ArticlePDF Available

Validating the Copenhagen Psychosocial Questionnaire (COPSOQ-II) Using Set-ESEM: Identifying Psychosocial Risk Factors in a Sample of School Principals

Authors:

Abstract and Figures

School principals world-wide report high levels of strain and attrition resulting in a shortage of qualified principals. It is thus crucial to identify psychosocial risk factors that reflect principals' occupational wellbeing. For this purpose, we used the Copenhagen Psychosocial Questionnaire (COPSOQ-II), a widely used self-report measure covering multiple psychosocial factors identified by leading occupational stress theories. We evaluated the COPSOQ-II regarding factor structure and longitudinal, discriminant, and convergent validity using latent structural equation modeling in a large sample of Australian school principals (N = 2,049). Results reveal that confirmatory factor analysis produced marginally acceptable model fit. A novel approach we call set exploratory structural equation modeling (set-ESEM), where cross-loadings were only allowed within a priori defined sets of factors, fit well, and was more parsimonious than a full ESEM. Further multitrait-multimethod models based on the set-ESEM confirm the importance of a principal's psychosocial risk factors; Stressors and depression were related to demands and ill-being, while confidence and autonomy were related to wellbeing. We also show that working in the private sector was beneficial for showing a low psychosocial risk, while other demographics have little effects. Finally, we identify five latent risk profiles (high risk to no risk) of school principals based on all psychosocial factors. Overall the research presented here closes the theory application gap of a strong multi-dimensional measure of psychosocial risk-factors.
Content may be subject to copyright.
ORIGINAL RESEARCH
published: 30 April 2018
doi: 10.3389/fpsyg.2018.00584
Frontiers in Psychology | www.frontiersin.org 1April 2018 | Volume 9 | Article 584
Edited by:
Sergio Machado,
Salgado de Oliveira University, Brazil
Reviewed by:
Kenn Konstabel,
National Institute for Health
Development, Estonia
Cesar Merino-Soto,
Universidad de San Martín de Porres,
Peru
*Correspondence:
Theresa Dicke
theresa.dicke@acu.edu.au
Specialty section:
This article was submitted to
Quantitative Psychology and
Measurement,
a section of the journal
Frontiers in Psychology
Received: 08 January 2018
Accepted: 06 April 2018
Published: 30 April 2018
Citation:
Dicke T, Marsh HW, Riley P,
Parker PD, Guo J and Horwood M
(2018) Validating the Copenhagen
Psychosocial Questionnaire
(COPSOQ-II) Using Set-ESEM:
Identifying Psychosocial Risk Factors
in a Sample of School Principals.
Front. Psychol. 9:584.
doi: 10.3389/fpsyg.2018.00584
Validating the Copenhagen
Psychosocial Questionnaire
(COPSOQ-II) Using Set-ESEM:
Identifying Psychosocial Risk Factors
in a Sample of School Principals
Theresa Dicke 1
*, Herbert W. Marsh2, Philip Riley 1, Philip D. Parker 1, Jiesi Guo 1and
Marcus Horwood 1
1Institute for Positive Psychology and Education, Australian Catholic University, Sydney, NSW, Australia, 2Department of
Education, University of Oxford, Oxford, United Kingdom
School principals world-wide report high levels of strain and attrition resulting in a
shortage of qualified principals. It is thus crucial to identify psychosocial risk factors that
reflect principals’ occupational wellbeing. For this purpose, we used the Copenhagen
Psychosocial Questionnaire (COPSOQ-II), a widely used self-report measure covering
multiple psychosocial factors identified by leading occupational stress theories. We
evaluated the COPSOQ-II regarding factor structure and longitudinal, discriminant,
and convergent validity using latent structural equation modeling in a large sample of
Australian school principals (N=2,049). Results reveal that confirmatory factor analysis
produced marginally acceptable model fit. A novel approach we call set exploratory
structural equation modeling (set-ESEM), where cross-loadings were only allowed within
a priori defined sets of factors, fit well, and was more parsimonious than a full ESEM.
Further multitrait-multimethod models based on the set-ESEM confirm the importance of
a principal’s psychosocial risk factors; Stressors and depression were related to demands
and ill-being, while confidence and autonomy were related to wellbeing. We also show
that working in the private sector was beneficial for showing a low psychosocial risk,
while other demographics have little effects. Finally, we identify five latent risk profiles
(high risk to no risk) of school principals based on all psychosocial factors. Overall the
research presented here closes the theory application gap of a strong multi-dimensional
measure of psychosocial risk-factors.
Keywords: COPSOQ-II, psychosocial risk factors, ESEM, school principals, occupational wellbeing,
psychometrics, burnout
INTRODUCTION
While teacher strain and consequent attrition have been identified as a worldwide problem
(Tsouloupas et al., 2010; Dicke et al., 2015a, 2017), research on school principals’ occupational
wellbeing is still scarce (Darmody and Smyth, 2016; though see Ilies et al., 2015; Fuller and
Hollingworth, 2017). Studies that have focussed on school principals reported high levels of strain
Dicke et al. The Copenhagen Psychosocial Questionnaire
leading to high attrition and a shortage of qualified principals
(e.g., Dewa et al., 2009; Riley, 2014, 2015, 2017; Grissom et al.,
2015; Darmody and Smyth, 2016). This is alarming considering
that principal leadership is crucial to a school environment that
fosters teachers’ wellbeing (Collie et al., 2016) and thus, indirectly
(Arens and Morin, 2016; Klusmann et al., 2016) and directly
students’ learning and wellbeing (Koh et al., 1995; Day, 2011).
This implies a need to identify risk factors for school principals
and to assist in the development of effective strategies to enhance
protective resources and wellbeing, for this at-risk occupational
group.
Although research has clearly established a link between
psychosocial factors and employee wellbeing, there remains a
gap between research and application, particularly with regard
to comprehensive measurement instruments (Bailey et al.,
2015; Zheng et al., 2015), and their construct validity (Schat
et al., 2005). The COPSOQ is a widely used as a tool for
psychosocial risk assessment in the workplace and has been
used in thousands of enterprise based risk assessments (Nübling
et al., 2014). The COPSOQ covers a broad array of important
psychosocial factors at work based on the leading concepts and
theories of occupational health and wellbeing. It is increasingly
being used for research purposes (Nübling and Hasselhorn,
2010). Evaluating the factor structure of instruments such as
the COPSOQ, which purposely include multiple related scales
(dimensions) of the same domain, requires new statistical
models that takes this substantive structure into account. In
particular, exploratory structural equation modeling (ESEM)
allows relaxation of the unique factor assumption, where each
item is hypothesized to load on one and only one factor (Marsh
et al., 2009, 2014). However, this technique loses parsimony with
an instrument such as the COPSOQ which presupposes logical
multiple factor loadings for its dimension level. Thus, a balance
of ESEM and parsimony is needed for such instruments. We
have developed such an approach, where we allow loadings on
multiple factors, but only within theoretically meaningful sets, an
approach we name set-ESEM.
The present research is one of the first comprehensive
studies of school principals psychosocial risk factors. We first
focus on the factor structure and validity of measuring school
principals’ psychosocial risk factors based on the COPSOQ using
an innovative new approach to ESEM (set-ESEM). We can
confirm the assumed structure of the COPSOQ which includes
34 distinct factors. Thus, set-ESEM shows better model fit than
CFA and comparable model fit to traditional ESEM. Almost all
120 items load more strongly on the set-ESEM factor that they
were designed to load on and then on all other factors. Finally,
a thorough multi-trait multi-method approach reveals strong
evidence for re-test reliability.
Based on the results of the set-ESEM model we examine
individual differences in the psychosocial risk factors
related to demographic characteristics of school principals.
Finally, we identify sub-groups within our sample that
show similar risk profiles. Taken together, the present
study takes an important step in bridging research and
application within the important and understudied field of
school principal wellbeing by bringing together new findings
of substantive, measurement related, and methodological
matters.
School Principals Declining Health
Changes to the principal role over the previous two decades is
the likely cause of a shortage of qualified school leaders due to the
attrition of the experienced teaching workforce and an increasing
reluctance to “step up” to the role of leader (For an overview
see Gates et al., 2006; Miller, 2013). Declining applications is
a worldwide concern (Gallant and Riley, 2014). Principals face
increasing accountability promoted by the global educational
reform movement (GERM: Sahlberg, 2015). A significant stressor
in GERM countries has been the increased emphasis by
governments on accountability for uniform curriculum delivery
along with the devolution of administrative tasks from central
to local control. This has led to increased job demands and
diminished resources, particularly decreased decision latitude.
More disturbing is that under these conditions younger people
appear to be at greater risk of coronary heart disease than their
older colleagues (Kuper and Marmot, 2003).
Phillips and Sen (2011) reported that, “work related stress
was higher in education than across all other industries. . . with
work-related mental ill-health. . . almost double the rate for all
industry” (pp. 177–178). In addition, retiring principals will
be replaced with younger, less experienced individuals who are
potentially more at risk of experiencing the negative impacts of
the role (Kuper and Marmot, 2003; Riley, 2014, 2015; Darmody
and Smyth, 2016). This is particularly alarming considering
that leadership is crucial to an effective school environment
that fosters students’ learning (Day, 2011; Leithwood and Louis,
2012). Put simply, declining school principals’ wellbeing leads
to a reduced ability to significantly impact school functioning
and teacher and student engagement and thus, whole-school
wellbeing also declines (Leithwood et al., 2008; Ten Bruggencate
et al., 2012; Arens and Morin, 2016; Collie et al., 2016; Klusmann
et al., 2016; Maxwell and Riley, 2017). Hence, it is essential to
further identify causes and predictors of principals’ diminishing
occupational health.
Psychosocial Risk Factors at Work
Cox and Griffiths (2005) define psychosocial risks at work
as aspects regarding work design as well as the social,
organizational, and management contexts of work that could
potentially cause physical or psychological harm. Indeed, the link
between occupational psychosocial aspects and mental health has
long been established (see Bailey et al., 2015 for an overview).
In line with these assumptions many models and theories that
are important in stress research (and beyond) focus on the
relationship of psychosocial aspects and employee mental health.
Kompier in his review (2003; p. 429), identified seven major
influential theories “to find the factors in work that affect stress
and psychological wellbeing”:
1) The Job Characteristics Model (JCM; Hackman and Oldham,
1976),
2) The Michigan Organizational Stress model (MOS; Caplan
et al., 1975),
Frontiers in Psychology | www.frontiersin.org 2April 2018 | Volume 9 | Article 584
Dicke et al. The Copenhagen Psychosocial Questionnaire
3) The Demand–Control–(Support) Model (DCM; Karasek,
1979, 1990),
4) The Sociotechnical Approach (Kuipers and Van Amelsvoort,
1993),
5) The Action–Theoretical Approach (Frese and Zapf, 1994),
6) The Effort–Reward–Imbalance model (ERI; Siegrist, 1996),
and
7) The Vitamin Model (Warr, 1996; De Jonge and Schaufeli,
1998; Kristensen et al., 2005).
Kompier found that, despite some differences, such as being
individually centered (1 and 2) vs. being centered on the
environment (3, 4, 5, and 7) or both (6), these theories
showed parallels in substantial areas, such as finding very
similar determinants of job related well-being, i.e., all models
agree on the importance of skill variety, demands, or social
support as psychosocial drivers at work (see Kompier, 2003 for
details).
Psychosocial Risk Factors for School
Principals
The principal’s occupation is multifaceted. It incorporates
a wide range of different work elements, including various
aspects of leadership, working with policy makers, providing a
service to clients (parents and students), financial budgeting,
recruitment, strategic projects, reporting, teacher evaluations
(Torff and Sessions, 2005) and of course teaching itself
(see also Dadaczynski and Paulus, 2015). Moreover, school
principals are challenged by a very diverse leadership role
where they are required to be visionaries and directors,
people developers, organization designers, and teaching and
learning program managers (Leithwood, 1994; Dadaczynski
and Paulus, 2015). Recent changes in society, such as an
increasing globalization, new technologies, and changes in
workforce demographics (Stiglbauer, 2017), have led to the
education system responding by changing the role of school
leaders (Dewa et al., 2009). Thus, principals now have greater
responsibility (particularly in managerial issues; Green et al.,
2001), higher time pressure (Grissom et al., 2015), less decision
latitude, and reduced autonomy (Riley, 2017). In addition,
Maxwell and Riley (2017) showed that emotional demands due
to the multitude of interactions with parents, teachers, and
other stakeholders predicted burnout. These results provide
empirical support for Friedman (2002) whose comprehensive
review of the literature resulted in a comprehensive list of
principal stressors. He showed that ongoing demands such
as interpersonal stress sources, including interactions with
staff and parents, affected burnout levels in addition to
general role overload and administrative constraints (Friedman,
2002; Poirel et al., 2012). These increased demands, but a
lack of adequate resources or reward to compensate these
consequently lead to the high levels of strain (Riley, 2014, 2015,
2017).
In the present study we thus investigate as stressor covariates:
sheer quantity of work, expectations of the employer, student
related issues, parent related issues, government initiatives,
lack of autonomy/authority, financial management issues,
and interpersonal conflicts. Furthermore, we investigate the
relationship of relevant COPSOQ dimensions with depression,
job related autonomy, and confidence.
The Role of Demographic Characteristics
As shown above, research on principals’ psychosocial risk factors
and job-related health and wellbeing are still understudied.
While numerous studies contribute to so called “laundry lists”
of stressors and demographic differences for teachers (see Dicke
et al., 2015b), it is hard to find such studies explicitly for principals
(but see, e.g., Friedman, 2002; Dewa et al., 2009; Darmody and
Smyth, 2016) and those that do mostly focus on burnout and
principal turnover (but see Federici and Skaalvik, 2012). The
studies that have investigated such demographic differences in
psychosocial risk factors and occupational wellbeing have found,
in part, inconsistent results (see also Dadaczynski and Paulus,
2015). While some report female school principals to show
higher levels of mental health related problems (Weber et al.,
2005; Dadaczynski and Paulus, 2011), others found no such (or
only negligible) differences (Friedman, 2002; Dewa et al., 2009;
Darmody and Smyth, 2016). Interestingly, most studies find
age to be negatively related to mental health, although years of
experience seems to be a protective factor (Darmody and Smyth,
2016). School location (e.g., urban vs. rural) had no effect on
mental health (Darmody and Smyth, 2016). Studies looking at
other psychosocial factors in school principals found no effects
of age or gender on self-efficacy or job satisfaction (Federici and
Skaalvik, 2012; Darmody and Smyth, 2016), except for Darmody
and Smyth (2016) finding a small negative effect of age on
job satisfaction. There were also no effects of school location
(e.g., urban vs. rural) on job satisfaction (Darmody and Smyth,
2016). However, job satisfaction was negatively correlated to the
length of experience a school principal had in the job, showing a
decline in job satisfaction over time (Darmody and Smyth, 2016).
Darmody and Smyth (2016) also found better quality school
facilities to foster job satisfaction. Although they did not explicitly
account for school type, better facilities are usually associated
with high income or resource schools, such as private (fee paying)
schools and are thus heavily related to the socio-economic status
of the school population.
Taken together, there is still a need to identify demographics
that are predictive of psychosocial factors and wellbeing to
identify high risk groups and develop individualized measures
for prevention. In the present study, we will examine differences
based on gender, age, school location (urban/rural/remote), and
school type (public/private).
Identifying Risk Profiles
A different approach to examining individual differences is
achieved by taking into account different subpopulations in
which the observed relations between variables may differ,
quantitatively and qualitatively. In these person-centered
analyses, as opposed to typical variable-centered approaches
that are based on the overall sample mean, it is then possible to
identify profiles of these subgroups (Morin et al., 2011; Morin
and Marsh, 2015). There is still a paucity of such profiles with
regard to occupational wellbeing for educational personnel.
Frontiers in Psychology | www.frontiersin.org 3April 2018 | Volume 9 | Article 584
Dicke et al. The Copenhagen Psychosocial Questionnaire
Some authors have investigated profiles for teachers (e.g.,
Klusmann et al., 2008; Collie et al., 2015), but to our knowledge
not yet for school leaders. For example, Klusmann et al.
(2008), identified four profiles: healthy–ambitious, unambitious,
excessively ambitious, and resigned. However, Klusmann
et al. (2008) investigated teachers and focused on only two
psychosocial factors, namely work engagement and resilience.
In order to more fully depict psychosocial risk profiles, it is
necessary to include a more comprehensive set of risk factors.
This need led us to choose the COPSOQ (Pejtersen et al., 2010b)
instrument to investigate these differences.
The COPSOQ a Developing Tool for
Assessing Psychosocial Risk Factors
The COPSOQ was developed as a tool for practice and research
(Kristensen, 2010; Nübling et al., 2014) and explicitly states as
one of its aims to develop valid and relevant instruments for
the assessment of psychosocial factors at work (Kristensen et al.,
2005, p. 439).
Theoretically, the original COPSOQ is based on this work
by Kompier (2003; see above) and the COPSOQ items have
been developed to cover all of the major theories of workplace
functioning and thus, the important aspects defined by them.
For the present study we will focus on the redefined long
version of the COPSOQ-II instrument (Pejtersen et al.,
2010b). The revised survey suggests seven overarching
domains based on Kompier’s (2003) aforementioned meta-
theoretical review, namely “Demands At Work,” “Work
Organisation and Job Contents,” “Interpersonal Relations and
Leadership,” “Work-Individual Interface,” “Personality,” “Values
at the workplace,” and “Health and Wellbeing.” These domains
then include several dimensions (Personality only includes
one), for example “Work-Individual Interface” consists of the
dimensions “Job insecurity,” “Job satisfaction,” “Work–family
conflict,” and “Family–work conflict” (see Table 1 for a list of all
domains and their dimensions). Thus, the COPSOQ includes
important psychosocial workplace dimensions including
predictors and outcomes, such as General Health, Burnout,
Influence, Trust in Management, and Emotional demands.
The COPSOQ questionnaire has been translated into over
25 languages to date (Berthelsen et al., 2016). Several studies
have validated these (translated) COPSOQ versions, finding
minimally important differences within scales (Pejtersen et al.,
2010a) and reliability (Thorsen and Bjorner, 2010). Furthermore,
construct validity has been tested by examining differential
item functioning (Bjorner and Pejtersen, 2010) and exploratory
analyses of the factor structure, ceiling, and floor effects
(Moncada et al., 2014). Further, some studies have focused on the
predictive quality of COPSOQ scales for important occupational
outcomes, such as vitality and mental health (Burr et al., 2010),
sickness absence (Clausen et al., 2012; Olesen et al., 2012;
Rugulies et al., 2016), and affective organizational commitment
(Clausen and Borg, 2010). These studies have mostly applied
analyses based on manifest scale scores, not taking measurement
error into account and rely on CFA like factor structures of the
COPSOQ-II dimensions, i.e., where every item loads on one
dimension and one dimension only. For a list of review articles
see the COPSOQ International Network website (https://www.
copsoq-network.org/validation-studies/).
To date, however, there is no published study that has
juxtaposed the specific factor structures (construct validity),
including discriminant, and convergent validity of the
COPSOQ using more appropriate SEM methods. Further,
the COPSOQ instrument is frequently used for assessing
changes in psychosocial variables (e.g., Clausen and Borg, 2010;
Nübling et al., 2013) and purposes such as improvement for
working conditions (Kristensen, 2010), all of which require
pre-and post-measures of the same instrument. Nevertheless,
its convergent and discriminant validity in relation to stability
over time (re-test reliability) has not sufficiently been tested,
particularly in a comprehensive matter including all dimensions
and their interrelations.
The Present Study
Utilizing a large sample of an at risk occupational group, namely
school principals, the aim of the present study is to identify and
closely examine individual differences in important psychosocial
risk factors for this at -risk occupation. Further, we validate the
COPSOQ-II instrument which, although widely used in applied
settings (Kristensen, 2010; Nübling et al., 2014), has not been
psychometrically scrutinized using state of the art methodology.
Thus, as a prerequisite to any substantive research questions
we will first validate the COPSOQ-II in our sample of school
principals; i.e., we will (a) investigate the factor structure of the
COPSOQ-II and (b) test for re-test reliability of these scales.
Next we will validate the importance of the psychosocial risk
factors by examining convergent and divergent validity with
external scales based on crucial principal covariates. For choosing
such substantively important external criteria we have defined
variables (see Table 2) that play a role in the development of
principals’ occupational wellbeing based on our literature review
of empirical findings (e.g., Carr, 1994; Caruso et al., 2004; Riley,
2017) and theoretical models of stress research e.g., the ERI
(Siegrist, 1996), DCM (Karasek, 1979), MOS (Caplan et al.,
1975). Thus, we define a priori with which COPSOQ-II scale
these covariates will correlate highest (see Table 2 which includes
references for the proposed relationships) and utilize a multitrait-
multimethod model (MTMM) for testing our assumptions.
Then, we examine individual differences due to demographic
characteristics in these factors. Finally, we will identify subgroups
within our sample and will determine risk profiles of these sub
groups.
Hypotheses and Research Questions:
Hypothesis 1:
a: The factor structure of the COPSOQ-II dimensions will
satisfactorily fit when constrained to a CFA model, but will
provide better fits, with regard to a priori defined cut-off
criteria, when using less restrictive modeling approaches
(ESEM), while maintaining the intended loading structure.
b: The COPSOQ-II dimensions will show strong convergent
and discriminant validity in relation to stability over time
Frontiers in Psychology | www.frontiersin.org 4April 2018 | Volume 9 | Article 584
Dicke et al. The Copenhagen Psychosocial Questionnaire
TABLE 1 | COPSOQ structure, number of items, omegas, and examples of all scales.
Domain Dimension Abbreviation Omega No. of
items
Version (Example) Items
Health and well-being General health rating GH 1 L,M,S In general, would you say your health is:
Excellent/very good/good/fair/poor
Burnout BO 0.91 4 L,M,S How often have you been emotionally exhausted?
Stress ST 0.89 4 L,M,S How often have you been stressed?
Troubles sleeping SL 0.89 4 L,M How often have you slept badly and restlessly?
Depressive symptoms DS 0.81 4 L How often have you felt sad?
Somatic stress symptoms SO 0.71 4 L How often have you had stomach ache?
Cognitive stress symptoms CS 0.87 4 L How often have you had problems concentrating?
Personality Self-efficacy SE 0.80 6 L It is easy for me to stick to my plans and reach my
objectives.
Work-individual
Interface
Job insecurity JI 0.72 4 L Are you worried about becoming unemployed?
Job satisfaction JS 0.82 4 L,M,S Satisfied with your job as a whole, everything taken
into consideration?
Work-family conflict WF 0.87 4 L,M,S Do you feel that your work takes so much of your
time that it has a negative effect on your private life?
Family-work conflict FW 2 L Do you feel that your private life takes so much of
your time that it has a negative effect on your work?
Interpersonal relations
and leadership
Job predictability PR 2 L,M,S Do you receive all the information you need in order
to do your work well?
Job rewards RE 0.87 3 L,M,S Does the management at your workplace respect
you?
Role clarity CL 0.86 3 L,M,S Do you know exactly what is expected of you at
work?
Role conflicts CO 0.84 4 L,M Are contradictory demands placed on you at work?
Quality of leadership QL 0.91 4 L,M,S Your supervisor gives high priority to job
satisfaction?
Social support from colleagues SC 0.78 3 L,M How often do you get help and support from your
colleagues?
Social support from supervisor SS 0.87 3 L,M,S How often do you get help and support from your
nearest superior?
Social community SW 0.81 3 L,M Do you feel part of a community at your place of
work?
Demands at work Quantitative demands QD 0.83 4 L,M,S Do you get behind with your work?
Work pace WP 0.87 3 L,M,S Do you have to work very fast?
Cognitive demands CD 0.77 4 L Do you have to keep your eyes on lots of things
while you work?
Emotional demands ED 0.79 4 L,M,S Is your work emotionally demanding?
Demands for hiding emotions HE 0.66 3 L Does your work require that you hide your feelings?
Work organization and
job
contents
Influence IN 0.74 4 L,M,S Do you have any influence on what you do at work?
Possibilities for development PD 0.80 4 L,M,S Does your work give you the opportunity to develop
your skills?
Variation VA 2 L Is your work varied?
Meaning of work MW 0.85 3 L,M,S Is your work meaningful?
Commitment to the workplace CW 0.77 4 L,M,S Do you enjoy telling others about your place of
work?
Values at workplace
level
Trust in management TM 0.75 4 L,M,S Can you trust the information that comes from the
management?
Mutual trust between
employees
TE 0.77 3 L,M Do the employees in general trust each other?
Justice JU 0.85 4 L,M,S Is the work distributed fairly?
Social responsibility SI 0.80 4 L Is there space for employees of a different race and
religion?
L, Long version; M, Medium version; S, Short version.
Frontiers in Psychology | www.frontiersin.org 5April 2018 | Volume 9 | Article 584
Dicke et al. The Copenhagen Psychosocial Questionnaire
TABLE 2 | A-priori prediction of covariates’ highest correlations with the COPSOQ.
Covariate Highest correlate
Topic Item References Domain Dimension(s)
Stress sources Sheer quantity of work Green et al., 2001 Demands at Work Quantitative Demands
Expectations of the employer Leithwood, 1994 Interpersonal Relations and Leadership Rewards
Student related issues Friedman, 2002 Demands at Work Emotional demands
Parent related issues Friedman, 2002 Demands at Work Emotional demands
Financial management issues Poirel et al., 2012 Interpersonal Relations and Leadership Role conflicts
Inability to get away from school/community Riley, 2014 Work-individual Interface Work-family conflict
Interpersonal conflicts Friedman, 2002 Interpersonal Relations and Leadership Trust in Management
Depression I am frequently depressed about my job. Carr, 1994 Health And Well-Being Depressive Symptoms
Autonomy …in providing strategic focus and direction to colleagues Riley, 2017 Work Organization And Job Content Influence
Confidence …in providing strategic focus and direction to colleagues Darmody and Smyth, 2016 - Self-Efficacy
(i.e., using time as the method variable in a multitrait-
multimethod analysis; Campbell and Fiske, 1959; Marsh,
1988).
Hypothesis 2:Typical school principals’ occupational
wellbeing variables will have high convergent and discriminant
validity in relation to matching COPSOQ-II scales (see
Table 2), i.e., the convergent relationships to be high and
higher than the relationships of the wellbeing variables
with non-matching (divergent) COPSOQ-II scales. Overall,
we expect stress sources to correlate with demands and/or
indicators of ill-being, while we expect autonomy and
confidence to correlate highest with personal resources and
well-being (e.g., Friedman, 2002; Poirel et al., 2012; Maxwell
and Riley, 2017).
Hypothesis 3: Although results regarding gender have been
inconsistent, we still expect female or older principals to
experience higher levels of those dimensions reflecting ill-
health and demanding factors (Dewa et al., 2009), but no
such effects for school type or school location (Darmody
and Smyth, 2016). We expect school principals that work
at private schools or school principals that have recently
started (Darmody and Smyth, 2016) to report higher job
satisfaction. We leave as a research question any other effects
of demographic differences on psychosocial risk factors.
Research Question 1:This study is one of the first studies
to investigate comprehensive school principals risk profiles
(person-centered approach) and due to the lack of existing
literature, is essentially exploratory. Thus, we leave as an open
question as to how many risk profiles and the nature of these
profiles.
METHODS
Participants
Participants were school principals working in Australia during
2011 (and partly in 2012). The sample (N=2,049) comprised
44.4% male and 55.6% female participants: 70.3% principals,
25.4% % assistant/deputy principals, and 3.3% campus principals
of a multi-campus school. The mean age of school principals in
our sample was 57.61 (SD =7.29) years. Regarding the school
types the principals managed, 64.0% were primary schools, 22.0%
were secondary schools, and 13.9% were combination schools
(both primary and secondary). The mean years of experience in
their current position was 5.2 years (Min. 0 and Max. 41 years)
and 12.5 years in leadership roles generally (Min. 0 and Max. 42
years).
Data on these school principals, were collected as part of a
large research project on principal health and wellbeing (Riley,
2014, 2015, 2017), where principals filled in a large survey
annually. In the present study, we focus on data assessed at
the first time wave of this larger project collected in 2011, but
additionally use data from the second wave (in 2012) to test
our second hypotheses. Missing data was assumed to be missing
at random and handled using the full information maximum
likelihood (FIML) approach (Enders, 2010).
Measures
We focused on the COPSOQ-II dimensions that consisted of
the Likert scaled item types (ranging on a “strongly agree” to
“strongly disagree” continuum (which excluded the Offensive
Behavior items)). Thus, we included 34 scales from the long
version. For an overview of all scales (included in each version
examined in the present study), example items, number of items,
and internal consistency, see Table 1.McDonald’s Omega (1999),
which reflects the proportion of variance in the scale scores
accounted for by a general latent factor, is reported as a measure
of internal consistency (see also Zinbarg et al., 2006; for alpha
values and confidence intervals for both, omegas and alphas see
Supplementary Material). In general, Omega coefficients were
0.79 on average. All scales showed omega values above 0.7, except
for Hiding Emotions (0.66).
Covariates. We included several other typical principals’
wellbeing variables as covariates. These included several stress
sources that are typical for school principals (i.e., Sheer
Quantity of Work, Expectations of the Employer, Student
Related Issues, Government Initiatives, Parent Related Issues,
Lack of Autonomy/Authority, Financial Management Issues,
and Interpersonal Conflicts) as well as three items to assess
Frontiers in Psychology | www.frontiersin.org 6April 2018 | Volume 9 | Article 584
Dicke et al. The Copenhagen Psychosocial Questionnaire
school principals’ level of depression, job related autonomy, and
confidence (see Table 2).
Analysis
In a first step we examined the factor structure of the COPSOQ-
II. Researchers typically use CFA models for validation of such
factor structures. In the present study however we also use
ESEM. The difference between the CFA and ESEM approaches
is that in ESEM all factor loadings are estimated, excluding
those constraints necessary for identification (see Asparouhov
and Muthén, 2009; Marsh et al., 2009, 2013). Although there are
many methodological and strategic advantages to CFAs, these
models typically do not provide an acceptable fit to the data
(Marsh et al., 2010a). This is most likely due to the overly
restrictive assumption of CFA (each item is hypothesized to
load on to only one factor), and the misspecification of factor
loadings (automatically constraining them to be zero), which
usually leads to distorted factors with over-estimated factor
correlations (Marsh et al., 2013). These positively-biased factor
correlations can lead to biased estimates in SEMs incorporating
other outcome variables (Asparouhov and Muthén, 2009; Marsh
et al., 2009; Schmitt and Sass, 2011).
ESEM, however, not only provides a better fit, but also results
in latent factors that are more differentiated (i.e., less correlated;
Asparouhov and Muthén, 2009) and accurately estimated. This
results from ESEM using two estimates of overlap between factors
(overlap in factor loadings and correlation between factors),
compared to CFA, which uses one estimate (correlation between
factors; Marsh et al., 2010a). In our study we will use ESEM with
target rotation (for details see Browne, 2001; Asparouhov and
Muthén, 2009). Despite its advantages when compared to CFA,
ESEMs with a large number observed indicators are naturally
not very parsimonious and can lead to convergence problems
(due to computational issues). Therefore, we also tested an
alternative set-ESEM model where all indictors are only allowed
to cross-load within an a priori defined set of factors based on a
theoretically meaningful structure. In case of the present study,
the specified cross-loadings were based on the suggested implicit
overarching domains of the COPSOQ-II (Pejtersen et al., 2010b).
These cross loadings within sets are then constrained to be close
to zero, while cross-loading between sets are constrained to be
zero.
Generally, given the known sensitivity of the chi-square test to
sample size, minor deviations from multivariate normality, and
minor misspecifications, applied SEM research focuses on indices
that are relatively sample-size independent (Hu and Bentler,
1999; Marsh et al., 2004) such as the Root Mean Square Error
of Approximation (RMSEA), the Tucker-Lewis Index (TLI), and
the Comparative Fit Index (CFI). Population values of TLI and
CFI vary along a 0-to-1 continuum, in which values >0.90 and
0.95 typically reflect acceptable and excellent fits to the data,
respectively. Values smaller than 0.08 and 0.06 for the RMSEA
show acceptable and good model fits.
As is recommended for self-report surveys that include a
mixture of positively and negatively worded items, we specified
correlated uniquenesses (CUs) relating the responses to each of
the negatively worded items (e.g., Marsh, 1996; Marsh et al.,
2010c) for avoiding method effects associated item wording
(Marsh et al., 2010a) and consequent bias.
The multitrait-multimethod model (MTMM) design, we
apply in the current study, is widely used to assess convergent
and discriminant validity and is one of the standard criteria for
evaluating psychological instruments (e.g., Campbell and Fiske,
1959; Marsh, 1988; Marsh et al., 1994). Campbell and O’Connell
(1967) and Marsh et al. (2010b) showed how operationalizing
the multiple methods in their MTMM paradigm across multiple
occasions provides a very strong approach to evaluating stability
of responses to a multidimensional instrument.
Thus, we compare convergent validities (correlations between
matching traits—test-retest correlations when method is based
on time), heterotrait-heteromethod (correlations between
different traits measured on different occasions), and heterotrait-
homomethod correlations (correlations among nonmatching
traits collected on the same occasion). Convergent validity is
supported when convergent validates (correlations) are high.
Discriminant validity, on the other hand, is supported when
convergent validities are larger than all other correlations.
We infer method effects in case heterotrait-homomethod
correlations involving a particular method are higher than
heterotrait-heteromethod correlations or approach 1.0.
We used the model-based clustering MCLUST package
(Fraley et al., 2014; Scrucca et al., 2016) in R (R Core Team,
2014) for identifying latent risk profiles by means finite Gaussian
mixture modeling fitted via EM algorithm. Thus, we assumed
that correlations between the indicators, i.e., for the present
research the psychosocial risk factors, can be explained by
an underlying latent categorical cluster variable representing
qualitatively and quantitatively distinct profiles of principals
within the sample population (Morin et al., 2011). We identified
the best fitting model solution with regard to number of profiles
and covariance structure based on the Bayesian information
criterion (BIC) and integrated complete-data likelihood criterion
(ICL; Scrucca et al., 2016). For evaluating our profile solution
we used probabilistic discriminant analysis (MclustDA; Fraley
and Raftery, 2007), where a model is fit to each class in the
training set. Observations of a test set are then assigned to the
class corresponding to the model in which they have the highest
posterior probability (Fraley and Raftery, 2007). It is then possible
to estimate errors for the training and the test data as well as a
cross-validation error.
RESULTS
Descriptives
The latent correlations of all factors based on the CFA, ESEM,
and set-ESEM are reported in Table SI1-SI3 (see Supplementary
Material and online for better readability of Tables SI1-SI5 and
SA1-SA16 https://figshare.com/s/c69bde7bbd73e8de70f2).
Factor Structure of the COPOSOQ-II
(Hypothesis 1a)
For examining the first-order factor structure of the COPSOQ-
II long version we compared three different models: (1) a CFA,
where cross-loadings are constrained to be zero, (2) ESEM with
Frontiers in Psychology | www.frontiersin.org 7April 2018 | Volume 9 | Article 584
Dicke et al. The Copenhagen Psychosocial Questionnaire
target rotation, were cross-loadings are allowed for all other
factors, and (3) a set-ESEM approach with target rotation, were
cross-loadings are only allowed within a defined set of factors.
(For results of these tests regarding the medium and short version
of the COPSOQ-II please see additional Supplementary Material
Annex Tables SA1–7).
CFA Model
The CFA model fit the data satisfactory (χ²=18,854, df =6,445,
CFI =0.907, TLI =0.898, RMSEA =0.031) with CFI and TLI
being minimally acceptable. However, correlations (Mean [M] | r
|=0.289) between some factors were very high with values up
to | r|=0.881 and 10% of the correlations had absolute values
above 0.50 (see Supplementary Material Table SI1). These high
correlations may be evidence that a CFA model is not appropriate
and overly restricted which causes inflated factor correlations.
ESEM
When running the model as an ESEM with target rotation, model
fit improved substantially (χ²=10,976, df =3,621, CFI =
0.971, TLI =0.94, RMSEA =024). Correlations dropped (M|r
|=0.205) with the highest | r|=0.77 and only 5% of the
correlations had absolute values above 0.50 (see Supplementary
Material Table SI2). This model, however, has a large number of
estimated parameters (df =3621) as cross loadings are allowed
for all factors in the model and thus lacks parsimony.
Set-ESEM
The set-ESEM approach with target rotation provides a method
to take advantage of EFA-like structures, thus providing a more
realistic model by taking into account the a priori factor structure,
but being more parsimonious (χ²=12,669, df =5,987, CFI
=0.947, TLI =0.938, RMSEA =023) as cross-loadings are
only allowed within a priori defined set of factors. Model fit
remained good and was not substantially different than the ESEM
model with regard to cut-off values (1CFI <0.01, 1TLI <
0.01, 1RMSEA <0.015). In fact, those fit indices that control
for parsimony remain almost unchanged (0.94 to 0.938 for
TLI;0.024 to 0.023 for RMSEA). And indeed, the χ2difference
test showed a non-significant difference between the full ESEM
and ESEM-set for the long version of the COPSOQ-II, further
indicating the appropriateness of the set-ESEM in line with our
expectations (H1).
Three scales were excluded from this set-ESEM structure and
included as CFAs: (1) The Self-Efficacy construct is the only
dimension in the personality domain, (2) The General Health
one-item scale was excluded for reasons of model identification,
and (3) the two item Variation scale was modeled as a CFA as
it showed very high unsystematic cross-loadings. On average,
correlations of this set-ESEM model (M|r|=0.263) were
slightly higher than those of the ESEM model, however, the
highest correlation was lower with |r|=0.760 and in this model
also only 5% of the correlations were above absolute 0.50 (see
Supplementary Material Table SI3).
Factor Loadings
The factor loadings for the CFA model are slightly higher than
those of the ESEM and set-ESEM models (M=0.736, M=0.638,
and M=0.678 respectively) but reveal only small differences
(see Supplementary Material annex Tables SA8–10). The higher
loadings within the CFA model were expected due to the highly
constrained nature of CFA models (Asparouhov and Muthén,
2009; Marsh et al., 2009). Importantly, almost all 120 items
load more strongly on the set-ESEM factor that they were
designed to load on Pejtersen et al. (2010b) and then on all other
factors.
Overall, the pattern of results for the long COPSOQ-II version
were largely replicated for the medium and short version of the
instrument (see Supplementary Material annex Tables SA1–7).
Re-test Reliability (Hypothesis 1b)
It was not possible to model the set-ESEM for two time
waves simultaneously for testing longitudinal invariance
due to inadequate memory and computing capacity of
statistical programs to process the large number of factors
and manifest indicators. Thus, for investigating the convergent
and discriminant validity in relation to stability over time of the
instrument we derived factor scores (based on the best fitting
ESEM-set model) of the long version of the COPSOQ-II at
T1 and T2 (one year later). We then utilized a MTMM-like
approach. In the classic MTMM model, two or more traits are
collected by two or more methods. In the present study the
traits will be represented by COPSOQ-II dimensions, while
method variation is reflected by different time waves. In the
matrix, we use 661scales (33 dimensions on 2 occasions). We
then evaluated the pattern of relations among the 33 latent
constructs to assess convergent and discriminant validity. The
33 convergent validities (those coefficients shaded in gray
in Supplementary Material Table SI4) represent correlations
between the same dimensions assessed by different methods
(monotrait–heteromethod correlations or test–retest stabilities
when the multiple methods are the different occasions). These
convergent validities were consistently substantive with a
mean of | r|=0.607 (SD =0.291). The correlations between
different dimensions assessed on different occasions (heterotrait–
heteromethod correlations in the framed square submatrix (Table
SI4), not including the convergent validities are substantially
lower than the convergent validities with a mean of | r|=0.235
(SD =0.124). In addition, the correlations between different
dimensions administered on the same occasion (heterotrait–
monomethod correlations in the triangular submatrices) are only
slightly larger than the heterotrait– heteromethod correlations,
with a mean value of | r|=0.302 (SD =0.168).
Thus, with regard to the original Campbell and Fiske (1959)
criteria, we provided strong support for convergent validity
(the substantive convergent validities) and strong support for
discriminant validity (heterotrait–monomethod and heterotrait–
heteromethod correlations that are substantively smaller than
the convergent validities). We, however, found some small
amount of method effect (heterotrait–monomethod correlations
higher than heterotrait–heteromethod correlations) which was
associated with the specific occasion of data assessment.
1The Job insecurity scale was not collected at T2.
Frontiers in Psychology | www.frontiersin.org 8April 2018 | Volume 9 | Article 584
Dicke et al. The Copenhagen Psychosocial Questionnaire
Correlations With Important Principal
Variables: Convergent and Discriminant
Validity (Hypothesis 2)
For evaluating convergent and discriminant validities with
external criterion, we again made use of the MTMM approach.
Thus, we correlated all COPSOQ-II dimensions with all
covariates (i.e., typical stress sources, autonomy, depression, and
confidence) based on our latent set-ESEM model. For each
criterion, the COPSOQ-II dimension to which it should be
most highly correlated was predicted a priori (see Table 2 for
an overview of a-priori hypothesis of relationships of these
covariates with the COPSOQ dimensions and in Table 3 shaded
in gray). Similar to Hypothesis 1, an evaluation of the construct
validity of the COPSOQ-II responses follows the logic of
MTMM analyses. In this model, (a) support for the convergent
validity of the COPSOQ-II responses requires that each predicted
correlation is statistically significant, and (b) support for the
discriminant validity of the COPSOQ-II responses requires that
each external validity criterion is more highly correlated with
the predicted COPSOQ-II dimension than any of the other
COPSOQ-II dimension.
The analysis model was based on the long set-ESEM
model at Time 1 and revealed good fit with χ²=1,4382,
df =7,206, p<0.001, CFI =0.95, TLI =0.94, RMSEA
=0.02. Overall results revealed a correlation pattern mostly
in line with our expectations (H2). While the matching
correlations showed an average of | r|=0.419 (SD =
0.087), the non-matching only showed an average of | r|
=0.193 (SD =0.104; for details see Table 3). In the summary
below we focus on the highest correlations only (for a more
detailed overview, see Supplementary Material). Namely, results
showed:
Stress Sources
Based on our literature review we examined correlations with
several typical stress sources of school principals.
The sheer quantity of work item as expected correlated
significantly highest with Quantitative Demands (r=0.553).
Further, it correlated moderately to highly with several negative
health outcomes, such as Perceived Stress (r=0.433) and
Sleeping Troubles (r=0.404) and with Work-Family Conflict (r
=0.541).
As hypothesized Expectations of the employer correlated
highest and negatively with Rewards (r= −0.411). It also
correlated with Stress, Sleeping Troubles, and Depression (r=
0.309, r=0.337, and r=327 respectively). Furthermore, it
correlated moderately with Job Satisfaction (r=0.388) and
Work-Family Conflict (r=0.341). Other notable correlations
where the ions Quantitative Demands (r=0.553) and Emotional
Demands (r=0.333). Excessive expectations are related to other
high demands as well as a feeling of reduced recognition by the
employer, less job satisfaction, higher work-family conflict and
consequently higher levels of ill-being.
Student related issues correlated moderately to highly with
Emotional Demands (r=0.335). Parent related issues correlated
moderately to highly with Emotional Demands (r=0.363). These
interpersonal issues thus, seem to reflect emotionally demanding
stressors.
Financial management issues correlated as expected highest
with Role Conflicts (r=0.324). Other notable correlations
showed with Emotional Demands (r=0.313).
Inability to get away from the school correlated as expected
highest with Work-family conflict (r=0.378). Interpersonal
conflicts in contrast to our expectations showed its highest
correlation (negative) with Role Conflicts (r= −0.384),
but closely followed by Trust in Management (r=0.376).
Additionally, it correlated moderately to highly with Sleeping
Troubles (r=0.332), Depressive Symptoms (r=0.335) and
Emotional Demands (r=0.333). Conflicts on the interpersonal
level are related to role conflicts, less trust and several indicators
of ill-being.
Overall, most of these results confirmed convergent and
divergent validity in line with our expectations (H3).
Depression, Autonomy, and Confidence
Depression in line with our a priori assumptions, correlated
highest with Depressive Symptoms (r=0.60), which indicated
convergent and divergent validity. However, depression
correlated highly with several psychosocial factors (see Table 3
and Supplementary Material), making it an important outcome
variable for school principals.
Autonomy as hypothesized correlated highest with Influence
(r=0.396). Further it showed moderate correlations with Role
Clarity (r=0.358), Job Satisfaction (r=0.318), and Meaning
Of Work (r=0.321). Autonomy is related to several important
occupational wellbeing indicators making it an important
resource for principals.
Confidence, only correlated significantly with Self-Efficacy (r
=0.449), which also provided strong evidence for convergent
and divergent validity. Therefore, we could again show
convergent and divergent validity of the COPSOQ-II (H3).
Demographic Variables as Predictors
(Hypothesis 3)
We included several demographic variables as predictors of the
psychosocial risk factors as identified by the COPSOQ-II. Again,
the analysis model was based on the long set-ESEM model at
Time 1 and revealed good fit with χ²=14,531, df =6,948, p
<0.001, CFI =0.941, TLI =0.929, RMSEA =0.017. Results
revealed a correlation pattern partly in line with our expectations
(H3). In the description, here we focus on significant coefficients
larger than one (|β0.1|; for details and more results see
Table SI5).
Gender
While women reported higher levels of negative indicators-
Somatic stress symptoms (β= −0.179), and Work pace (β=
0.115), Cognitive Demands (β= −0.102), showing that they
perceived higher demands, they also reported higher levels of
positive indicators, Possibilities for development (β= −0.123),
Variation (β= −0.106), and Commitment (β= −0.132).
Frontiers in Psychology | www.frontiersin.org 9April 2018 | Volume 9 | Article 584
Dicke et al. The Copenhagen Psychosocial Questionnaire
TABLE 3 | Correlations of covariates with the COPSOQ scales.
Sheer
quantity of
work
Expectations
of the
employer
Student
related
issues
Parent
related
issues
Financial
management
issues
Inability to
get away
from school
Inter-
personal
conflicts
Depress. Autono. Confid.
General health rating 0.215 0.208 0.112 0.122 0.135 0.202 0.156 0.297 0.12 0.114
Burnout 0.281 0.25 0.147 0.155 0.169 0.246 0.188 0.361 0.054 0.051
Stress 0.433 0.309 0.201 0.241 0.217 0.337 0.249 0.432 0.097 0.095
Troubles sleeping 0.404 0.37 0.237 0.283 0.267 0.374 0.332 0.543 0.122 0.153
Depressive symptoms 0.212 0.327 0.221 0.248 0.198 0.278 0.335 0.6 0.195 0.289
Somatic stress
symptoms
0.269 0.254 0.156 0.216 0.183 0.283 0.227 0.393 0.095 0.074
Cognitive stress
symptoms
0.293 0.254 0.173 0.189 0.17 0.256 0.269 0.426 0.181 0.208
Self-efficacy 0.11 0.138 0.138 0.182 0.115 0.137 0.182 0.294 0.204 0.449
Job insecurity 0.121 0.237 0.117 0.185 0.137 0.242 0.177 0.35 0.158 0.159
Job satisfaction 0.292 0.388 0.214 0.224 0.233 0.281 0.282 0.596 0.318 0.214
Work-family conflict 0.541 0.341 0.198 0.248 0.259 0.378 0.235 0.374 0.079 0.093
Family-work conflict 0.064 0.036 0.056 0.055 0.092 0.018 0.051 0.099 0.01 0.044
Job predictability 0.254 0.325 0.17 0.177 0.215 0.212 0.175 0.307 0.256 0.075
Job rewards 0.238 0.411 0.154 0.184 0.203 0.207 0.2 0.38 0.198 0.098
Role clarity 0.133 0.181 0.121 0.105 0.111 0.18 0.197 0.32 0.358 0.248
Role conflicts 0.311 0.377 0.234 0.295 0.324 0.288 0.385 0.378 0.105 0.061
Quality of leadership 0.173 0.259 0.056 0.071 0.142 0.137 0.114 0.237 0.146 0.008
Social support from
colleagues
0.141 0.189 0.13 0.143 0.115 0.222 0.167 0.285 0.205 0.132
Social support from
supervisor
0.187 0.277 0.083 0.067 0.107 0.084 0.116 0.195 0.09 0.02
Social community 0.063 0.122 0.141 0.168 0.1 0.193 0.363 0.316 0.214 0.248
Quantitative demands 0.553 0.318 0.203 0.195 0.214 0.261 0.206 0.304 0.089 0.136
Work pace 0.399 0.221 0.113 0.14 0.109 0.18 0.095 0.168 0.03 0.078
Cognitive demands 0.265 0.216 0.117 0.152 0.187 0.191 0.126 0.121 0.158 0.212
Emotional demands 0.383 0.339 0.335 0.363 0.312 0.279 0.333 0.414 0.022 0.006
Demands for hiding
emotions
0.261 0.249 0.173 0.232 0.152 0.22 0.247 0.256 0.022 0.02
Influence 0.315 0.343 0.195 0.181 0.12 0.202 0.197 0.335 0.396 0.225
Possibilities for
development
0.087 0.162 0.125 0.116 0.032 0.15 0.118 0.298 0.29 0.253
Meaning of work 0.054 0.052 0.077 0.069 0.03 0.049 0.1 0.277 0.321 0.262
Commitment to the
workplace
0.291 0.319 0.231 0.288 0.192 0.32 0.264 0.57 0.113 0.102
Variation 0.022 0.119 0.093 0.072 -0.015 0.128 0.09 0.241 0.202 0.136
Trust in management 0.102 0.181 0.148 0.164 0.167 0.203 0.376 0.33 0.206 0.14
Mutual trust between
employees
0.11 0.239 0.106 0.101 0.094 0.136 0.188 0.249 0.203 0.091
Justice 0.082 0.179 0.121 0.131 0.051 0.123 0.211 0.309 0.28 0.169
Social responsibility 0.01 0.048 0.057 0.101 0.067 0.088 0.086 0.121 0.096 0.11
A-priori prediction of covariates highest correlations with the COPSOQ scales are shaded in gray. Insignificant (p >0.05) correlations are in italic font. (see additional figure file).
Leadership Experience
School leaders with higher levels of experience reported lower
levels of Job predictability (β= −0.103), Quality of Leadership
(β= −0.129), and particularly Social Support (β= −0.222). In
addition, higher levels of experience positively predicted the level
of Role conflicts (β=0.106), and unexpectedly cognitive and
emotional demands (β=0.114 and β=0.111), respectively.
School Location
The location (urban/suburban/large town/rural/remote)
of the school only predicted Mutual Trust between
employees, where school leaders in rural areas reported
higher levels (β=0.105) in comparison to their urban
counterparts.
School Sector
Overall, school principals working at private (vs. public) schools
seemed to be better of reporting higher levels of Job Satisfaction
(β=0.115; as expected), Job Predictability (β=0.138), Job
Rewards (β=0.173), Social Support (β=0.13), and Influence
(β=0.143), while reporting lower levels of Role Conflicts (β=
0.147) and Demands for Hiding Emotions (β= − 0.105).
Frontiers in Psychology | www.frontiersin.org 10 April 2018 | Volume 9 | Article 584
Dicke et al. The Copenhagen Psychosocial Questionnaire
Age
In Contrast to our expectations older school leaders seemed to
thrive as they reported lower levels of Burnout (β= −0.212),
Stress (β= −0.177), Cognitive Stress (β= −0.106), Cognitive
Demands (β= −0.103), and Demands for hiding Emotions
(β= −0.105). Additionally, they reported higher levels of Job
Predictability (β=0.138), Role Clarity (β=0.128), Meaning of
Work (β=0.165), and Justice (β=0.139).
Taken together, being older and working at a private school
seemed to be of advantage for most psychosocial risk factors,
while gender revealed inconclusive results, and experience did
not protect, but rather was a disadvantage with regard to
psychosocial risk factors.
School Principal’s Risk Profiles (Research
Question 1)
Next we applied model based cluster analysis for identifying
the latent risk profiles based on the 34 COPSOQ-II factor
scores. The results of these risk profiles are shown in Figure 1
(see Supplementary Material for an alternative depiction).
The best fitting model (fixed variances and constrained
covariances) showed a five-profile solution based on the BIC
= −125,874 and the ICL = −126056.6. We confirmed this
solution by means of a discriminant analyses, where the
profile solution on the whole sample served as known classes.
Analyses of test and training data revealed very small errors
(0.01 and 0.03, respectively) demonstrating a reliable profile
solution.
The distribution of participants (N: Profile 1 =191, Profile 2
=153, Profile 3 =227, Profile 4 =852, and Profile 5 =626) in
profiles showed that the majority (72%), of school principals were
either in Profile 4 or 5. The profiles overall revealed level effects
rather than shape effects (see Figure 1). Indeed, participants
in Risk Profile 2 showed values clearly below the mean (high
risk profile), Profile 5 showed values majorly below the mean
(moderate risk profile), Risk Profile 1 fluctuating around the
mean (average risk profile), Risk Profile 3 majorly above the
mean (low risk profile), and Risk Profile 4 clearly above the mean
(minimal Risk profile). Two risk factors however stand out in
this five-profile solution, namely Job Insecurity and Family Work
conflicts. Job Insecurity seems to be one of the most extreme
values in all profiles in line with the direction of most risk factors.
Family work conflict however, stands out not just because it is
also an extreme value in most profiles, but for two profiles (3
and 5) it is additionally in opposite direction of most other risk
factors.
DISCUSSION
We evaluated the psychometric properties of the COPSOQ-II
using a sample of Australian school principals. Results revealed
that the dimensional structure of the COPSOQ-II holds up
well in CFA and ESEM-set. Further, the COPSOQ-II showed
high re-test reliability as well as convergent and divergent
validity in relation to covariates that play a key role for school
principals.
Factor Structure of the COPSOQ (H1a)
Although the good psychometric properties of the COPSOQ-
II have so far been successfully validated with regard to
different cultures, mixed occupations, and aspects of validity (see
Kristensen, 2010 and Berthelsen et al., 2016 for an overview),
validation studies based on state of the art methodology such
as SEM were still missing. Our approach allows for not only
measurement error and differences in item loadings to be taken
into account, but also provides more flexibility with regard to
model constraints. Thus, even though the CFA model including
all dimensions (of that particular COPSOQ-II version) fit the
data acceptable, those models where we relaxed overly restricted
constraints of items only loading on one factor, fit the data even
better, in line with similar studies in research on personality
traits that revealed cross-loadings between traits to be a better
reflection of reality (see Marsh et al., 2013 for an overview).
Applying exploratory approaches to models with a large number
of dimensions and consequently items, such as the COPSOQ-
II, however, results in very complex models with a large number
of estimated parameters which require very high computational
and processing capacities. The innovative approach of the present
study was in using a more parsimonious set-ESEM approach,
where we only allowed cross-loadings between dimensions of
the same implicit domain structure. Model fit was very similar
compared to the full ESEM and hence, better than the CFA
model. This is noteworthy, as the set-ESEM has a strong
confirmatory basis if there is good justification for the sets.
Indeed, set-ESEM could be placed as midway between CFA and
ESEM (with target rotation), depending on the number of factor
loadings constrained to be zero. The aforementioned pattern
of results (set-ESEM fitting similar to ESEM and better than
CFA) emerged for all three COPSOQ-II versions. Despite the
only modest differences in fit between the CFA and both ESEM
models, at least for the short version of the COPSOQ-II, our
findings suggest the ESEM solution to be more appropriate, as it
decreased the inflated correlations of closely related dimension to
an acceptable level. Additionally, Chi2difference tests showed a
non-significant difference between the full and ESEM-set models
for the long version of the COPSOQ-II. This indicates that the
set-ESEM approach is particularly beneficial in case of very high
model complexity. This ESEM-set model should be confirmed in
other samples, albeit we consider our results a strong indicator
for the models suitability in future research with the COPSOQ-II
scales, particularly when considering several theoretically closely
related scales simultaneously.
Regarding test-score reliability of the scales we can report
omega values above 0.7 for all scales, except for Hiding Emotions
(0.66). However, the reliability coefficients are dependent on the
number of items per scale (Marsh et al., 1998) and one feature of
the COPSOQ is to provide a large number of divers scales, thus,
including less numbers of items per scale. Further results of our
MTMM analyses provide strong evidence for re-test reliability
(see below).
Factor loadings are high for the CFA model with exception
of the Variation factor which shows a weak factor loading
for one item. This scale should thus, be closely examined
in future research. More importantly however, in the more
Frontiers in Psychology | www.frontiersin.org 11 April 2018 | Volume 9 | Article 584
Dicke et al. The Copenhagen Psychosocial Questionnaire
FIGURE 1 | The five-profile solution based on finite Gaussian mixture modeling (Fraley et al., 2014). See Table 1 for a list of scale Abbreviations. For an alternative
depiction see Supplementary Material.
appropriate ESEM-set model (and the full ESEM model) target
loadings were always higher than cross-loadings, indicating a
good representation of factors through their indicators (Marsh
et al., 2013).
Overall, we can report good model fit for all three versions
of COPSOQ-II, thus confirming Hypothesis 1a(H1a). Therefore,
the instrument offers flexibility for using it in research and the
applied context and also offers the user a choice between using
different levels of comprehensibility and therefore length.
Re-test Reliability (H1b)
Our results showed strong evidence for convergent and
discriminant validity in relation to stability over time of the
COPSOQ-II dimensions in line with other studies that have
investigated the COPSOQ-II’s test-retest reliability (Rosário et al.,
2014). Indeed, in our MTMM analysis we found strong support
for both convergent and discriminant validity in relation to
time as the method. The small method effects (of multitrait
monomethod correlations higher than multitrait multimethod
correlations) we found were as expected, with correlations
of theoretically related dimensions being high at the same
measurement occurrence, particularly within some domains. We
can therefore confirm Hypothesis 1b.
Convergent and Divergent Validity (H2)
To test for divergent and convergent validity we correlated
all COPSOQ-II scales with covariates of particular importance
for school principals’ occupational wellbeing, (i.e., another
MTMM model), based on our theoretical review and assumed
a-priori relationships. Indeed, we found all of our hypothesized
convergent relationships to show the stronger correlations on
average than the non-matching ones. Further, almost all of the
predicted relationships reflected the highest correlation of that
specific covariate with the predicted COPSOQ-II scale, thus also
providing strong evidence for divergent validity.
For covariates that represented typical stress sources, one
scale, however, did not show the expected relationships.
Interpersonal conflicts showed its highest correlation with Role
Conflicts and not as hypothesized with Trust in Management.
However, Trust in Management was the second highest
correlation and the difference seemed marginal.
In line with our expectations depression correlated highest
with the depressive symptoms scale. Nevertheless, it showed very
high correlations with various COPSOQ-II scales. The reason for
this pattern might be because depression can be viewed as an
outcome variable or consequence of being repeatedly exposed to
stressors/demands over time, rather than being a demand itself
that may or may not cause such mental health outcome (Dicke
et al., 2014). Such a differentiation between stressors and strain
has become standard in research on occupational wellbeing and
is reflected in models such as the aforementioned JCM (Hackman
and Oldham, 1976), DCM (Karasek, 1979), ERI (Siegrist, 1996),
and the more recent Job-Demands Resources model (Schaufeli
and Bakker, 2004; Bakker and Demerouti, 2007). Thus, a high
level of depression will be related to the appearance of high
levels of other strain variables (as reflected by correlations with
indicators such as sleeping troubles or exhaustion which can be
considered as a symptom of depression), but also with high levels
of demands that caused this strain in the first place. Overall,
the results reported here show that the identified principal
stressors are significantly related to principals’ demands and to
ill-health outcomes, while autonomy and confidence are related
Frontiers in Psychology | www.frontiersin.org 12 April 2018 | Volume 9 | Article 584
Dicke et al. The Copenhagen Psychosocial Questionnaire
to positive outcomes such as self-efficacy and job satisfaction.
Future research should explore these sequential and causals
relationships of psychosocial factors in school principals, with
regard to important stress models the COPSOQ-II is based on
to confirm these assumptions longitudinally. This will enable
derivation of empirical and practical implications and further
advance research into this red flagged occupational group.
Individual Differences Due to Demographic
Characteristics (H3)
Surprisingly our results did not reveal large differences due to
demographic characteristics in the psychosocial risk factors of
the school principals. In contrast to our assumptions we found
inconsistent results for gender predicting ill-being as some other
researchers had demonstrated as well (Friedman, 2002; Dewa
et al., 2009; Darmody and Smyth, 2016). In addition, despite
there only being small effects, older principals showed mostly
lower levels negative risk factors and thus higher wellbeing
than younger ones, while less experienced principals didn’t have
higher job satisfaction, but rather showed higher demands and
lower wellbeing. This could indicate that individuals entering the
role of school principal are more vulnerable in very early stages,
but that more experience, while protective, increases skepticism
about the role. This result also suggests that the increased risks
to younger people in the role needs to be investigated more
thoroughly as there is going to be a significant lowering of
the age of principals, and previous research indicates significant
increased risk for this incoming cohort (Kuper and Marmot,
2003).
In line with what we expected location and school type
didn’t show meaningful effects on the psychosocial risk factors
in general. The strongest predictions were through working at a
private rather than a public school. Indeed, working as a school
principal in the private sector was predictive of many positive
factors (Darmody and Smyth, 2016). This is not surprising as
these school principals will have higher resources and greater
autonomy. A deeper investigation of what exactly is a protective
job or personal resource of these principals is important with
regard to practical implications such as best practices that could
be transferred to the public sector.
School Principal’s Risk Profiles (H4)
The profiles revealed predominantly level effects with profiles
from high to no risk. Here we want to discuss two risk factors
that stood out, namely Job Insecurity and Family-Work Conflict.
Job Insecurity. Job Insecurity seems to be one of the most
extreme values in all profiles and as it’s direction is in line with
most scales, we propose Job Insecurity to be one of the main
drivers of the level of the profiles. Early on seminal work, such
as Karasek et al. (1998) recognized the important role of job
insecurity in the stress process. However, job insecurity has not
yet been examined closely in school principals (or even teachers
but see Vander Elst et al., 2014) indicating an important area
for future research. It is an important factor as it is increasingly
used as a new public management tool for both teachers and
principals.
Family-work conflict. Family work-conflict (which is often
confounded with work-family conflict in stress research) has
been investigated with female principals (Loder, 2005), who
showed that these such conflicts are an increasing problem for
women administrators. Research with a more general sample of
teachers can confirm that family-work and work family-conflicts
predict strain and burnout (e.g., Gali Cinamon and Rich, 2010).
The results presented here show an interesting pattern of two
profiles were family-work conflict, but not work-family conflict,
which shows a high value in the opposite direction of all other
risk profiles. Put simply in Profile 3 family-work conflict shows
a high-risk influence, while all other risk factors show low risk
(and vice versa for Profile 5). This could indicate that for school
principals in these profiles family-work conflict is involved in a
trade-off. This would mean, that these principals can either have
an overall low risk (high wellbeing), but this comes with the cost
of a high family work conflict (Profile 3), or that they can have
a good family-work balance, but that comes with the cost of an
otherwise higher risk (low wellbeing). Further, the difference in
the patterns for family-work and work-family conflict are another
interesting area for future research, not just in the occupation of
school principals.
Interestingly, Research has focused on the relationship
between job insecurity and work-family conflict [which is often
time confounded with family work conflict; Vander Elst et al.
(2014)]. Vander Elst et al. (2014) could show a reciprocal
relationship of these two risk factors for men in a sample of
teachers and explain this relationship through spillover effects of
job insecurity on the spouse and negative effects that impact any
children. Next steps should be to investigate if the profiles are
meaningful predictors of additional outcomes such as attrition or
turn-over.
LIMITATIONS AND FUTURE RESEARCH
As mentioned above, one limitation of the present study was that
the high number of items and factors included in the COPSOQ-
II lead to very complex and computationally demanding models,
making standard analyses, as in our case tests for longitudinal
invariance, impossible. Researchers who desire to use the
instrument longitudinally should focus on a limited number of
scales relevant to their research, and test invariance only for these
scales. Alternatively, they can make use of the MTMM approach
suggested in the present study.
Additionally, due to the nature of data collection in our study
we were able to look at the correlations with our covariates only
cross-sectionally. Future studies need to include longitudinal
predictions and outcomes to fully explore convergent and
divergent validity of the instrument. It would be of importance
to continue investigating the correlations with other established
questionnaires that assess work place wellbeing.
Finally, the aim of the present study was to validate the
COPSOQ-II in an at risk occupational group, namely Australian
school principals, to be able to provide a practical benefit
for future studies considering this group. Although many
studies have shown the applicability of the COPSOQ-II in
Frontiers in Psychology | www.frontiersin.org 13 April 2018 | Volume 9 | Article 584
Dicke et al. The Copenhagen Psychosocial Questionnaire
other languages and cultures already, future research should
test for invariance and compare the instruments properties
over countries and occupations to provide evidence for the
generalizability of the COPSOQ-II.
CONCLUSION
The present study provides an important contribution to
investigating the still understudied occupational group of
school principals and validating the comprehensive COPSOQ-
II instrument, considering its rapidly growing popularity
with researchers and workplaces alike. The proposed factorial
structure of the COPSOQ-II shows a very good fit to our large
data set of school principals. In addition, the COPSOQ-II showed
good support for convergent and discriminant validity over time.
We found expected associations with covariates considered to be
important for school principals’ occupational wellbeing, which
provided strong evidence for convergent and divergent validity
in relation to external variables. We found overall very little
effects of demographic variables in predicting the psychosocial
risk factors. Working in the private sector however, seems to be
beneficial for health and demands alike. School principals can
be clustered in five profiles of different risk levels (from high to
minimal risk). Job insecurity and family-work conflict play an
important role in the formation of these profiles. Overall, the
COPSOQ-II can be considered a beneficial tool for investigating
psychosocial factors for school principals. Identifying these
factors is of major importance, as principal’s wellbeing is not
only crucial in its own right, but also effects, teacher’s and
students’ wellbeing and achievement. The present study took an
important additional step in confirming a comprehensive and
validated measurement instrument, thereby further closing the
theory application gap and enabling the development of effective
workplace interventions.
ETHICS STATEMENT
This study was carried out in accordance with the
recommendations of Monash University and Australian
Catholic University Human Research Ethics Committees
(HREC) with written informed consent from all subjects. All
subjects gave written informed consent in accordance with the
HREC recommendations. The protocol was approved by the
Monash University and Australian Catholic University HREC.
AUTHOR CONTRIBUTIONS
TD was the main initiator of this research. HM supported
in the application of exploratory structural equation modeling
(ESEM), while PR is an expert and advised in substantive
matters regarding school principals as he has been working
with them for years. PP and JG both supported the lead
author with the methods applied in this research. MH was
involved in data preparation and substantively edited the
manuscript.
FUNDING
This article was supported by a grant from the Australian
Research Council to HM and PR (LP160101056).
REFERENCES
Arens, A. K., and Morin, A. J. (2016). Relations between teachers’ emotional
exhaustion and students’ educational outcomes. J. Educ. Psychol. 108, 800–803.
doi: 10.1037/edu0000105
Asparouhov, T., and Muthén, B. (2009). Exploratory structural
equation modeling. Struct. Equat. Model. Multidiscip. J. 16, 397–438.
doi: 10.1080/10705510903008204
Bailey, T. S., Dollard, M. F., and Richards, P. A. (2015). A national standard
for psychosocial safety climate (PSC): PSC 41 as the benchmark for low risk
of job strain and depressive symptoms. J. Occup. Health Psychol. 20, 15–26.
doi: 10.1037/a0038166
Bakker, A. B., and Demerouti, E. (2007). The job demands-resources model: state
of the art. J. Manag. Psychol. 22, 309–328. doi: 10.1108/02683940710733115
Berthelsen, H., Hakanen, J., Kristensen, T., Lönnblad, A., and Westerlund, H.
(2016). A qualitative study on the content validity of the social capital scales
in the Copenhagen Psychosocial Questionnaire (COPSOQ II). Scand. J. Work
Organ. Psychol. 1:5. doi: 10.16993/sjwop.5
Bjorner, J. B., and Pejtersen, J. H. (2010). Evaluating construct validity
of the second version of the Copenhagen Psychosocial Questionnaire
through analysis of differential item functioning and differential item
effect. Scand. J. Public Health 38, 90–105. doi: 10.1177/140349480935
2533
Browne, M. W. (2001). An overview of analytic rotation in
exploratory factor analysis. Multivariate Behav. Res. 36, 111–150.
doi: 10.1207/S15327906MBR3601_05
Burr, H., Albertsen, K., Rugulies, R., and Hannerz, H. (2010). Do dimensions from
the Copenhagen Psychosocial Questionnaire predict vitality and mental health
over and above the job strain and effort—reward imbalance models? Scand. J.
Public Health 38, 59–68. doi: 10.1177/1403494809353436
Campbell, D. T., and Fiske, D. W. (1959). Convergent and discriminant
validation by the multitrait-multimethod matrix. Psychol. Bull. 56, 81–105.
doi: 10.1037/h0046016
Campbell, D. T., and O’Connell, E. J. (1967). Methods factors in multitrait-
multimethod matrices: multiplicative rather than additive? Multivariate Behav.
Res. 2, 409–426. doi: 10.1207/s15327906mbr0204_1
Caplan, R., Cobb, S., French, J., and Harrison, R. (1975). Job Demands and Worker
Health, Main Effects and Occupational Differences. Washington, DC: NIOSH.
Carr, A. (1994). Anxiety and depression among school principals–warning,
principalship can be hazardous to your health. J. Educ. Admin. 32, 18–34.
doi: 10.1108/09578239410063094
Caruso, C. C., Hitchcock, E. M., Dick, R. B., Russo, J. M., and Schmit, J. M. (2004).
Overtime and Extended Work shifts: Recent Findings on Illnesses, Injuries, and
Health Behaviors. Cincinnati, OH: U. S. Department of Health and Human
Services, Centers for Disease Control and Prevention, National Institute for
Occupational Safety and Health.
Clausen, T., and Borg, V. (2010). Psychosocial work characteristics as predictors
of affective organisational commitment: a longitudinal multi-level analysis
of occupational well-being. Appl. Psychol. Health Well-Being 2, 182–203.
doi: 10.1111/j.1758-0854.2010.01031.x
Clausen, T., Nielsen, K., Carneiro, I. G., and Borg, V. (2012). Job demands, job
resources and long-term sickness absence in the Danish eldercare services:
a prospective analysis of register-based outcomes. J. Adv. Nurs. 68, 127–136.
doi: 10.1111/j.1365-2648.2011.05724.x
Collie, R. J., Shapka, J. D., Perry, N. E., and Martin, A. J. (2015). Teachers’
beliefs about social-emotional learning: identifying teacher profiles and
Frontiers in Psychology | www.frontiersin.org 14 April 2018 | Volume 9 | Article 584
Dicke et al. The Copenhagen Psychosocial Questionnaire
their relations with job stress and satisfaction. Learn. Instr. 39, 148–157.
doi: 10.1016/j.learninstruc.2015.06.002
Collie, R. J., Shapka, J. D., Perry, N. E., and Martin, A. J. (2016). Teachers’
psychological functioning in the workplace: Exploring the roles of contextual
beliefs, need satisfaction, and personal characteristics. J. Educ. Psychol. 108,
788–799. doi: 10.1037/edu0000088
Cox, T., and Griffiths, A. (2005). “The nature and measurement of work- related
stress: Theory and practice,” in Evaluation of Human Work, eds J. R. Wilson and
N. Corlett (London, UK: CRS Press), 553–571.
Dadaczynski, K., and Paulus, P. (2011). Psychische Gesundheit aus Sicht
von Schulleitungen: Erste Ergebnisse einer internationalen Onlinestudie für
Deutschland [Psychological health from principals’ perspective: First results
of a German online study]. Psychol. Erziehung Unterricht 58, 306–318.
doi: 10.2378/peu2011.art08d
Dadaczynski, K., and Paulus, P. (2015). “Healthy principals–healthy schools?
A neglected perspective to school health promotion,” in Schools for Health
and Sustainability, eds V. Simovska and P. McNamara (Dordrecht: Springer),
253–273.
Darmody, M., and Smyth, E. (2016). Primary school principals’ job
satisfaction and occupational stress. Int. J. Educ. Manag. 30, 115–128.
doi: 10.1108/IJEM-12-2014-0162
Day, D. (2011). “Leadership development, in The SAGE Handbook of Leadership,
eds A. Byrman, D Collinson, K. Grint, B Jackson and M. Uhil-Bien (Thousand
Oaks, CA; London: SAGE), 37–50.
De Jonge, J., and Schaufeli, W. B. (1998). Job characteristics and employee
well-being: a test of Warr’s Vitamin Model in health care workers
using structural equation modelling. J. Organ. Behav. 19, 387–407.
doi: 10.1002/(SICI)1099-1379(199807)19:4<387::AID-JOB851>3.0.CO;2-9
Dewa, C. S., Dermer, S. W., Chau, N., Lowrey, S., Mawson, S., and Bell, J. (2009).
Examination of factors associated with the mental health status of principals.
Work 33, 439–448.
Dicke, T., Elling, J., Schmeck, A., and Leutner, D. (2015a). Reducing
reality shock: the effects of classroom management skills training on
beginning teachers. Teach. Teacher Educ. 48, 1–12. doi: 10.1016/j.tate.2015.
01.013
Dicke, T., Parker, P. D., Holzberger, D., Kunter, M., and Leutner, D. (2015b).
Beginning teachers’ efficacy and emotional exhaustion: Latent changes,
reciprocity, and the influence of professional knowledge. Contemp. Educ.
Psychol. 41, 62–72. doi: 10.1016/j.cedpsych.2014.11.003
Dicke, T., Parker, P. D., Marsh, H. W., Kunter, M., Schmeck, A., and Leutner, D.
(2014). Self-efficacy in classroom management, classroom disturbances, and
emotional exhaustion: a moderated mediation analysis of teacher candidates.
J. Educ. Psychol. 106, 569–583. doi: 10.1037/a0035504
Dicke, T., Stebner, F., Linninger, C., Kunter, M., and Leutner, D. (2017).
A Longitudinal Study of Teachers’ Occupational Well-Being: Applying the
Job Demands-Resources Model. J. Occup. Health Psychol. 23, 262-277.
doi: 10.1037/ocp0000070
Enders, C. K. (2010). Applied Missing Data Analysis. New York, NY; London:
Guilford Press.
Federici, R. A., and Skaalvik, E. M. (2012). Principal self-efficacy: relations with
burnout, job satisfaction and motivation to quit. Soc. Psychol. Educ. Int. J. 15,
295–320. doi: 10.1007/s11218-012-9183-5
Fraley, C., and Raftery, A. E. (2007). Bayesian regularization for normal
mixture estimation and model-based clustering. J. Classificat. 24, 155–181.
doi: 10.1007/s00357-007-0004-5
Fraley, C., Raftery, A. E., and Scrucca, L. (2014). MCLUST for R: Normal
Mixture Modeling and Model-based Clustering.Classification, and Density
Estimation. Available online at: https://www.stat.washington.edu/sites/default/
files/files/reports/2012/tr597.pdf
Frese, M., and Zapf, D. (1994). “Action as the core of work psychology: a German
approach,” in Handbook of Industrial and Organizational Psychology, 2nd Edn,
Vol. 4, eds H. C. Triandis, M. D. Dunnette, and L. M. Hough (Palo Alto, CA:
Consulting Psychologists Press), 271–340.
Friedman, I. A. (2002). Burnout in school principals: role related antecedents. Soc.
Psychol. Educ. 5, 229–251. doi: 10.1023/A:1016321210858
Fuller, E., and Hollingworth, L. (2017). Questioning the use of outcome measures
to evaluate principal preparation programs. Leadership Policy Schools 1–22.
doi: 10.1080/15700763.2016.1270332
Gali Cinamon, R., and Rich, Y. (2010). Work family relations: antecedents and
outcomes. J. Career Assess. 18, 59–70. doi: 10.1177/1069072709340661
Gallant, A., and Riley, P. (2014). Early career teacher attrition: new
thoughts on an intractable problem. Teacher Dev. 18, 562–580.
doi: 10.1080/13664530.2014.945129
Gates, S. M., Ringel, J. S., Santibanez, L., Guarino, C., Ghosh-Dastidar, B., and
Brown, A. (2006). Mobility and turnover among school principals. Econ. Educ.
Rev. 25, 289–302. doi: 10.1016/j.econedurev.2005.01.008
Green, R., Malcolm, S., Greenwood, K., Small, M., and Murphy, G. (2001). A survey
of the health of Victorian primary school principals. Int. J. Educ. Manag. 15,
23–30. doi: 10.1108/09513540110366114
Grissom, J. A., Loeb, S., and Mitani, H. (2015). Principal time management
skills: Explaining patterns in principals’ time use, job stress, and perceived
effectiveness. J. Educ. Admin. 53, 773–793. doi: 10.1108/JEA-09-2014-0117
Hackman, J. R., and Oldham, G. R. (1976). Motivation through the design
of work: test of a theory. Organ. Behav. Hum. Perform. 16, 250–279.
doi: 10.1016/0030-5073(76)90016-7
Hu, L. T., and Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance
structure analysis: conventional criteria versus new alternatives. Struct. Equat.
Model. Multidiscip. J. 6, 1–55. doi: 10.1080/10705519909540118
Ilies, R., Huth, M., Ryan, A. M., and Dimotakis, N. (2015). Explaining the links
between workload, distress, and work–family conflict among school employees:
physical, cognitive, and emotional fatigue. J. Educ. Psychol. 107, 1136–1149.
doi: 10.1037/edu0000029
Karasek, R. (1990). Lower health risk with increased job control among white collar
workers. J. Organ. Behav. 11, 171–185. doi: 10.1002/job.4030110302
Karasek, R., Brisson, C., Kawakami, N., Houtman, I., Bongers, P., and Amick,
B. (1998). The Job Content Questionnaire (JCQ): an instrument for
internationally comparative assessments of psychosocial job characteristics. J.
Occup. Health Psychol. 3, 322–355. doi: 10.1037/1076-8998.3.4.322
Karasek, R. A. Jr. (1979). Job demands, job decision latitude, and mental strain:
implications for job redesign. Adm. Sci. Q. 24, 285–308. doi: 10.2307/2392498
Klusmann, U., Kunter, M., Trautwein, U., Lüdtke, O., and Baumert, J.
(2008). Teachers’ occupational well-being and quality of instruction: the
important role of self-regulatory patterns. J. Educ. Psychol. 100:702.
doi: 10.1037/0022-0663.100.3.702
Klusmann, U., Richter, D., and Lüdtke, O. (2016). Teachers’ emotional exhaustion
is negatively related to students’ achievement: Evidence from a large-scale
assessment study. J. Educ. Psychol. 108, 1193–1203. doi: 10.1037/edu0000125
Koh, W. L., Steers, R. M., and Terborg, J. R. (1995). The effects of transformational
leadership on teacher attitudes and student performance in Singapore. J. Organ.
Behav. 16, 319–333. doi: 10.1002/job.4030160404
Kompier, M. (2003). “Job design and well-being,” in The Handbook of Work and
Health Psychology, eds M. J. Schabracq, J. A. M. Winnubst and C. L. Cooper
(Chichester: Wiley), 429–454.
Kristensen, T. S. (2010). A questionnaire is more than a questionnaire. Scand. J.
Public Health 38, 149–155. doi: 10.1177/1403494809354437
Kristensen, T. S., Hannerz, H., Høgh, A., and Borg, V. (2005). The Copenhagen
Psychosocial Questionnaire-a tool for the assessment and improvement of the
psychosocial work environment. Scand. J. Work Environ. Health 31, 438–449.
doi: 10.5271/sjweh.948
Kuper, H., and Marmot, M. (2003). Job strain, job demands, decision latitude,
and risk of coronary heart disease within the Whitehall II study. J. Epidemiol.
Commun. Health 57, 147–153. doi: 10.1136/jech.57.2.147
Kuipers, H., and Van Amelsvoort, P. J. L. M. (1993). Slagvaardig Organiseren:
Inleiding in de Sociotechniek als Integrale Ontwerpleer [Successful Organization:
Introduction to the Socio-Technic Approach as IntegralDesign Theory] Deventer:
Kluwer.
Leithwood, K. (1994). Leadership for school restructuring. Educ. Admin. Quart.
30, 498–518. doi: 10.1177/0013161X94030004006
Leithwood, K., Harris, A., and Hopkins, D. (2008). Seven strong claims
about successful school leadership. School Leadership Manag. 28, 27–42.
doi: 10.1080/13632430701800060
Leithwood, K., and Louis, K. S. (2012). Linking Leadership to Student Learning. San
Francisco, CA: Jossey-Bass.
Loder, T. L. (2005). Women administrators negotiate work-family conflicts in
changing times: an intergenerational perspective. Educ. Admin. Quart. 41,
741–776. doi: 10.1177/0013161X04273847
Frontiers in Psychology | www.frontiersin.org 15 April 2018 | Volume 9 | Article 584
Dicke et al. The Copenhagen Psychosocial Questionnaire
Marsh, H. W. (1988). “Multitrait-multimethod analyses,” in Educational Research
Methodology, Measurement and Evaluation: An International Handbook, ed J.
P. Keeves (Oxford: Pergamon Press), 570–580.
Marsh, H. W. (1996). Positive and negative global self-esteem: a substantively
meaningful distinction or artifactors? J. Pers. Soc. Psychol. 70, 810–819.
doi: 10.1037/0022-3514.70.4.810
Marsh, H. W., Hau, K. T., Balla, J. R., and Grayson, D. (1998). Is more ever too
much? The number of indicators per factor in confirmatory factor analysis.
Multivariate Behav. Res. 33, 181–220. doi: 10.1207/s15327906mbr3302_1
Marsh, H. W., Hau, K. T., and Wen, Z. (2004). In search of golden rules: comment
on hypothesis-testing approaches to setting cutoff values for fit indexes and
dangers in overgeneralizing Hu and Bentler’s (1999) findings. Struct. Equat.
Model. 11, 320–341. doi: 10.1207/s15328007sem1103_2
Marsh, H. W., Lüdtke, O., Muthén, B. O., Asparouhov, T., Morin, A. J. S.,
and Trautwein, U. (2010a). A new look at the Big Five factor structure
through exploratory structural equation modeling. Psychol. Assess. 22, 471–491.
doi: 10.1037/a0019227
Marsh, H. W., Martin, A. J., and Jackson, S. (2010b). Introducing a short version
of the physical self description questionnaire: new strategies, short-form
evaluative criteria, and applications of factor analyses. J. Sport Exer. Psychol.
32, 438–482. doi: 10.1123/jsep.32.4.438
Marsh, H. W., Morin, A. J., Parker, P. D., and Kaur, G. (2014). Exploratory
structural equation modeling: an integration of the best features of exploratory
and confirmatory factor analysis. Annu. Rev. Clin. Psychol. 10, 85–110.
doi: 10.1146/annurev-clinpsy-032813-153700
Marsh, H. W., Muthén, B., Asparouhov, T., Lüdtke, O., Robitzsch, A., Morin, A.
J., et al. (2009). Exploratory structural equation modeling, integrating CFA and
EFA: application to students’ evaluations of university teaching. Struct. Equat.
Model. Multidiscip. J. 16, 439–476. doi: 10.1080/10705510903008220
Marsh, H. W., Nagengast, B., and Morin, A. J. (2013). Measurement invariance of
big-five factors over the life span: ESEM tests of gender, age, plasticity, maturity,
and la dolce vita effects. Dev. Psychol. 49, 1194–1218. doi: 10.1037/a0026913
Marsh, H. W., Richards, G. E., Johnson, S., Roche, L., and Tremayne, P.
(1994). Physical Self-Description Questionnaire: psychometric properties and
a multitrait-multimethod analysis of relations to existing instruments. J. Sport
Exer. Psychol. 16, 270–305. doi: 10.1123/jsep.16.3.270
Marsh, H. W., Scalas, L. F., and Nagengast, B. (2010c). Longitudinal tests
of competing factor structures for the Rosenberg Self-Esteem Scale: traits,
ephemeral artifacts, and stable response styles. Psychol. Assess. 22, 366–381.
doi: 10.1037/a0019225
Maxwell, A., and Riley, P. (2017). Emotional demands, emotional labour and
occupational outcomes in school principals: modelling the relationships. Educ.
Manag. Admin. Leadership 45, 484–502. doi: 10.1177/1741143215607878
McDonald, R. P. (1999). Test theory: A Unified Treatment. Mahwah, NJ: Lawrence
Erlbaum.
Miller, A. (2013). Principal turnover and student achievement. Econ. Educ. Rev. 36,
60–72. doi: 10.1016/j.econedurev.2013.05.004
Moncada, S., Utzet, M., Molinero, E., Llorens, C., Moreno, N., Galtés, A., et al.
(2014). The copenhagen psychosocial questionnaire II (COPSOQ II) in Spain
- a tool for psychosocial risk assessment at the workplace. Am. J. Ind. Med. 57,
97–107. doi: 10.1002/ajim.22238
Morin, A. J., and Marsh, H. W. (2015). Disentangling shape from level effects
in person-centered analyses: An illustration based on university teachers’
multidimensional profiles of effectiveness. Struct. Equat. Model. Multidiscipl.
J. 22, 39–59. doi: 10.1080/10705511.2014.919825
Morin, A. J., Morizot, J., Boudrias, J. S., and Madore, I. (2011). A
multifoci person-centered perspective on workplace affective commitment:
a latent profile/factor mixture analysis. Organ. Res. Methods 14, 58–90.
doi: 10.1177/1094428109356476
Nübling, M., Burr, H., Moncada, S., and Kristensen, T. S. (2014). COPSOQ
International Network: co-operation for research and assessment
of psychosocial factors at work. Public Health Forum 22, A1–A3.
doi: 10.1016/j.phf.2013.12.019
Nübling, M., and Hasselhorn, H. M. (2010). The Copenhagen Psychosocial
Questionnaire in Germany: from the validation of the instrument to the
formation of a job-specific database of psychosocial factors at work. Scand. J.
Public Health 38, 120–124. doi: 10.1177/1403494809353652
Nübling, M., Seidler, A., Garthus-Niegel, S., Latza, U., Wagner, M., Hegewald,
J., et al. (2013). The Gutenberg Health Study: measuring psychosocial
factors at work and predicting health and work-related outcomes with
the ERI and the COPSOQ questionnaire. BMC Public Health 13:538.
doi: 10.1186/1471-2458-13-538
Olesen, K., Carneiro, I. G., Jørgensen, M. B., Rugulies, R., Rasmussen, C. D.,
Søgaard, K., et al. (2012). Associations between psychosocial work environment
and hypertension among non-Western immigrant and Danish cleaners. Int.
Arch. Occup. Environ. Health 85, 829–835. doi: 10.1007/s00420-011-0728-2
Pejtersen, J. H., Bjorner, J. B., and Hasle, P. (2010a). Determining
minimally important score differences in scales of the Copenhagen
Psychosocial Questionnaire. Scand. J. Public Health 38, 33–41.
doi: 10.1177/1403494809347024
Pejtersen, J. H., Kristensen, T. S., Borg, V., and Bjorner, J. B. (2010b). The second
version of the Copenhagen Psychosocial Questionnaire. Scand. J. Public Health
38, 8–24. doi: 10.1177/1403494809349858
Phillips, S., and Sen, D. (2011), “Stress in head teachers,” in Handbook of Stress
in the Occupations, eds J. Langan-Fox and C. L. Cooper (Cheltenham: Edward
Elgar Publishing), 177–201.
Poirel, E., Lapointe, P., and Yvon, F. (2012). Coping with administrative
constraints by Quebec school principals. Canad. J. School Psychol. 27, 302–318.
doi: 10.1177/0829573512461131
R Core Team (2014). R: A Language and Environment for Statistical Computing.
Vienna: R Foundation for Statistical Computing.
Riley, P. (2014). The Australian Principal Occupational Health, Safety & Well-
Being Survey: 2011-2014 Data. Melbourne: ACU. Available Online at: www.
principalhealth.org/au/reports.php.
Riley, P. (2015). The Australian Principal Occupational Health, Safety & Well-Being
Survey: 2015 Data. Melbourne: ACU. Available Online at: www.principalhealth.
org/au/reports.php.
Riley, P. (2017). The Australian Principal Occupational Health, Safety & Well-Being
Survey: 2016 Data. Melbourne, VIC: ACU.
Rosário, S., Fonseca, J., and da Costa, J. T. (2014). “Cultural and linguistic
adaptation and validation of the long version of Copenhagen psychosocial
questionnaire II (COPSOQ II) in Portuguese,” in Occupational Safety and
Hygiene II - Selected Extended and Revised Contributions from the International
Symposium Occupational Safety and Hygiene, eds P. Arezes, J. S. Baptista, M. P.
Barroso, P. Carneiro, P. Cordeiro, N. Costa, R. B. Melo, S. A. Miguel, and G.
Perestrelo (SHO), 441–446. doi: 10.1201/b16490-78
Rugulies, R., Hasle, P., Pejtersen, J. H., Aust, B., and Bjorner, J. B. (2016).
Workplace social capital and risk of long-term sickness absence. Are
associations modified by occupational grade? Eur. J. Public Health 26, 328–333.
doi: 10.1093/eurpub/ckv244
Sahlberg, P. (2015). “Finnish Schools and the Global Education Reform
Movement,” in Flip the System: Changing Education from the Ground
Up, eds J. Evers, and R. Kneyber (London; New York, NY: Routledge),
162–174.
Schat, A. C., Kelloway, E. K., and Desmarais, S. (2005). The Physical
Health Questionnaire (PHQ): construct validation of a self-report
scale of somatic symptoms. J. Occup. Health Psychol. 10, 363–381.
doi: 10.1037/1076-8998.10.4.363
Schaufeli, W. B., and Bakker, A. B. (2004). Job demands, job resources, and their
relationship with burnout and engagement: a multi-sample study. J. Organ.
Behav. 25, 293–315. doi: 10.1002/job.248
Schmitt, T. A., and Sass, D. A. (2011). Rotation criteria and hypothesis
testing for exploratory factor analysis: implications for factor pattern
loadings and interfactor correlations. Educ. Psychol. Meas. 71, 95–113.
doi: 10.1177/0013164410387348
Scrucca, L., Fop, M., Murphy, T. B., and Raftery, A. E. (2016). mclust 5: Clustering,
classification and density estimation using gaussian finite mixture models. R J.
8, 289–317.
Siegrist, J. (1996), Adverse health effects of high effort-low reward conditions at
work. J. Occup. Health Psychol. 1, 27–43.
Stiglbauer, B. (2017). Under what conditions does job control moderate the
relationship between time pressure and employee well-being? Investigating
the role of match and personal control beliefs. J. Organ. Behav. 38, 730–748.
doi: 10.1002/job.2165
Ten Bruggencate, G., Luyten, H., Scheerens, J., and Sleegers, P. (2012).
Modeling the influence of school leaders on student achievement: how
can school leaders make a difference? Educ. Admin. Quart. 48, 699–732.
doi: 10.1177/0013161X11436272
Frontiers in Psychology | www.frontiersin.org 16 April 2018 | Volume 9 | Article 584
Dicke et al. The Copenhagen Psychosocial Questionnaire
Thorsen, S. V., and Bjorner, J. B. (2010). Reliability of the Copenhagen
psychosocial questionnaire. Scand. J. Public Health 38, 25–32.
doi: 10.1177/1403494809349859
Torff, B., and Sessions, D. N. (2005). Principals’ Perceptions of
the Causes of Teacher Ineffectiveness. J. Educ. Psychol. 97:530.
doi: 10.1037/0022-0663.97.4.530
Tsouloupas, C. N., Carson, R. L., Matthews, R., Grawitch, M. J., and Barber, L.
K. (2010). Exploring the association between teachers’ perceived student
misbehaviour and emotional exhaustion: the importance of teacher
efficacy beliefs and emotion regulation. Educ. Psychol. 30, 173–189.
doi: 10.1080/01443410903494460
Vander Elst, T., Van den Broeck, A., De Cuyper, N., and De Witte, H. (2014).
On the reciprocal relationship between job insecurity and employee well-
being: mediation by perceived control? J. Occup. Organ. Psychol. 87, 671–693.
doi: 10.1111/joop.12068
Warr, P. (ed.). (1996). “Employee well-being,” in Psychology at Work, 4th Edn
(Chichester: John Wiley & Sons).
Weber, A., Weltle, D., and Lederer, P. (2005). Ill healt h and early retirement among
school principals in Bavaria. Int. Arch. Occup. Environ. Health 78, 325–331.
doi: 10.1007/s00420-004-0555-9
Zheng, X., Zhu, W., Zhao, H., and Zhang, C. (2015). Employee well-being
in organizations: theoretical model, scale development, and cross-
cultural validation. J. Organ. Behav. 36, 621–644. doi: 10.1002/job.
1990
Zinbarg, R. E., Yovel, I., Revelle, W., and McDonald, R. P. (2006). Estimating
generalizability to a latent variable common to all of a scale’s indicators:
a comparison of estimators for ωh. Appl. Psychol. Meas. 30, 121–114.
doi: 10.1177/0146621605278814
Conflict of Interest Statement: The authors declare that the research was
conducted in the absence of any commercial or financial relationships that could
be construed as a potential conflict of interest.
Copyright © 2018 Dicke, Marsh, Riley, Parker, Guo and Horwood. This is an open-
access article distributed under the terms of the Creative Commons Attribution
License (CC BY). The use, distribution or reproduction in other forums is permitted,
provided the original author(s) and the copyright owner are credited and that the
original publication in this journal is cited, in accordance with accepted academic
practice. No use, distribution or reproduction is permitted which does not comply
with these terms.
Frontiers in Psychology | www.frontiersin.org 17 April 2018 | Volume 9 | Article 584
... The structure of the COPSOQ-II consists of higher order domains and contributing subdomains/scales. These have been found to be very robust and stable measures of the psychosocial work environment and health and wellbeing (Burr, Albertsen, Rugulies, & Hannerz, 2010;Dicke et al, 2018;Kiss, De Meester, Kruse, Chavee, & Braeckman, 2013;Thorsen & Bjorner, 2010). All COPSOQ domain scores are transformed to 0-100 aiding comparisons across domains. ...
... The structure of the COPSOQ-II consists of higher order domains and contributing subdomains/scales. These have been found to be very robust and stable measures, by both ourselves (Dicke et al., 2018) and others (Burr, Albertsen, Rugulies, & Hannerz, 2010;Kiss, De Meester, Kruse, Chavee, & Braeckman, 2013;Thorsen & Bjorner, 2010). All COPSOQ domain scores are transformed to 0-100 aiding comparisons across domains. ...
Technical Report
Full-text available
This report presents the average scores for all New Zealand primary school leaders on the main dimensions of the psychosocial work environment and health and wellbeing. Where possible, leaders’ responses in 2021 are compared with the average scores for the New Zealand working population to illustrate the similarities and unique challenges of leaders’ work. We also compare leaders’ responses in 2021 with the responses of leaders in previous rounds of the survey (2016 to 2020) to show changes over time.
... The structure of the COPSOQ-II consists of higher order domains and contributing subdomains/scales. These have been found to be very robust and stable measures of the psychosocial work environment and health and wellbeing (Burr, Albertsen, Rugulies, & Hannerz, 2010;Dicke et al, 2018;Kiss, De Meester, Kruse, Chavee, & Braeckman, 2013;Thorsen & Bjorner, 2010). All COPSOQ-II domain scores are transformed to 0-100 aiding comparisons across domains. ...
... The structure of the COPSOQ-II consists of higher order domains and contributing subdomains/scales. These have been found to be very robust and stable measures, by both ourselves (Dicke et al., 2018) and others (Burr, Albertsen, Rugulies, & Hannerz, 2010;Kiss, De Meester, Kruse, Chavee, & Braeckman, 2013;Thorsen & Bjorner, 2010). All COPSOQ domain scores are transformed to 0-100 aiding comparisons across domains. ...
Technical Report
Full-text available
This report presents the average scores for all New Zealand primary teachers on the main dimensions of the work environment and health and wellbeing. Where possible, teachers’ responses in 2021 are compared with the average scores for the New Zealand working population to illustrate the similarities and unique challenges of teaching work. We also compare teachers’ responses in 2021 with the responses of teachers in previous rounds of the survey (2019 and 2020) to show changes over time.
... In this study, we employed the Copenhagen Psychosocial Questionnaire (COPSOQ III) to measure psychosocial work environment factors and burnout. The COPSOQ questionnaire has being translated into more than 25 different languages, and covers a wide range of contexts, namely health workers (Wagner et al., 2020), the manufacturing industry (Nuruzzakiyah & Hanida, 2020), educational workers (Dicke et al., 2018), and technicians (Moncada et al., 2014), providing evidence of good reliability and validity. ...
Article
Full-text available
The purpose of this paper is to examine the psychosocial work environment factors that influence burnout amongst knowledge workers in the information technology industry. Psychosocial work environment was measured by its six dimensions whereas as burnout was measured as an outcome variable. Two hundred and thirty-two respondents provided their input on self-administrated questionnaire from IT industry. Structural equation modelling was performed to empirically test the relationship between psychosocial workplace environments with burnout. The study found that out of six factors, four factors (conflict & offensive behaviour, interpersonal relationship & leadership, social capital and work organization & job content have significant impact on burnout. However, the study did not find the significant impact of demand at work and work individual interface on burnout. Further the SEM emphasized the significance of conflicts and offensive behaviours among all factors towards burnout. The findings of the study provide organizations with a clear consideration of the critical factors that need to be taken into account to reduce the occurrence of burnout amongst the knowledge workers in the IT industry. The study offers some directions for organizations in preventing burnout amongst knowledge workers in the IT industry.
... The structure of the COPSOQ-II consists of higher order domains and contributing subdomains/scales. These have been found to be very robust and stable measures, by both ourselves (Dicke et al., 2018) and others (Burr, Albertsen, Rugulies, & Hannerz, 2010;Kiss, De Meester, Kruse, Chavee, & Braeckman, 2013;Thorsen & Bjorner, 2010). All COPSOQ domain scores are transformed to 0-100 aiding comparisons across domains. ...
Technical Report
Full-text available
This report presents the average scores for all New Zealand secondary school leaders on the key components of the psychosocial work environment and mental and physical health and wellbeing.
... Thus, a balance of ESEM and parsimony is needed for such instruments. In this study, we employed a recently developed approach-Set-ESEM with target rotationwhere cross-loadings are only allowed within an a priori defined set of factors (Dicke et al., 2018;. For example, Self-Control items are only allowed to cross-load on Responsibility and Persistence facets that belong to the Task Performance domain (see Figure 1). ...
Article
Full-text available
Social-emotional skills have been shown to be beneficial for many important life outcomes for students. However, previous studies on the topic have suffered from many issues (e.g., consideration of only a small subset of skills, single-informant, and single-cohort design). To address these limitations, this study used a multi-informant (self, teacher, and parent) and multi-cohort (ages 10 and 15 from Finland, N = 5,533) perspective to study the association between 15 social-emotional skills and 20 educational (e.g., school grades), social (e.g., relationships with teachers), psychological health (e.g., life satisfaction), and physical health outcomes (e.g., sleep trouble). Results showed that (a) there was a modest level of inter-rater agreement on social-emotional skills, with the highest agreement between students and parents (mean r = .41); (b) inclusion of multi-informant ratings substantially enhanced the ability of social-emotional skills in predicting outcome variables, with parent- and self-rated skills playing important, unique roles; (c) by modeling skills at the facet-level rather than at the domain-level, we identified the key skills for different outcomes and found significant variation in facets’ predictive utility even within the same domain; (d) although the older cohort showed lower levels of most social-emotional skills (9/15), there were only minor changes in the inter-rater agreement and predictive utility on outcomes. Overall, Self-Control, Trust, Optimism, and Energy were found among the four most important skills for academic and life success. We further identified the unique contribution of each skill for specific outcomes, pointing the way to effective and precise interventions.
... With its advantages, there is a growing interest in using ESEM to examine an instrument's multidimensionality in various fields such as education and medical research field (Dicke et al., 2018;Karlgren et al., 2020;Pommier et al., 2020;Sancho et al., 2020;Neff et al., 2021). In the sports psychology literature as a whole, however, few studies have utilized ESEM to examine the psychometric properties of the instrument as a model-based analysis. ...
Article
Full-text available
The purpose of this study was to validate the Korean version of the Student-Athletes’ Motivation toward Sports and Academics Questionnaire (SAMSAQ) using exploratory structural equation modeling (ESEM). A total of 412 (men 77%; women 23%) South Korean collegiate student-athletes competing in 27 types of sports from 13 different public and private universities across South Korea were analyzed for this study. ESEM statistical approach was employed to examine the psychometric properties of SAMSAQ-KR. To assess content validity, the SAMSAQ-KR was inspected by a panel of content subject experts. The Athletic Identity Measurement Scale was used to obtain convergent validity. The results of this study illustrated that the SAMSAQ-KR appears to be a robust and reliable instrument.
Article
Purpose Extant literature has demonstrated connections between workplace environment and worker stress, as well as between worker stress and direct service provision. Current research on direct service provision to people experiencing homelessness, however, has not established a clear association between the workplace environment and the quality of direct services provided to clients receiving case management. This study extends the existing research by establishing connections between all of these constructs, specifically within the context of case management services to people experiencing homelessness. Method For this mixed methods study, the authors sampled 16 case managers providing direct services to people experiencing homelessness in one homelessness services organization (HSO) in a large metropolitan area. Through focus group interviews and web-based surveys, the authors collected data on the workplace environment, worker stress, and direct service provision. The authors then analyzed the data using a concurrent nested approach for mixed methods analysis. Results The results of this study suggest that case managers in homelessness HSOs often experience a stressful workplace environment due to workplace cultural norms, inefficient processes, and high expectations placed upon them by both clients and administrators. The stressful workplace environment can interact with client trauma to produce secondary traumatic stress in direct service providers, which then influences client-case manager rapport development. Discussion Study findings point toward specific policies and practices that homelessness HSOs ought to adopt in order to mitigate case managers’ workplace stress and secondary traumatic stress, and negative influences of these stressors on rapport development between case managers and clients experiencing homelessness.
Article
We explore whether decentralization of decision-making influences school principals’ subjective experience of autonomy, job demands, burnout, and job satisfaction. Using six-years of longitudinal data, we used two Australian education reforms as a natural experiment of the effect of decentralization. Exploiting state-to-state variation in the policies, we used difference-in-differences models, finding that the decentralization policies had a small influence on increasing self-perceptions of autonomy without increasing job demands. We also found that the policies had a small positive effect on job satisfaction.