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Teachers’ Adaptability 1
Collie, R. J., & Martin, A. J. (2017). Teachers' sense of adaptability: Examining links with
perceived autonomy support, teachers' psychological functioning, and students' numeracy
achievement. Learning and Individual Differences, 55, 29-39.
http://dx.doi.org/10.1016/j.lindif.2017.03.003
This article may not exactly replicate the authoritative document published in the journal. It is not the
copy of record. The exact copy of record can be accessed via the DOI: 10.1016/j.lindif.2017.03.003.
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Teachers’ Adaptability 2
Teachers’ Sense of Adaptability: Examining Links with Perceived Autonomy Support,
Teachers’ Psychological Functioning, and Students’ Numeracy Achievement
Abstract
In the current study, we examined teachers’ sense of adaptability alongside their perceptions of
principal autonomy support, well-being, and organizational commitment. Associations between
the teacher constructs and students’ numeracy achievement were also conducted. With a sample
of 115 high school mathematics teachers (and 1685 students from their classrooms), we
conducted (single- and multilevel) structural equation modeling. Findings showed that perceived
autonomy support was positively associated with teachers’ adaptability, and that both constructs
were positively associated with teachers’ well-being and organizational commitment. In addition,
there were several associations between the teacher constructs and students’ numeracy
achievement. Findings have implications for understanding teachers’ responses to the inherently
changing demands of their work.
Keywords: teachers; adaptability; well-being; autonomy support; students’ numeracy
achievement
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Teachers’ Adaptability 3
Teachers’ Sense of Adaptability: Examining Links with Perceived Autonomy Support,
Teachers’ Psychological Functioning, and Students’ Numeracy Achievement
1. Introduction
The work of teachers involves constant change. Teachers are expected to respond to the
different and changing needs of students, effectively interact with new colleagues and different
parents/carers, pro-actively manage adjustments in timetabling and shifting daily activities, and
integrate new professional learning or curriculum into their instructional practices (Collie &
Martin, 2016). Being able to respond effectively to these changes is an important capacity for
healthy and effective workplace functioning. This has been referred to as adaptability and
defined as individuals’ capacity to adjust their thinking, actions, and emotions in response to
changing, new, or uncertain situations (Martin, Nejad, Colmar, & Liem, 2012, p.66). Although
the literature has regularly postulated that teachers’ adaptability is a central factor in effective
teaching (e.g., Corno, 2008; Kunter et al., 2013; Mansfield, Beltman, Price, & McConney, 2012;
Parsons, Williams, Burrowbridge, & Mauk, 2012; Vaughn & Parsons, 2013, 2016), there is
limited supporting empirical work. Given the constantly changing demands that teachers face in
their daily work, more research on this topic is important.
We therefore set out to examine teachers’ sense of adaptability (henceforth,
predominantly referred to as adaptability). To gain a better idea of its place, it was investigated
alongside three salient workplace factors. Specifically, we examined the extent to which
teachers’ perceptions of autonomy support provided by their principal (referred to as perceived
autonomy support; PAS) are associated with their adaptability, and whether both constructs are
associated with teachers’ reports of well-being and organizational commitment (Phase 1). We
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were also interested in determining the extent to which these constructs are related to students’
numeracy achievement (Phase 2). Figure 1 shows the examined models.
1.1. The Importance of Adaptability for Teachers
Teaching work involves inherent change and teachers are called upon to regularly adapt
in order to manage new or uncertain demands and situations (Collie & Martin, 2016). As such,
we suggest that adaptability is a crucial capacity for teachers. The literature on this topic,
although limited, has provided support for this. Some of this work has considered adaptability
with respect to instructional practices (e.g., Brühwiler & Blatchford, 2011; Corno, 2008; Parsons
et al., 2012; Vaughn & Parsons, 2013, 2016). For example, Corno (2008) establishes the
importance of adapting instruction to meet the needs of different groups and individuals in the
classroom on micro levels (small changes in the moment of teaching) and macro levels (larger-
scale program changes based on assessments), and that support should be adjusted continuously
to match learners’ changing needs as they develop. In an empirical study, Parsons (2012)
examined micro adaptations in literacy instruction and provided examples of how this occurred
to promote effective instruction such as changing how lesson objectives were met, inventing
examples, and inserting mini-lessons. Brühwiler and Blatchford (2011) conducted a related study
showing that teachers’ capacity to suggest alternative strategies for planning and instructional
practices in relation to vignette and video samples of others’ teaching was positively associated
with their own quality of instruction, and in turn, students’ achievement.
Other studies have considered teachers’ sense of adaptability as a central component of
teacher resilience (e.g., Gu & Day, 2007; Hargreaves, 2005; Le Cornu, 2009; Mansfield et al.,
2012). For example, Mansfield and colleagues (2012) analyzed early career teachers’
descriptions of what it means to be a resilient teacher and found that alongside self-efficacy,
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optimism, and other personal capacities, being adaptable and flexible was a core theme and
included actions such as adjusting to new roles, accepting changes, and having back-up
arrangements for when things do not go to plan. In sum, emerging work supports the assertion
that adaptability is an important capacity for teachers. Given the nascent nature of the empirical
base, however, more research is necessary (Parsons, 2012).
1.2. Theorizing Underpinning Sense of Adaptability
Although there are various constructs related to adaptability in the broader psychological
literature (e.g., Cullen, Edwards, Casper, & Gue, 2014; Pulakos, Arad, Donovan, & Plamondon,
2000), there have been calls for greater conceptual and methodological clarity such as the need to
disentangle the capacity of adaptability from associated motivational processes (e.g., Jundt et al.,
2015; VandenBos, 2007). In the current study, we employed an operationalization that addresses
these calls: the tripartite model of adaptability (Martin, Nejad et al., 2012).
1.2.1. Tripartite model of adaptability. According to the tripartite model, adaptability
refers to an individual’s capacity to use strategies to regulate their thoughts, actions, and
emotions in order to effectively respond to new, changing, or uncertain situations (Martin, Nejad
et al., 2012, 2013). For example, if a teacher is asked to teach a new class, adaptability may
involve the teacher regulating his/her thoughts to think about connections he/she can make with
this new group of students, regulating behavior by seeking advice from others who have taught
this class before, and regulating emotions such as potential anxiety or excitement to ensure they
are best able to plan effectively for the new class (Collie & Martin, 2016).
In describing the model, Martin, Nejad et al (2012) refer to the lifespan theory of control
(Heckhausen, Wrosch, & Schulz, 2010), which proposes that development across the lifespan is
influenced by an individual’s ability to play an active role in effectively adapting to the
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opportunities and constraints in the environment. A key factor in positive development is
compensatory control, which refers to altering one’s actions or thoughts to effectively respond to
circumstances or events (Tomasik, Silbereisen, & Heckhausen, 2010). As this definition suggests
though, the lifespan theory of control differs from the tripartite model in that it does not consider
adjustments to emotions. Moreover, whereas the lifespan theory largely considers goal
disengagement, the tripartite model is more specific in that it refers to adjustments in response to
novelty, change, or uncertainty. Despite these differences, the lifespan theory of control and the
concept of compensatory control provide a theoretical basis for understanding how adaptability
functions (Martin, Nejad et al., 2012, 2013).
1.2.2. Differentiating adaptability from cognate constructs. In order to better
understand adaptability, it is important to establish how it is distinct from cognate constructs
such as self-regulated learning, resilience, coping, and self-efficacy. Self-regulated learning
involves several phases of strategies undertaken to achieve one’s goals (Winne & Hadwin,
2008). Models of self-regulated learning tend to culminate in an adaptation phase where the
individual self-evaluates his/her performance and identifies cognitive and behavioral
modifications necessary to improve in future (e.g., Winne & Hadwin, 2008). This adaptation
phase is related to adaptability. However, whereas self-regulated learning is broadly focused on
academic or work tasks and demands, adaptability focuses specifically on individuals’ capacity
to employ strategies to effectively adjust in the face of novelty, change, and uncertainty (Martin,
Nejad et al., 2013). In that sense, adaptability has been positioned as a special case of self-
regulation (Martin, Nejad et al., 2013).
Turning to resilience, this has been conceptualized as a process, whereby teachers’
responses to situations are influenced by a constellation of protective and risk factors (Mansfield,
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Beltman, & Price, 2014). Protective factors include personal factors such as self-efficacy and
optimism, as well as contextual factors such as social support (Gu & Day, 2007; Mansfield et al.,
2014). We suggest that adaptability is another personal protective factor that is relevant to
resilience as a process, but is nonetheless a distinct construct. Coping refers to cognitive and
behavioral efforts to manage difficult or challenging demands (Lazarus & Folkman, 1984). Thus,
the focus on setback differentiates coping from adaptability, which is concerned with novelty,
change, and uncertainty (Martin, Nejad et al., 2013). Finally, whereas self-efficacy is focused on
confidence regarding a future task, adaptability concerns the use of strategies to effectively
navigate present and past experiences. Taken together, therefore, adaptability is related to, but
different from these cognate constructs—and emerging empirical evidence supports these
distinctions (see Martin, Nejad et al., 2012, 2013).
1.2.3. The tripartite model and teachers. Although the tripartite model has been
effectively applied in research among school and university students (e.g., Collie, Holliman, &
Martin, 2016; Martin, Nejad et al., 2012, 2013; Martin, Nejad, Colmar, Liem, & Collie, 2015),
researchers have yet to examine it among teachers. We suggest a specific focus on teachers is
important given the unique nature of teaching work compared with other professions. For
instance, teaching involves a high level of variability moving between different groups of
students and potentially teaching different subjects (Lin, Schwartz, & Hatano, 2005). In addition,
unlike other professions where it is probable that only one situation arises at a time, teachers may
face multiple instances of novelty, change, and uncertainty simultaneously (Lin et al., 2005)—for
example, some students may be struggling with a task while others are nearly finished. Thus,
investigating adaptability and how it is associated with teachers’ other experiences at work is an
important area of research.
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1.3. Conceptual Framework: The Job Demands-Resources Model
In the current study, we harnessed the job demands-resources model (JD-R model;
Schaufeli & Bakker, 2004) and focus on the role of job and personal resources. It has been well
established that access to job and personal resources plays a central role in employees’
psychological functioning at work (Schaufeli & Bakker, 2004; Schaufeli & Taris, 2014; Van den
Broeck, Van Ruysseveldt, Els, & Witte, 2013). Job resources refer to psychological, physical,
social, or organizational supports that help individuals to meet work goals, develop personally
and professionally, and reduce job demands (e.g., social support; Van den Broeck et al., 2013).
Personal resources refer to personal capacities reflecting individuals’ potential to effectively
control and impact the environment (e.g., emotional competence, self-efficacy, optimism; Van
den Broeck et al., 2013).
One premise in the JD-R model is that job resources play a role in predicting personal
resources (Van den Broeck et al., 2013). For example, job resources such as perceived principal
support and teaching self-efficacy have been positively associated with personal resources such
as teachers’ general sense of optimism, work motivation and engagement, and emotion
regulation ability (Brackett et al., 2010; Desrumaux et al., 2015; Dicke, Stebner, Linninger,
Kunter, & Leutner, 2017; Fernet, Trépanier, Austin, & Levesque-Côté, 2016). A second premise
of the JD-R model is that both job and personal resources predict employees’ psychological
outcomes because they promote positive psychological functioning and enable employees to
better respond to their work demands (Van den Broeck et al., 2013). Typically, work engagement
and burnout are considered direct outcomes of resources; however, research has also examined
other direct outcomes—including the teacher outcomes of interest in the current study, well-
being and organizational commitment (e.g., Bogler & Nir, 2014; Fernet et al., 2016).
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Despite a relatively broad literature on the JD-R model, the impact of personal resources
is one area where further work is needed (Van den Broeck et al., 2013). Moreover, although
several personal resources have been investigated (e.g., self-efficacy, optimism), adaptability has
yet to be considered. We suggest adaptability is applicable as a personal resource given that it
contributes to “individuals’ potential to successfully control and influence the environment”
(Van den Broeck et al., 2013, p.87). With respect to the other key components of the JD-R
model, we positioned PAS as a job resource, and well-being and organizational commitment as
teachers’ outcomes. We introduce these constructs below.
1.4. Perceived Autonomy Support
The extent to which teachers feel supported by principals plays a significant role in
assisting their positive workplace experiences. Indeed, when teachers experience effective
support from principals, this helps them to feel more empowered in their work, a greater sense of
organizational trust, more motivated and engaged in their work, and a greater sense of
commitment to the school (e.g., Collie, Shapka, Perry, & Martin, 2016; Fernet, Trépanier,
Austin, Gagné, & Forest, 2015; Klassen, Perry, & Frenzel, 2012; Lee & Nie, 2014; Leithwood,
Harris, & Hopkins, 2008). These positive outcomes occur because principals play a central role
in shaping the school environment. Indeed, if teachers are highly competent and motivated, but
work in a dysfunctional school context, their personal strengths will not necessarily translate to
effective teaching (Leithwood & Jantzi, 2006). Thus, considering the role of principals is
important. In the current study, we examined principal support by way of teachers’ PAS, which
refers to teachers’ perceptions that their principal supports their interests, is respectful of their
opinions, and promotes their volition and autonomy (Deci & Ryan, 2012). With respect to the
JD-R model, PAS is considered a job resource because it enables teachers to meet their work
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goals, develop personally and professionally, and manage their work demands (e.g., Schaufeli &
Taris, 2014).
1.5. Well-being and Organizational Commitment
Researchers have highlighted the significance of psychological functioning for healthy
and effective teachers (e.g., Collie et al., 2016; Kunter et al., 2013). In the current study, we
focused on two forms of psychological functioning. The first, well-being, refers to teachers’
positive evaluations of and healthy functioning at work (Van Horn, Taris, Schaufeli, & Schreurs,
2004). The second, (affective) organizational commitment, refers to teachers’ psychological or
emotional attachment to the school of employment (Allen & Meyer, 1996). The two outcomes
were chosen because they reflect optimal states of being that are important not only for teachers
themselves, but also for students and schools. Indeed, well-being and commitment are associated
with teachers’ improved mental and physical health (e.g., Hakanen, Bakker, & Schaufeli, 2006),
lower absenteeism and turnover from work (e.g., Buchannan, Prescott, Schuck, Aubusson, &
Burke, 2013), and higher quality teacher-student relationships (e.g., Spilt, Koomen, & Thijs,
2011). Moreover, emerging research is indicating an important connection between teachers’
well-being and students’ achievement (e.g., Arens & Morin, 2016).
1.6. Students’ Numeracy Achievement
Thus far, we have introduced the teacher-related constructs and how they are positioned
with regard to the JD-R model (Schaufeli & Bakker, 2004). Alongside the current study’s first
aim of examining associations among these constructs, an additional aim was to examine how
the teacher-related constructs are associated with students’ numeracy achievement. This second
aim, thus, moves beyond the conceptual framework. As intimated above, there is a growing
awareness of the important role that teachers’ psychological functioning plays in influencing
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students’ outcomes (Briner & Dewberry, 2007; Gu, 2014; Safe Work Australia, 2013). For
example, Briner and Dewberry (2007) demonstrated that teachers’ well-being explains 8% of the
variance in students’ test scores—which can amount to substantial cumulative academic costs
over the career of a teacher who experiences poor psychological functioning and for students
taught by teachers with poor psychological functioning. More recently, researchers have shown a
negative association between teachers’ emotional exhaustion and students’ achievement (e.g.,
Arens & Morin, 2016). Other researchers have shown that teacher turnover—an inverse correlate
of organizational commitment—is associated with lower student achievement (Ronfeldt, Loeb,
& Wyckoff, 2013). Thus, there is emerging research in this area; however, more is needed to
corroborate existing work and to examine the link between teachers’ commitment and students’
achievement. It is also important that researchers investigate the extent to which other workplace
experiences and perceptions—such as teachers’ adaptability and PAS—are associated with
students’ achievement given that these types of constructs influence how teachers approach their
work.
1.7. Relevant Teacher Covariates
In endeavoring to better understand the construct of teachers’ adaptability and its
association with the other constructs, it is helpful to control for the influence of teachers’ socio-
demographic variables. For example, male teachers, older teachers, and more experienced
teachers report higher general well-being (Collie, Shapka, Perry, & Martin, 2015; Gloria, Faulk,
& Steinhardt, 2013), and more experienced teachers report greater PAS (Lee & Nie, 2014) and
lower organizational commitment (Henkin & Holliman, 2009). Seniority and level of extra duties
may also be implicated in teachers’ well-being and commitment (e.g., workload is a salient
factor in teachers’ stress; Collie, Shapka, & Perry, 2012). However, other research has shown
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that teachers’ characteristics have no associations with well-being (gender, age; Chan, 2013) and
PAS (gender, age, tenure; Freire & Fernandes, 2015). Given these mixed findings, there is a need
for additional research that controls for teachers’ socio-demographic characteristics and
determines the extent to which these are associated with adaptability.
1.8. The Current Study
The aim of the current study was to examine teachers’ sense of adaptability and its links
with salient teacher and student outcomes. Our research was conducted within mathematics
classrooms. We sought to understand the extent to which PAS (a job resource) is associated with
teachers’ adaptability (a personal resource) and the extent to which both constructs are associated
with teachers’ workplace well-being and organizational commitment (teachers’ outcomes; Phase
1; see Figure 1). Following this, we examined students’ numeracy achievement as an outcome
associated with these teacher-related constructs. (Phase 2; see Figure 1). In both phases of
analysis, we controlled for teachers’ socio-demographic characteristics (gender, age, teaching
experience, seniority, amount of extra duties, and grade-level taught). Although our model was
grounded in the JD-R model—which provides strong theoretical guidance for ordering (Van den
Broeck et al., 2013)—we also tested for alternative models given that clear directional links
among our substantive variables have yet to be established in the literature. Five research
questions guided the study:
1. To what extent is PAS positively associated with teachers’ adaptability, and are PAS and
adaptability positively associated with teachers’ well-being and organizational
commitment? (RQ1)
2. To what extent is PAS indirectly associated with teachers’ well-being and organizational
commitment via adaptability? (RQ2)
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3. To what extent are teachers’ adaptability, well-being, and organizational commitment
positively associated with students’ numeracy achievement? (RQ3)
4. To what extent are teachers’ PAS and adaptability indirectly associated with students’
numeracy achievement via teachers’ well-being or organizational commitment? (RQ4)
5. To what extent are our hypothesized models more appropriate than alternative models
with different construct ordering?
For Phase 1, we expected that PAS would be positively associated with teachers’
adaptability. More specifically, high quality supervisory support is promotive of individual
control (e.g., Hakanen et al., 2006)—and (compensatory) control plays a key role in teachers’
capacity to effectively adapt to opportunities and constraints in their environment (e.g.,
Heckhausen et al., 2010). We anticipated that both PAS and adaptability would be directly and
positively associated with well-being and commitment. This is because high quality principal
support promotes a working environment that is supportive of teachers’ needs and psychological
functioning (Collie, Shapka, & Perry, 2011, 2012). Moreover, adaptability may help teachers to
successfully manage their work demands, which promotes positive experiences at work (Van den
Broeck et al., 2013). We also hypothesized that PAS may be indirectly associated with the
teacher outcomes via adaptability based on the direct associations expected among these
constructs.
Turning to Phase 2, we expected that teachers’ well-being and commitment would be
positively associated with students’ numeracy achievement (e.g., Briner & Dewberry, 2007).
With respect to links between adaptability and students’ numeracy achievement, we anticipated
that a positive association may occur given that adaptability may help teachers to better adjust to
student learning needs. Next, we hypothesized positive indirect relationships from PAS and
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adaptability to students’ numeracy achievement (via teachers’ well-being and commitment):
when teachers feel supported by their principals and are adaptable, this may be positively
associated with students’ numeracy achievement via a boost given to teachers’ well-being and
commitment. Finally, given the strong empirical support for the JD-R model, we expected that
our hypothesized model would be equal or superior to alternative models tested in both phases of
analysis.
2. Methods
2.1. Sample and Procedures
The teacher sample comprised 115 high school mathematics teachers (data from their
mathematics classrooms are described below). Teachers completed an online questionnaire about
PAS, their sense of adaptability, well-being, organizational commitment, as well as background
factors (gender, teaching experience, etc.). Teachers were 52% male and had an average age of
44.7 (SD=12.4) years. Average teaching experience was 16.9 (SD=11.5) years and participants
taught only girls (30%), only boys (34%) or both girls and boys (36%) at their school of
employment. Teachers worked at 17 Australian independent (84%) or systemic Catholic (16%)
schools in the states of New South Wales, Victoria, and Western Australia. The average number
of teachers per school was six (SD=3.6). In Phase 1, we employed the full sample of 115 teachers
to examine associations among the teacher constructs.
In Phase 2, we examined the teacher constructs alongside students’ numeracy achievement
data. For this, we did not have matching student data for 15 of the teachers—and thus, our
analyses only involved 100 teachers along with data from the students in their classrooms. To
match teacher and student data, we compared identifying information collected from all
participants (e.g., day and time of mathematics class, name of class). Matched students
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comprised 1685 high school students in grades 7 to 9 (57% male, M age = 13.5 [SD=0.95] years,
86% spoke mostly English at home). There were on average 17.9 students per class (SD=5.6).
Student data were drawn from a larger dataset (n = 4226 students) in a national project
investigating students’ academic goals in mathematics—however, for the present study, only
students residing in the sample teachers’ classrooms were included. Students completed an
online questionnaire that included a brief numeracy test (along with other motivational constructs
that were collected for the larger project and not examined here).
Data for both teachers and students were collected simultaneously, in one sitting during
class, and in the latter half of the school year. Teacher and student recruitment was based on
schools that agreed to participate in the larger student-focused project. Students and teachers in
grades 7, 8, and 9 mathematics classrooms were invited to participate. Although we were unable
to ascertain response rates for teachers, some evidence of representativeness is provided by the
fact that our sample statistics were very similar to the population parameters for teachers in
Australian schools. Among Australian secondary teachers, 42% are male, the average age is
around 45 years old, and the average teaching experience is 17 years (McKenzie, Weldon,
Rowley, Murphy, & McMillan, 2014). With respect to students, participation was dependent on
parental consent and student attendance on the day of the survey. Response rates were around
75% of the total eligible sample. Ethics approval was received from the [Institutional Review
Board Name]. Approval was also received from principals of each participating school, and
consent/assent was obtained from teachers, students, and students’ parents.
2.2. Measures
2.2.1. Teachers’ perceived autonomy support. Klassen, Perry, and Frenzel’s (2012)
adapted version of 6-item short form of the Work Climate Questionnaire (Baard, Deci, & Ryan,
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2004) was used to assess teachers’ perceptions of principal (autonomy) support (e.g., “My
principal encourages me to ask questions”). The items were scored from 1 (Strongly disagree) to
7 (Strongly agree). Klassen et al. (2012) and Collie et al. (2016) have demonstrated evidence of
reliability and validity for this scale among teachers. In the current study, the Cronbach’s alpha
was .94 suggesting an adequate level of internal consistency.
2.2.2. Teachers’ sense of adaptability.
We used the 9-item Adaptability Scale (Martin,
Nejad et al., 2013) to assess teachers’ sense of cognitive (e.g., “I am able to adjust my
thinking or expectations to assist me in a new situation”), behavioral (e.g., “To assist me in a
new situation, I am able to change the way I do things”), and emotional adaptability (e.g., “I
am able to reduce negative emotions [e.g., fear] to help me deal with uncertain situations”).
Following psychometric work, Martin, Nejad and colleagues (2012, 2013) demonstrated that
adaptability can be operationalized and analyzed as a single factor; thus, for parsimony this
operationalization is used in modeling here. Items were scored from 1 (Strongly disagree) to 7
(Strongly agree). Researchers using the scale among other populations have provided
evidence of reliability, factor structure, convergent validity, and measurement invariance for
the scale (e.g., Martin, Nejad et al., 2012, 2013, 2015). The Cronbach’s alpha was at an
adequate level of .91 in the current study.
2.2.3. Teachers’ well-being. Four items from Parker and Martin (2009) were used to
assess teachers’ well-being (e.g., “When I'm at work I feel pretty happy”). Items were scored
from 1 (Strongly disagree) to 7 (Strongly agree). Prior research has provided evidence of
reliability and validity for the scale among teachers (Parker & Martin, 2009). The Cronbach’s
alpha in the current study was .91.
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2.2.4. Teachers’ organizational commitment. Four items were used to assess teachers’
organizational commitment (e.g., “I am proud to belong to this school community”). As per
Collie et al. (2016), these were adapted from Vandenberghe and Bentein (2009) to focus on
school as the workplace. Items were scored from 1 (Strongly disagree) to 7 (Strongly agree).
Previous work has shown adequate reliability and factor structure for the original scale (e.g.,
Vandenberghe & Bentein, 2009) and the adapted teacher-specific scale (Collie et al., 2016). The
Cronbach’s alpha in the current study was an adequate level of .75.
2.2.5. Students’ numeracy achievement. Students’ numeracy achievement was assessed
via a multiple-choice test involving 10 items on basic mathematics skills (e.g., 22 + 30 + 44 = [a]
127, [b] 84, [c] 96, or [d] 106; Integrate: 3 - 2x + 6x2 = [a] 3x2, [b] 3x– x2 + 2x3, [c] 3x2 – x, or [d]
-2x – 3 + x3). These numeracy items form a subset of a quiz used by Martin, Anderson, Bobis,
Way, and Vellar (2012) and include questions at a variety of difficulty levels similar to the Wide
Range Achievement Test (WRAT, Wilkinson, 1993). To test reliability, we calculated intraclass
correlations (ICCs). We calculated the ICC(1) to determine the proportion of total variance
attributable to between-class differences (Lüdtke, Trautwein, Kunter, & Baumert, 2006). This
was .44 and indicates substantial classroom-level variance (Byrne, 2012). We also calculated the
ICC(2) to determine classroom-level reliability for the scale (Lüdtke et al., 2006). This statistic,
which accounts for number of students per class and is interpreted like Cronbach’s alpha, was at
an adequate level of .93.
2.2.6. Covariates. We examined seven teacher covariates: gender, age, teaching
experience, seniority, school type (e.g., independent or Catholic), amount of extra duties, and
grade. Gender was coded as 0 for females and 1 for males. Age and teaching experience were
continuous variables. School type was coded as 0 for independent schools and 1 for Catholic
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schools. Teachers rated their seniority in the workplace from 1 (“Low”) to 5 (“High”) and their
involvement in extra duties compared with other staff at their school from 1 (“A lot less than
others”) to 5 (“A lot more than others”). Grade represented the grade level of the matched
students.
2.3. Data Analysis
Preliminary analyses involved calculating reliability coefficients, means, standard
deviations, skewness, and kurtosis statistics for all substantive variables. Our main analyses
centered on confirmatory factor analysis (CFA) and (single- and multilevel) structural equation
modeling (SEM). We used Mplus version 7.4 (Muthén & Muthén, 2015) to conduct our
analyses. Robust maximum likelihood (MLR) was used as the method of estimation along with
school set as the “cluster” variable under the “type=complex” option to account for the
hierarchical clustering of teachers within schools (Phase 1 and 2) and students within classrooms
(Phase 2). Given the modest number of teachers relative to estimated parameters at the item
level, we conducted our analyses with item parceling (Little, Rhemtulla, Gibson, & Schoemann,
2013). Preliminary exploratory factor analyses (EFAs) confirmed the unidimensionality of the
scales (Little et al., 2013) and two items (three for adaptability) were randomly assigned to
parcels for each scale.
Missing data were 0.4% for teacher variables and 5.5% for student variables, and were
dealt with using the Full Information Maximum Likelihood defaults in Mplus. Model fit was
assessed with the comparative fit index (CFI), the root-mean-square error of approximation
(RMSEA), and standardized root-mean-square residual (SRMR). For multilevel modeling in
Phase 2 (details below), we also refer to the SRMR at within (SRMRwithin) and between levels
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(SRMRbetween). CFI values greater than .90, RMSEA values less than .08, and SRMR values less
than .08 indicate adequate fit (Hu & Bentler, 1999).
2.3.1. Phase 1: Modeling with teacher constructs. A CFA was run to provide
measurement support and then SEM was run to examine the influence of PAS on adaptability,
and the influence of both constructs on well-being and organizational commitment (see Figure 1;
RQ1). Teacher covariates were included as predictors of all constructs. Next, we assessed
indirect associations from PAS to teachers’ well-being and organizational commitment via
teachers’ adaptability (RQ2) using a non-parametric bootstrapping approach (1000 draws; Shrout
& Bolger, 2002) with bias-corrected confidence intervals (MacKinnon, 2008). Here, we sought
to determine whether teachers’ adaptability plays a linking role in how PAS is associated with
teachers’ workplace outcomes. Although our construct ordering was based on understanding
from theory and research, bootstrapping also provided the opportunity to test alternative ordering
(RQ5). More precisely, we tested five alternative models with different construct ordering (e.g.,
adaptability→well-being and commitment→PAS; adaptability→ PAS→well-being and
commitment). We compared the strength of indirect associations in these alternative models with
our original model as preliminary support for hypothesized ordering (model fit indices were
identical in all models given fully forward nature).
2.3.2. Phase 2: Multilevel modeling with students’ numeracy achievement. In Phase
2, we conducted analyses involving student data using multilevel CFA and multilevel SEM with
the same parcels for the teacher constructs. Students’ numeracy achievement was modeled at
both the student- and class-level (the latter representing class means). The teacher constructs
(and covariates) were modeled at the class level. Efforts to run one model with both teacher well-
being and organizational commitment ran into issues of multicollinearity, so two separate models
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Teachers’ Adaptability 20
were run. More precisely, we examined the associations that PAS has with adaptability, that both
constructs have with well-being (in Model 1) or organizational commitment (in Model 2), and
that adaptability and well-being or commitment have with students’ numeracy achievement (see
Figure 1; RQ3). Covariates were entered as predictors of all teacher constructs. For the
multilevel SEM, non-significant paths between substantive constructs starting with the lowest
standardized coefficients were deleted for reasons of parsimony one at a time until only
significant paths remained in the model.
The second analysis of Phase 2 involved testing indirect associations—bootstrapping was
not available in Mplus (Muthén & Muthén, 2015) with multilevel SEM so we aggregated the
student numeracy achievement variable at the class-level and ran bootstrapping with a single-
level SEM (1000 draws; bias-corrected confidence intervals). As a second option, we also
applied Preacher, Zyphur, and Zhang’s (2010) approach to multilevel SEM indirect effects
analysis. We assessed indirect associations from PAS and adaptability to students’ numeracy
achievement via teachers’ well-being/commitment (RQ4). Here, we wanted to determine
whether PAS and adaptability were indirectly associated with students’ numeracy achievement.
The final aspect of Phase 2 involved testing alternative models with different construct ordering
(RQ5). We compared these alternative models with our hypothesized construct ordering using
the chi-square fit statistic.
3. Results
3.1. Preliminary Analyses: Reliabilities, Descriptive Statistics, and Correlations
Table 1 presents reliability coefficients, means, standard deviations, skewness, and
kurtosis statistics for all substantive variables. Reliability coefficients for the scales ranged from
.75 to .94. The skewness and kurtosis values for all substantive variables suggest approximately
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Teachers’ Adaptability 21
normal distributions. Thus, the reliability coefficients and the distributional data provide support
for further analyses.
3.2. Phase 1: Modeling with Teacher Constructs
The CFA yielded adequate fit: χ2(56) =59.80, p =.34, RMSEA =.024 (90% CI [.000,
.064]), CFI =.99, SRMR = .026. Factor loadings of the item parcels are shown in Table 1 and
latent correlations are shown in Table 2. Correlations among the constructs revealed that the
covariates played a relatively minor role. Of note, teachers working at Catholic schools reported
greater PAS (r=.22, p=.007). Older teachers (r=.20, p=.04) and teachers with more seniority
(r=.19, p=.03) reported greater sense of adaptability. In addition, teachers working in higher
grades reported greater well-being (r=.18, p=.002), and older teachers (r=.30, p=.012), more
experienced teachers (r=.26, p=.003), and teachers with greater seniority (r=.33, p<.001)
reported higher levels of commitment. As expected, PAS, adaptability, well-being, and
commitment were all positively intercorrelated (M correlation =.59, p’s<.001). Combined, these
correlations provide preliminary support for associations among constructs of interest. These
were, thus, further analyzed in multivariate analyses that provided appropriate controls for shared
variance.
Given that the same parameters were estimated in the SEM, the fit was identical to the
CFA. Figure 2 shows the results of the SEM involving the teacher constructs (RQ1). Table 3
reports all standardized beta paths. A positive relationship was revealed between PAS and
adaptability (β=.46, p<.001). In addition, PAS and adaptability were positively associated with
well-being (β=.35, p=.004; β=.46, p<.001, respectively) and commitment (β=.57, p<.001; β=.25,
p=.01, respectively). Turning to the covariates, teachers working in Catholic schools reported
greater PAS (β=.27, p=.004), but lower adaptability (β=-.19, p=.03) and lower well-being (β=-
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Teachers’ Adaptability 22
.14, p=.03). Older teachers (β=.25, p=.001) and those teaching higher grades (β=.27, p=.001)
reported significantly greater well-being, and teachers who completed more extra duties reported
greater organizational commitment (β=.14, p=.02).
The indirect analyses (RQ2) indicated a significant association from PAS → adaptability
→ well-being (β=.22, p<.001, 95% CI [.13, .34]). Another significant indirect association was
found from PAS → adaptability → organizational commitment (β=.11, p=.04, 95% CI [.02,
.24]). Combined, these findings indicate that teachers’ adaptability plays a linking role in how
PAS is associated with teachers’ workplace outcomes.
In order to provide support for the construct ordering in our model, we ran five
alternative models (e.g., adaptability → well-being and commitment → PAS; adaptability →
PAS → well-being and commitment; RQ5). For four of the alternative models, the indirect
associations were non-significant indicating they did not explain the relationships as well as our
hypothesized model. The only model that had significant indirect associations was that for
adaptability → PAS → well-being and commitment. These were similar in strength to the
hypothesized model suggesting that this may be another plausible model. Nonetheless, we chose
to retain our original ordering on the basis of the conceptual framework provided earlier
(however, we do acknowledge the importance of longitudinal research below).
3.2. Phase 2: Multilevel Modeling with Students’ Numeracy Achievement.
Phase 2 analyses involved examining PAS, adaptability, well-being, and organizational
commitment alongside students’ numeracy achievement (RQ3). The multilevel CFA yielded
excellent fit for the well-being model: χ2(43) =39.19, p =.64, RMSEA <.01, CFI =1.0,
SRMRwithin<.01, SRMRbetween=.028. Regarding the latent correlations at the class-level, only
teachers’ well-being was positively associated with students’ numeracy achievement (r=.22,
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Teachers’ Adaptability 23
p=.02). The organizational commitment CFA also yielded adequate fit: χ2(43) =48.0, p =.28,
RMSEA =.01, CFI =.99, SRMRwithin<.01, SRMRbetween=.031. However, there were no significant
relationships with students’ numeracy achievement in this model.
The well-being SEM yielded adequate fit: χ2(52) =46.42, p =.69, RMSEA <.01, CFI
=1.0, SRMRwithin<.01, SRMRbetween=.042. Figure 2 shows the results for this analysis. Table 4
shows all standardized beta paths. Teachers’ well-being was positively associated with students’
numeracy achievement (β=.21, p=.031). Associations among the teacher constructs and between
these and the covariates were very similar to Phase 1 (see Table 4).1 For completeness, we ran
the multilevel SEM for the organizational commitment model. This yielded adequate fit (χ2[52]
=56.73, p =.30, RMSEA =.01, CFI =0.99, SRMRwithin<.01, SRMRbetween=.042) and confirmed no
significant relationship between organizational commitment and students’ numeracy
achievement (β=-.08, ns).
To examine indirect associations using aggregated class means of students’ numeracy
achievement (RQ4), we proceeded with the well-being model only. Bootstrapping revealed two
significant indirect associations: PAS → teachers’ well-being → students’ numeracy
achievement (β=.15, p=.03, 95% CI [.04, .33]) and teachers’ adaptability → teachers’ well-being
→ students’ numeracy achievement (β=.17, p=.04, 95% CI [.04, .35]). Indirect effects using
Preacher and colleagues’ multilevel SEM approach showed the same pattern of findings (this
approach yields unstandardized betas): PAS → teachers’ well-being → students’ numeracy
achievement (B=.16, p=.002, 95% CI [.06, .26]) and teachers’ adaptability → teachers’ well-
1 Given research positively linking the different, yet related construct of adaptive teaching competency with smaller
class sizes (Brühwiler & Blatchford, 2011), for completeness we also tested our model with class size as an
additional covariate. It was not significantly associated with any of the substantive constructs and the remaining
relationships showed the same pattern as our main model. Thus, we conclude that class size was not a significant
factor in our model.
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Teachers’ Adaptability 24
being → students’ numeracy achievement (B=.08, p=.04, 95% CI [.01, .15]). Combined, these
findings indicate that PAS and adaptability were associated with students’ numeracy
achievement via teachers’ well-being.
Alternative models were run next to provide support for our hypothesized construct
ordering (RQ5). Given the potential for many different alternative orderings, we utilized
knowledge gained from Phase 1 to determine the models to test. We first tested three models
where the teacher constructs were kept in the same order as our hypothesized model, but where
the location of students’ numeracy achievement differed (i.e., students’ numeracy achievement
→ PAS → adaptability → well-being; PAS → students’ numeracy achievement → adaptability
→ well-being; PAS → adaptability → students’ numeracy achievement → well-being). Given
that one alternative model tested in Phase 1 also appeared plausible, we ran a fourth model
involving adaptability → PAS → WB → students’ numeracy achievement. There were no
significant differences in fit indices between the original and alternative models (e.g., χ2[9]
=7.30, ns). Although this does not indicate our model is superior, it does indicate that none of the
other alternative models provided better fit. Thus, on the basis of the conceptual framework we
outlined earlier, we once again chose to retain our hypothesized model.
4. Discussion
The aim of the current study was to examine teachers’ sense of adaptability and to
ascertain its links with salient workplace experience and students’ numeracy achievement.
Findings revealed that teachers’ perceptions of autonomy support (PAS) and adaptability were
positively associated. In addition, PAS and adaptability were positively associated with teachers’
well-being and organizational commitment. In analyses involving student data, teachers’ well-
being was directly associated with students’ numeracy achievement, and PAS and adaptability
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Teachers’ Adaptability 25
were indirectly associated with students’ numeracy achievement via teachers’ well-being.
Together, these findings provide important preliminary evidence of the soundness of the
tripartite model (and its accompanying scale; Martin, Nejad et al., 2012) for assessing
adaptability among teachers—that is, given that measurement support was evident and
associations among constructs were as expected based on hypotheses developed from the JD-R
model (Van den Broeck et al., 2013). Several key findings are discussed below.
4.1. Links Among the Teacher Constructs
The first finding of note was that teachers reporting greater PAS also tended to report
higher adaptability. This finding is consistent with processes established by the JD-R model (Van
den Broeck et al., 2013) and research showing the importance of PAS for teachers’ personal
resources (e.g., Desrumaux et al., 2015). One explanation for why this finding occurred is that
when teachers feel they are supported at work this may help them to enact compensatory control.
As noted earlier, compensatory control stems from the lifespan theory of control (Heckhausen et
al., 2010) and involves adjusting one’s actions or thoughts to deal with circumstances or events
(Tomasik et al., 2010). When teachers feel supported by their principal, they may experience
more (compensatory) control over their work and, thus, be more capable of adapting as needed.
At this point, it is important to recognize that in our testing of alternative models, a model where
adaptability preceded PAS was also plausible. We retained our hypothesized ordering based on
theoretical support; nonetheless, longitudinal research is an important area for research to
ascertain whether reciprocal relationships may also exist among these constructs.
The positive association between PAS and teachers’ adaptability is an important one
because it provides evidence that the context within which teachers work may be associated with
their capacity to adjust to new, changing, or uncertain situations. For practice, this finding speaks
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Teachers’ Adaptability 26
to the importance of shaping school environments to promote autonomy-supportive leadership—
although it is important to note that we assessed perceptions of autonomy support, rather than
actual practices. Thus, there is a need to specifically measure principal practices (e.g., see
Leithwood et al., 2008; Leithwood & Jantzi, 2006). To the extent that PAS reflects principals’
actual autonomy supportive practices, potential efforts by principals may include conveying
confidence in teachers’ abilities to do their job in a self-directed manner, inviting teachers’ input
in decision making, listening to teachers’ needs, and encouraging teachers’ initiative (Collie et
al., 2016; Baard et al., 2004; Klassen et al., 2012).
A second major finding is that teachers who were more adaptable also experienced
greater well-being and organizational commitment. As suggested in the Introduction, perhaps by
being adaptable, teachers are able to better meet the demands of their work (Van den Broeck et
al., 2013). This finding extends emerging qualitative work discussing adaptability as a central
piece in teachers' resilience (e.g., Mansfield et al., 2012) with a quantitative examination of
adaptability and its links to important teacher outcomes. In doing so, it adds weight to our
suggestion that adaptability is relevant for teachers’ positive psychological functioning.
Moreover, it extends prior work involving the JD-R model by examining adaptability as a novel
personal resource. Consistent with previous research (e.g., Collie et al., 2016), PAS was also
positively associated with the two teacher outcomes. It is possible this occurred because PAS
reflects a responsive and caring working environment that is central for teachers’ positive
psychological functioning (e.g., Klassen & Chiu, 2011; Klassen et al., 2012).
The third finding to be discussed concerns the role of teachers’ characteristics. These
tended to play a relatively minor role in teachers’ adaptability. Significant findings that merit
discussion, however, include the correlations that age and seniority had with adaptability.
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Teachers’ Adaptability 27
According to the lifespan theory of control (Heckhausen et al., 2010), positive development is
influenced by the extent to which an individual is able to alter his/her actions or thoughts to
effectively respond to circumstances. Our finding here suggests that perhaps this capacity to alter
one’s thoughts, actions, and emotions improves over time as individuals gain experience. This
aligns with research showing that teaching knowledge (which comes with greater experience) is
positively associated with adaptive expertise (i.e., the ability to transfer expertise in familiar
situations to novel situations; Bohle Carbonelle, Stalmeijer, Königs, Segers, & van Merriënboer,
2014). Age, experience, and/or seniority also had several positive associations with well-being
and commitment. Perhaps as teachers get older and gain experience, they feel more competent in
their role (to a certain age/experience; Klassen & Chiu, 2011), which may foster well-being and
commitment. Taken together, these findings offer important information on the role of teachers’
characteristics. Moving forward there is a need for additional research with other characteristics
(e.g., teachers’ position and tenure, school socio-economic status).
4.2. Links Between the Teacher and Student Constructs
The positive relationship between teachers’ well-being and students’ numeracy
achievement was a notable result. The finding corroborates prior research (e.g., Briner &
Dewberry, 2007) and, in so doing, provides additional evidence on the merits of attending to
teachers’ well-being—teachers who are faring well tend to have higher performing students. For
practice, there may be value in efforts to promote teachers’ well-being. Mindfulness
interventions (Roeser et al., 2013) and ensuring teachers have adequate job resources to
effectively deal with their work demands (e.g., Bakker & Demerouti, 2007) are possible
approaches. Teachers’ organizational commitment was not associated with students’ numeracy
achievement. Perhaps this is because organizational commitment is more relevant to students in
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Teachers’ Adaptability 28
others ways (e.g., positively impacting their perceptions of the classroom/school). Research
looking at the association between commitment (and well-being) on other students’ outcomes
(e.g., perceived teacher support) is an important future direction for research.
On a related note, teachers’ adaptability was not directly associated with students’
numeracy achievement; however, it was indirectly linked with students’ numeracy achievement
via teachers’ well-being. This finding suggests that adaptability may influence students’
outcomes via teachers’ well-being rather than directly. Future studies examining other ways in
which adaptability may influence students’ numeracy achievement (e.g., via classroom
management approaches) and whether a work-specific assessment of adaptability is more
directly linked with students’ numeracy achievement are important. Nonetheless, the findings
provide encouraging preliminary evidence for teachers’ adaptability (and PAS, which was also
implicated indirectly) and its association with positive student outcomes.
With respect to adaptability, the findings extend prior theorizing on the importance of
adaptive teaching for student learning outcomes (e.g., Corno, 2008) by indicating that
adaptability more broadly is (indirectly) associated with students’ numeracy achievement. This
indicates that when teachers feel supported and are adaptable, this is positively associated with
students’ numeracy achievement via the boost given to teachers’ well-being. For practice, the
findings further underscore the relevance of efforts by school leadership to be autonomy-
supportive.
4.3. Limitations and Future Directions
It is important to consider the limitations of the study while interpreting the findings.
First, our teacher data were self-report in nature. Although this is not an inappropriate method
when attempting to measure teachers’ intrapsychic experiences, there is a need for further
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Teachers’ Adaptability 29
research using other measures—such as external observations or ratings of teachers’ adaptability
(e.g., from students, principals). In addition, our results make the assumption that teachers’
perceptions of principal support (i.e., PAS) reflect principals’ actual practices, and that teachers’
perceptions of well-being and commitment reflect their actual experiences. Moving forwards
there is a need to examine whether this is in fact the case (e.g., see Leithwood et al., 2008;
Leuthwood & Jantzi, 2006). Nonetheless, the inclusion of students’ numeracy achievement
strengthened the study’s design. Second, our data were cross-sectional in nature, which means
that we were not able to test for causality. The ordering of constructs in our model was based
upon key premises from the JD-R model (Schaufeli & Bakker, 2004). Although some
preliminary support was provided by our tests of alternative ordering, the existence of other
plausible models highlights the need for longitudinal research—for example, to examine whether
reciprocal relationships may also exist. Third, we were not able to ascertain response rates for
teachers and thus cannot determine the characteristics of those teachers who chose not to
participate. Despite this, support for representativeness was evidenced by the similarity between
our sample statistics and the population parameters for teachers in Australian schools.
Nonetheless, in future research it will be important to maximize response rates and to make
stronger efforts to understand why participants may choose not to respond.
Fourth, although we harnessed understanding about job and personal resources from the
JD-R model, we did not examine the typical outcomes of work engagement and burnout.
Moreover, we did not examine teachers’ instructional practices, which are important to consider
when building on the initial findings provided in the student-focused analyses here. Fifth,
although we were able to conduct some measurement work (via the CFA), there is a need for
research with larger samples to examine the factor structure and measurement invariance of the
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Teachers’ Adaptability 30
scale among teachers—and to see whether the processes in our models generalize to other
workplaces. On a related point, we used item parceling, which can absorb inherent heterogeneity
in a factor and thus may influence the results. Given that our factors showed high reliability and
that preliminary EFAs supported unidimensionality of the factors (Little et al., 2013), we feel
that item parceling was an appropriate approach given the sample size. Nonetheless, larger
sample sizes are important in future research so that item parceling is not needed in modelling.
Finally, we examined domain-general adaptability. An important avenue of future research is to
examine the construct with respect to teachers’ work and how it is associated with other cognate
constructs.
5. Conclusion
The aim of the current study was to examine sense of adaptability among a sample of
teachers and to ascertain its links with salient teacher and student outcomes. Findings provide
evidence that adaptability is associated with teachers’ functioning at work. In particular, positive
associations between PAS and adaptability, and between these two constructs and teachers’ well-
being and organizational commitment were evident. In addition, teachers’ well-being was
directly and positively associated with students’ numeracy achievement, and PAS and
adaptability were indirectly associated via teachers’ well-being. Taken together, the findings
have implications for researchers and educational leaders endeavoring to better understand
teachers’ responses to the often changing demands of their work and the links this may have with
teachers’ psychological functioning and students’ academic outcomes.
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Figure 1. The proposed models under examination.
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Table 1
Reliabilities and Descriptive Statistics for Substantive Variables
α M SD
Skewness Kurtosis Factor loadings
Teacher variables
PAS .94 5.02 1.30 -0.48 -0.20 .94, .93
Adaptability .91 5.73 0.66 0.25 -0.77 .88, .89, .82
Well-being .91 5.97 0.84 -0.73 0.24 .87, .94
Organizational commitment .75 5.70 0.90 -0.47 -0.47 .69, .90
Student variable
Numeracy achievement .93a 7.58 1.73 -0.89 0.95 —
Note. Adaptability, well-being, commitment, student participation, disengagement, and perceived autonomy support were all scored from 1 to 7. a This reliability
is the intraclass correlations at the class level (ICC[2])—see Methods for further details. Numeracy achievement was a single-item factor treated as latent. PAS =
Perceived autonomy support.
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Table 2
Latent Correlations Among Teacher Constructs (Phase 1)
1. 2. 3. 4. 5. 6. 7. 8. 9. 10.
Covariates
1. Gender (F/M)
2. Age .08
3. Teaching exp. .14 .74***
4. School type -.26** .06 .05
5. Seniority .07 .22* .30** -.10
6. Extra duties .16 -.03 .11 -.12 .33***
7. Grade .01 -.23* -.20 .08 -.09 .03
Teacher variables
8. PAS .04 .01 -.04 .22** .11 -.05 .02
9. Adaptability .06 .20* .14 -.09 .19* .02 -.11 .43***
10. Well-being .10 .18 .05 -.07 .09 -.04 .18** .53*** .62***
11. Org commitment .03 .30* .26** .06 .33*** .15 -.07 .66*** .56*** .71***
Note. Teaching exp.= teaching experience. Org. commitment=organizational commitment. PAS = Perceived autonomy support.
*p<.05, ** p<.01, *** p<.001.
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Table 3
Standardized Beta Coefficients from SEM Involving the Teacher Constructs (Phase 1)
PAS Adaptability Well-being Organizational
Commitment
Teacher covariates
Gender (F/M) .13 -.03 .02 -.08
Age .05 .18 .25** .17
Teaching exp. -.16 .01 -.11 .09
School type .27** -.19* -.14* -.05
Seniority .19 .08 -.04 .11
Extra duties -.09 .01 -.03 .14*
Grade -.01 -.06 .27** .01
Teacher variables
PAS .46*** .35** .57***
Adaptability .46*** .25*
Note. PAS = Perceived autonomy support.
*p<.05, ** p<.01, *** p<.001.
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Table 4
Standardized Beta Coefficients from Multilevel SEM Involving the Teacher and Student Constructs (Phase 2)
Teacher variables Student variable
PAS Adaptability Well-being Numeracy
achievement
Teacher covariates
Gender (F/M) .17 -.07 -.01 —
Age -.01 .23 .26*** —
Teaching exp. -.11 -.08 -.10 —
School type .26** -.21* -.14* —
Seniority .15 .13 -.02 —
Extra duties -.10 .03 -.02 —
Grade -.07 -.03 .30*** —
Teacher variables
PAS .46*** .39** —
Adaptability .44*** —
Well-being .21*
Note. PAS = Perceived autonomy support.
*p<.05, ** p<.01, *** p<.001.
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Figure 2. Structural equation modeling involving teacher constructs (Phase 1) and multilevel structural equation modeling involving
teacher and student constructs (Phase 2). Only standardized beta coefficients for substantive parameters are indicated here (at p < .05).
Covariates not shown. Findings involving organizational commitment not shown here given non-significance. See Table 3 for all
standardized beta coefficients from Phase 1 and Table 4 for all standardized beta coefficients from Phase 2.
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