potential contextual features that may explain measurement non-invariance across

teachers therefore evidently merits attention in future research. One potential way of doing so

is to investigate cluster bias in the student-specific TSES with respect to violators at the within-

and between-teacher level separately for each teaching domain. Such analyses may be more

feasible in that they would allow for smaller sample sizes to achieve adequate statistical power,

and may overcome the issue of multicollinearity.

ADDRESSING CHALLENGES REGARDING THE FORMATION AND DEVELOPMENT OF TSE

Although ample research has attested to the predictive power of TSE for a range of student

and teacher outcomes (Chapter 2), there has been a noticeable lack of efforts to investigate the

various sources of TSE and the processes through which these beliefs are formed. Chapters 4

and 5 of this dissertation, therefore, were specifically targeted at exploring these unresolved

challenges, both fully concentrating on TSE at the student-specific level. Generally, results

from Chapter 4 seem to verify the assumption made in Chapter 1 that personal characteristics

of individual students, including their externalizing, internalizing, and prosocial behavior, may

serve as potent sources of teachers’ self-efficacy. Other background characteristics, such as

students’ gender and age, did not appear to be predictive of teachers’ student-specific self-

efficacy beliefs.


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Perhaps the most important finding of Chapter 4 is that elementary school teachers were likely

to experience the lowest levels of self-efficacy in relation to individual students who exhibited

externalizing behavior in class. These externalizing student behaviors explained most of the

variance in teachers’ student-specific self-efficacy for behavior management, followed by

domains of student engagement, instructional strategies, and emotional support. Remarkably,

those outcomes seem to suggest that teachers’ beliefs in their capability to establish warm

connections with individual students and to be sensitive and responsive to their needs are least

affected by externalizing student behavior. It could be that teachers may use their emotional

supports as a way to increase externalizing students’ academic and social behaviors. Prior

research from Henricsson and Rydell (2004), for instance, has shown that disruptive children

generally get more attention, praise, and encouragement from their teachers than

unproblematic students, whose behaviors are more likely to be maintained by intrinsic interest

in their schoolwork (e.g., Beaman & Wheldall, 2000). Such positive communications may

provide teachers with the type of enactive mastery experiences that, in part, help them

overcome unsuccessful dealings with disruptive children and, in turn, increase their sense of

efficacy toward these students in the emotional support domain (Bandura, 1997). Possibly, this

might also explain why individual students’ prosocial behavior appeared to be the most

important source of TSE for emotional support, followed hot on the heels by domains of

behavior management, instructional strategies, and student engagement.

Spurred by finding that disruptive student behaviors are likely to be one of the more vital

sources of teachers’ self-efficacy, these behaviors were investigated further in Chapter 5. Here,

the quality of the student–teacher relationship was explored as an intermediary mechanism by

which the association between students’ disruptive behaviors and student-specific TSE could

be explained. Results from this short-term longitudinal study only partly supported the idea

that disruptive student behaviors, as sources of student-specific TSE, become instructive to

TSE only through teachers’ subjective evaluations of these behaviors in the context of their

daily interactions with individual students. More specifically, tentative evidence was found for

the hypothesis that teacher-perceived conflict in the student–teacher relationship mediates the

longitudinal association between students’ disruptive behavior and teachers’ student-specific

self-efficacy beliefs. Contrary to expectations, however, teacher-perceived closeness was not

found to mediate the link between disruptive student behavior and student-specific TSE.

Rather, support was provided for an alternative model, representing the hypothesis that

student-specific TSE, irrespective of teaching domain, mediated the association between


GENERAL DISCUSSION

187

disruptive student behavior and teachers’ perceptions of closeness in the student–teacher

relationship over time.

The complex outcomes reported in Chapter 5 seem to complement and extend the cross-

sectional results from Chapter 4, both empirically and theoretically. On the empirical front, the

differences in the magnitude of coefficients between Chapter 4 and Chapter 5 suggest that the

much larger cross-sectional associations between disruptive student behavior and student-

specific TSE in Chapter 4 were probably inflated by the stable characteristics of the association

between these two constructs (Little, 2013). As such, the longitudinal, cross-lagged approach of

Chapter 5 probably yielded more reliable coefficients.

Theoretically, the differential patterns of association for closeness and conflict seem to

underscore the importance of viewing teachers’ self-efficacy beliefs as both products and

constructors of daily interactions and relationships with individual children. Prior studies echo

this standpoint (e.g., Bandura, 1997; Fives & Alexander, 2004; Raudenbusch, Rowan, &

Cheong, 1992; Wyatt, 2016). Those studies suggest that teachers’ self-efficacy beliefs in relation

to individual students in specific contexts and their knowledge and belief structures (i.e., mental

representational models) should be considered in relation to one another, rather than

independently, to reveal a fuller portrait of the forms of knowledge and beliefs that teachers

draw upon when interacting with a particular child. Unfortunately, however, very little research

to date has explored such reciprocal associations, probably because TSE, in contrast to dyadic

student–teacher relationships, is usually defined at the classroom-level of analysis and has not

previously been integrated with attachment, dynamic systems, or bio-ecological theories.

PUTTING SOCIAL-COGNITIVE THEORY INTO PRACTICE

The current dissertation may yield some important insights into how teachers can be assisted in

their quest to deal with a variety of students with different behaviors, needs, and (dis)abilities.

First, teachers ought to be made aware of the potential influence their capability beliefs may

have on their students. Findings from forty years of research on TSE suggest that teachers’

sense of self-efficacy, at least at the classroom-level, may essentially be intertwined with their

teaching behaviors and practices in class and, as such, affect their students’ academic

adjustment. Moreover, at the student-specific level, it has been suggested that TSE may change the

quality of teachers’ relationships with individual (disruptive) students in ways that either

confirm or disconfirm their capability beliefs toward these children. To be more precise, a


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healthy sense of self-efficacy tends to allow teachers to get into “yes mode”. Such optimistic

feelings may help them create supportive learning environments that are more in tune with

students' social, affective, and academic needs. Negative self-efficacy beliefs, however, may

stymie teachers’ effective practices in class, block their flow of positive thoughts, and

ultimately, hamper students’ motivation for their schoolwork and academic performance. This

may be especially true for disruptive students, toward whom teachers generally feel the least

efficacious. Helping teachers to be aware of, and reflect on their behaviors and feelings toward

particular students may therefore be a first step forward in the process of increasing their self-

efficacy. To facilitate such a reflective process, the relationship-focused reflection program of

Spilt, Koomen, Thijs, and van der Leij (2012) may be a particularly helpful tool.

Relatedly, the domain- and student-specific nature of teachers’ self-efficacy judgments

underscores the importance of ascertaining in which cases such capability beliefs may be

particularly problematic and therefore require intervention. Thus far, teachers seem to

experience the lowest levels of self-efficacy for behavior management in relation to students

who display disruptive, externalizing behavior, and the lowest levels of self-efficacy for

emotional support toward internalizing students. These findings, though preliminary, align well

with a recent Dutch report on appropriate education (Smeets, Ledoux, Regtvoort, Felix, & Mol

Lous, 2015), in which two third of Dutch elementary school teachers expresses a need for

further skill development in areas of interpersonal teaching and dealing with challenging

student behavior. Accordingly, training and development programs for preservice and inservice

teachers should incorporate more strategies teachers might use to bolster their skills in these

domains, such as anticipating problems, setting consistent expectations for student behavior,

providing clear routines, and engaging more in social conversations with individual students

(cf. Hamre et al., 2014). At the same time, it is worthwhile to replicate and extend the current

findings focusing on TSE in relation to students who differ in ability, motivation, and

educational needs. Such research may shed further light on the processes by which a diverse

student population may shape their teachers’ sense of student-specific self-efficacy in different

teaching domains. This information can be used to identify further training needs among

elementary school teachers.

Lastly, the high correlations among the domains of student-specific TSE give some reason to

believe that teachers, next to more generic teaching skills and capabilities, may develop various

instructional, behavioral, and affective subskills specific to individual students. If this is the case,


GENERAL DISCUSSION

189

then educational researchers and school psychologists should work to identify both the content

of, and possible deficiencies in such student-specific skills and capabilities to develop coaching

programs that are specifically tailored to teachers’ problems and needs with regard to individual

students. Such programs may help them better deal with difficult students in class and, in turn,

develop a healthier sense of efficacy toward these students and potentially, toward the class as

a whole.

CONCLUDING REMARKS

Research on teacher self-efficacy continues to be a vibrant and productive field of study. As

has been noted throughout this dissertation, there has been a steady increase of studies

examining the nature, measurement, sources, and consequences of TSE over the past forty

years. These studies have produced new insights demonstrating that teachers’ sense of self-

efficacy, at least at the classroom-level of analysis, may set the tone for a high-quality classroom

environment, may play a role in their students’ academic performance and motivation for their

schoolwork, and might affect their own sense of well-being in class. At the same time,

however, this body of work has raised many new challenges in the study of TSE that need to

be addressed if the field is to evolve over the next couple of years in ways benefiting both

theory and practice.

In this dissertation, therefore, we have taken stock of the current state of theory and research

on TSE and aimed to address several challenges the field is currently facing by taking teachers’

self-efficacy beliefs to the student-specific level. This refinement and extension of Bandura’s

original ideas enabled us, first, to provide a new benchmark for how the teacher self-efficacy

construct could be operationalized – as a personal belief of capability tailored to various

domains of teaching and learning, as well as individual students. Directly associated with this

benchmark is our newly developed, multifactorial instrument, which may capture variation in

teachers’ sense of self-efficacy across teaching domains and individual students. Hopefully, this

measure of self-efficacy may contribute to more adequate analyses of TSE in future years and

generate more comparable results that are less difficult to interpret.

Taking teacher’ sense of self-efficacy to the student-specific level may, in part, also address

challenges regarding the sources and underlying processes of TSE. Contrary to the modest or

non-significant results of prior research on the sources of TSE (e.g., Ruble, Usher, & McGrew,


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2011; Tschannen-Moran & Woolfolk Hoy, 2007), this dissertation provided evidence that

individual students’ social-emotional behaviors in class serve as relatively strong predictors of

student-specific TSE, and disruptive behavior in particular. Moreover, teachers’ perceptions of

conflict and closeness in the student–teacher relationship each appear to play a different role in

the development of teachers’ sense of self-efficacy toward disruptive students in various

teaching domains. Although these findings are preliminary, they seem to underscore a need to

integrate social-cognitive insights with theoretical perspectives on student–teacher

relationships, including attachment, dynamic systems, and self-determination theories. On a

more practical note, this evidence also calls for a need to set up (intervention) studies that

examine how teachers’ sense of self-efficacy toward and their affective relationships with

difficult children affect one another in a reciprocal fashion.

Overall, the current studies on student-specific teacher self-efficacy may hold some promise

for the future. Yet, much more has to be done to further define and move the field forward on

this front. In our view, this will be an important challenge in the study of teacher self-efficacy

for the years to come.


191

SUMMARY

FROM GENERAL TO STUDENT-SPECIFIC TEACHER SELF-EFFICACY

_________________________________________________________________________

Diversity in elementary classrooms nowadays seems to be the rule rather than the exception.

Since the inception of inclusive and appropriate education in the Netherlands, the number of

students with behavioral, learning, and other educational disabilities in regular elementary

classes has been shown to increase slowly but surely. Although some teachers handle this new

reality with notable ease, many others do not seem to feel up to the task of dealing with a

diverse student population and providing all students with an equal opportunity to learn.

Thereby, these teachers not only run the risk of physical stress and burnout, but may, in the

long run, also hamper their students’ academic performance and school engagement.

One psychological factor that has potential to explain why teachers may be more or less

successful in dealing with classroom diversity, is teachers’ sense of self-efficacy (TSE). Over

the past 40 years, these capability beliefs have been explored in multiple ways, such that

currently much information is available on teachers’ general ability to give shape to their

common actions in class and to motivate and regulate their execution. Yet, the absence of a

clear understanding of the nature, sources, and consequences of TSE, and psychometrically

sound instruments that adequately measure the construct seem to have hampered our efforts

to identify useful research-based insights about TSE that help teachers better deal with

individual students who may differ in behavior, needs, and abilities. For this reason, the present

dissertation aimed to take stock of the current state of research on TSE and address the

challenges the field is currently facing by gradually taking teachers’ general self-efficacy beliefs

to the student-specific level.

Starting out at the most general, classroom-level of analysis, Chapter 2 aimed to address

current challenges regarding the nature of TSE and its consequences. Using a process-oriented

framework, 40 years of social-cognitive research on TSE were reviewed and synthesized to

elucidate the nature of this construct and explore its direct and indirect associations with the

quality of classroom processes, students’ academic adjustment, and teachers’ psychological


SUMMARY

192

well-being. Based on 165 studies conducted among samples of both pre- and in-service

teachers and their students in pre-, elementary, middle, and high school, it could generally be

inferred that general, classroom-level TSE is a positive predictor of various teacher practices

related to the classroom quality. Among others, these include teachers’ dealings with problem

behavior, their instructional strategies and goals, and their student-centered teaching styles.

Modest positive associations were also noted between TSE and students’ motivation and

academic performance in a range of subjects. Teacher practices defining the classroom quality,

and particularly those related to emotional support, have been found to mediate these

associations. Lastly, aspects of teachers’ well-being, such as job satisfaction, stress and burnout

symptoms, commitment, and attrition and retention, were found to be predicted by general

TSE, either directly or, in some cases, indirectly, through instructional and socioemotional

teaching practices. Yet, several issues, including the cross-sectional nature of most reviewed

research, the sometimes poor research designs and methodologies of these studies, and the

limited breadth of the global scales used to measure TSE, prevented definitive conclusions

from this review.

Largely in an attempt to address such methodological issues, Chapter 3 therefore focused on

the development and evaluation of a new instrument that may capture fluctuations in TSE

across various teaching domains and individual students. Results from 841 third- to sixth-grade

students and their 107 regular elementary school teachers supported the existence of one

higher-order factor (General TSE) and four lower-order factors (TSE for Instructional

Strategies, Behavior Management, Student Engagement, and Emotional Support), both at the

between- and within-teacher level. These domains of TSE appeared to vary more within

teachers than between teachers, signifying that TSE, despite reflecting some degree of trait

variability, is likely to vary across characteristics of students toward whom teachers’ behaviors

are directed. It should be noted, however, that the presence of cluster bias in the majority of

teacher self-efficacy items suggested that student-specific TSE at the between-teacher level

may have a different interpretation than this construct at the within-teacher level. The

moderate association between general TSE and student-specific TSE at the between-teacher

level supports this claim. Accordingly, these findings underscore the importance of shifting

focus from general to student-specific teacher self-efficacy.

Fully refraining from general TSE at the classroom-level of analysis, then, Chapters 4 and 5

aimed to address current challenges regarding the sources of student-specific TSE and the


SUMMARY

193

underlying processes by which such sources become instructive to those capability beliefs. To

begin with the sources of TSE, Chapter 4 explored the predictive value of teachers’

perceptions of individual students’ internalizing, externalizing, and prosocial behaviors for

student-specific TSE, and the moderating role of teacher-perceived common classroom

misbehavior and years of experience. Using a cross-sectional sample of 69 teachers and 526

third-to-sixth graders, teachers were generally found to experience lower self-efficacy beliefs in

relation to individual students with externalizing behavior and higher self-efficacy beliefs

toward prosocial students, irrespective of teaching domain. Moreover, students’ internalizing

behavior appeared to be a negative source of student-specific TSE for instructional strategies

and emotional support, and a positive source of TSE for behavior management. Lastly,

teachers’ perceived levels of classroom misbehavior exacerbated the negative association

between externalizing student behavior and TSE for behavior management. Together, these

outcomes suggest that individual students’ externalizing behavior, more than other socio-

emotional behaviors, may be the most potent source of teachers’ student-specific self-efficacy.

Further insight into the association between externalizing, or disruptive student behavior and

student-specific TSE was, for that reason, gained in Chapter 5. In this chapter, teachers’

perceptions of closeness and conflict in the student–teacher relationship were explored as

intermediary mechanisms by which individual students’ disruptive behavior may affect student-

specific TSE over time. Short-term longitudinal results from a sample of 524 third-to-sixth

graders and their 69 teachers indicated that the association between disruptive student behavior

and student-specific TSE may be far more complex than expected on the basis of the results of

Chapter 4. Specifically, teachers generally experienced more conflict in dyadic relationships

with disruptive students, which, in turn, translated into lower student-specific TSE across

teaching domains. For closeness, a different pattern of associations was found, suggesting that

student-specific TSE, irrespective of teaching domain, mediated the link between externalizing

student behavior and teachers' perceptions of closeness in the student–teacher relationship.

These findings indicate that student–teacher conflict and closeness each may play a different

role in the development of teachers’ sense of self-efficacy toward disruptive students in various

teaching domains. For the development of empirically-based intervention programs for

teachers, it is essential to spur further understanding of the complex interrelationships among

individual students’ behaviors and needs, student–teacher relationships, and student-specific

TSE across domains of teaching and learning.


SUMMARY

194

In sum, this dissertation aimed to address several conceptual and methodological challenges

the field of TSE is currently facing by gradually taking teachers’ self-efficacy beliefs to the

student-specific level. In so doing, further research-based insights were gained about the highly

specific and multifaceted nature of TSE, possible ways to adequately measure this construct,

the student characteristics and relationship processes through which teachers’ self-efficacy

beliefs may develop, and lastly, the various consequences of TSE at a more general level. How

this information may help teachers better deal with a diverse student population and inform

future studies and interventions about TSE is discussed in Chapter 6.


195

SAMENVATTING (SUMMARY IN DUTCH)

VAN ALGEMENE NAAR LEERLING-SPECIFIEKE LEERKRACHT SELF-EFFICACY

_________________________________________________________________________

Diversiteit in de klas lijkt tegenwoordig eerder regel dan uitzondering. Sinds de invoering van

inclusief en passend onderwijs in Nederland is het aantal leerlingen met gedrags–, leer– en

andere onderwijsproblemen in het regulier basisonderwijs langzaam maar zeker aan het stijgen.

Hoewel sommige leerkrachten deze nieuwe realiteit met gemak tegemoet lijken te treden,

voelen andere zich niet opgewassen tegen de taak met een diverse leerlingpopulatie om te gaan

en al hun leerlingen gelijke leerkansen te bieden. Hierdoor lopen deze leerkrachten niet enkel

het risico op fysieke stress- en burn-outklachten, maar belemmeren zij mogelijk ook de

leerprestaties en schoolse betrokkenheid van hun leerlingen op langere termijn.

Eén psychologische factor die mogelijk kan verklaren waarom leerkrachten in meer of mindere

mate succesvol zijn in het omgaan met diversiteit in de klas, is leerkracht self-efficacy (LSE). In de

afgelopen 40 jaar zijn deze self-efficacy-, of doelmatigheidspercepties van leerkrachten op diverse

manieren verkend. Hierdoor is momenteel veel informatie voorhanden over de algemene

doelmatigheid van leerkrachten om hun eigen handelen in de klas te kunnen motiveren en

reguleren. Door een gebrek aan inzicht in de aard, bronnen en consequenties van LSE, en

instrumenten die dit construct betrouwbaar en valide kunnen meten, blijkt het niettemin lastig

om tot relevante inzichten over LSE te komen die leerkrachten helpen beter om te gaan met

individuele leerlingen die verschillen in gedrag, behoeften, en leervermogen. Om deze reden

werd in dit proefschrift de balans opgemaakt van de huidige staat van onderzoek over LSE en

werden bovenstaande kwesties aangepakt door de focus te verleggen van algemene naar

leerling-specifieke self-efficacy.

In Hoofdstuk 2 werden problemen met betrekking tot de aard en consequenties van LSE

nader onder de loep genomen. De algemene mate van doeltreffendheid van leerkrachten ten

aanzien van de hele klas vormde hierbij het vertrekpunt. Om de aard en directe en indirecte

gevolgen van LSE helder te krijgen, werd gebruik gemaakt van een proces-georiënteerd,

heuristisch raamwerk waarmee 40 jaar aan sociaal-cognitief onderzoek over LSE geëvalueerd



196

en samengevat kon worden. Op basis van 165 studies, uitgevoerd onder startende en ervaren

leerkrachten en hun leerlingen in peuterklassen, basisscholen en het middelbaar onderwijs,

werd geconcludeerd dat algemene LSE op klasniveau een positieve predictor is van diverse

leerkrachthandelingen en –opvattingen die gerelateerd zijn aan de kwaliteit van het klasklimaat.

Voorbeelden daarvan zijn het adequaat omgaan van leerkrachten met probleemgedrag, hun

instructiestrategieën en –doelen, en hun leerling-gecentreerde doceerstijlen. Daarnaast werden

bescheiden positieve associaties van LSE met de motivatie en leerprestaties van leerlingen in

diverse vakken gevonden. Leerkrachthandelingen die met name gerelateerd zijn aan emotionele

ondersteuning van leerlingen bleken deze associaties bovendien te mediëren. Tot slot werd

gevonden dat aspecten van het welzijn van leraren, zoals stress en burnout, werktevredenheid,

betrokkenheid en werkuitval, voorspeld worden door algemene LSE, zowel direct als indirect,

door leerkrachthandelingen gericht op instructie en sociaal-emotionele ondersteuning. Echter

dient opgemerkt te worden dat de cross-sectionele aard van veel van de geëvalueerde

onderzoeken, de matige onderzoeksdesigns en de beperkte reikwijdte van de instrumenten die

zijn gebruikt om LSE te meten meer definitieve conclusies uitsluiten.

In een poging om vat te krijgen op bovengenoemde methodologische problemen lag de focus

in Hoofdstuk 3 daarom op het ontwikkelen en evalueren van een nieuw instrument dat variatie

in LSE over diverse leerdomeinen en individuele leerlingen bloot kan leggen. Resultaten uit een

steekproef van 107 reguliere basisschoolleerkrachten in relatie tot 841 leerlingen uit groep 5 tot

en met 8 ondersteunden één hogere orde factor (Algemene LSE) en vier lagere orde factoren,

of domeinen, van leerkracht self-efficacy (LSE voor Instructiestrategieën, Gedragsmanagement,

Leerlingmotivatie en Emotionele Ondersteuning). Deze domeinen van LSE werden zowel

tussen als binnen leerkrachten gevonden. Daarnaast bleken de verschillen in self-efficacy binnen

leerkrachten aanzienlijk groter te zijn dan dan verschillen in LSE tussen leerkrachten. Dit

suggereert dat leerkracht self-efficacy waarschijnlijk niet volledig stabiel is, maar kan veranderen,

afhankelijk van de kenmerken van individuele leerlingen uit de klas van de leerkracht. Echter

dient opgemerkt te worden dat het merendeel van de items niet geheel zuiver gemeten kon

worden tussen leerkrachten. Deze vraagonzuiverheid (cluster bias) geeft aan dat potentiële

verschillen in leerkrachtkenmerken kunnen leiden tot verschillen in leerling-specifieke LSE

tussen leerkrachten die niet verklaard kunnen worden door het construct zelf. De

middelmatige correlatie tussen de algemene en leerling-specifieke self-efficacy-percepties van

leerkrachten lijkt dit idee te ondersteunen. Deze bevindingen onderstrepen dus het belang van

een focusverschuiving van algemene naar leerling-specifieke LSE.



197

In Hoofdstukken 4 en 5 lag de nadruk daarom volledig op LSE in relatie tot individuele

leerlingen. Centraal in deze hoofdstukken stonden uitdagingen met betrekking tot de bronnen

van deze doelmatigheidspercepties en de onderliggende processen waardoor deze bronnen

mogelijk betekenis krijgen voor de self-efficacy-gevoelens van leerkrachten. Om te beginnen met

de bronnen van LSE, werd in Hoofdstuk 4 de voorspellende waarde van leerkrachtpercepties

over het internaliserend, externaliserend en pro-sociaal gedrag van individuele leerlingen voor

LSE onderzocht. Tevens werd de modererende rol van leerkrachtpercepties over storend

leerlinggedrag in de klas en onderwijservaring geëxploreerd. Uit een cross-sectionele steekproef

van 69 leraren en 526 leerlingen (groep 5 t/m 8) bleek dat leerkrachten zichzelf over het

algemeen minder doelmatig voelden in relatie tot individuele leerlingen met externaliserend

gedrag en meer doelmatig voelden in relatie tot pro-sociale kinderen. Bovendien bleken

symptomen van internaliserend gedrag te fungeren als een negatieve bron van leerling-

specifieke LSE in het domein van instructiestrategieën en emotionele ondersteuning. Tot slot

kwam naar voren dat de associatie tussen externaliserend leerlinggedrag en LSE voor gedrags-

management sterker werd als leerkrachten tevens veel storend gedrag in de klas ondervonden.

Samen suggereren deze uitkomsten dat, van alle sociaal-emotionele leerlinggedragingen,

externaliserend gedrag mogelijk de belangrijkste bron van leerling-specifieke LSE is.

Verder inzicht in de associatie tussen externaliserend, of storend leerlinggedrag en leerling-

specifieke LSE werd om deze reden verkregen in Hoofdstuk 5. In dit hoofdstuk werd gekeken

of leerkrachtpercepties over nabijheid en conflict in de leerkracht–leerlingrelatie mogelijk

fungeren als mediërend mechanisme waarlangs storend gedrag van individuele leerlingen

invloed uitoefent op leerling-specifieke LSE over tijd. Korte-termijn longitudinale resultaten uit

een steekproef van 524 bovenbouwleerlingen in relatie tot hun 69 leraren suggereerden dat de

relatie tussen storend leerlinggedrag en leerling-specifieke LSE veel complexer is dan werd

verwacht op basis van de resultaten uit Hoofdstuk 4. Gevonden werd dat leraren over het

algemeen meer conflict in dyadische relaties met storende leerlingen beleefden, wat zich

vervolgens vertaalde in negatievere leerling-specifieke doelmatigheidspercepties over

leerdomeinen heen. Wat nabijheid betreft werd een ander patroon aangetoond. Dit patroon

was indicatief voor een mediërend effect van leerling-specifieke LSE, ongeacht leerdomein, op

de associatie tussen storend leerlinggedrag en leerkrachtpercepties van nabijheid in de

leerkracht–leerlingrelatie. Deze bevindingen roepen de gedachte op dat conflict en nabijheid in

de leerkracht–leerlingrelatie elk een andere rol spelen in de ontwikkeling van LSE in diverse

leerdomeinen en in relatie tot individuele leerlingen met storend gedrag. Voor het ontwikkelen



198

van evidence-based interventieprogramma’s voor leerkrachten is het daarom essentieel om meer

inzicht te krijgen in de complexe inter-relaties tussen de behoeften en gedragingen van

individuele leerlingen, leerkracht–leerlingrelaties en leerling-specifieke LSE in diverse

leerdomeinen.

Samenvattend werd in dit proefschrift gepoogd om diverse conceptuele en methodologische

kwesties in het veld van leerkracht self-efficacy aan te pakken door de focus te verleggen van

algemene naar leerling-specifieke self-efficacy van leerkrachten. Hierdoor werden verdere, op

onderzoek gebaseerde, inzichten verkregen over de zeer specifieke en veelzijdige aard van LSE,

over mogelijke manieren om dit construct op een adequate manier te meten, over

leerlingkenmerken en relatieprocessen waardoor leerling-specifieke LSE mogelijk tot

ontwikkeling kan komen en, tot slot, over diverse gevolgen van LSE op een meer algemeen

niveau. Hoe deze informatie leerkrachten kan helpen bij het omgaan met een diverse

leerlingpopulatie en bij kan dragen aan toekomstige studies en interventies is besproken in

Hoofdstuk 6.


199

REFERENCES _________________________________________________________________________

Abidin, R. R., & Robinson, L. L. (2002). Stress, biases, or professionalism: What drives teachers’ referral

judgments of students with challenging behaviors? Journal of Emotional and Behavioral Disorders, 10, 204–212. doi:10.1177/10634266020100040201

Abuh-Tineh, A. M., Khasawneh, S. A., & Khalaileh, H. A. (2011). Teacher self-efficacy and classroom manage-ment styles in Jordanian schools. Management in Education, 25, 175–181. doi:10.1177/0892020611420597

Ahmad, T. B. T., Basha, K. B., Marzuki, A. M., Hisham, N. A. H. I., & Sahari, M. S. (2010). Faculty's acceptance of computer based technology: Cross-validation of an extended model. Australasian Journal of Educational Technology, 26, 268–279.

Ahnert, L., Pinquart, M., & Lamb, M. E. (2006). Security of children's relationships with nonparental care providers: A meta-analysis. Child Development, 77, 664–679. doi:10.1111/j.1467-8624.2006.00896.x

Ahsan, M. T., Sharma, U., & Deppeler, J. M. (2012). Exploring pre-service teachers’ perceived teaching-efficacy, attitudes and concerns about inclusive education in Bangladesh. International Journal of Whole Schooling, 8, 1–20.

Allen, I. E., & Seaman, C. A. (2007). Likert scales and data analyses. Quality Progress, 40, 64–65. Allinder, R. M. (1995). An examination of the relationship between teacher efficacy and curriculum-based

measurement and student achievement. Remedial & Special Education, 16, 247–255. Almog, O., & Shechtman, Z. (2007). Teachers' democratic and efficacy beliefs and styles of coping with be-

havioural problems of pupils with special needs. European Journal of Special Needs Education, 22, 115–129. doi:10.1080/08856250701267774

Aloe, A. M., Amo, L. C., & Shanahan, M. E. (2014). Classroom management self-efficacy and burnout: A multi-variate meta-analysis. Educational Psychology Review, 26, 101–126. doi:10.1007/s10648-013-9244-0

Andreou, E., & Rapti, A. (2010). Teachers' causal attributions for behaviour problems and perceived efficacy for class management in relation to selected interventions. Behaviour Change, 27, 53–67. doi:10.1375/bech.27. 1.53

Angle, J., & Moseley, C. (2010). Science teacher efficacy and outcome expectancy as predictors of students' end-of-instruction (EOI) biology I test scores. School Science and Mathematics, 109, 473–483. doi:10.1111/ j.1949-8594.2009.tb18294.x

Arbeau, K. A., & Coplan, R. J. (2007). Kindergarten teachers' beliefs and responses to hypothetical prosocial, asocial, and antisocial children. Merrill-Palmer Quarterly, 53, 291–318. doi:10.1353/mpq.2007.0007

Arbeau, K. A., Coplan, R. J., & Weeks, M. (2010). Shyness, teacher–child relationships, and socio-emotional adjustment in grade 1. International Journal of Behavioral Development, 34, 259–269. doi:10.1177/0165025 409350959.

Armor, D., Conroy-Oseguera, P., Cox, M., King, N., McDonnell, L., Pascal, A., … Zellman, G. (1976). Analysis of the school preferred reading programs in selected Los Angeles minority schools (Rep. No. R-2007-LAUSD). Santa Monica, CA: Rand Corporation.

Arnold, D. H. (1997). Co-occurrence of externalizing behavior problems and emergent academic difficulties in young high-risk boys: A preliminary evaluation of patterns and mechanisms. Journal of Applied Developmental Psychology, 18, 317–330. doi:10.1016/S0193-3973(97)80003-2

Ashton, P. T., Olejnik, S., Crocker, L., & McAuli e, M. (1982). Measurement problems in the study of teachers’ sense of efficacy. Paper presented at the annual meeting of the American Educational Research Association, New York, NY.

Avanzi, L., Miglioretti,, M., Velasco, V., Balducci, C., Vecchio, L., Fraccaroli, F., & Skaalvik, E. M. (2012). Cross-validation of the Norwegian Teacher’s Self-Efficacy Scale (NTSES). Teaching and Teacher Education, 31, 69–78. doi:10.1016/j.tate.2013.01.002


REFERENCES

200

Baker, J. A., Grant, S., & Morlock, L. (2008). The teacher-student relationship as a developmental context for children with internalizing or externalizing behavior problems. School Psychology Quarterly, 23, 3–15. doi:10.1037/1045-3830.23.1.3

Bakker, A. B., Hakanen, J. J., Demerouti, E., & Xanthopoulou, D. (2007). Job resources boost work engagement, particularly when job demands are high. Journal of Educational Psychology, 99, 274–284. doi: 10.1037/0022-0663.99.2.274

Bandura, A. (undated). Teacher Self-Efficacy Scale. Retrieved from http://www.coe.ohio-state.edu/ahoy/ researchinstruments. htm#Ban

Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84, 191–215. doi:10.1037/0033-295X.84.2.191

Bandura, A. (1982). Self-efficacy mechanism in human agency. American Psychologist, 37, 122–147. doi:10.1037/ 0003-066X.37.2.122

Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice Hall. Bandura, A. (1997). Self-efficacy: The exercise of control. New York, NY: W.H. Freeman. Bandura, A. (2000). Exercise of human agency through collective efficacy. Current Directions in Psychological Science, 9,

75–78. doi:10.1111/1467-8721.00064 Bandura, A. (2001). Social cognitive theory: An agentic perspective. Annual Review of Psychology, 52, 1–26.

doi:10.1146/annurev.psych.52.1.1 Bandura, A. (2006). Guide for constructing self-efficacy scales. In F. Pajares & T. Urdan (Eds.), Adolescence and

education: Vol. 5. Self-efficacy and adolescence (pp. 307–337). Greenwich, CT: Information Age. Barouch Gilbert, R., Adesopea, O. O., & Schroeder, N. L. (2013). Efficacy beliefs, job satisfaction, stress and their

influence on the occupational commitment of English-medium content teachers in the Dominican Republic. Educational Psychology, 34, 1–23. doi:10.1080/01443410.2013.814193

Beaman, R., & Wheldall, K. (2000). Teachers' use of approval and disapproval in the classroom. Educational Psychology, 20, 431–446. doi: 10.1080/713663753

Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107, 238–246. doi:10.1037/0033-2909.107.2.238

Bentler, P. M. (1992). On the fit of models to covariances and methodology to the Bulletin. Psychological Bulletin, 112, 400–404. doi:10.1037/0033-2909.112.3.400.

Berman, P., & McLaughlin, M. (1977). Factors affecting implementation and continuation (Report No R-1589/7-HEW). In Federal programs supporting educational change, Vol. II. Santa Monica, CA: Rand Corporation.

Birch, S. H., & Ladd, G. W. (1998). Children's interpersonal behaviors and the teacher–child relationship. Developmental Psychology, 34, 934–946. doi:10.1037/0012-1649.34.5.934

Blackburn, J. J., & Robinson, J. S. (2008). Assessing teacher self-efficacy and job satisfaction of early career agriculture teachers in Kentucky. Journal of Agricultural Education, 49, 1–11.

Bogler, R., & Somech, A. (2004). Influence of teacher empowerment on teachers’ organizational commitment, professional commitment and organizational citizenship behavior in schools. Teaching and Teacher Education, 20, 277–289. doi:10.1016/ j.tate.2004.02.003

Brady, K., & Woolfson, L. (2008). What teacher factors influence their attributions for children’s difficulties in learning? British Journal of Educational Psychology,78, 527–544. doi:10.1348/000709907X268570

Briones, E., Tabernero, C., & Arenas, A. (2010). Job satisfaction of secondary school teachers: Effect of demographic and psycho-social factors. Revista de Psicología del Trabajo y de las Organizaciones, 26, 115–122. doi:10.5093/tr2010v26n2a3

Brissie, J. S., Hoover-Dempsey, K. V., & Bassler, O. C. (1988). Individual, situational contributors to teacher burnout. The Journal of Educational Research, 82, 106–112.

Bronfenbrenner, U., & Morris, P. A. (1998). The ecology of developmental processes. In W. Damon (Series Ed.) & R. M. Lerner (Vol. Ed.), Handbook of child psychology: Vol. 1. Theory (5th ed.). New York, NY: Wiley.

Brophy, J. E.(1996). Teaching problem students. New York, NY: Guilford Press. Brouwers, A., Evers, W. J. G., & Tomic, W. ( 2001). Self-efficacy in eliciting social support and burnout among

secondary-school teachers. Journal of Applied Social Psychology, 31, 1474–1491. doi:10.1111/j.15591816. 2001.tb02683.x

Brouwers, A., & Tomic, W. (2000). A longitudinal study of teacher burnout and perceived self-efficacy in classroom management. Teaching and Teacher Education, 16, 239–253. doi:10.1016/S0742-051X(99)00057-8


REFERENCES

201

Brown, E. T. (2005). The influence of teachers’ efficacy and beliefs regarding mathematics instruction in the early childhood classroom. Journal of Early Childhood Teacher Education, 26, 239–257. doi:10.1080/109010205003 69811

Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen, & J. S. Long (Eds.), Testing structural equation models (pp. 136–162). Newbury Park, CA: Sage.

Brownell, M. T., & Pajares, F. (1999). Teacher efficacy and perceived success in mainstreaming students with learning and behavior problems. Teacher Education and Special Education, 22, 154–164. doi:10.1177/088840 649902200303

Bru, E. (2009). Academic outcomes in school classes with markedly disruptive pupils. Social Psychology Review, 12, 461–479. doi:10.1007/s11218-009-9095-1

Brudnik, M., (2009). Perception of self-efficacy and professional burnout in general education teachers. Human Movement, 10, 170–175. doi:10.2478/v10038-009-0013-3

Bruinsma, M., & Jansen, E W. P. A. (2010). Is the motivation to become a teacher related to pre service teachers’ intentions to remain in the profession? European Journal of Teacher Education, 33,185–200. doi:10.1080/ 02619760903512927

Buyse, E., Verschueren, K., Doumen, S., Van Damme, J., & Maes, F. (2008). Classroom problem behavior and teacher-child relationships in kindergarten: The moderating role of the classroom climate. Journal of School Psychology, 46, 367–391. doi:10.1016/ j.jsp.2007.06.009

Canrinus, E. T., Helms-Lorenz, M., Beijaard, D., Buitink, J.,& Hofman, A. (2010). Self-efficacy, job satisfaction, motivation and commitment: Exploring the relationships between indicators of teachers’ professional identity. European Journal of Psychological Education, 27, 115–132. doi:10.1007/s10212-011-0069-2

Cantrell, S. C., Almasi, J. F., Carter, J. C., & Rintamaa, M. (2013). Reading intervention in middle and high schools: Implementation fidelity, teacher efficacy, and student achievement. Reading Psychology, 34, 26–58. doi:10.1080/02702711.2011.577695

Cantrell, S. C., & Hughes, H. K. (2008). Teacher efficacy and content literacy implementation: An exploration of the effects of extended professional development with coaching. Journal of Literacy Research, 40, 95–127. doi:10.1080/10862960802070442

Cantrell, P., Young, S., & Moore, A. (2003). Factors affection science teaching efficacy of preservice elementary teachers. Journal of Science Teacher Education, 14, 177–192. doi:10.1023/A:1025974417256

Capa-Aydin, Y., Sungur, S., & Uzuntiryaki, E. (2009). Teacher self regulation: Examining a multidimensional construct. Educational Psychology, 29, 345–356. doi:10.1080/01443410902927825

Caprara, G. V., Barbaranelli, C., Borgogni, L., & Steca, P. (2003). Efficacy beliefs as determinants of teachers’ job satisfaction. Journal of Educational Psychology, 95, 821–832. doi:10.1016/j.jsp.2006.09.001

Caprara, G. V., Barbaranelli, C., Steca, P., & Malone, P. S. (2006). Teachers’ self-efficacy beliefs as determinants of job satisfaction and students’ academic achievement: A study at the school level. Journal of School Psychology, 44, 473–490. doi:10.1016/j.jsp.2006.09.001

CBS Statline (2015a). (Speciaal) basisonderwijs; culturele minderheden, (achterstands)leerlingen. [(Special) primary education; cultural minorities, (at-risk) students]. Retrieved from http://statline.cbs.nl

CBS Statline (2015b). (Speciaal) basisonderwijs en speciale scholen; leerlingen, schoolregio. [(Special) primary education and special schools; students, school region]. Retrieved from http://statline.cbs.nl

Chacon, C. T. (2005). Teachers’ perceived efficacy among English as a foreign language teachers in middle schools in Venezuela. Teaching and Teacher Education, 21, 257–272. doi:10.1016/j.tate.2005.01.001

Chan, D. W. (2008). Self-efficacy, job satisfaction, motivation and commitment: Exploring the relationships between indicators of teachers’ professional identity. Educational Psychology, 28, 397–408. doi:10.1080/014 43410701668372

Chan, W. T., Lau, S., Nie, Y., Lim, S., & Hogan, D. (2008). Organizational and personal predictors of teacher commitment: The mediating role of teacher efficacy and identification with school. American Educational Research Journal, 45, 597–630.

Chang, I. H. (2011). A study of the relationships between distributed leadership, teacher academic optimism and student achievement in Taiwanese elementary schools. School Leadership and Management, 31, 491–515. doi:10.1080/13632434.2011.614945

Chen, F. F. (2007). Sensitivity of goodness of fit indexes to lack of measurement invariance. Structural Equation Modeling, 14, 464–504. doi:10.1080/10705510701301834


REFERENCES

202

Chen, R. J. (2010). Investigating models for preservice teachers’ use of technology to support student-centered learning. Computers & Education, 55, 32–42. doi:10.1016/j.compedu.2009.11.015

Cheung, H. Y. (2008). Teacher efficacy: A comparative study of Hong Kong and Shanghai primary in-service teachers. The Australian Educational Researcher, 35, 103–123. doi:10.1007/BF03216877

Cheung, G. W., & Rensvold, R. B. (2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling, 9, 233–255. doi:10.1207/ S15328007SEM0902_5.

Cho, Y., & Shim, S. S. (2013). Predicting teachers’ achievement goals for teaching: The role of perceived school goal structure and teachers’ sense of efficacy. Teaching and Teacher Education, 32, 12–21. doi:10.1016/ j.tate.2012.12.003

Chong, W. H., Klassen, R. M., Huan, V. S.,Wong, I., Kates, A. D. (2010). The relationships among school types, teacher efficacy beliefs, and academic climate: Perspective from Asian middle schools. The Journal of Educational Research, 103, 183–190. doi:10.1080/00220670903382954

Chung, L. C., Marvin, C. A., & Churchill, S. L. (2005). Teacher factors associated with preschool teacher-child relationships: Teaching efficacy and parent-teacher relationships. Journal of Early Childhood Teacher Edu-cation, 25, 131–142. doi:10.1080/1090102050250206

Ciani, K. D., Summers, J. J., & Easter, M. A. (2008). A “top-down” analysis of high school teacher motivation. Contemporary Educational Psychology, 33, 533–560. doi:10.1016/j.cedpsych.2007.04.002

Clunies-Ross, P., Little, E., & Kienhuis, M. (2008). Self-reported and actual use of proactive and reactive classroom management strategies and their relationship with teacher stress and student behaviour. Educational Psychology, 28, 693–710. doi:10.1080/01443410802206700

Coladarci, T. (1992). Teachers' Sense of Efficacy and Commitment to Teaching. The Journal of Experimental Edu-cation, 60, 323–337. doi:10.1080/00220973.1992.9943869

Coladarci, T., & Breton, W. A. (1997). Teacher efficacy, supervision, and the special education resource-room teacher. The Journal of Educational Research, 90, 230–239. doi:10.1080/00220671.1997.10544577

Cole, D. A., & Maxwell, S. E. (2003). Testing mediational models with longitudinal data: questions and tips in the use of structural equation modeling. Journal of Abnormal Psychology, 112, 558–577. doi:10.1037/0021843X. 112.4.558

Collie, R. J., Shapka, J. D., & Perry, N. E. (2012). School climate and social–emotional learning: Predicting teacher stress, job satisfaction, and teaching efficacy. Journal of Educational Psychology, 104, 1189–1204. doi:10.1037 /a0029356

Coolahan, K.C., Fantuzzo, J., Mendez, J.L. & McDermott, P.A. (2000). Preschool peer interactions and readiness to learn: Relationships between classroom peer play and learning behaviors and conduct. Journal of Educational Psychology, 92, 458–465. doi:10.1037/0022-0663.92.3.458

Coplan, R. J. (2000). Assessing nonsocial play in early childhood: Conceptual and methodological approaches. In K. Gitlin-Weiner & A. Sandgrund (Eds.), Play diagnosis and assessment (2nd ed., pp. 563-598). New York, NY: Wiley.

Coplan, R. J., & Prakash, K. (2003). Spending time with teacher: Characteristics of preschoolers who frequently elicit versus initiate interactions with teachers. Early Childhood Research Quarterly, 18, 143–158. doi:10. 1016/S0885-2006(03)00009-7

Crosnoe, R., Johnson, M. K., & Elder, G. H. (2004). Intergenerational bonding in school: The behavioral and contextual correlates of student-teacher relationships. Sociology of Education, 77, 60–81. doi:10.1177/ 003804070407700103

Davis, H. A. (2003). Conceptualizing the role and influence of student-teacher relationships on children's social and cognitive development. Educational Psychologist, 38, 207–234. doi:10.1207/S15326985EP3804_2

Deemer, S. (2004). Classroom goal orientation in high school classrooms: Revealing links between teacher beliefs and classroom environments. Educational Research, 46, 73–90. doi:10.1080/0013188042000178836

DeForest, P. A., & Hughes, J. N. (1992). Effect of teacher involvement and teacher self-efficacy on ratings of consultant effectiveness and intervention acceptability. Journal of Educational and Psychological Consultation, 3, 301–316.

De Jong, R., Mainhard, T., Tartwijk, J., Veldman, I., Verloop, N., & Wubbels, T. (2014). How pre service teachers' personality traits, self efficacy, and discipline strategies contribute to the teacher–student relationship. British Journal of Educational Psychology, 84, 294–310. doi:10.1111/bjep.12025

Dellinger, A. B. (2005).Validity and the review of literature. Research in the Schools, 12, 41–54.


REFERENCES

203

Dellinger, A.B., Bobbett, J. J., Olivier, D. F., & Ellett, C. D. (2008). Measuring teachers’ self-efcacy beliefs: Development and use of the TEBS-Self. Teaching and Teacher Education, 24, 751–766. doi:10.1016/j.tate. 2007.02.010

Derriks, M., Ledoux, G., Overmaat, M. & Van Eck, E. (2002). Omgaan met verschillen. Competenties van leerkrachten en schoolleiders. Amsterdam: SCO-Kohnstamm Instituut.

Dickey, W. C., & Blumberg, S. J. (2004). Revisiting the factor structure of the strengths and difficulties questionnaire: United States, 2001. Journal of the American Academy of Child & Adolescent Psychiatry, 43, 1159–1167. doi:10.1097/01.chi.0000132808.36708.a9

Diefenbach, M. A., Weinstein, N. D., & O'Reilly, J. (1993). Scales for assessing perceptions of health hazard susceptibility. Health Education Research, 8, 181–192.

Doménech-Betoret, F. (2006). Stressors, self-efficacy, coping resources and burnout among secondary school teachers in Spain. Educational Psychology, 26, 519–539. doi:10.1080/01443410500342492

Doménech-Betoret, F. (2009). Self efficacy, school resources, job stressors and burnout among Spanish primary and secondary school teachers: A structural equation approach. Educational Psychology, 29, 45–68. doi:10.1 080/01443410802459234

Doumen, S., Verschueren, K., Buyse, E., Germeijs, V., Luyckx, K., & Soenens, B. (2008). Reciprocal relations between teacher–child conflict and aggressive behavior in kindergarten: A three-wave longitudinal study. Journal of Clinical Child and Adolescent Psychology, 37, 588–599. doi:10.1080/15374410802148079

Downer, J., Sabol, T. J., & Hamre, B. (2010). Teacher–child interactions in the classroom: Toward a theory of within-and cross-domain links to children's developmental outcomes. Early Education and Development, 21, 699–723. doi:10.1080/10409289.2010.497453

Duffy, R. D., & Lent, R. W. (2009). Test of a social cognitive model of work satisfaction in teachers. Journal of Vocational Behavior, 75, 212–223. doi:10.1016/j.jvb.2009.06.001

Dunfield, K. A., & Kulhmeier, V. A. (2013). Classifying prosocial behavior: Children's responses to instrumental need, emotional distress, and material desire. Child Development, 84, 1766–1776. doi:10.1111/cdev.12075

Dunfield, K. A., Kuhlmeier, V. A., O’Connell, L., & Kelley, E. (2011). Examining the diversity of prosocial Behavior: helping, sharing, and comforting in infancy. Infancy, 16, 227–247. doi:10.1111/j.1532-7078. 2010.00041.x

Dunn, K. E., Airola, D. T., Lo, W. J., & Garrison, M. (2013). Becoming data driven: The influence of teachers’ sense of efficacy on concerns related to data-driven decision making. The Journal of Experimental Education, 81, 222–241. doi:10.1080/00220973.2012.699899

Dunn, K. E., & Rakes, G. C. (2011). Teaching teachers: An investigation of beliefs in teacher education students. Learning Environments Research, 14, 39–58. doi:10.1007/s10984-1-9083-1

DUO (2014). Leeftijd van personeel in het primair onderwijs. [Age of employees in primary education]. Retrieved from http://www.onderwijsincijfers.nl/kengetallen/primair-onderwijs/personeelpo/leeftijd-personeel

Dussault, M. (2006). Teachers’ self-efficacy and organizational citizenship behaviors. Psychological Reports, 98, 427–432. doi:10.2466/pr0.98.2.427-432

Dyer, N. G., Hanges, P. J., & Hall, R. J. (2005). Applying multilevel confirmatory factor analysis techniques to the study of leadership. The Leadership Quarterly, 16, 149–167. doi:10.1016/j.leaqua.2004.09.009

Ebmeier, H. (2003). How supervision influences teacher efficacy and commitment: An investigation of a path model. Journal of Curriculum & Supervision, 18, 110–142.

Egyed C. J., & Short, R. J. (2006). Teacher self-efficacy, burnout, experience and decision to refer a disruptive student. School Psychology International, 27, 462–474. doi:10.1177/ 0143034306070432

Eisenberg, N. (1982). The development of reasoning about prosocial behavior. In N. Eisenberg (Ed.), The development of prosocial behavior (pp. 219–249). New York, NY: Academic Press.

Eisenberg, N., Cumberland, A., Spinrad, T. L., Fabes, R. A., Shepard, S. A., Reiser, M., ... & Guthrie, I. K. (2001). The relations of regulation and emotionality to children's externalizing and internalizing problem behavior. Child development, 72, 1112–1134. doi:10.1111/1467-8624.00337

Emmer, E., & Hickman, J. (1991). Teacher efficacy in classroom management and discipline. Educational and Psychological Measurement, 51, 755–765. doi:10.1177/0013164491513027

Emmer, E. T., & Stough, L. M. (2001). Classroom management: A critical part of educational psychology, with implications for teacher education. Educational Psychologist, 36, 103–112. doi:10.1207/S15326985EP3602_5


REFERENCES

204

Eren, A. (2009). Examining the teacher efficacy and achievement goals as predictors of Turkish student teachers’ conceptions about teaching and learning. Australian Journal of Teacher Education, 34, 69–87. doi:10.14221/ ajte.2009v34n1.6

Eren, A. (2012). Prospective teachers’ future time perspective and professional plans about teaching: The mediating role of academic optimism. Teaching and Teacher Education, 28, 111–123. doi:10.1016/j.tate. 2011.09.006

Eslami, Z. R., & Fatahi, A. (2008). Teachers' sense of self-efficacy, English proficiency, and instructional strategies: A study of nonnative EFL teachers in Iran. TESL-EJ, 11, 1–19.

Eun, B., & Heining-Boynton, A. L. (2007). Impact of an English-as-a-second-language professional development program. The Journal of Educational Research, 101, 36–49. doi:10.3200/JOER.101.1.36-49

Evans, E. D., & Tribble, M. (1986). Perceived teaching problems, self-efficacy, and commitment to teaching among preservice teachers. The Journal of Educational Research, 80, 81–85.

Evers, W., Brouwers, A., & Tomic, W. (2002). Burnout and self-efficacy: A study on teachers’ beliefs when implementing an innovative educational system in the Netherlands. British Journal of Educational Psychology, 72, 227–243. doi:10.1348/000709902158865

Evers, W. J. G., Tomic, W., & Brouwers, A. (2004). Burnout among teachers: Students’ and teachers’ perceptions compared. School Psychology International, 25, 131–148. doi:10.1177/0143034304043670

Evers, W. Tomic, W. & Brouwers, A. (2005). Does equity sensitivity moderate the relationship between self-efficacy beliefs and teacher burnout? Representative Research in Social Psychology, 28, 35–46.

Fernet, C., Guay, F., Senecal, C., & Austin, S. (2012). Predicting intraindividual changes in teacher burnout: The role of perceived school environment and motivational factors. Teaching and Teacher Education, 28, 514–525. doi:10.1016/j.tate.2011.11.013

Feuerborn, L., & Chinn, D. (2012). Teacher perceptions of student needs and implications for positive behavior supports. Behavioral Disorders, 37, 219–231.

Fives, H., & Alexander, P. A. (2004). How schools shape teacher efficacy and commitment: another piece in the achievement puzzle. Big Theories Revisited, 4, 329–359.

Fives, H., Hamman, D., & Olivarez, A. (2007). Does burnout begin with student-teaching? Analyzing efficacy, burnout, and support during the student-teaching semester. Teaching and Teacher Education, 23, 916–934. doi:10.1016/j.tate.2006.03.013

Fredricks, J. A., & Eccles, J. S. (2002). Children's competence and value beliefs from childhood through adolescence: Growth trajectories in two male sex-typed domains. Developmental Psychology, 38, 519–533. doi:10.1037//0012-1649.38.4.519.

Friedman, I. A. (2003). Self-efficacy and burnout in teaching: The importance of interpersonal-relations efficacy. Social Psychology of Education, 6, 191–215.

Friedman, I.A. (2006). Classroom management and teacher stress and burnout. In C.M. Evertson & C.S. Weinstein (Eds.), Handbook of classroom management: Research, practice, and contemporary issues (pp. 925–944). Mahwah, NJ: Lawrence Erlbaum.

Friedman, I. A., & Kass, E. (2002). Teacher self-efcacy: A classroom-organization conceptualization. Teaching and Teacher Education, 18, 675–686. doi:10.1016/S0742-051X(02)00027-6

Gao,W., & Mager, G. (2011). Enhancing preservice teachers’ sense of efficacy and attitudes toward school diversity through preparation: A case of one U.S. inclusive teacher education program. International Journal of Special Education, 26, 92–107.

Gazelle, H., & Ladd, G. W. (2003). Anxious solitude and peer exclusion: A diathesis–stress model of internalizing trajectories in childhood. Child development, 74, 257–278. doi:10.1111/1467-8624.00534

Geijsel, F. P., Sleegers, P. J. C., Stoel, R. D., & Kruger, M. L. (2009). The effect of teacher psychological and school organizational and leadership factors on teachers’ professional learning in Dutch schools. The Elementary School Journal, 109, 406–427.

Ghaith, G., & Shaaban, K. (1999). The relationship between perceptions of teaching concerns, teacher efficacy, and selected teacher characteristics. Teaching and Teacher Education, 15, 487–496. doi:10.1016/S0742-051X (99)00009-8

Ghaith, G., & Yaghi, M. (1997). Relationships among experience, teacher efficacy and attitudes toward the implementation of instructional innovation. Teaching and Teacher Education, 13, 451–458. doi:10.1016/ S0742-051X(96)00045-5


REFERENCES

205

Gibbs, S., & Powell, B. (2011). Teacher efcacy and pupil behaviour: The structure of teachers’ individual and collective beliefs and their relationship with numbers of pupils excluded from school. British Journal of Educational Psychology, 82, 564–584. doi:10.1111/j.2044-8279.2011.02046.x

Gibson, S., & Dembo, M. (1984). Teacher efficacy: a construct validation. Journal of Educational Psychology, 76, 569–582. doi:10.1037/0022-0663.76.4.569

Goddard, R. D., & Goddard, Y. L. (2001). A multilevel analysis of teacher and collective efficacy. Teaching and Teacher Education, 17, 807–818. doi:10.1016/S0742-051X(01)00032-4

Goddard, R. D., Hoy, W. K., & Woolfolk Hoy, A. E. (2004). Collective efficacy beliefs: Theoretical developments, empirical evidence, and future directions. Educational Researcher, 33, 3–13.

Godfrey, C. (2001). Computers in school: Changing technologies. Australian Educational Computing, 16, 14–17. Goffin, R. D., & Gellatly, I.R. (2001). A multi-rater assessment of organizational commitment: Are self-report

measures biased? Journal of Organizational Behavior, 22, 437–451. doi:10.1002/job.94 Goodman, A., Lamping, D., & Ploubidis, G.B. (2010). When to use broader internalising and externalising

subscales instead of the hypothesised five subscales on the Strengths and Difficulties Questionnaire (SDQ): Data from British parents, teachers and children. Journal of Abnormal Child Psychology, 38, 1179–1199. doi:10.1007/s10802-010-9434-x

Gorozidis, G., & Papaioannou, A. (2011). Teachers’ self-efficacy, achievement goals, attitudes and intentions to implement the new Greek physical education curriculum. European Physical Education Review, 17, 231–253. doi:10.1177/1356336X11413654

Greene, R. W., Abidin, R. R., & Kmetz, C. (1997). The Index of Teaching Stress: A measure of student-teacher compatibility. Journal of School Psychology, 35, 239–259. doi:10.1016/S0022-4405(97)00006-X

Greene, R. W., Beszterczey, S. K., Katzenstein, T., Park, K., & Goring, J. (2002). Are students with ADHD more stressful to teach?: Patterns of teacher stress in an elementary school sample. Journal of Emotional and Behavioral Disorders, 10, 79–89. doi:10.1177/10634266020100020201

Gresham, F. M., & Kern, L. (2004). Internalizing behavior problems in children and adolescents. In R. Rutherford, M. Quinn, & S. Mathur (Eds.), Handbook of research in behavior disorders (pp. 262–281). New York, NY: The Guilford Press.

Guo, Y., McDonald Connor, C., Yang, Y., Roehring, A. D., & Morrison, F. J. (2012). The effects of teacher qualification, teacher self-efficacy, and classroom practices on fifth graders’ literacy outcomes. The Elementary School Journal, 113, 3–24. doi:10.1086/665816

Guo, Y., Piasta, S. B., Justice, L. M., & Kaderavek, J. N. (2010). Relations among preschool teachers' self-efficacy, classroom quality, and children's language and literacy gains. Teaching and Teacher Education,26, 1094–1103. doi:10.1016/j.tate.2009.11.005

Guo, Y., Sawyer, B. E., Justice, L. M., & Kaderavek, J. N. (2013). Quality of the literacy environment in inclusive early childhood special education classrooms. Journal of Early Intervention, 35, 40–60. doi:10.1177/ 1053815113500343

Guskey, T. R. (1981). Measurement of responsibility teachers assume for academic successes and failures in the classroom. Journal of Teacher Education, 32, 44–51. doi:10.1177/002248718103200310

Hamre, B., Hatfield, B., Pianta, R., & Jamil, F. (2014). Evidence for general and domain-specific elements of teacher–child interactions: Associations with preschool children's development. Child Development, 85, 1257–1284. doi:10.1111/cdev.12184

Hamre, B. K., & Pianta, R. C. (2001). Early teacher-child relationships and the trajectory of children's school outcomes through eighth grade. Child Development, 72, 625–638.

Hamre, B. K., & Pianta, R. C. (2004). Self-reported depression in nonfamilial caregivers: Prevalence and associations with caregiver behavior in child-care settings. Early Childhood Research Quarterly, 19, 297–318. doi:10.1016/j.ecresq.2004.04.006

Hamre, B. K., & Pianta, R. L. (2005). Can instructional and emotional support in the first grade classroom make a difference for children at risk of school failure? Child Development, 76, 949–967. doi:10.1111/j.1467-8624.2005.00889.x

Hamre, B. K., & Pianta, R. L. (2010). Classroom environments and developmental processes. In Meece, J. L., & J. S. Eccles. Handbook of research on schools, schooling, and human development (pp. 25–41). New York, NY: Routledge.


REFERENCES

206

Hamre, B. K., Pianta, R. C., Downer, J. T., Decoster, J., Jones, S., Brown, J., Cappella, E, … Kaefer, T. (2013). Teaching through interactions: testing a developmental framework of effective teaching in over 4,000 classrooms. The Elementary School Journal, 113, 461–487. doi:10.1086/669616

Hamre, B. K., Pianta, R. C., Downer, J. T., & Mashburn, A. J. (2008). Teachers’ perceptions of conflict with young students: Looking beyond problem behaviors. Social Development, 17, 115–136. doi:10.1111/j.1467-9507.2007.00418.x

Hardré, P.L., Huang, S.H., Chen, C.H., Chiang, C.T., Jen, F.L., & Warden, L. (2006). High school teachers’ motivational perceptions and strategies in an East Asian nation. Asia-Pacific Journal of Teacher Education 34, 199–221. doi:10.1080/13598660600720587

Hardré, P. L., & Sullivan, D. W. (2008). Teacher perceptions and individual differences: How they influence rural teachers’ motivating strategies. Teaching and Teacher Education , 24, 2059– 2075. doi:10.1016/j.tate.2008.04. 007

Hardré, P. L., & Sullivan, D. W. (2009). Motivating adolescents: high school teachers’ perceptions and classroom practices. Teacher Development, 13, 1–16. doi:10.1080/13664530902858469

Hargreaves, A. (1998). The emotional practice or teaching, Teaching and Teacher Education, 14, 835-854. Hargreaves, A. (2000). Mixed emotions: Teachers’ perceptions of their interactions with students. Teaching and

Teacher Education, 16, 811–826. doi:10.1016/S0742-051X(00)00028-7 Hastings, R. P., & Bham, M. S. (2003).The relationship between student behaviour patterns and teacher burnout.

School Psychology International, 24, 115–127. doi:10.1177/0143034303024001905 Haverback, H. R. (2009). Situating pre service reading teachers as tutors: implications of teacher self efficacy on

tutoring elementary students. Mentoring & Tutoring: Partnership in Learning, 17, 251–261. doi:10.1080/1361 1260903050171

Helms-Lorenz, M., Slof, B., Vermue, C. E., & Canrinus, E. T. (2012). Beginning teachers’ self-efficacy and stress and the supposed effects of induction arrangements. Educational Studies, 38, 189–207. doi:10.1080/ 03055698.2011.598679

Heneman, H.G., Kimball, S., & Milanowski, A. (2006). The Teacher Sense of Efficacy Scale: Validation evidence and behavioral prediction (WCER Working Paper No. 2006-7). Madison, WI: Wisconsin Center for Education Research.

Henricsson, L., & Rydell, A. M. (2004). Elementary school children with behavior problems: Teacher–child relations and self-perception. A prospective study. Merrill-Palmer Quarterly, 50, 111–138. doi:10.1353/ mpq.2004.0012

Henson, R. (2001). Teacher self-efficacy: Substantive implications and measurement dilemmas. (ERIC Document Reproductive Service No. ED 452208).

Henson, R. K. (2002). From adolescent angst to adulthood: Substantive implications and measurement dilemmas in the development of teacher efficacy research. Educational Psychologist, 37, 137–150. doi:10.1207/ S15326985EP3703_1

Hines, M. T. (2008). The interactive effects of race and teacher self-efficacy on the achievement gap in school. National Forum of Multicultural Issues Journal, 7, 1–11.

Hipp, K. A., & Bredeson, P. V. (1995). Exploring connections between teacher efficacy and principals’ leadership behavior. Journal of School Leadership, 5, 136–150.

Høigaard, R., Giske, R., & Sundsli, K. (2012). Newly qualified teachers’ work engagement and teacher efficacy influences on job satisfaction, burnout, and the intention to quit. European Journal of Teacher Education, 35, 347–357. doi:10.1080/02619768.2011.633993

Holzberger, D., Philipp, A., & Kunter, M. (2013). How teachers’ self-efficacy is related to instructional quality: A longitudinal analysis. Journal of Educational Psychology, 105, 774–786. doi:10.1037/a0032198

Hornstra, L., van der Veen, I., Peetsma, T., & Volman, M. (2013). Developments in motivation and achievement during primary school: A longitudinal study on group-specific differences. Learning and Individual Differences, 23, 195–204. doi:10.1016/j.lindif.2012.09.004

Howes, C., Hamilton, C. E., & Matheson, C. C. (1994). Children's Relationships with Peers: Differential Associations with Aspects of the Teacher Child Relationship. Child Development, 65, 253–263. doi:10.11 11/j.1467-8624.1994.tb00748.x

Hox, J. (2002). Multilevel analysis: Techniques and applications. Mahwah, NJ: Erlbaum.


REFERENCES

207

Hoy, W. K., & Woolfolk, A. E. (1990). Socialization of student teachers. American Educational Research Journal, 27, 279–300.

Hoy, W. K. & Woolfolk, A. E. (1993). Teachers’ sense of efficacy and the organizational health of schools. The Elementary School Journal, 93, 356–372.

Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indices in covariance structure analysis: conventional versus new alternatives. Structural Equation Modeling, 6,1–55. doi:10.1080/10705519909540118

Hughes, G. D. (2012). Teacher retention: Teacher characteristics, school characteristics, organizational characteristics, and teacher efficacy. The Journal of Educational Research, 105, 245–255. doi:10.1080/ 00220671.2011.584922

Hughes, J. N., Barker, D., Kemenoff, S., & Hart, M. (1993). Problem ownership, causal attributions, and self-efficacy as predictors of teachers' referral decisions. Journal of Educational & Psychological Consultation, 4, 369–384.

Hultell, D., Melin, B., & Gustavsson, J. P. (2013). Getting personal with teacher burnout: A longitudinal study on the development of burnout using a person-based approach. Teaching and Teacher Education, 32, 75–86. doi:10.1016/j.tate.2013.01.007

Imants, J., & Van Zoelen, A. (1995). Teachers’ sickness absence in primary schools, school climate and teachers’ sense of efficacy. School Organisation, 15, 77–86.

Jak, S. (2014). Testing strong factorial invariance using three-level structural equation modeling. Frontiers in psychology, 5, 1–7. doi:10.3389/fpsyg.2014.00745

Jak, S., Oort, F. J., & Dolan, C. V. (2013). A test for cluster bias: detecting violations of measurement invariance across clusters in multilevel data. Structural Equation Modeling, 20, 265–282. doi:10.1080/10705511. 2013.769392

Jak, S., Oort, F. J., & Dolan, C. V. (2014). Measurement bias in multilevel data. Structural Equation Modeling,21, 31–39. doi:10.1080/10705511.2014.856694

Jennings, P. A., & Greenberg, M. T. (2008). The prosocial classroom: Teacher social and emotional competence in relation to student and classroom outcomes. Review of Educational Research, 79, 491–525. doi:10.3102/ 0034654308325

Jerome, E. M., Hamre, B. K., & Pianta, R. C. (2009). Teacher–Child Relationships from Kindergarten to Sixth Grade: Early Childhood Predictors of Teacher perceived Conflict and Closeness. Social Development, 18, 915–945. doi:10.1111/j.1467-9507.2008.00508.x

Jimmieson, N. L., Hannam, R. L.. & Yeo, G. B. (2010). Teacher organizational citizenship behaviours and job efficacy: Implications for student quality of school life. British Journal of Psychology, 101, 453–479. doi:10.1348/000712609X470572

Johnson, S., Cooper, C., Cartwright, S., Donald, I., Taylor, P., & Millet, C. (2005). The experience of work-related stress across occupations. Journal of managerial psychology, 20, 178-187. doi:10.1108/02683940510579803

Justice, L. M., Mashburn, A. J., Hamre, B. K., & Pianta, R. C. (2008). Quality of language and literacy instruction in preschool classrooms serving at-risk pupils. Early Childhood Research Quarterly, 23, 51–68. doi:10.1016/ j.ecresq.2007.09.004

Kagan, D. M. (1990). Ways of evaluating teacher cognition: Inferences concerning the goldilocks principle. Review of Educational Research, 60, 419–469. doi:10.3102/ 00346543060003419

Kao, C. P., Wu, Y. T., & Tsai, C. C. (2011). Elementary school teachers’ motivation toward web-based professional development, and the relationship with Internet self-efficacy and belief about web-based learning. Teaching and Teacher Education, 27, 406–415. doi:10.1016/j.tate.2010.09.010

Keiley, M. K., Lofthouse, N., Bates, J. E., Dodge, K. A., & Pettit, G. S. (2003). Differential risks of covarying and pure components in mother and teacher reports of externalizing and internalizing behavior across ages 5 to 14. Journal of Abnormal Child Psychology, 31, 267–283. doi:10.1023/A:1023277413027

Kellam, S.G., Ling, X., Merisca, R., Brown, C.H., & Ialongo, N. (1998). The effect of the level of aggression in the first grade classroom on the course and malleability of aggressive behavior in middle school. Development and Psychopathology, 10, 165–185.

Kirsch, I. (1985). Self-efficacy and expectancy: Old wine with new labels. Journal of Personality and Social Psychology, 49, 824–830.


REFERENCES

208

Kislenko, K., & Grevholm, B. (2008, July). The Likert scale used in research on affect: A short discussion of terminology and appropriate analyzing methods. Paper presented at the 11th International Congress on Mathematical Education, Monterrey, Mexico.

Klassen, R. M., & Chiu, M. M. (2010). Effects on teachers’ self-efficacy and job satisfaction: teacher gender, years of experience, and job stress. Journal of Educational Psychology, 102, 741–756. doi:10.1037/a0019237

Klassen, R. M., & Chiu, M. M. (2011). The occupational commitment and intention to quit of practicing and pre-service teachers: Influence of self-efficacy, job stress, and teaching context. Contemporary Educational Psychology, 36, 114–129. doi:10.1016/j.cedpsych.2011.01.002

Klassen, R. M., Bong, M., Usher, E. L., Chong, W. H., Huan, V. S., Wong, I. Y., & Georgiou, T. (2009). Exploring the validity of a teachers’ self-efficacy scale in five countries. Contemporary Educational Psychology, 34, 67–76. doi:10.1016/j.cedpsych.2008.08.001

Klassen, R. M., & Tze, V. M. (2014). Teachers’ self-efficacy, personality, and teaching effectiveness: A meta-analysis. Educational Research Review, 12, 59–76. doi:10.1016/j.edurev.2014.06.001

Klassen, R. M., Tze, V. M., Betts, S. M., & Gordon, K. A. (2011). Teacher efficacy research 1998 –2009: Signs of progress or unfulfilled promise? Educational Psychological Review, 23, 21–43. doi:10.1007/s10648-010-91418

Klassen, R., Wilson, E., Siu, A. F. Y., Hannok, W., Wong, M. W., Wongsri, N., … Jansem, A. (2013). Preservice teachers’ work stress, self-efficacy, and occupational commitment in four countries. European Journal of Psychological Education, 28, 1289–1309. doi:10.1007/s10212-012-0166-x

Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed.).New York, NY: Guilford. Kokkinos, C. M., & Kargiotidis, A. (2014). Rating students’ problem behaviour: the role of teachers’ individual

characteristics. Educational Psychology, (ahead-of-print), 1–17. doi:10.1080/01443410.2014.993929 Kokkinos, C. M., Panayiotou, G., & Davazoglou, A. M. (2004). Perceived seriousness of pupils’ undesirable

behaviours: The student teachers’ perspective. Educational Psychology, 24, 109–120. doi:10/1080.01443410 32000146458

Kokkinos, C. M., Panayiotou, G., & Davazoglou, A. M. (2005). Teacher appraisals of student behaviors. Psychology in the Schools, 42, 79–89. doi:10.1002/pits.20031

Koomen, H. M. Y., & Spilt, J. L. (2011). Emotionele en gedragsproblemen [Emotional and behavioral problems]. In P. F. de Jong & H. M. Y. Koomen (Eds.), Interventie bij Onderwijsleerproblemen. Antwerp: Garant.

Koomen, H. M. Y. Verschueren, K., & Pianta, R. C. (2007). Leerling–Leerkracht Relatie Vragenlijst (LLRV): Handleiding. [Student–Teacher Relationship Scale: Manual]. Houten: Bohn Stafleu van Loghum

.Koomen, H. M. Y., Verschueren, K., Van Schooten, E., Jak, S., & Pianta, R. C. (2012). Validating the Student-Teacher Relationship Scale: Testing factor structure and measurement invariance across child gender and age in a Dutch sample. Journal of School Psychology, 50, 215–234. doi:10.1016/j.jsp.2011.09.001

Kuncel, N. R., Credé, M., & Thomas, L. L. (2005). The validity of self-reported grade point averages, class ranks, and test scores: A meta-analysis and review of the literature. Review of Educational Research, 75, 63–82.

Kyriacou, C. (2001). Teacher stress: Directions for future research. Educational Review, 53, 27–35. doi:10.1080/ 00131910120033628

Labone, E. (2004). Teacher efficacy: Maturing the construct through research in alternative paradigms. Teaching and Teacher Education, 20, 341–359. doi:10.1016/j.tate.2004.02.013

Lachman, M. E. (2006). Perceived control over aging-related declines: Adaptive beliefs and behaviors. Current Directions in Psychological Science, 15, 282–286.

Ladd, G. W., & Burgess, K. B. (1999). Charting the relationship trajectories of aggressive, withdrawn, and aggressive/withdrawn children during early grade school. Child Development, 70, 910–929.

Lakshmanan, A., Heath, B. P., Perlmutter,A., & Elder, M. (2011). The impact of science content and professional learning communities on science teaching efcacy and standards-based instruction. Journal of Research in Science Teaching, 48, 534–551. doi:10.1002/tea.20404

Lambert, R. G., McCarthy, C., O'Donnell, M., & Wang, C. (2009). Measuring elementary teacher stress and coping in the classroom: Validity evidence for the Classroom Appraisal of Resources and Demands. Psychology in the Schools, 10, 973–988. doi:10.1002/pits.20438

La Paro, K. M., Pianta, R. C., & Stuhlman, M. (2004). The Classroom Assessment Scoring System: Findings from the prekindergarten year. The Elementary School Journal, 104, 409–426.


REFERENCES

209

Lee, B., Cawthon, S., & Dawson, K. (2013). Elementary and secondary teacher self-efficacy for teaching and pedagogical conceptual change in a drama-based professional development program. Teaching and Teacher Education, 30, 84–98. doi:10.1016/j.tate.2012.10.010

Lee, V. E., Dedrick, R. F., & Smith, J. B. (1991). The effect of the social organization of schools on teachers' efficacy and satisfaction. Sociology of Education, 64, 190–208. doi:10.2307/2112851

Lee, M. H., & Tsai, C. C. (2010). Exploring teachers’ perceived self-efficacy and technological pedagogical content knowledge with respect to educational use of the World Wide Web. Instructional Science, 38, 1–21. doi:10.1007/s11251-008-9075-4

Lent, R. W., & Brown, S. D. (2006). On conceptualizing and assessing social cognitive constructs in career research: A Measurement Guide. Journal of Career Assessment, 14, 12–35. doi:10.1177/1069072705281364

Lent, R. W., Nota, L., Soresi, S., Ginevra, M. C., Duffy, R. D., & Brown, S. D. (2011). Predicting the work and life satisfaction of Italian teachers: test of a social cognitive model. Journal of Vocational Behavior, 79, 91–97. doi:10.1016/j.jvb.2010.12.006

Leroy, N., Bressoux, P., Sarrazin,P., & Trouilloud, D. (2007). Impact of teachers’ implicit theories and perceived pressures on the establishment of an autonomy supportive climate. European Journal of Psychology of Education, 22, 529–545. doi:10.1007/BF03173470

Letcher, P., Smart, D., Sanson, A., & Toumbourou, J. W. (2009). Psychosocial precursors and correlates of differing internalizing trajectories from 3 to 15 years. Social Development, 18, 618–646. doi:10.1111/j.1467-9507.2008.00500.x

Leung, S. O. (2011). A comparison of psychometric properties and normality in 4-, 5-, 6-, and 11-point Likert scales. Journal of Social Service Research, 37, 412–421. doi:10.1080/01488376.2011.580697

Liljequist, L., & Renk, K. (2007). The relationships among teachers’ perceptions of student behaviour, teachers’ characteristics, and ratings of students’ emotional and behavioural problems. Educational Psychology, 27, 557–571. doi:10.1080/01443410601159944

Little, T. D. (2013). Longitudinal structural equation modeling. New York, NY: The Guildford Press. Lumpe, A., Czerniak, C., Haney, J. & Beltyukova, S. (2012). Beliefs about Teaching Science: The relationship

between elementary teachers’ participation in professional development and student achievement. International Journal of Science Education, 34, 153–166. doi:10.1080/09500693.2010.551222

Maassen, G. H., & Bakker, A. B. (2001). Suppressor variables in path models: Definitions and interpretations. Sociological Methods & Research, 30, 241–270. doi:10.1177/0049124101030002004

MacKinnon, D. P., Lockwood, C. M., & Williams, J. (2004). Confidence limits for the indirect effect: Distribution of the product and resampling methods. Multivariate Behavioral Research, 39, 99–128. doi:10.1207/ s15327906mbr3901_4

Malecki, C. K., & Elliott, S. N. (2002). Children’s social behaviors as predictors of academic achievement: A longitudinal analysis. School Psychology Quarterly, 17, 1–23. doi:10.1521/scpq.17.1.1.19902

Malinen, O. P., Savolainen, H., Engelbrecht, P., Xu, J., Nel, M., Nel, N., & Tlale, D. (2013). Exploring teacher self-efficacy for inclusive practices in three diverse countries. Teaching and Teacher Education, 33, 34–44. doi:10.1016/j.tate.2013.02.004

Malinen, O. P., Savolainen, H., & Xu, J. (2012). Beijing in-service teachers’ self-efficacy and attitudes towards in-clusive education. Teaching and Teacher Education, 28, 526–534. doi:10.1016/j.tate.2011.12.004

Malow-Iroff, M. S., O'Connor, E. A., & Bisland, B. M. (2007). Intention to return: Alternatively certified teachers’ support, ideology and efficacy beliefs. Teacher Development, 11, 263–275. doi:10.1080/13664530701644573

Martin, N. K., & Sass, D. A. (2010). Construct validation of the Behavior and Instructional Management Scale. Teaching and Teacher Education, 26, 1124–1135. doi:10.1016/j.tate.2009.12.001

Martin, N. K., Sass, D. A., & Schmitt, T. A. (2012). Teacher efficacy in student engagement, instructional management, student stressors, and burnout: A theoretical model using in-class variables to predict teachers' intent-to-leave. Teaching and Teacher Education, 28, 546–559. doi:10.1016/j.tate.2011.12.003

Mashburn, A. J., Hamre, B. K., Downer, J. T., Pianta, R. C. (2006). Teacher and classroom characteristics associated with teachers’ ratings of prekindergartners’ relationships and behaviors. Journal of Psychoeducational Assessment, 24, 367–380. doi:10.1177/0734 282906290594

Meece, J. L., Anderman, E. M., & Anderman, L. H. (2006). Classroom goal structure, student motivation, and academic achievement. Annual Review of Psychology, 57, 487–503. doi:10.1146/annurev.psych.56.091103. 070258


REFERENCES

210

Meijer, C., & Foster, S. (1988). The effect of teacher self-efficacy on referral chance. Journal of Special Education, 22, 378–385.

Mejia, T. M., & Hoglund, W. L. (2016). Do children's adjustment problems contribute to teacher–child relationship quality? Support for a child-driven model. Early Childhood Research Quarterly, 34, 13–26. doi:10.1016/j.ecresq.2015.08.003

Merrell, K.W. (1999). Behavioral, social, and emotional assessment of children and adolescents. Mahwah, NJ: Erlbaum. Midgley, C., Anderman, E., & Hicks, L. (1995). Differences between elementary and middle school teachers and

students: A goal theory approach. Journal of Early Adolescence, 15, 90–113. Midgley, C., Feldlaufer, H., & Eccles, J. (1989). Change in teacher e cacy and student self- and task-related

beliefs in mathematics during the transition to junior high school. Journal of Educational Psychology, 81, 247–258. doi:10.1037/0022-0663.81.2.247

Ministry of Education, Culture and Science (2014). Wet Passend Onderwijs [Appropriate Education Act]. Retrieved from https://www.passendonderwijs.nl/wp-content/uploads/2014/11/Wet-passend-onderwijs.pdf

Moè, A., Pazzaglia, F., & Ronconi, L. (2010). When being able is not enough. The combined value of positive affect and self-efficacy for job satisfaction in teaching. Teaching and Teacher Education, 26, 1145–1153. doi:10.1016/j.tate.2010.02.010

Mohamadi, F. S., & Asadzadeh, H. (2012). Testing the mediating role of teachers’ self-efficacy beliefs in the relationship between sources of efficacy information and students achievement. Asia Pacific Educational Review, 13, 427–433. doi:10.1007/s12564-011-9203-8

Mojavezi, A., & Poodineh Tamiz, M. (2012). The impact of teacher self-efficacy on the students’ motivation and achievement. Theory and Practice in Language Studies, 2, 483–491. doi:10.4304/tpls.2.3.483-491

Moore, V. & Esselman, M. (1992, April). Teacher efficacy, empowerment, and focused instructional climate: Does student achievement benefit? Paper presented at the annual meeting of the American Educational Research Association, San Francisco.

Morris-Rothschild, B. K., & Brassard, M. R. (2006). Teachers' conflict management styles: The role of attachment styles and classroom management efficacy. Journal of School Psychology, 44, 105–121. doi:10.1016/j.jsp. 2006.01.004

Mueller, J., Wood, E., Willoughby, T., Ross, C., & Specht, J. (2008). Identifying discriminating variables between teachers who fully integrate computers and teachers with limited integration. Computers & Education, 51, 1523–1537. doi:10.1016/j.compedu.2008.02.003

Murray, C., & Murray, K. M. (2004). Child level correlates of teacher–student relationships: An examination of demographic characteristics, academic orientations, and behavioral orientations. Psychology in the Schools, 41, 751–762. doi:10.1002/pits.20015

Murray, C., & Zvoch, K. (2011). Teacher–Student Relationships Among Behaviorally At-Risk African American Youth From Low-Income Backgrounds: Student Perceptions, Teacher Perceptions, and Socioemotional Adjustment Correlates. Journal of Emotional and Behavioral Disorders, 19, 41–54. doi:10.1177/106342660 9353607

Muthén, B. O. (1994). Multilevel covariance structure analysis. Sociological Methods and Research, 22, 376–398. doi:10.1177/0049124194022003006

Muthén, B., & Asparouhov, T. (2013). New Methods for the Study of Measurement Invariance with Many Groups. Technical report. Retrieved from http://www.statmodel.com.

Muthén, L. K., & Muthén, B. O. (1998-2012). Mplus user’s guide (7th ed.). Los Angeles, CA: Muthén & Muthén. Nesdale, D., & Pickering, K. (2006). Teachers’ reactions to children's aggression. Social Development, 15, 109–127.

doi: 10.1111/j.1467-9507.2006.00332.x Newberry, M., & Davis, H. A. (2008). The role of elementary teachers’ conceptions of closeness to students on

their differential behaviour in the classroom. Teaching and Teacher Education, 24, 1965–1985. doi:10.1016/ j.tate.2008.02.015

Nie, Y., Tan, G. H., Liau, A. K., Lau, S., & Chua, B. L. (2013). The roles of teacher efficacy in instructional innovation: Its predictive relations to constructivist and didactic instruction. Educational Research for Policy and Practice,12, 67–77. doi:10.1007/s10671-012-9128-y

Ngidi, D. P. (2012). Academic optimism: An individual teacher belief. Educational Studies, 38, 139–150. doi:10.1080/03055698.2011.567830

Olson, J., & Cooper, P. (2001). Dealing with disruptive children in the classroom. London: Routledge.


REFERENCES

211

O’Connor, K. E. (2008). “You choose to care”: Teachers, emotions and professional identity. Teaching and Teacher Education, 24, 117–126. doi:10.1016/j.tate.2006.11.008

O'Connor, E. (2010). Teacher–child relationships as dynamic systems. Journal of School Psychology, 48, 187–218. doi:10.1016/j.jsp.2010.01.001

O’Neill, S. C., & Stephenson, J. (2011). The measurement of classroom management self-efficacy: A review of measurement instrument development and influences. Educational Psychology, 31, 261–299. doi:10.1080/ 01443410.2010.545344

Oort, F. J. (1992). Using restricted factor analysis to detect item bias. Methodika, 6, 150–166. Pajares, F. (1996, April). Assessing self-efficacy beliefs and academic outcomes: The case for specificity and correspondence. Paper

presented at the annual meeting of the American Educational Research Association, New York, NY. Pajares, F. (1997). Current directions in self-efficacy research. In H. W. Marsh, R. G. Craven, & D. M. McInerney

(Eds.), International advances in self research (pp. 1–49). Greenwich, CT: Information Age Publishing. Pakarinen, E. Lerkkanen, M. K., Poikkeus, A. M., Kiuru, N., Siekkinen, M., Rasku-Puttonen, H., & Nurmi, J. E.

(2010). A validation of the Classroom Assessment Scoring System in Finnish kindergartens. Early Education and Development, 21, 95–124. doi:10.1080/10409280902858764

Parker, P. L., McDaniel, H. S., & Crumpton-Young, L. L. (2002). Do research participants give interval or ordinal answers in response to Likert scales? Retrieved from http://citeseerx.ist..psu.edu/viewdoc/download?doi=10.1.1.19. 6352&rep=rep1&type=pdf

Pas, E. T., Bradshaw, C. P., Hershfeldt, P. A., & Leaf, P. J. (2010). A multilevel exploration of the influence of teacher efficacy and burnout on response to student problem behaviour and school-based service use. School Psychology Quarterly, 25, 13–27. doi:10.1037/a0018576

Pianta, R. C. (1999). Assessing child-teacher relationships. In R. C. Pianta, Enhancing relationships between children and teachers (pp. 85–104). Washington, DC: American Psychological Association.

Pianta, R. C. (2001). STRS: Student-teacher Relationship Scale: Professional manual. Lutz, FL: Psychological Assessment Resources.

Pianta, R. L., & Hamre, B. K., (2009). Conceptualization, measurement, and improvement of classroom pro-cesses: Standardized observation can leverage capacity. Educational Researcher, 38, 109–119. doi:10.3102/ 0013189X09332374

Pianta, R. C., Hamre, B., & Stuhlman, M. (2003). Relationships between teachers and children. In W. M. Reynolds & G. E. Miller (Eds.), Handbook of psychology: Educational psychology (Vol. 7, pp. 199–234). Hoboken, NJ: Wiley.

Pianta, R. C., La Paro, K. M., & Hamre, B. K. (2008). Classroom Assessment Scoring System. Baltimore, MD: Brookes. Pianta, R. C., La Paro, K. M., Payne, C., Cox, M. J., & Bradley, R. (2002). The relation of kindergarten classroom

environment to teacher, family, and school characteristics and child outcomes. The Elementary School Journal, 102, 225–238.

Piantadosi, S., Byar, D. P., & Green, S. B. (1988). The ecological fallacy. American Journal of Epidemiology, 127, 893–904.

Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40, 879–891. doi:10.3758/BRM.40.3.879

Preacher, K. J., & Selig, J. P. (2012). Advantages of Monte Carlo confidence intervals for indirect effects. Communication Methods and Measures, 6, 77–98. doi:10.1080/19312458. 2012.679848

Putnam, R.F., Luiselli, J.K., Handler, M.W., & Jefferson, G.L. (2003). Evaluating student discipline practices in a public school through behavioral assessment of office referrals. Behavior Modification, 27, 505–523. doi:10.1177/0145445503255569

Ransford, C. R., Greenberg, M. T., Domitrovich, C. E., Small, M., Jacobson, L. (2009). The role of teachers’ psychological experiences and perceptions of curriculum supports on the implementation of a social and emotional learning curriculum. School Psychology Review, 38, 510–532.

Raudenbusch, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods. Newbury Park: Sage.

Raudenbusch, S. W., Rowan, B., & Cheong, Y. F. (1992). Contextual effects on the self-perceived efficacy of high school teachers. Sociology of Education, 65, 150–167.


REFERENCES

212

Reyes, M. R., Brackett, M. A., Rivers, S. E., White, M., & Salovey, P. (2012). Classroom emotional climate, student engagement, and academic achievement. Journal of Educational Psychology, 104, 700–712. doi:10.1037/ a0027268

Riggs, I. M., & Enochs, L. G. (1990). Toward the development of an elementary teacher’s science teaching efficacy belief instrument. Science Education, 74, 625–637. doi:10.1002/sce.3730740605

Rimm-Kaufman , S. E., & Chui, Y. J. I. (2007). Promoting social and academic competence in the classroom: An intervention study examining the contribution of the Responsive Classroom approach. Psychology in the Schools, 44, 397–413. doi:10.1002/pits.20231

Rimm-Kaufman, S. E., La Paro, K. M., Downer, J. T., & Pianta, R. C. (2005). The contribution of classroom setting and quality of instruction to children’s behavior in kindergarten classrooms. The Elementary School Journal, 105, 377–394. doi:10.1086/429948

Robertson, C., & Dunsmuir, S. (2013). Teacher stress and pupil behavior explored through a rational-emotive behaviour therapy framework. Educational Psychology, 33, 215–232. doi:10.1080/01443410.2012.730323

Roehrig, A. D., Pressley, M., & Talotta, D. (2002). Stories of beginning teachers: First year challenges and beyond. Notre Dame, IN: University of Notre Dame Press.

Roeser, R W, Eccles, J. S., & Sameroff, A J. (2000). School as a context of early adolescents' academic and social-emotional development: A summary of research findings. The Elementary School Journal, 100, 443–447.

Rohaan, E. J., Taconis, R., & Jochems, W. M. G. (2012). Analysing teacher knowledge of technology education and its effects on pupils' concept and attitude. Retrieved from https://www.iteaconnect.org/Conference/PATT/PATT22/ Rohaan.pdf

Roorda, D. L., Koomen, H. M., Spilt, J. L., & Oort, F. J. (2011). The influence of affective teacher–student relationships on students’ school engagement and achievement a meta-analytic approach. Review of Educational Research, 81, 493–529. doi:10.3102/0034654311421793

Roorda, D. L., Koomen, H. M., Spilt, J. L., Thijs, J. T., & Oort, F. J. (2013). Interpersonal behaviors and complementarity in interactions between teachers and kindergartners with a variety of externalizing and internalizing behaviors. Journal of School Psychology, 51, 143–158. doi:10.1016/j.jsp.2012.12.001

Roorda, D. L., Verschueren, K., Vancraeyveldt, C., Van Craeyevelt, S., & Colpin, H. (2014). Teacher–child relationships and behavioral adjustment: Transactional links for preschool boys at risk. Journal of School Psychology, 52, 495–510. doi:10.1016/j.jsp.2014.06.004

Rose, J. S., & Medway, F. J. (1981). Measurement of teachers' beliefs in their control over student outcome. Journal of Educational Research, 74, 185–190.

Ross, J. A. (1992). Teacher efficacy and the effects of coaching on student achievement. Canadian Journal of Education, 17, 51–65. doi:10.2307/1495395

Ross, J. A. (1994). The impact of an in-service to promote cooperative learning on the stability of teacher efficacy. Teaching and Teacher Education, 10, 381–394.

Ross, J. A. (1998). The antecedents and consequences of teacher efficacy. In J. Brophy (Ed.), Advances in Research on Teaching (Vol.7, pp. 49–73). Greenwitch, CT: JAI.

Ross, J. A., Cousins, J. B., & Gadalla, T. (1996). Within-teacher predictors of teacher efficacy. Teaching and Teacher Education, 12, 385–400.

Ross, J. A., Hogaboam-Gray, A., & Hannay, L. (2001). Effects of teacher efficacy on computer skills and computer cognitions of Canadian students in grades K-3. The Elementary School Journal, 102, 141–156.

Rots, I., Aelterman, A., Vlerick, P., & Vermeulen, K. (2007). Teacher education, graduates’ teaching commitment and entrance into the teaching profession. Teaching and Teacher Education, 23, 543–556. doi:10.1016/ j.tate.2007.01.012

Rotter, J. B. (1966). Generalized expectancies for internal versus external control of reinforcement. Psychological Monographs, 80, 1–28.

Rubie-Davies, C. M., Flint, A., & McDonald, L. G. (2011). Teacher beliefs, teacher characteristics, and school contextual factors: What are the relationships? British Journal of Educational Psychology, 82, 270–288. doi:10.1111/j.2044-8279.2011.02025.x

Rubin, K. H., & Copian, R. J. (2004). Paying attention to and not neglecting social withdrawal and social isolation. Merrill-Palmer Quarterly, 50, 506–534.


REFERENCES

213

Ruble, L. A., Usher, E. L., & McGrew, J. H. (2011). Preliminary investigation of the sources of self-efficacy among teachers of students with autism. Focus on Aautism and Other Developmental Disabilities, 26, 67–74. doi:10.1177/1088357610397345

Rudasill, K. M. (2011). Child temperament, teacher–child interactions, and teacher–child relationships: A longitudinal investigation from first to third grade. Early Childhood Research Quarterly, 26, 147–156. doi:10.1016/j.ecresq.2010.07.002

Ryan, R.M., & Deci, E.L. (2002). Overview of self-determination theory: An organismic dialectical perspective. In E.L. Deci & R.M. Ryan (Eds.), Handbook on Self-Determination Research (pp. 3–33). Rochester, NY: University of Rochester Press.

Ryu, E. (2014). Factorial invariance in multilevel confirmatory factor analysis. British Journal of Mathematical and Statistical Psychology, 67, 172–194. doi:10.1111/bmsp.12014

Saklofske, D. H., Michayluk, J. O., & Randhawa, B. S. (1988). Teachers’ efficacy and teaching behaviors. Psychological Reports, 63, 407–414.

Salanova, M., Llorens, S., & Schaufeli, W. B. (2011). “Yes, I can, I feel good, and I just do it!” On gain cycles and spirals of efficacy beliefs, affect, and engagement. Applied Psychology: An International Review, 60, 255–285. doi:10.1111/j.1464-0597.2010.00435.x

Sameroff, A.J., & Fiese, B.H. (2000). Transactional regulation: The developmental ecology of early intervention. In J.P. Shonkoff, & S.J. Meisels (Eds.), Handbook of early childhood intervention, (2nd Edition, pp. 135-159), New York. NY: Cambridge University Press.

Sang, G., Valcke, M., van Braak, J., & Tondeur, J. (2010). Student teachers’ thinking processes and ICT integration: Predictors of prospective teaching behaviors with educational technology. Computers & Education, 54, 103–112. doi:10.1016/j.compedu.2009.07.010

Sass, D. A., Seal, A. K., & Martin, N. K. (2011). Predicting teacher retention using stress and support variables. Journal of Educational Administration, 49, 200–215. doi:10.1108/09578231111116734

Satorra, A. (2000). Scaled and adjusted restricted tests in multi-sample analysis of moment structures. In R. D. H. Heijmans, D. S. G. Pollock, & A. Satorra (Eds.), Innovations in multivariate statistical analysis. A Festschrift for Heinz Neudecker (pp. 233–247). London, UK: Kluwer Academic Publishers.

Satorra, A., & Bentler, P. M. (2010). Ensuring positiveness of the scaled difference chi-square test statistic. Psychometrika, 75, 243–248. doi:10.1007/s11336-009-9135-y

Schram, E., Van der Meer, F., & Van Os, S. (2012). Omgaan met verschillen: (g)een kwestie van maatwerk. Naar een doorgaande lijn in de toerusting van leraren voor passend onderwijs. Enschede: SLO.

Schwarzer, R. (Ed.) (1992). Self-efficacy: Thought control of action. Washington, DC: Hemisphere. Schwarzer, R., & Hallum, S. (2008). Perceived teacher self-efficacy as a predictor of job stress and burnout:

Mediation analyses. Applied Psychology: An International Review, 57, 152–171. doi:10.1111/j.1464-0597. 2008.00359.x

Schwarzer, R., & Jerusalem, M. (1995). Generalized self-efficacy scale. In J. Weinman, S. Wright, & M. Johnston (Eds.), Measures in health psychology: A user’s portfolio (pp. 35–38). Windsor: NFER-Nelson.

Schwarzer, R., Schmitz, G.S., & Daytner, G.T. (1999). The Teacher Self-Efficacy scale. Retrieved from http://www.fu-berlin.de/gesund/skalen/t_se.htm

Schwarzer, R., Schmitz, G. S., & Tang, C. (2000). Teacher burnout in Hong Kong and Germany: A cross-cultural validation of the Maslach Burnout Inventory. Anxiety, Stress, & Coping, 13, 309–326. doi:10.1080/1061 5800008549268

Shyman, E. (2010). Identifying predictors of emotional exhaustion among special education paraeducators: A preliminary investigation. Psychology in the Schools, 47, 828–841. doi:10.1002/pits.20507

Siwatu, K.O. (2007). Pre-service teachers’ culturally responsive teaching self-efficacy and outcome expectancy beliefs. Teaching and Teacher Education, 23, 1086–1101. doi:10.1016/j.tate.2006.07.011

Siwatu, K. O. (2011). Preservice teachers’ culturally responsive teaching self-efficacy forming experiences: A mixed methods study. Journal of Educational Research, 104, 360–369.doi:10.1080/00220671.2010.487081

Skaalvik, E.M., & Skaalvik, S. (2007). Dimensions of teacher self-efficacy and relations with strain factors, perceived collective teacher efficacy, and teacher burnout. Journal of Educational Psychology, 99, 611–625. doi:10.1037/0022-0663.99.3.611

Skaalvik, E. M., & Skaalvik, S. (2010). Teacher self-efficacy and teacher burnout: A study of relations. Teaching and Teacher Education, 26, 1059–1069. doi:10.1016/j.tate.2009.11.001


REFERENCES

214

Smeets, E., Ledoux, G., Blok, H., Felix, C., Heurter, A., Van Kuijk, J., & Vergeer, M. (2013). Op de drempel van passend onderwijs: Beleid en aanbod rond specifieke onderwijsbehoeften in zes samenwerkingsverbanden. Nijmegen: ITS.

Smeets, E., Ledoux, G., Regtvoort, A., Felix, C. & Mol Lous, A. (2015). Passende competenties voor passend onderwijs: Onderzoek naar competenties in het basisonderwijs. Nijmegen: ITS.

Smeets, E. & Rispens, J. (2008). Op zoek naar passend onderwijs: Overzichtsstudie van de samenhang tussen regulier en speciaal (basis)onderwijs. Nijmegen: ITS.

Snijders, T. A. B., & Bosker, R. J. (1999). Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling. London, UK: Sage Publishers.

Sobel, M. E. (1990). Effect analysis and causation in linear structural equation models. Psychometrika, 55, 495–515. doi:10.1007/BF02294763

So-kum Tang, C., Au, W. T., Schwarzer, R., & Schmitz, G. (2001). Mental health outcomes of job stress among Chinese teachers: Role of stress resource factors and burnout. Journal of Organizational Behavior, 22, 887–901. doi:10.1002/job.120

Soodak, L. C., & Podell, D. M. (1993). Teacher efficacy and student problem as factors in special education referral. Journal of Special Education, 27, 66–81.

Soodak, L. C., & Podell, D. M. (1996). Teacher efficacy: Toward the understanding of a multi-faceted construct. Teaching and Teacher Education, 12, 401–411.

Soodak, L. C., Podell, D. M., & Lehman, L. R. (1998). Teacher, student, and school attributes as predictors of teachers’ responses to inclusion. The Journal of Special Education, 31, 480–497.

Spilt, J. L., & Koomen, H. M. Y. (2009). Widening the view on teacher–child relationships: Teachers’ narratives concerning disruptive versus nondisruptive children. School Psychology Review, 38, 86–101.

Spilt, J. L., Koomen, H. M. Y., & Thijs, J. T. (2011). Teacher wellbeing: The importance of teacher–student relationships. Educational Psychology Review, 23, 457–477. doi:10.1007/ s10648-011-9170-y

Spilt, J. L., Koomen, H. M., Thijs, J. T., & van der Leij, A. (2012). Supporting teachers’ relationships with disruptive children: The potential of relationship-focused reflection. Attachment & Human Development, 14, 305–318. doi:10.1080/14616734.2012.672286

Stephanou, G., Gkavras, G., & Doulkeridou, M. (2013). The role of teachers’ self-and collective-efficacy beliefs on their job satisfaction and experienced emotions in school. Psychology, 4, 268–278. doi:10.4236/ psych.2013.43A040

Stipek, D., & Miles, S. (2008). Effects of aggression on achievement: Does conflict with the teacher make it worse?. Child Development, 79, 1721–1735. doi:10.1111/j.1467-8624.2008.01221.x

Stuhlman, M. W., & Pianta, R. C. (2002). Teachers' narratives about their relationships with children: Associations with behavior in classrooms. School Psychology Review, 31, 148–163.

Sutherland, K. S., & Oswald, D. P. (2005). The relationship between teacher and student behavior in classrooms for students with emotional and behavioral disorders: Transactional processes. Journal of Child and Family Studies, 14, 1–14. doi:10.1007/s10826-005-1106-z

Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (5th ed.).Boston, MA: Allyn and Bacon. Tejeda-Delgado, M. D. C. (2009). Teacher efficacy, tolerance, gender, and years of experience and special

education referrals. International Journal of Special Education, 24, 112–119. Temiz, T., & Topcu, M. S. (2013). Preservice teachers’ teacher efficacy beliefs and constructivist-based teaching

practice. European Journal of Psychology of Education, 28, 1435–1452, doi:10.1007/s10212-013-0174-5 Teo, T. (2009). Examining the relationship between student teachers' self-efficacy beliefs and their intended uses

of technology for teaching: A structural equation modelling approach. The Turkish Online Journal of Educational Technology, 8, 1–16.

Thijs, J., Westhof, S., & Koomen, H. (2012). Ethnic incongruence and the student–teacher relationship: The perspective of ethnic majority teachers. Journal of School Psychology, 50, 257–273. doi:10.1016/j.jsp.2011. 09.004

Thomas, D. E., Bierman, K. L., & The Conduct Problems Prevention Research Group (2006). The impact of classroom aggression on the development of aggressive behavior problems in children. Developmental Psychopathology, 18, 471–487. doi:10.1017/S0954579406060251

Thoonen, E. E. J., Sleegers, P. J. C., Oort, F. J., Peetsma, T. T. D., & Geijsel, F. P. (2011a). How to improve teaching practices: The role of teacher motivation, organizational factors, and leadership practices. Educational Administration Quarterly, 47, 496–536. doi:10.1177/0013161X11400185


REFERENCES

215

Thoonen, E. E. J., Sleegers, P. J. C., Peetsma, T. T. D., & Oort, F. J. (2011b). Can teachers motivate students to learn? Educational Studies, 37, 345–360. doi:10.1080/03055698.2010.507008

Thornberry, T. P., & Krohn, M. D. (1997). Peers, drug use, and delinquency. In D. M. Stoff, J. Breiling, & J. D. Maser (Eds.), Handbook of antisocial behavior (pp. 218–233). New York, NY: Wiley.

Throndsen, I., & Turmo, A. (2013). Primary mathematics teachers’ goal orientations and student achievement. Instructional Science,41,307–322. doi:10.1007/s11251-012-9229-2

Tournaki, N., & Podell, D. M. (2005). The impact of student characteristics and teacher efficacy on teachers’ predictions of student success. Teaching and Teacher Education, 21, 299–314. doi:10.1016/j.tate.2005.01.003

Tschannen-Moran, M., & Johnson, D. (2011). Exploring literacy teachers’ self-efficacy beliefs: Potential sources at play. Teaching and Teacher Education, 27, 751–761. doi:10.1016/ j.tate.2010.12.005

Tschannen Moran, M., & McMaster, P. (2009). Sources of self efficacy: Four professional development formats and their relationship to self efficacy and implementation of a new teaching strategy. The Elementary School Journal, 110, 228–245. doi:10.1086/605771

Tschannen-Moran, M., Woolfolk Hoy, A., & Hoy, W.K. (1998). Teacher efficacy: Its meaning and measure. Review of Educational Research, 68, 202–248. doi:10.3102/00346543068002202

Tschannen-Moran, M., & Woolfolk Hoy, A. (2001). Teacher efficacy: capturing an elusive construct. Teaching and Teacher Education, 17, 783–805. doi:10.1016/S0742-051X(01)00036-1

Tschannen-Moran, M., & Woolfolk Hoy, A. (2007). The differential antecedents of self-efficacy beliefs of novice and experienced teachers. Teaching and Teacher Education, 23, 944–956. doi:10.1016/j.tate.2006.05.003

Tsigilis, N., Koustelios, A., & Grammatikopoulos, V. (2010). Psychometric properties of the Teachers' Sense of Efficacy Scale within the Greek educational context. Journal of Psychoeducational Assessment, 28, 153–162. doi:10.1177/0734282909342532

Tsouloupas, C. N., Carson, R. L., Matthews, R., Grawitch, M. J., & Barber, L. K. (2010). Exploring the association between teachers’ perceived student misbehaviour and emotional exhaustion: the importance of teacher efficacy beliefs and emotion regulation. Educational Psychology, 30, 173–189. doi:10.1080/014434109 03494460

Van Gennip, H., Marx, T., & Smeets, E. (2007). Gedragsproblemen in de basisschool en competenties van leraren. Nijmegen: ITS.

Van Leeuwen, K., Meerschaert, T., Bosmans, G., De Medts, L., & Braet, C. (2006). The strengths and difficulties questionnaire in a community sample of young children in Flanders. European Journal of Psychological Assessment, 22, 189–197.

Vannatta, R. A., & Fordham, N. (2004). Teacher dispositions as predictors of classroom technology use. Journal of Research on Technology in Education, 36, 253–271.

Van Uden, J. M., Ritzen,H., & Pieters, J. M. (2013). I think I can engage my students: Teachers’ perceptions of student engagement and their beliefs about being a teacher. Teaching and Teacher Education, 32, 43–54. doi:10.1016/j.tate.2013.01.004

Van Widenfelt, B. M., Goedhart, A. W., Treffers, P. D. A., & Goodman, R. (2003). Dutch version of the strengths and difficulties questionnaire (SDQ). European Child & Adolescent Psychiatry, 12, 281–289.

Verschueren, K., & Koomen, H. M. (2012). Teacher–child relationships from an attachment perspective. Attachment & Human Development, 14, 205–211. doi:10.1080/14616734.2012.672260

Viel-Ruma, K., Houchins, D., Jolivette, K., & Benson, G. (2010). Efficacy beliefs of special educators: The relationships among collective efficacy, teacher self-efficacy, and job satisfaction. Teacher Education and Special Education, 33, 225–233. doi:10.1177/ 0888406409360129

Ware, H. W., & Kitsantas, A. (2007). Teacher and collective efficacy beliefs as predictors of professional commitment. The Journal of Educational Research,100, 303–310. doi:10.3200/JOER.100.5.303-310

Ware, H. W., & Kitsantas, A. (2011). Predicting teacher commitment using principal and teacher efficacy variables: An HLM approach. The Journal of Educational Research, 104, 183–193. doi:10.1080/0022067 1003638543

Wehby, J. H., Symons, F. J., Canale, J. A., & Go, F. J. (1998). Teaching practices in classrooms for students with emotional and behavioral disorders: Discrepancies between recommendations and observations. Behavioral Disorders, 24, 51–56.

Wentzel, K. R. (1993). Does being good make the grade? Social behavior and academic competence in middle school. Journal of Educational Psychology, 85, 357–364.


REFERENCES

216

Werthamer-Larsson, L., Kellam, S. G., & Wheeler, L. (1991). Effect of first-grade classroom environment on shy behavior, aggressive behavior, and concentration problems. American Journal of Community Psychology, 19, 585–602. doi:10.1007/BF00937993

Wertheim, C., & Leyser, Y. (2002). Efficacy beliefs, background variables, and differentiated instruction of Israeli prospective teachers. Journal of Educational Research,96,54–63.

Weshah, H. A. (2012). Teaching efficacy and teaching performance among student teachers in a Jordanian childhood education program. Journal of Early Childhood Teacher Education, 33,163-177. doi:10.1080/ 10901027.2012.675941

Westling, D. L. (2010). Teachers and challenging behavior: Knowledge, views, and practices. Remedial and Special Education, 31, 48–63. doi:10.1177/0741932508327466

Wheatley, K. F. (2005). The case for reconceptualizing teacher efficacy research. Teaching and Teacher Education, 21, 747–766. doi:10.1016/j.tate.2005.05.009

Wolters, C. A., & Daugherty, S. G. (2007). Goal structures and teachers’ sense of efficacy: their relation and association to teaching experience and academic level. Journal of Educational Psychology, 99, 181–193.

Wong, K. T., Teo, T., & Russo, S. (2010). Influence of gender and computer teaching efficacy on computer acceptance among Malaysian student teachers: An extended technology acceptance model. Australasian Journal of Educational Technology, 28, 1190–1207.

Woolfolk, A. E., & Hoy, W. K. (1990). Prospective teachers’ sense of e cacy and beliefs about control. Journal of Educational Psychology, 82, 81–91.

Woolfolk, A. E., Rosoff, B., & Hoy, W. K. (1990). Teachers’ sense of efficacy and their beliefs about managing students. Teaching and Teacher Education, 6, 137–148. doi:10.1016/0742-051X(90)90031-Y

Woolfolk Hoy, A., & Burke-Spero, R. (2005). Changes in teacher efcacy during the early years of teaching: A comparison of four measures. Teaching and Teacher Education, 21, 343–356. doi:10.1016/j.tate.2005.01.007

Woolfolk Hoy, A., & Davis, H. A. (2005). Teachers’ sense of efficacy and its influence on the achievement of adolescents. In T. Urdan & F. Pajares (Eds.), Adolescence and education: Volume V: Self-efficacy beliefs during adolescence (pp. 117–137). Greenwich, CT: Information Age.

Woolfolk Hoy, A., Hoy, W. K., & Davis, H. A. (2009). Teachers’ self-efficacy beliefs. In K. Wentzel & A. Wigfield (Eds.), Handbook of Motivation at School (pp. 627–653). New York, NY: Routledge.

Woolfolk Hoy, A., Hoy, W. K., & Kurz, N. M. (2008). Teacher's academic optimism: The development and test of a new construct. Teaching and Teacher Education, 24, 821–835. doi:10.1016/j.tate.2007.08.004

Woolfson, L. M., & Brady, K. (2009). An investigation of factors impacting on mainstream teachers’ beliefs about teaching students with learning difficulties. Educational Psychology, 29, 221–238. doi:10.1080/0144341080 2708895

Wyatt, M. (2014). Towards a re-conceptualization of teachers' self-efficacy beliefs: Tackling enduring problems with the quantitative research and moving on. International Journal of Research & Method in Education, 37, 166–189. doi:10.1080/1743727X.2012.742050

Wyatt, M. (2016). “Are they becoming more reflective and/or efficacious?” A conceptual model mapping how teachers’ self-efficacy beliefs might grow. Educational Review, 68, 114–137. doi:10.1080/00131911.2015. 1058754

Yeo, L. S., Ang, R. P., Chong, W. H., Huan, V. S., & Quek, C. L. (2008). Teacher efficacy in the context of teaching low achieving students. Current Psychology, 27, 192–204. doi:10.1007/s12144-008-9034-x

Yildirim, K., & Ates, S. (2012). Turkish pre-service teachers` perceived self-efficacy beliefs and knowledge about using expository text as an instructional tool in their future classroom settings. Australian Journal of Teacher Education, 37, 12–31. doi:10.14221/ajte.2012v37n8.4

Yilmaz, C. (2011). Teachers' perceptions of self-efficacy, English proficiency, and instructional strategies. Social Behavior and Personality, 39, 91–100. doi:10.2224/sbp.2011.39.1.91

Yoon, J. S. (2002). Teacher Characteristics as predictors of teacher-student relationships: Stress, negative affect, and self-efficacy. Social Behavior and Personality, 30, 485–494. doi:10.2224/sbp.2002.30.5.485

Yoon, J. S. (2004). Predicting teacher interventions in bullying situations. Education & Treatment of Children, 27, 37–45.

Yuan, K.H. (2005). Fit indices versus test statistics. Multivariate Behavioral Research, 40, 115–148. doi:10.1207/ s15327906mbr4001_5


REFERENCES

217

Yuan, K. H., & Bentler, P. M. (2000). Three likelihood-based methods for mean and covariance structure analysis with nonnormal missing data. Sociological Methodology, 30, 165–200. doi:10.1111/0081-1750.00078

Zee, M., De Jong, P. F., & Koomen, H. M. Y. (2016). Teachers' self-efficacy in relation to individual students with a variety of social-emotional behaviors: A multilevel investigation. Journal of Educational Psychology. Advance online publication. doi:10.1037/edu0000106

Zee, M. & Koomen, H. M. Y. (2015, March). Student-specific teacher self-efficacy: Investigating the factorial, convergent, and concurrent validity of a new instrument. Poster presented at the biennial meeting of SRCD, Philadelphia, PA.

Zee, M., Koomen, H. M. Y., Jellesma, F. C., Geerlings, J., & de Jong, P. F. (2016). Inter- and intra-individual differences in teachers' self-efficacy: A multilevel factor exploration. Journal of School Psychology, 55, 39–56. doi:10.1016/j.jsp.2015.12.003

Zee, M., Koomen, H. M., & Van der Veen, I. (2013). Student–teacher relationship quality and academic adjustment in upper elementary school: The role of student personality. Journal of school psychology, 51, 517–533. doi:10.1016/j.jsp.2013.05.003

Zhang, X., & Sun, J. (2011). The reciprocal relations between teachers’ perceptions of children's behavior problems and teacher–child relationships in the first preschool year. The Journal of Genetic Psychology, 172, 176–198. doi:10.1080/00221325.2010.528077

Zimmer-Gembeck, M. J., Geiger, T. C., & Crick, N. R. (2005). Relational and physical aggression, prosocial behavior, and peer relations: Gender moderation and bidirectional associations. Journal of Early Adolescence, 25, 421–452. doi:10.1177/0272431605279841


219

DANKWOORD

(ACKNOWLEDGMENTS IN DUTCH)

_________________________________________________________________________

“No man is an island, entire of itself”

– John Donne

Toen ik begon met studeren, vroeg mijn vader zich bezorgd af of ik, met elke stap voorwaarts

die ik binnen de wetenschap zette, niet teveel op een eilandje terecht zou komen. Nu ik tien

jaar verder ben en een proefschrift rijker, kan ik niets anders zeggen dan dat hiervan geenszins

sprake is. Wat heb ik een fantastische groep mensen om me heen met wie ik kan lachen,

huilen, discussiëren, nadenken, praten over (n)iets en bij wie ik simpelweg mezelf kan zijn! Een

aantal van hen wil ik op deze plek in het bijzonder bedanken.

Lieve Helma en Peter, jullie wil ik allereerst enorm bedanken voor de kans die jullie mij

geboden hebben om bij jullie te komen promoveren. Peter, het zal me niet verbazen als ik de

meest eigenwijze, irritante en veeleisende promovenda ben die jij ooit hebt begeleid. Des te

bijzonderder vind ik het dat je mij de ruimte en het vertrouwen gegeven hebt mijn aio-plek veel

eerder dan gepland in te ruilen voor een postdoc. Ik kijk enorm uit naar onze toekomstige

gedachtewisselingen, gezamenlijke papers en gezellige praatjes op de gang. Jouw mening

waardeer ik altijd enorm. Helma, met jou als begeleider viel ik echt met mijn neus in de boter.

Wij lijken het per definitie wel met elkaar eens te zijn, vullen elkaar naadloos aan en hebben

aan een half woord genoeg. Zowel in het laatste jaar van mijn research master als tijdens mijn

promotie kreeg je het altijd weer voor elkaar mijn werk naar een hoger niveau te tillen en bood

je me daarnaast een luisterend oor wanneer ik dat nodig had. Ik ken maar weinig mensen die

zo intelligent, betrokken en bevlogen zijn als jij. Je bent me enorm dierbaar en ik sta te

springen om alle ideeën die we samen hebben ten uitvoer te gaan brengen!

Ook wil ik de leden van mijn promotiecommissie, prof. dr. Karine Verschueren, prof. dr.

Alexander Minnaert, prof. dr. Thea Peetsma, prof. dr. Geert Jan Stams en prof. dr. Frans Oort,


DANKWOORD (ACKNOWLEDGMENTS IN DUTCH)

220

hartelijk danken. Ik waardeer het enorm dat jullie de tijd en moeite hebben willen nemen om

mijn proefschrift te lezen en te beoordelen.

Jochem en Jolien, jullie wil ik bedanken voor de constructieve samenwerking tijdens de

afgelopen tweeënhalf jaar. Met name het gezamenlijk werven van scholen, het plannen en

uitvoeren van het interventie-onderzoek, en het uitwisselen van ervaringen in deze laatste fase

van het project heb ik als prettig ervaren. Bedankt ook Francine, voor je feedback op mijn

eerste paper en de mogelijkheid om mee te schrijven aan één van je onderzoeken.

Ik zou lang niet zoveel plezier en inspiratie uit mijn werk op de UvA halen als ik niet omringd

zou worden door de geweldige collegae en mede-promovendi van POWL. Lieve OLPeople, bij

jullie voel ik me echt thuis! Marlie, als iemand voor een vrolijk en warm welkom kan zorgen,

dan ben jij het wel! Bedankt dat je me zo geweldig hebt opgevangen op mijn eerste werkdag,

bedankt voor je grenzeloze enthousiasme en gezelligheid, de vele thee-haal-momenten en je

immer doeltreffende regelacties. Op jou kunnen we bouwen! Sietske, als ik twijfel over mijn

statistiek, taal, of APA, iets dringends kwijt moet, of een heldere blik nodig heb, dan ben jij de

eerste tot wie ik me wend. Ik vind het ontzettend fijn om terecht te kunnen bij iemand die

dezelfde ambities en drijfveren heeft als ik. Hopelijk kunnen we nog lang van en met elkaar

leren en mooie dingen beleven! Madelon, jij bent mijn grote voorbeeld wat betreft prachtige

papers, hoge hakken en kleine wagens met grote knuffelfactor, en Bettina, stille kracht met

geweldige humor, wat was het fijn om samen met jou een “eilandje” te bewonen in D8.12.

Haytske, we hebben hetzelfde meegemaakt en doen allebei onderzoek naar leerkracht–

leerlingrelaties. Dat schept een band. Ik waardeer jouw wilskracht en niet aflatende interesse in

het wel en wee van anderen enorm en praat graag weer eens met je bij! Loes, Janneke en

Alexander, wat zijn jullie ook weer een geweldige aanwinst voor het team. Met jullie is het

nooit saai! Jans en Klepel, jullie O&O Bowlingcompetitie verdient nu al met recht het stempel

legendarisch! Mariska, Merlijn en Annette, leuk dat we af en toe onder het genot van een kop

koffie of thee even bij kunnen kletsen. Dat houden we erin! Heel veel dank ook funky-

geweldige-briljante-megalieve Elise. Als jij er bent, gaat de zon spontaan schijnen. Wat leer ik

veel van jou! Ik beschouw het als een groot voorrecht dat ik met jou samen mag werken.

Dan mijn paranimfen, Britt en Debora. Lieve B, wij vonden elkaar toen we het erover eens

waren dat de veel te lange vlecht van een spreker op het Graduate School Colloquium toch

echt afgeknipt moest worden. Ook onze voorliefde voor katten en poezen bleek een gemene



221

deler te zijn. Inmiddels wisselen we veel meer uit dan enkel plaatjes van en verhalen over onze

harige viervoeters. Vooral tijdens de laatste periode van onze promotie zijn we echt naar elkaar

toe gegroeid. Hoe bijzonder was het om in deze fase samen op te kunnen trekken, kleine en

grote successen te vieren en samen de eindstreep te halen. Ik heb enorm veel zin in ons cross-

over paper met Elise en Helma en ik hoop dat ik nog vaak bij jou en Charlotte de pannen mag

komen leeglikken (zonder gloet ish goed!). Lieve Debster, wat ben ik blij dat jij in het eerste jaar

van mijn promotie weer terugkwam naar de UvA. Niet enkel liggen we helemaal op één lijn

wat onderzoeksinteresses betreft, maar ook buiten de wetenschap blijken we een gouden team

te zijn! Met Haytske bij jou in Leuven logeren, samen een outfit scoren voor het SRCD, New

York onveilig maken, de vele borrels bij de Roeter… ik had het allemaal voor geen goud willen

missen! Ik ben benieuwd wat we deze zomer allemaal gaan beleven in Vilnius en Riga!

Graag wil ik nog een aantal student-assistenten bij naam noemen die hebben bijgedragen aan

dit proefschrift. Kelly, Emma, Marjolein, Lisa, Annouschka, Jaleesah, Jantine, Mailis, Carlijne

en Malou, jullie hulp bij het werven van scholen, invoeren van data, en het afnemen van

vragenlijsten en interviews was onmisbaar! Uiteraard ben ik ook de ruim 140 leerkrachten en

3000 leerlingen die bereid waren deel te nemen aan ons project zeer erkentelijk. Ik had het

genoegen een groot deel van jullie zelf op school te mogen bezoeken. Vooral de leerkrachten

wil ik enorm danken voor hun gastvrijheid, tijd, moeite en betrokkenheid. Zonder u was dit

proefschrift er niet geweest.

Tot slot mijn familie. Lieve Meindert en Marcia, wat is het fijn om in goede en slechte tijden op

je familie terug te kunnen vallen. Bedankt dat jullie er altijd en onvoorwaardelijk zijn. Hetzelfde

geldt voor jullie, Paula en Ton, dank voor jullie goede zorgen, interesse, betrokkenheid,

heerlijke etentjes en gezellige uitjes. Jullie betekenen heel veel voor me! Lieve Bob, Florien, Pa

en Ma, jullie zijn mijn alles. Bob, wat leer ik veel van jouw nuchtere kijk op het leven en jouw

relativeringsvermogen in tijden van stress. Jij weet altijd het juiste te doen en te zeggen. Een

betere zwager had ik me niet kunnen wensen! Flo, grote zus, jij bent mijn beste vriendin.

Bedankt voor je wijze raad en steun, altijd en overal. Zonder jou weet ik me geen raad. Ik ben

enorm trots op je. Sprokkeltje, over niet al te lange tijd ben jij de nieuwste telg in de familie.

Mag ik je vaak komen voorlezen en knuffelen? Mama, weet je nog dat we samen een boek

zouden schrijven? Nou, hier is hij dan eindelijk! Een thriller is het niet geworden (hoewel de

meningen daarover ongetwijfeld verdeeld zullen zijn), maar ik hoop dat dit boekje iets van

jouw creativiteit en warmte bevat. Ik mis je nog elke dag, maar weet dat je altijd dicht bij me



222

bent. Allerliefste paatje, dit boekje draag ik op aan jou. Ik kan niet vaak genoeg herhalen hoe

trots ik op je ben en hoeveel ik van je houd. Net als mama heb jij me de ingrediënten

meegegeven waarmee ik dit proefschrift heb kunnen schrijven. Hard werken, doorzetten, niet

opgeven en schouders eronder: falen is geen optie. Geen Zee te hoog!


223

ABOUT THE AUTHOR _________________________________________________________________________ Marjolein Zee was born on June 20, 1987, in Hoorn, the Netherlands. After completing

secondary school at the RSG Enkhuizen in 2005, she continued her education at the University

of Amsterdam (UvA), where she studied Media and Culture for half a year. In the spring of

2006, she spent some time working at Bricomarché Lourches, France, to brush up her French.

Later that year, however, she decided to return to the UvA, earning both her honors certificate

and bachelor’s degree in Educational Sciences in 2009 (Cum Laude). During her bachelor’s,

she gained experience as a student mentor and later, as a coordinator of the student mentoring

program for first-year undergraduates in the Pedagogical and Educational Sciences. For this

work, she received the Student-of-the-Year Award in 2009. From 2009 to 2011, she attended

her Research Master studies in Educational Sciences at the UvA and contributed as a junior

teacher to several first-year courses on education. Under the supervision of dr. Helma Koomen

and dr. Ineke van der Veen, she ultimately completed her studies with a thesis on the quality of

student–teacher relationships in upper elementary school.

After having worked as a junior educationist at the Center for Evidence-Based Education,

Academic Medical Center, Amsterdam, Marjolein started in September 2013 on a four-year

PhD project at UvA’s Research Institute of Child Development and Education, with prof. dr.

Peter de Jong and dr. Helma Koomen as her supervisors. She completed her dissertation in

January 2016 and subsequently started as a postdoctoral researcher at the same department,

continuing her research on student-specific teacher self-efficacy and student–teacher

relationships, and teaching various courses in the College and Graduate School of Child

Development and Education.


224

LIST OF PUBLICATIONS

_________________________________________________________________________

INTERNATIONAL PEER-REVIEWED PUBLICATIONS Zee, M., Koomen, H. M. Y., & van der Veen, I. (2013). Student–teacher relationship quality and academic

adjustment in upper elementary school: The role of student personality. Journal of School Psychology, 51,

517–533. doi:10.1016/j.jsp.2013.05.003

Zee, M., de Boer, M., & Jaarsma, A. D. C. (2014). Acquiring evidence-based medicine and research skills in the

undergraduate medical curriculum: Three different didactical formats compared. Perspectives on Medical

Education, 3, 357–370. doi:10.1007/s40037-014-0143-y

Jellesma, F. C., Zee, M., & Koomen, H. M. Y. (2015). Children’s perceptions of the relationship with the teacher:

Associations with appraisals and internalizing problems in middle childhood. Journal of Applied

Developmental Psychology, 36, 30–38. doi:10.1016/j.appdev.2014.09.002

Zee, M., de Jong, P. F., & Koomen, H. M. Y. (2016). Teachers' self-efficacy in relation to individual students with

a variety of social-emotional behaviors: A multilevel investigation. Journal of Educational Psychology.

Advance online publication. doi:10.1037/edu0000106

Zee, M., & Koomen, H. M. Y. (2016). Teacher self-efficacy and its effects on classroom processes, student

academic adjustment, and teacher well-being: A synthesis of 40 years of research. Review of Educational

Research. Advance online publication. doi:10.3102/0034654315626801

Zee, M., Koomen, H. M. Y., Jellesma, F. C., Geerlings, J., & de Jong, P. F. (2016). Inter- and intra-individual

differences in teachers' self-efficacy: A multilevel factor exploration. Journal of School Psychology, 55, 39–56.

doi:10.1016/j.jsp.2015.12.003

PAPERS UNDER REVIEW Zee, M., & de Bree, E. H. (2015). Self-regulation and academic achievement in middle childhood: The role of student–teacher

relationships. Manuscript in revision.

Zee, M., de Jong, P. F., & Koomen, H. M. Y. (2015). Students’ disruptive behavior and the development of teachers’ self-

efficacy: The role of teacher-perceived closeness and conflict in the student–teacher relationship. Manuscript submitted

for publication.

POPULAR van Viersen, S., Zee, M., & Hakvoort, B. E. (2016). Advies Platform Onderwijs2032 ontbeert verbinding tussen

wetenschap en praktijk. The Post Online. http://politiek.tpo.nl/2016/03/14/advies-platform-

onderwijs2032-ontbeert-verbinding-wetenschap-en-praktijk/

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