ABSTRACT
Research was conducted on the predictors of burnout in a sample
of teachers in Queensland private schools. A total of 246 teachers
responded to scales that assessed burnout, school and classroom
environments, work pressure, role overload, role ambiguity, role
conflict, teaching efficacy, external locus of control, and selfesteem.
The Maslach Burnout Inventory was used to assess three facets of
burnout: emotional exhaustion, depersonalisation and personal accomplishment.
An hypothesized model of burnout was tested in a LISREL analysis
with post hoc modifications indicating that role overload, work
pressure, classroom environment and selfesteem were predictors
of emotional exhaustion. Depersonalisation was significantly related
to emotional exhaustion, role conflict, selfesteem
and school environment. Teaching efficacy, selfesteem and depersonalisation
were predictors of personal accomplishment.
In 1974, Freudenberger introduced the term burnout to describe the
inability to function effectively in one's job as a consequence
of prolonged and extensive job related stress. Since that time,
incidences of, and research into stress and burnout have increased
with popular emphasis on employees in the human services sector
including social workers, nurses, teachers, lawyers, medical doctors
and police officers (Jackson, Schwab, & Schuler, 1986; Maslach
& Jackson, 1981). A common characteristic of these occupations
is that the nature of the work can be highly emotional. For teachers,
the potential for emotional stress is high since they work with
classes of up to 35 students for long periods of time. The intensely
relational nature of classrooms means that teachers are vulnerable
to emotionally draining and discouraging experiences (Maslach &
Leiter, 1999). Such experiences can lead to dysfunctional teacher
behaviour with obvious implications for the teacher’s wellbeing
and student learning.
This article reports the findings of a study of burnout in Queensland
private school teachers. Specifically, the study investigated the
influence of several hypothesized predictor variables. To provide
a contextual basis for the research, background information on theoretical
and empirical perspectives relating to this research is provided.
THEORETICAL AND EMPIRICAL PERSPECTIVES ON BURNOUT
According to Byrne (1991) and Maslach, Jackson, & Leiter (1996),
the burnout syndrome has three distinct but loosely coupled dimensions:
emotional exhaustion (feelings of being emotionally overextended
and exhausted with one's work), depersonalisation (the
development of negative and uncaring attitudes towards others),
and negative personal accomplishment (the loss of feelings of selfcompetence
and dissatisfaction with one's achievements). Maslach et al. have
developed and validated the Maslach Burnout Inventory
(MBI), an instrument that assesses these three dimensions. This
instrument has been used in burnout research across a wide range
of human environments.
Australian and overseas research has shown that high school teachers
exhibit high levels of stress when compared to other white collar
workers (Bransgrove, 1994). Otto (1986) showed that as many as one
third of Australian teachers reported being very or extremely stressed.
Teachers operating under high levels of stress for significant periods
of time can develop burnout characteristics including less sympathy
towards students, reduced tolerance of students, failure to prepare
lessons adequately and a lack of commitment to the teaching profession.
It follows that the study of teacher burnout is of great importance
to the productivity of teachers and subsequent student learning.
Early attempts to describe stress and burnout emphasized their personal
nature and, accordingly, blamed the individual teacher. This view
has been superseded by a more social view of burnout that recognizes
both background personality variables of the individual and
school characteristics as contributing to burnout in teachers. However,
most studies of burnout have focused largely on the investigation
of background variables like marital status, age, years of teaching
and gender as predictors of burnout (Anderson & Iwanicki, 1984;
Byrne, 1991, 1994; Malik, Mueller, & Meinke, 1991; Maslach &
Jackson, 1981). In fact, empirical studies involving psychosocial
environment dimensions of schools and classrooms as antecedents
to teacher burnout are rare.
According to Guglielmi and Tatrow (1998), serious conceptual problems
have confronted stress and burnout research. Two examples demonstrate
the divergent findings that can arise if variables are operationalized
in quite different ways. On the influence of student
misbehaviour on teacher stress, Hart, Wearing and Conn (1995) concluded
that ‘there is little point in trying to reduce teacher stress
by reducing student misbehaviour’ (p. 27). By contrast, Boyle,
Borg, Falzon and Baglioni (1995) reported that workload and student
misbehaviour accounted for the most variance in predicting teaching
stress. Hart et al. measured student misbehaviour with a single
selfreport item that assessed the time that the teacher spent dealing
with student misbehaviour. It could be argued that such a simplistic
and naïve conceptualisation of student misbehaviour does not
in any way reflect the complex student misbehaviour issues that
teachers handle on a daily basis and which are not related to time.
Similarly, the measure of organizational climate employed by Hart
et al. is simplistic and does not reflect advances in school climate
research since the early 1980s (see, e.g. Fraser, 1994). It seems
clear that different researchers operationalize constructs in quite
different ways. Recent research involving burnout has investigated
links between teacher burnout and perceived selfefficacy in classroom
management (Brouwers & Tomic, 2000), compared stress and burnout
in rural and urban schools (Abel & Sewell, 1999), and studied
the sources of stress and burnout in Hong Kong teachers (Tang &
Yeung, 1999). Although research on learning environments and teacher
burnout have shown remarkable progress over the past 25 years, no
studies utilizing the latest approaches to research in these two
fields have been conducted. The recognition of school and classroom
environments as possible predictors of burnout is consistent with
Lens’s and Jesus’ (1999) psychosocial interpretation of
teacher stress and burnout and Maslach’s (1999) view that the
social environment is at the heart of both understanding the teacher
burnout phenomenon and ameliorating it.
Design of Present Study
The aims of the present study were to:
• validate scales to assess possible predictors of teacher
burnout (viz. school and classroom environment, work pressure, role
overload, role conflict, role ambiguity, teaching efficacy, locus
of control, selfesteem) and Maslach Burnout Inventory scales (viz.
emotional exhaustion, depersonalisation, and personal accomplishment),
and
• investigate whether the postulated model of relationships
among the above predictors and Maslach Burnout Inventory scales
shown in Figure 1 fits the data through the use of structural equation
modelling.
Figure 1. Postulated structural model for teacher burnout (observed
variables,
fixed path loadings from observed variables to latent variables
and error
variances for observed variables have been omitted.)
As shown in Figure 1, both organizational and personality variables
predict burnout variables. It is noteworthy that this model was
based on Byrne’s (1994) research that has related a host of
variables with the three scales of the Maslach Burnout Inventory.
METHOD
Participants
The sample employed in this study consisted of 246 teachers who
teach in private (i.e. nongovernment) schools in Queensland. Table
1 describes the sample which consisted of 99 primary, 103 secondary
and 44 teachers from combined primary and secondary schools. As
indicated earlier in this article, age and gender have been shown
to influence teacher burnout. While Table 1 describes the sample
in terms of gender and age group, these two variables are not the
focus of the present investigation.
Instrumentation
A test battery consisting of several instruments was administered
to each respondent. All instruments had been employed in previous
research in the United States but it was considered mandatory that
the psychometric properties of each scale be reported. Details of
the specific instruments which are described in Table 2 are as follows:
 Classroom environment. A 24item
instrument which assesses teacher’s perceptions of their
classroom psychosocial environments was used. Items were taken
from four scales of a contemporary classroom environment instrument,
the What is Happening in this Classroom (Aldridge & Fraser,
2000; Fraser, 1998). These scales assessed Interactions, Cooperation,
Task Orientation, and Order and Organization in the classroom.
Because of the problematic nature of conducting structural equation
modelling with a large number of observed variables, a single
classroom environment score based on a linear combination of item
responses using
factor scores as coefficients was computed and used in subsequent
modelling. These factor scores were obtained from a confirmatory
factor analysis (CFA). All classroom environment items employed
a 5point response format (viz. Strongly Disagree, Disagree, Not
Sure, Agree, and Strongly Agree).
 School environment. In an analogous
manner to the assessment of classroom environment, schoollevel
environment, was assessed with 36 items from an instrument employed
previously in school environment research (Dorman, Fraser, &
McRobbie, 1997).
These items were from six underlying scales (viz. mission consensus,
empowerment, student support, affiliation, professional interest,
and resource adequacy). As with classroom environment, a single
school environment score based on a linear combination of item
responses using factor scores from a CFA as coefficients was computed.
The response format for all school environment items was the same
as for classroom environment items.
 Role conflict, role ambiguity and role
overload. Three 5item scales that have been validated
in previous research by Pettegrew and Wolf (1982) were used. These
scales have been successfully used by Byrne (1994) in teacher
burnout research in North America. All items employed a five point
response format (viz. Strongly Disagree, Disagree, Not Sure, Agree,
and Strongly Agree).
Table 2: Descriptive Information for Nine Predictor and Three Burnout
Scales
 Teaching efficacy. This 7item
scale is from the Patterns of Adaptive Learning Survey(PALS) (Midgley
et al., 1997). Items employed the same five point response format
used for the above scales.
 Selfesteem. Seven
items from the adult form of Coopersmith’s (1981) SelfEsteem
Inventory were used. A fivepoint scoring format was used (viz.
Very Unlike Me, Unlike Me,Neither, Like Me, Very Like Me).
 External locus of control.
Byrne (1994) suggested the use of Rotter's Locus of Control scale
(MacDonald, 1974; Rotter, 1966) as a measure of locus of control
in burnout research. A modified 10item form of Rotter's Locus
of Control scale was used. Items relating
to internal locus of control were reversescored so that scale
scores were an indication of the respondent's perceived level
of external locus of control. Items used a five point response
format: 1 (Strongly Disagree), 2 (Disagree), 3 (Not Sure), 4 (Agree),
and 5 (Strongly Agree).
 Burnout. A set of 19 items from
the latest version of the Maslach Burnout Inventory Form ES (MBI)
(Maslach et al., 1996) which has been developed especially for
educational institutions was used to provide a selfassessment
of each teacher's perceived burnout level. The original 22item
MBI has three factoranalytically derived scales: emotional exhaustion,
depersonalisation and personal accomplishment. Whereas emotional
exhaustion and depersonalisation are positively related to burnout,
personal accomplishment is negatively related to burnout. A fivepoint
Likert response format ranging from Almost Never to Almost Always
was used to score each item.
Data Analysis and Interpretation
To investigate relationships among the above variables, structural
equation modelling (SEM) using LISREL 8.3 (Joreskog & Sorbom,1993)
was conducted. A weighted least squares (WLS) method with data from
polychoric correlation and asymptotic covariance matrices was
used in the analyses. The WLS method was preferred because item
data had five response categories, and polychoric correlations rather
than Pearson product.moment correlations were computed. In these
circumstances, Joreskog and Sorbom (1993) have argued that WLS is
the appropriate method of analysis.
There were two distinct components to the analyses conducted in
the present study. First, measurement models for each of the variables
were explored. While confirming the measurement of a particular
variable, each of these models provided factor scores to be used
in generating composite factor scores from items. Using theory described
by HolmesSmith and Rowe (1994), these congeneric measurement models
maximized the reliability of composite and latent variables. This
was achieved by computing scale scores as linear combinations of
items with factor scores as item coefficients. According to HolmesSmith
and Rowe, the composite score reliability (e.g. Cronbach alpha)
is maximized if the weights on each item (i.e. coefficients) are
corresponding factor scores rather than unity. Second, computed
composite variables were used subsequently in structural equation
modelling that examined relationships among latent variables. Munck
(1979) showed that path loadings
and error variances
for observed variables can be fixed in structural equation modelling
and that, provided correlation matrices are analysed, they are related
to reliability (r) by the formulae
These formulae allow for paths from observed composite variables
to latent variables and error variances of observed composite variables
to be fixed. The advantage of this approach is that the number of
parameters to be estimated by LISREL is sharply reduced with consequent
improvement in model robustness.
Of the many indices available to report model fit, model comparison
and model parsimony in structural equation modelling, three indices
are reported in the present article: the Root Mean Square Error
of Approximation (RMSEA), the TuckerLewis Index (TLI) and the Parsimony
Normed Fit Index (PNFI). Whereas the RMSEA assesses model fit, the
TLI and PNFI assess model comparison and model parsimony respectively.
To interpret these indices, the following rules which are generally
accepted in the SEM literature as reflecting good models were adopted:
RMSEA should be below .05 with perfect fit indicated by an index
of zero, TLI should be above 0.90 with perfect fit indicated when
TLI = 1.00, and PFNI should be above 0.50 with indices above 0.70
unlikely even in a very sound fitting model. Further discussion
on indices and acceptable values is provided in Byrne (1998), Kelloway
(1998) and Schumacker and Lomax (1998). While the use of ¥ö2
tests to report goodness of fit of the model to the data is acknowledged
as problematic in SEM, it was used in the present study to report
improvements to the overall model fit as post hoc adjustments were
made.
Statistics reported in the present study included squared multiple
correlation coefficients (R2) for each structural equation and a
total coefficient of determination (Joreskog & Sorbom, 1989).
While R2 is a measure of the strength of a linear relationship,
the total coefficient of determination is the amount of variance
in the set of dependent variables explained by the set of independent
variables. In addition to overall fit statistics, it is important
to consider the strength and statistical significance of individual
parameters in the model. Each path was tested using a ttest (p
<.05).
