Personality, psychosocial risks at work, and health

 Personality, psychosocial risks at work, and health
Katharine Parkes, PhD
Department of Experimental Psychology,
University of Oxford, UK
A report prepared for the “Collège d’expertise sur le suivi statistique
des risques psychosociaux au travail” and commissioned by the DREES
March 2010
Summary
This report responds to questions formulated by the Expert Committee about the
role of personality in the process whereby work-related psychosocial risk factors
are implicated in mental and physical health impairment. There are four main
parts to the report. In Section 1, as a background to the specific questions raised,
models of work stress are described, and the various pathways by which
personality and work-related psychosocial factors jointly impact on health, are
examined with reference to empirical findings.
Section 2 documents a systematic review of journal articles (published 20002009) describing prospective studies that evaluate work-related psychosocial risks
and one or more personality measures as predictors of mental and physical health
outcomes. A total of 33 studies which met these and other pre-determined criteria
were identified. Findings from the studies are summarised and discussed, with
particular reference to evidence of the additive, interactive, and mediator roles of
personality in relations between psychosocial risk factors and health outcomes.
Section 3 responds to a further question concerned with changes in personality
across the life course. Normative age-related changes in mean levels of
personality variables are described, and work-related factors associated with
individual change are considered, noting evidence of reciprocal influence between
work experiences and personality change.
Psychometric issues relevant to the development and validation of personality
measures are outlined in Section 4, and the psychometric properties of several
widely-used measures are described in Section 5. Particular attention is given to
evidence of the reliability and validity of the personality measures identified in
Section 2 as significant predictors of health outcomes in the prospective studies
reviewed. Examples of personality measures available in the published literature,
and the scoring methods used, are shown in the Appendix.
Index
1. Introduction
1.1 Demand-control-support model
1.2 Effort-reward imbalance model
1.3 Organizational justice model
1.4 The role of personality in work stress
1
1
2
2
2. Psychosocial risks, personality and health: A systematic review
2.1 Background
2.2 Literature search
2.3 Results
2.4 Findings for individual personality characteristics
2.5 Conclusions
8
10
12
14
20
3. Personality change over the life course
3.1 Mean-level changes in personality across the life course
3.2 Individual differences in patterns of personality change
3.3 Implications
41
44
46
4. Psychometric issues: Reliability and validity of personality tests
4.1 Scale development
4.2 Reliability and validity
4.3 Fairness in psychometric testing
48
48
49
5. Psychometric properties of specific personality measures
5.1 The NEO Five-Factor personality measures
5.2 Over-commitment
5.3 Negative affectivity
5.4 Hostility
5.5 Sense of coherence
5.6 Locus of control
5.7 Core self evaluations
5.8 General issues
50
52
54
56
58
60
61
62
6. References
64
Appendix: Examples of selected tests
80
1
1.
Introduction
Evidence from prospective studies shows that exposure to work-related psychosocial risks
has an adverse impact on long-term health. Much of this evidence comes from research
based on two current models of work stress, the job demand-control-support model
(DCS)1 and the Effort-Reward model (ERI)2. These main features of these models,
together with a more recent model, the Organizational Justice model3, are outlined below.
1.1
Demand-control-support model
In the DCS model, high demands (e.g. time pressures, work overload), low control (few
opportunities to make decisions at work, limited skill utilization), and low social support
are predicted to lead to high psycho-physiological strain and, over time, to adverse health
outcomes. The significance of the DCS dimensions for health outcomes, particularly
cardiovascular disease4-6 and affective well-being7-9 has been widely demonstrated.
Significant findings have also been reported for other outcome measures, including minor
health complaints10, workability11, absenteeism12, and suicide/attempted suicide13,14.
However, the relative importance of the three DCS dimensions in predicting health
outcomes varies across studies. Moreover, concerns have been raised about
methodological limitations, particularly in relation to cross-sectional survey studies 7,15.
Relatively little evidence supports the demand x control interaction originally predicted by
the job strain model16; a review of ‘high quality’ longitudinal studies concluded that there
was only modest support for the interactive hypothesis7. More usually, additive effects are
reported17,18.
1.2
Effort-reward imbalance model
The ERI model proposes that an imbalance between effort (e.g. extrinsic job demands,
responsibilities, and obligations) and rewards (money, promotion prospects, job security)
is a risk factor for poor health2. Unlike the DCS model, recent versions of the ERI model
include an intrinsic personality component, designated ‘over-commitment’ (OC). The
nature and hypothesised role of OC has evolved in the development of the ERI model; it
now refers to a personality trait combining excessive striving with needs for approval and
esteem, and is regarded as a potential moderator variable19.
Findings from prospective studies support the ERI model in that high effort coupled with
low reward is associated with poor mental and physical (particularly cardiovascular)
health 20,21. Job insecurity has been found to add to the adverse effects of ERI22. However,
a recent study questions the value of combining effort and reward into a single measure23,
and other evidence suggests that causal relations between ERI and health may be
reciprocal rather than unidirectional24.
The role of over-commitment has been less widely examined, but it was found to be
significant as an additive risk factor, over and above ERI measures, in four out of five
studies of CVD incidence, and in five out of eleven studies of CVD symptoms in a review
2
of ERI research19. However, the review provided little support for the interactive model
although a more recent survey, using measures from both the ERI and the DCS models,
reported that low control (DCS model) and high OC (ERI model) combined
synergistically to give rise to a high levels of depressive symptoms25.
1.3
Organizational justice model
Lack of organizational justice in the workplace has been recognised as a psychosocial risk
factor that can lead to adverse mental and physical health outcomes3. The ‘Organizational
Justice’ model has two components, procedural injustice (decisions at work lack
consistency, openness and input from all affected parties) and relational injustice (lack of
considerate and fair treatment of employees by supervisors); these components have been
found to predict sleeping problems26, poor mental health27 and cardiovascular mortality28
in prospective studies and to explain variance in health outcomes over and above that
accounted for by ERI measures27.
1.4
The role of personality in work stress
The models outlined above do not incorporate individual personality characteristics as
predictors of mental and physical health outcomes (with the exception of the OC measure
in the ERI model). However, personality is known to be significant in relation to longterm health29-31, and several work stress models include paths among personality traits,
objective and perceived work stressors and health outcomes. One such model, the
‘Michigan’ model32,33 has been particularly influential in guiding research into the joint
effects of personality variables and psychosocial work stressors, and is used as the basis
for the present discussion.
As represented in this model, shown in Figure 1.1, objective work characteristics influence
subjective perceptions of work stress; these perceptions give rise to short-term affective,
cognitive, behavioural and physiological responses which, with continued stressor
exposure, lead to chronic long-term health impairment. However, the model also
incorporates bi-directional pathways and feedback loops; for instance, long-term health
impairment may lead individuals to perceive their work conditions less favourably, or to
seek an objectively less demanding job.
The influence of individual differences operates at several points in the stress process
represented by the Michigan model. For instance, personality traits (and other individual
characteristics) may act by influencing selection into different types of job and hence
exposure to objective stressors40, or by influencing work perceptions34, or by directly
influencing stress responses and health35,36. Moreover, the model includes not only direct
effects of personality, but also mediating and moderating effects, and bi-directional paths.
Personality variables, particularly negative affectivity, are potentially involved in each of
these mechanisms37.
MODERATOR VARIABLES: INDIVIDUAL AND SITUATIONAL CHARACTERISTICS
Social support
Management
style
Personality
Coping resources
Demographic
e.g. age, gender,
education
Objective work stressors
Work overload
Long work hours
Paced work, time pressures
Lack of control over work tasks
Shift work
Organizational re-structuring,
down-sizing, job insecurity
Behavioural
e.g. exercise
diet
Short-term responses
Perceived work
stressors
Genetic
e.g. family history
of illness
Long-term outcomes
Affective
Medical
Cognitive
Psychological
Behavioural
Physiological
e.g. Cardiovascular disease
e.g. Chronic depression
Anxiety disorder
Behavioural
e.g. Alcoholism
Figure 1.1 Conceptual model of the stress process
Note: Solid lines represent direct effects among variables. Broken lines represent interaction effects.
Adapted from Israel et al. (1996)
3
4
1.4.1
Personality and work stress: Mediating effects
Mediation refers to an indirect process by which the effect of one variable on an outcome
measure is transmitted through an intervening variable (see Baron and Cohen38 for
statistical methods of testing mediation effects). Thus, personality may influence how
individuals perceive the objective work demands to which they are exposed and these
perceptions in turn may lead to short-term affective, physiological, and behavioural
responses, and eventually to chronic health effects. In this presumed sequence, the effects
of personality on long-term health are mediated through perceived work stressors and
short-term stress responses.
In a longitudinal study of mediation effects using a cross-lagged panel design, Schwarzer
et al.39 examined the role of self-efficacy in the process by which work stress led to
burnout in teachers. The results showed that self-efficacy was a protective factor;
resourceful individuals high in self-efficacy experienced less job stress, which in turn
reduced subsequent burnout. Moreover, over the one-year follow-up period, the path from
earlier self-efficacy to later burnout was stronger than the (non-significant) reverse causal
path. Similarly, a measure of personality resources assessed in childhood and early
adulthood was found to predict job satisfaction in middle adulthood, and this relationship
was partially mediated by job complexity40.
A different form of mediation was reported by Kivimaki et al.41 from a 7 yr follow-up
study of hostility and sickness absence in which the personality variable ‘Sense of
Coherence’ (SOC)42 acted as mediator, reducing the association between individual
hostility and subsequent sickness absence by 33-50% depending on the outcome measure.
In a further study, Feldt et al.43 found that the longitudinal relationship between
psychosocial stressors (including job insecurity) and psychosomatic/affective outcomes
was mediated by SOC. These two studies suggest that change in SOC may result from
exposure to individual and work-related psychosocial factors.
1.4.2
Personality and work stress: Confounding effects
As used in work stress research, the term ‘confounding’ refers to the role of personality
traits (or other factors such as demographic or socioeconomic variables) in creating an
apparent link between measures of work-related psychosocial risks and health, which may
be solely or partly due to a ‘third factor’ effect, the influence of the confounding
variable(s) on both perceptions of job characteristics and health.
In relation to personality, attention has been focused primarily on neuroticism/NA as a
potential confounding factor, i.e. as a source of self-report bias that should be statistically
controlled in studies that seek to link perceived job stressors and health outcomes44,45.
Accordingly, self-report studies of psychosocial stressors and strain responses often
include NA as a control variable46,47. If inclusion of NA in a multivariate analysis reduces
the relationship between perceived stressors and outcome, then confounding is a possible
explanation (although it should be noted that the statistical test for confounding is the
same as that for mediation; interpretation depends on the nature of the variables
concerned, and the underlying theoretical viewpoint).
5
The view that NA is simply a ‘nuisance’ variable and a potential source of bias has been
disputed by other researchers, particularly Spector and his colleagues34,48, who argue that
there is little evidence to support a general bias effect that cuts across all stressor and
strain variables. They suggest that NA plays an important role in work stress processes
which should be addressed rather than statistically controlled, and they describe a series of
substantive mechanisms through which NA could affect job stressors and strains, each
supported by research evidence34:
•
•
•
•
•
•
Perception. This mechanism reflects the tendency of individuals high in NA to view
the world in a negative light, but their negative self-reports of job stressors are seen
as valid indicators of their actual perceptions and experiences.
Hyper-responsivity. High NA individuals may respond to the same objective level of
stressors more strongly than their low NA counterparts, in which case NA would act
as a moderating variable in relations between job stressors and affective responses.
Selection. High NA individuals may be in more stressful jobs than those low in NA,
because they are recruited for less favourable jobs, or because of self-selection.
Stressor creation. High NA individuals may, by their own behaviour, create job
stressors for themselves, for example, by creating interpersonal conflicts at work, or
by managing their workload less effectively than low NA individuals.
Mood. Transitory mood may affect the assessment of NA. If so, and if mood is also
affected by job conditions, then a correlation between NA and job stressors could
reflect the indirect influence of job stressors on reports of NA, rather than the effects
of NA on reports of job stressors.
Causality. Exposure to high levels of job stressors may tend to make individuals
higher in NA; thus, this mechanism proposes that job characteristics affect the trait
level of NA, as assessed empirically.
Whilst these possible mechanisms are discussed in relation to NA by Spector et al.34, they
are not necessarily specific to NA; for example, Hoge et al.49 found that sense of
coherence had not only direct effects on strain measures, but also showed significant
perception, selection, and stressor-creation effects, although work stressors remained
substantial predictors of strain.
1.4.3
Personality and work stress: Reciprocal effects and reverse causation
Although theoretical models represent job stressors as having a causal effect on health
outcomes, evidence of reciprocal and reverse causal relationships between work-related
psychosocial risk factors and health outcomes is increasing. In some instances, also,
personality variables are implicated in these reverse or reciprocal relationships. A review
of longitudinal field studies of organizational stress, found that about half of the 43 studies
evaluated reverse causation in stressor-strain relationships, and some evidence of reversed
causal effects was found in about 33% of the studies concerned50. More recently, in an
evaluation of the demand-control model, Dalgard et al.51 found evidence of reverse
causation over an 11-yr follow-up period; in this case, it applied to job demands but not to
job control.
6
Several studies have applied structural modelling to longitudinal panel data which allows
evaluation not only of direct and reverse causation but also of reciprocal causal effects
between exposures and health measures. Although at least one study using structural
modelling has affirmed the theoretical causal ordering among DCS job characteristics and
work-related psychological well-being52, reciprocal longitudinal findings have also been
reported53. Similarly, in an evaluation of cross-lagged reciprocal relationships among job
demands, work-home interference, and general health, a reciprocal model proved to be
superior to both the direct causation and the reversed causation models54.
Structural modelling has also been used to evaluate causal paths between ERI measures
and health; using three waves of data collection, Shimazu et al.24 found cross-lagged and
causally dominant effects of ERI on employee psychological and physical health, but also
evidence of reciprocal effects. Similarly, Xanthopoulou et al.55 found that the best-fit
model was one in which not only were personal resources (self-esteem, self-efficacy and
optimism) and job resources (e.g. autonomy, support) reciprocally related to work
engagement, but there was also a reciprocal relationship between job and personal
resources.
Taken together, these longitudinal studies do not provide strong support for uni-directional
reverse causal paths from strains to stressors although, consistent with the ERI and DCS
theoretical models, there is evidence of causal influences from psychosocial stressors to
health-related outcomes. However, it is also clear that reciprocal paths play an important
role in stressor-strain relationships; moreover, these reciprocal pathways may involve
personality variables in addition to measures of psychosocial risk factors and health. Thus,
even though personality characteristics are conceptualised to be stable over time, as
empirically assessed, they appear to be subject to influence by work conditions over
relatively short time periods.
1.4.4
Personality and work stress: Interactive effects
Additive (or main) effects imply that two or more predictor variables contribute
independently to explaining variance in an outcome measure; in contrast, interactive (or
moderating) effects imply that the magnitude and direction of the effect of one predictor
(for example, a psychosocial risk factor) on a outcome measure depends on the level of a
second predictor (for example, a personality characteristic). Two forms of interaction can
be identified, ‘vulnerability/resilience’ and ‘person-environment fit’.
•
Vulnerability implies that a high level of a maladaptive personality trait if
combined with a high level of a psychosocial risk factor will result in a
disproportionately adverse outcome relative to their additive effects. Thus, for
instance, high NA individuals were found to show significantly greater reactivity
to high job demands than low NA subjects56. Conversely, resilience or buffering
implies that a high level of an adaptive personality resource protects individuals
from adverse effects of exposure to psychosocial risk factors.
7
•
Person-environment fit. In cross-over interactions, neither high nor low levels of a
personality factor are necessarily maladaptive; rather, adverse outcomes arise from
incongruence or ‘lack of fit’ between personality and the environment. For
instance, perceived job control and an individual’s locus of control may be
congruent or incongruent, and have favorable or unfavorable effects, respectively,
on outcomes57.
Although the DCS model does not include personality variables, evidence that personality
characteristics may act as moderators of the effects of exposure to high strain (or high isostrain) conditions has been reported. For instance, a significant three-way interaction was
found to predict affective well-being in cross-sectional and longitudinal data58; demand
and control combined interactively for externals whereas additive effects were found for
internals. Other researchers have also found that personality characteristics moderate the
effects of DCS dimensions on health outcomes57,59, and have advocated further research to
extend the demand/control model with personal characteristics59.
The points in the causal process at which personality may moderate work stress effects are
not yet clearly established. In particular, moderating variables may influence either or both
links in the paths by which objective stressors influence perceptions of work stress, and
perceptions of work stress are related to psychological and physiological responses. In
particular, little is known about how personality variables may moderate relations between
objective and perceived work stressors, although significant dispositional moderators of
relations between objective and perceived characteristics of laboratory tasks have been
identified60.
8
2.
2.1
Psychosocial risks, personality and health: A systematic review
Background
Over the past five years, prospective research into psychosocial risk factors in relation to
physical and mental health outcomes has been the subject of several systematic reviews. In
particular, detailed reviews of coronary heart disease (CHD)61,62 and mental health
outcomes, including depression21,63,64, in relation to work-related psychosocial exposures
have been published. In these reviews, two of which include meta-analyses63,64, risk
factors defined by two theoretical models of work stress, the demand-control-support
model (DCS)1 and the effort-reward model (ERI)2 play a major role. A further review,
which includes both prospective and cross-sectional findings, focuses on the role of work
stress in relation to coronary risk factors (including hypertension, blood lipids, and
metabolic syndrome)65. More specifically, Van Vegchel et al19 examine research evidence
for the extrinsic (effort reward imbalance) and intrinsic (over-commitment) components of
the ERI model in relation to health-related outcomes.
In general, and with some significant reservations, these reviews conclude that
components of DCS and ERI models are prospective risk factors for heart disease and
poor mental health, particularly depression. Indeed, referring to their meta-analytic review
of work-related psychosocial risk factors in relation to mental health outcomes, Stansfeld
and Candy63 conclude that it provides “robust consistent evidence that (combinations of)
high demands and low decision latitude and (combinations of) high efforts and low
rewards are prospective risk factors for common mental disorders” (p. 443).
Other authors have reviewed the effects of exposure to psychosocial risks from a wider
perspective, considering both individual and environmental factors66-68. In particular, in a
systematic review of prospective epidemiological data, Kuper et al.68 concluded that there
was evidence for the roles of depression, lack of social support, and psychosocial work
characteristics on the aetiology and prognosis of CHD, but that evidence for an effect of
anxiety or Type A /hostility was less consistent. In discussing this research, Kuper et al.
draw attention to the difficulty of determining the extent of bias in the reporting of
psychosocial findings; they also note that bias may occur after publication as strongly
positive results are more likely to be cited by other papers than weak or negative findings.
In the context of the present review, it is relevant that the 71 studies of CHD (published up
to 2001) summarized by Kuper et al. include almost no prospective research that examines
work-related psychosocial risk factors for CHD and personality measures. Moreover, in
two studies reviewed by Kuper et al. that do report both work exposures and personality
characteristics, the latter are used only as control variables, the main interest being the role
of psychosocial work exposures69,70.
Conversely, prospective studies that focus on individual personality traits as predictors of
long-term health outcomes rarely include psychosocial work exposures. Adverse health
outcomes, such as all-cause mortality, cardiovascular mortality, coronary heart disease,
depression and other mental health problems, suicide and attempted suicide, have been
9
linked to aspects of personality in long-term prospective studies. Thus, a 2006 review of
the literature on the development and course of physical illness highlights the adverse
effects of negative affectivity and anger/hostility, and the positive role of optimism29,
while more recent studies identify extraversion, conscientiousness, and sense of coherence
as significant predictors of favourable long-term mental and physical health
outcomes30,71,72. Possible mechanisms underlying the effects of personality on long-term
health include shared genetic influences, health behaviours, and the influence of
personality on appraisal, coping, and physiological reactivity29; personality may also
influence exposure to potential stressors (including those in the workplace) and the stressreducing resources (e.g. social support) available.
One possible explanation of the relative lack of long-term prospective research that
analyses both personality measures and psychosocial work exposures is that the job
strain/iso-strain model1, which has attracted much research attention, only includes work
environment characteristics; it does not take into account the possible role of individual
differences in personality and coping in responses to adverse work conditions. Proponents
of the DCS model point to evidence suggesting that individual differences are unlikely to
account for the association between job strain and CHD6, although the need for further
research into interactions between environmental stressors and personality characteristics
is recognized.
An important exception to the paucity of research combining work exposures with
individual personality characteristics is a study based on data from the Whitehall II study
of UK civil servants5. The analysis included measures of hostility, Type A behaviour,
competitiveness, negative affectivity, minor psychiatric disorder, and two coping patterns,
together with measures of job control, in relation to newly reported CHD events over a 5.3
yr follow-up period. The results showed that when age-adjusted odds ratios for the effects
of low job control on CHD outcomes were compared across sub-groups identified as
having or not having each negative personality characteristic, differences were small and
not consistently in one direction, nor consistently in the same direction for men and
women.
These results were not substantially changed by including other control variables, by use
of different analyses, or when job demand and social support were used as exposure
measures. The authors concluded that adverse effects of low job control could not be
explained by confounding effects of negative personal characteristics, or by a generalized
tendency for neurotic individuals to complain. They also ruled out possible mediating or
moderating effects of negative personality factors, concluding that their findings “seem to
justify the relative disregard of personal factors and individual differences concerning low
job control” (p. 406). However, the authors noted some psychometric problems with their
measures, and suggested that other personal attributes, e.g. locus of control, or use of
different work stress models, might produce different results. Moreover, this study only
considered CHD outcomes, and the authors emphasised that personal characteristics
should certainly not be neglected in the broader field of job stress research.
10
In contrast to the job strain model, the Effort-Reward Imbalance (ERI) model incorporates
an intrinsic component, a personality characteristic designated over-commitment (OC),
which reflects excessive striving combined with a strong need for approval and esteem.
Studies reviewed by Van Vegchel et al.19 suggest that there is evidence for a significant
additive role of OC, but there is little support for the model that predicts that OC and ERI
act synergistically to predict health outcomes.
The aim of the present review is to bring together findings from prospective research
which examines personality factors and work-related psychosocial risks as joint predictors
of mental and physical health outcomes. The review focuses on two main questions:
•
First, is there evidence that personality variables contribute to explaining health
outcomes over and above the effects of work-related psychosocial risk factors? In
this context, not only direct relationships, but also possible confounding,
mediating, and/or reverse or reciprocal effects among personality variables,
psychosocial risk factors, and health outcomes are potentially relevant.
•
Second, is there evidence that personality factors act as moderators of relations
between work-related psychosocial risks and health outcomes?
As described below, a systematic search of the literature published from 2000-2009
inclusive was undertaken to address these questions.
2.2
Literature search
The main literature search was carried out using the ISI Web of Science, and two
components of the ‘Scopus’ database ‘Health Sciences’ (which includes Medline) and
‘Social Sciences & Humanities’ (which covers journals in Psychology and Social
Sciences). Additional searches were carried out using OvidSP. Further material was found
by examining journal articles listed as having cited the articles located in these searches.
The search terms identified prospective/longitudinal studies published in peer-reviewed
journals in the years 2000-2009, in which measures of work-related psychosocial risks,
including (but not restricted to) dimensions from the Demand-Control-Support (DCS)
model and the Effort-Reward Imbalance (ERI) model, together with one or more
personality measures, were used to predict health-related outcomes. Search terms for
personality included specific personality variables in addition to the general terms
‘personality’, disposition*, and trait*. The main search terms used are shown in Table 2.1.
2.2.1
Criteria for selecting studies
The many documents located in these searches were examined to identify studies that met
pre-determined criteria, specified with reference to recent systematic reviews of
psychosocial risk factors in relation to health outcomes63,64,68.
11
Table 2.1
Main terms used in the literature search
Personality
Personality
Disposition*
Trait*
Vulnerable / vulnerability
Resilient / resilience
Negative affect*
Positive affect*
Neuroticism
Extraversion
Optimism
Locus of control (LOC)
Self-efficacy
Sense of coherence (SOC)
Hostility
Type A
Over-commitment (OC)
Coping resources
NEO inventory
NEO-FFI
Outcomes
Mortality
Cardiovascular
Heart disease
Coronary
Psychiatric
Mental health
Distress
Anxiety
Depression
Mood
Affect*
Psychosomatic
Somatic symptoms
Well-being
Job satisfaction
Illness
Sickness absence
Sick leave
Turnover
Psychosocial work environment
(Work* OR job OR occupation*)
AND (stress OR psychosocial)
AND
Method
Demand*
Control
Discretion OR decision authority
OR decision latitude OR autonomy
Social support
Iso-strain OR job strain
Effort AND reward
Organizational injustice
Organizational justice
Work hours OR time pressure
Workload
Job security OR job insecurity
Re-structuring OR down-sizing OR
relocation
Longitudinal*
Prospective*
Follow-up
Search terms in different groups were
joined by AND, and those within groups
by OR
The following criteria were applied in selecting the studies to be included:
•
Longitudinal studies of working age adults which included one or more
psychosocial work exposures AND one or more personality characteristics as
predictors of health-related outcomes.
12
•
•
•
•
•
•
•
•
Outcomes were cardiovascular disease, psychiatric diagnoses, mental and physical
health, psychosomatic complaints, insomnia, job-related affective responses, and
job-related behavioural outcomes (e.g. sickness absence, turnover), identified by
hospital or company records, assessed by formal diagnostic interview, or by
referenced self-report scales.
Clear descriptions of the measures used.
A follow-up period of at least one year
Control for initial level of dependent measure, and/or exclusion of baseline ‘cases’.
A study size of at least 200 participants
Sample located in Europe, North America, New Zealand, Australia, Japan or
Russia
English language articles published in peer-reviewed journals, 2000-2009
Must report statistical results relating to personality variables (i.e. not simply state
that they were used as controls)
A total of 33 studies met the criteria and were included in the present review; the relatively
small number of studies selected was primarily due to the triple requirement for
prospective data in which both personality and psychosocial risk exposures were assessed.
As others have noted73, the majority of studies in this research area are either crosssectional and/or do not include measures of both personality and work characteristics.
2.3
Results
Details of the 33 studies included in the review are summarized in Table 2.2 (pages 2638). The follow-up durations generally ranged from 1-10 years, although two studies used
childhood measures. Both men and women were included in almost all the studies, but
separate results were not always presented; more usually, gender was treated as a control
variable. The analysis methods included multiple regression and structural modelling
(with continuous variables) and, more frequently, logistic regression models (based on
groups with different levels of exposure). The psychosocial exposure measures and types
of outcome variables used are outlined below. The personality variables, their main effects
on outcome measures, and interactions between personality and psychosocial risks, are
then examined in more detail.
2.3.1
Psychosocial work exposures
Almost half the studies reviewed used measure from (or conceptually similar to) the DCS
model or the ERI model to assess psychosocial work exposures.
DCS model. Ten studies used measures of work-related psychosocial exposures derived
directly from the DCS model, or based on similar constructs [6, 7, 8, 10, 12, 16, 19, 21,
a
32, 33] . Most of these studies assessed job demand, control, and social support, although
a
The numbers in square brackets in the text identify the studies summarised in Table 2.2
13
in some cases only two of the three measures were used. If only one of the three DCS
measures was used, the study was included in the ‘other exposures’ group.
ERI model. Five studies included in the review were based on the ERI model [1, 2, 4, 9,
31]. All of these studies assessed effort, reward and effort/reward imbalance, although
proxy measures were used in some instances, e.g. [31]. Each of these studies assessed the
intrinsic effort component of the ERI model, over-commitment (OC), but only two studies
[2, 31] tested the OC x ERI interaction.
DCS and ERI models. Three studies [11, 13, 29] used both ERI and DCS measures.
Other exposure measures. Non-theoretically based measures were used to assess work
exposures in 15 studies; these studies were more heterogeneous than those based on the
DCS or ERI models. Some focused specifically on a single exposure measure, assessed
subjectively, e.g. job control [15] or objectively, e.g. unemployment [5]; others used
objective and subjective assessments of the same work exposure [3]. In some studies,
specific objective work events were assessed in addition to a general perceived work stress
measure [e.g. 14]. One study based on the ‘Organizational Justice’ model [27] used
measures of relational justice and procedural justice as exposure measures.
2.3.2
Outcome variables
Overall, half the studies used only one outcome measure, although those that assessed jobrelated affective responses tended to use several measure, e.g. scales from the Maslach
Burnout Inventory (MBI). Four different types of outcome variables were identified in the
studies reviewed.
General mental health outcomes [1] to [15]. The mental health outcomes assessed in these
studies included general measures of mental health/psychological distress, specific
measures of depression and/or anxiety, self-reported health, and insomnia. In most cases,
outcomes were assessed by standard self-report scales, but in two studies [6, 7] by
interviews carried out either face-to-face or by telephone.
Job-related affective well-being [16] to [25]. Six studies in this group used scales from the
Maslach Burnout Inventory (MBI)74, sometimes coupled with more general measures (e.g.
fatigue), for assessing outcome [16, 17, 18, 20, 22, 24]. Participants in these studies were
primarily teachers, healthcare workers, and other human services personnel, although
three studies [18, 20, 25] were based on random samples of the general population
(screened to include only employed individuals) or trades unions. The majority of the
studies used non-theoretical measures to assess work characteristics, but three studies used
measures from the DCS model [16, 19, 21].
Behavioural outcomes [26] to [30]. Of the five studies that used behavioural measures,
four derived the outcome data from formal organizational records relating to sickness
absence rates [27, 30]; health-care usage [28] and physician visits [29]. Self-reported
voluntary job turnover was used as the outcome in the remaining study [26]. One study
14
[29] included measures from both the DCS and the ERI models, and one used measures of
organizational justice [27]; in the remaining three studies, non-theoretically based
measures (down-sizing, stressful work events, and work challenge/hindrance stress) were
used [26, 28, 30].
Cardiovascular disease outcomes. [31] to [33]. The three studies in this group analysed
longitudinal data from the Whitehall II prospective study of UK civil servants; in each
case the mean follow-up period was approximately 11-12 yrs. Of the three studies, one
study [31] used proxy measures of effort, reward, and OC to test the ERI model, while two
studies [32, 33] used measures from the DCS model of work stress.
2.3.3
Personality variables used in the studies
The majority of the studies reviewed assessed only one or two personality characteristics
(20 studies reported one personality measure, and 5 studies reported two measures). In all,
there were 56 instances in which personality measures were analysed in relation to one or
more outcome variables. The personality variables most frequently reported were the OC
component of the ERI model and neuroticism/NA, but three other personality measures
(hostility/Type A, locus of control/hardiness, and sense of coherence) were each used in at
least five of the 33 studies, although not always assessed by the same scales. Of the
remaining 11 measures, 7 were used in only one study.
Information about the main and interactive effects of the personality variables is
summarised in Table 2.2. The statistical data are taken from the fully adjusted models,
although several studies noted the problem of possible over-adjustment, e.g. by including
as control variables, health behaviours and/or lifestyle variables which may act as
mediators of relations between personality/psychosocial factors, and health outcomes.
Additive effects and, if reported, tests of the moderating effects of personality on relations
between psychosocial work exposures and outcomes are shown, together with notes on
confounding or mediating effects. 12 of the studies reviewed reported testing interactions
between personality and work exposures but, in several cases, only limited details were
provided. Findings for each of the main personality measures used are reported below.
2.4
Findings for individual personality characteristics
2.4.1
Over-commitment (OC)
The personality characteristic of over-commitment describes individual attitudes,
behaviours and emotions reflecting excessive work-related striving combined with a need
for approval and esteem. The measure forms an integral part of the Effort-Reward
Imbalance (ERI) model75. A recent psychometric evaluation demonstrated that the current
version of the OC measure and the other ERI scales have stable psychometric properties76.
15
Main effects. Eight of the studies included a measure of OC, together with the effort and
reward measures from the ERI model; three of these studies also used DCS measures.
None of the studies that used an OC measure used any other personality measures. OC
showed significant main effects, additively with ERI measures, in all except one of the
studies [29]. Among men, high OC was consistently predictive of adverse health-related
outcomes, including psychological ‘caseness’[1], self-reported depression, anxiety, and/or
somatic symptoms [2, 4], insomnia [11], poor subjective health ratings [9], and incident
CHD [31]. Risk ratios (if reported) were generally in the ‘moderate’ range, 1.5 - 2.0,
except for CHD for which the HR value for OC was 1.26 (CI 1.09-1.46).
Among women, the effects of OC were less consistent; for instance, OC did not predict
subjective health ratings among women [9], nor was it a predictor of anxiety or somatic
symptoms, although it did predict depression in women [4]. Findings from one study
suggested that high OC was a risk factor for professionals, but not for manual workers
[13]. Entirely non-significant findings for the main effects of OC were reported in only
one study. In this case, separate components of the ERI model (but not OC or effortreward imbalance) were significant predictors of an objective measure of health care usage
[29]. Thus, the strongest results for OC as a psychosocial risk factor were found for men
in relation to a range of health-related outcomes, primarily of a psychological or
psychosomatic nature.
Interactive effects. Although the ERI model includes predicted interactions between OC
and effort-reward imbalance, only two out of the eight studies that used the OC measure
reported testing OC x ERI interactions. In both cases [2, 31], the results were nonsignificant. The present review therefore provides no evidence that OC acts as a moderator
of ERI effects in relation to mental health outcomes or incident coronary heart disease,
thus contributing further non-significant results to the generally inconsistent evidence
reviewed by Van Vegchel et al.19. However, the failure to report tests of interactions in six
of the studies concerned weakens any wider conclusions that can be drawn. Differences
between men and women in the extent to which OC acts as a risk factor for adverse health
outcomes suggest that interactions between gender and OC might contribute significantly
explaining outcome variance.
2.4.2
Neuroticism / negative affectivity (NA)
NA is used here to refer to both neuroticism and negative affectivity, both of which reflect
emotional vulnerability, pessimism, and a general disposition to react negatively to life
and work stressors; individuals high in NA tend to be anxious, easily upset, often moody
or depressed, and focused on negative aspects of self, other people and the world in
general.
In the studies reviewed, NA was the most frequently reported personality characteristic; it
was assessed in 11 of the 33 studies, distributed across all the four types of outcome
variables. Five of the studies based on the DCS model and six of the non-theoretically
based studies included NA, although none of the ERI studies did so. In six of the studies
that used a measure of NA, other aspects of personality were also assessed. NA is often
16
regarded as a confounding variable in the link between psychosocial work stressors and
health outcomes, and several noted that it was included for that reason [3, 7, 8].
Main effects. In 9 out the 11 studies, including all the DCS studies, NA was a significant
risk factor in multivariate predictive models, particularly in relation to mental health
outcomes [3, 7, 8, 14], but also for job-related affective outcomes [16, 23], and for CHD
[32, 33]. Risk ratios for NA in relation to mental health outcomes were reported for two
studies; the values were 1.98 (1.66–2.36) men / 1.55(1.33–1.81) women [7], and 3.59
(2.06–6.26) men/women [14], representing moderate to strong associations. NA was also
found to be a significant risk factor for sickness absence in both men and women [27], but
it was not related to voluntary job turnover [26].
In the two studies of CHD incidence [32, 33], NA was a main focus of interest rather than
being included only as a potential confounder. Adjusted for demographic factors, those in
the highest NA tertile (top one-third of scores) were at significant, albeit not large, risk for
incident CHD events, hazard ratio=1.32 (1.09 - 1.60); controlling for job strain, health
behaviours, and other potential confounders in the multivariate model had little effect on
this finding [32]. However, the positive dispositional counterpart of NA, positive
affectivity (PA) did not show significant effects, nor did the ‘affect balance’ score (the
difference between PA and NA scores).
A further study of NA in relation to CHD [33], based on a similar Whitehall II dataset,
used the ‘Relative Index of Inequality’ (RII) as the risk index. The RII represents the
hazard ratio for the extremes of the observed score distribution. In this study the RII value
for NA was 1.64 (1.17 – 2.32). NA and inflammatory biomarkers were found to be
independent predictors of incident CHD; there was no evidence of mediation effects.
Confounding effects of NA. Three studies of mental health outcomes [3, 7, 8] explicitly
noted the role of NA as a potential confounder of relations between psychosocial factors
and mental health outcomes and reported the significance of NA as a main effect, but only
one study reported data showing the effects of controlling for NA. In this study [7], the
effects of high job demand (highest tertile scores) remained significant when NA was
included in the multivariate model, and the reduction in RR values was small for both men
and women.
Interactive effects of NA. Two very different studies [16, 27] included tests of interactions
between NA and psychosocial risk factors in predicting outcomes; both reported
significant findings.
Elovainio et al.77 [27] examined the roles of NA and hostility as moderators of relations
between measures of organizational justice and sickness absence rates. In addition to a
significant main effect of NA in both men and women, NA moderated the effect of
relational justice on sickness absence rates among men, but not among women. Thus,
consistent with a personality vulnerability model, the combination of a stressful
psychosocial context (low relational justice) and high NA among men led to particularly
high rates of absence.
17
Houkes et al.78 [16] used LISREL structural modelling to evaluate additive and interactive
effects of NA in a cross-lagged panel design. In the additive model, inclusion of a direct
causal path from Time 1 NA to Time 2 emotional exhaustion significantly improved the
fit of the synchronous model; however, reverse causal and reciprocal paths were not
significant. Interactive effects were evaluated by sub-group analyses; a significant
moderating effect of NA on relations between workload and exhaustion was found. The
total effect of Time 1 workload on Time 2 emotional exhaustion was .06 in the low NA
group and .27 in the high NA group. This analysis confirmed and extended previous
findings56, and illustrated one of the substantive mechanisms identified by Spector et al.34.
In summary, NA was found to act as a significant additive risk factor in almost all the
studies in which it was included, but there was little evidence of it acting as a confounding
variable. There was also no evidence of reversed causal effects from psychosocial
stressors to NA measures, or of reciprocal effects, although these were tested in only one
study [16]. Significant interactive effects were found in two studies [16, 27] both of which
were consistent with a vulnerability model of NA.
2.4.3
Hostility / Type A behaviour
Main effects of hostility /Type A behaviour. Although both hostility and Type A behaviour
pattern have been linked with the incidence of cardiovascular disease79,80, in the studies
reviewed, these personality characteristics were assessed in relation to mental health and
behavioural outcomes. Measures of Type A behaviour [20, 28], and hostility [27, 30] were
each reported in two studies; in addition, one study [12] assessed both these
characteristics, and one study used teacher ratings of hostility at age 8 yrs to predict adult
health outcomes in relation to employment status [5].
A significant effect of hostility was found in both the studies that used sickness absence as
an outcome measure. In one study, the risk of absence was higher by a factor of 1.2–1.4
among individuals high in hostility than among others [30]. Similarly, the second study
reported significant regression coefficients for hostility as a predictor of sickness absence
in both men (.15*) and women (.03**) [27]. Hostility, but not Type A, predicted increased
depression over a 3-yr follow-up in a large French cohort [12]. Type A was also nonsignificant in relation to health care usage [28], and in an exploratory path analysis in
which job demand and workload, and several personality measures, were used to predict
fatigue and exhaustion [20].
Confounding/mediating effects of hostility/Type A behaviour. There was no evidence in
these studies that hostility or Type A acted as confounding variables in relations between
psychosocial factors and health outcomes, or that its effects on outcome measures were
mediated by psychosocial factors.
Interactive effect of hostility/Type A behaviour pattern. Three studies tested hypothesised
interactions between psychosocial risk factors and hostility [5, 27, 30]. In each case, the
predicted interactions were significant; high hostility was a risk factor for individuals
experiencing stressful conditions, specifically, unemployment [5], low job control during
18
organizational down-sizing [30], or lack of procedural justice [27], although in two cases
the findings applied only to men [5, 27]. In addition, one study tested multiple interactions
on an exploratory basis [12]; hostility was a component of one of the significant
interactions found.
2.4.4
Locus of control / hardiness
Locus of control (LOC) measures assess the extent to which individuals believe that
outcomes are determined by personal effort and ability rather than by external influences
such as fate, chance and powerful others81-83. Locus of control, considered together with
hardiness (a composite measure in which LOC is the major component84) was used in five
studies [6, 15, 20, 21, 28]. These studies reported very diverse findings, specifically, a
significant negative main effect of internal LOC on incidence of depression in a large
sample of Canadian employees [6]; non-significant results for the main effect of hardiness
on health-care usage [28]; moderator effects of LOC on relations between DCS
dimensions and job dissatisfaction [21]; a significant negative effect of hardiness on
emotional exhaustion in a causal model [20]; and a significant path by which personal
control mediated the effect of work control on health over a 10-yr follow-up period [15].
Main effects. Findings from two studies [6, 20] pointed to additive roles for LOC and
hardiness, respectively, in attenuating negative affective states associated with
psychosocial exposures; in two other studies [15, 21] tests of meditation and moderation
rather than possible additive effects were the main focus.
Confounding. One study [6] noted that inclusion of personality variables in the regression
model reduced the effect of work demand to non-significance; this effect appeared to be
due to the effects of two personality variables, locus of control and sense of cohesion,
acting either as confounders or mediators.
Personal control (LOC) as mediator. Latent growth curve modelling was used to evaluate
the roles of work control and personal control (similar to LOC) in predicting health
outcomes in a 10-yr follow-up study of men employed in a rural U.S. state85 [15]. The
final model demonstrated an indirect influence of work control on the two health
outcomes; the effects of initial level of work control and subsequent change in work
control on health at follow-up were fully mediated through the corresponding initial level
and changes in level of personal control. The findings suggest that personal control can be
influenced by work experiences, and that change in personal control has a long-term
impact on health, although no reverse causal paths were tested.
Moderating effects of locus of control. Significant moderating effects of LOC in relation to
the exposure measures of the DCS model were demonstrated in one study [21]. In data
collected over a 2 yr follow-up period, a significant four-way interaction was found; job
demand and control interacted to predict job dissatisfaction only for internal locus of
control participants under conditions of high social support. However, the form of the
interaction was not readily interpretable, and the interaction term accounted for only a
very small proportion of variance in the final model.
19
2.4.5 Sense of Coherence (SOC)
Individuals high in SOC view their environment as manageable and comprehensible; they
are more likely to perceive themselves as able to adapt to the demands they experience,
less vulnerable to stress, and more able to maintain health42. SOC is highly negatively
correlated with NA86 and would therefore be expected to have favourable effects in
attenuating or moderating relations between work-related psychosocial risks and health
measures. Five of the studies reviewed used a measure of SOC, in relation to measures of
general mental health [6, 8, 10] or job-related affective outcomes [24, 25].
Main effects of SOC. Two studies found significant negative relationships of SOC with
measures of mental health in multivariate models including psychosocial risk factors and
confounding variables [6, 8]. SOC was also included in a causal model of relations
between psychosocial work dimensions and four measures of job-related affective wellbeing [25]. There was no significant path between SOC and T2 affective responses,
although one reverse causal path from a T1 affective measure to T2 organizational climate
was found.
SOC as a mediator. In a best-fit structural model, SOC acted as a mediator of the effects
of organizational climate and job insecurity on emotional exhaustion and psychosomatic
symptoms; also, change in SOC mediated the effect of change in organizational climate on
change in both indicators of well-being [24].
SOC as moderator. Moderating effects of SOC were found in one study [10]; under
conditions of high job strain, high SOC increased the probability of ‘excellent’ SRH
ratings (OR=2.35**), and reduced the probability of musculoskeletal pain (OR=.34**).
2.4.6
Other personality variables
The main findings for other personality variables used in the studies reviewed are
summarised below.
•
•
•
•
•
Four studies included extraversion [14, 20, 23, 26] but it was significant only in
relation to voluntary job turnover [26]. Positive affectivity (PA), which is closely
linked to extraversion, was non-significant in relation to incident CHD [32].
Low self-esteem was a risk factor for increase in depression [12]; it was also
negatively related to mental distress and emotional exhaustion in men but not women
[18]. It was non-significant in two other studies [6, 19].
Conscientiousness was used in two studies [20, 26,] as part of the Five-Factor
Personality Inventory87. It was not significant in either study.
Self-efficacy was also used in two studies. In one study [22] it was a significant
predictor of burnout, the path being mediated by work stress. In the other study [17],
it was a direct predictor of client engagement (the reverse of burnout).
Other personality measures, including optimism (significantly negatively related to
job-related affective distress among men [18]), and openness (non-significant in
relation to onset of depression [14]) were each used only in a single study.
20
2.5
Conclusions
The findings outlined above demonstrate that personality is implicated in several different
ways in work stress processes. Thus, the effects of work-related psychosocial stressors on
health may be independent of personality; they may be confounded by personality; they
may be moderated by personality; or they may mediate the effects of personality. These
and other more complex relationships (including reverse causation and reciprocal effects)
were potentially within the scope of the present review. However, although additive
effects were tested in almost every study, some important mechanisms were evaluated
only in relatively few of the studies concerned.
In particular, in spite of the potential importance of moderator effects in stress processes,
only 12 out of the 33 studies (36.4%) reported tests of interactions between psychosocial
stressors and personality. This proportion agrees closely with findings from a systematic
review of multivariate analyses in 56 papers published in 2002, in which self-reported
health was the outcome variable and which met other specified criteria88. Failure to report
tests of interactions was the most common problem identified; it applied to 63% of the
studies examined, a proportion virtually identical to that found in the present review.
Thus, almost two thirds of the studies considered here did not test any hypotheses
predicting interactions between personality and psychosocial stressors, or even evaluate
possible interactions on an exploratory basis (or, if such tests were carried out, they were
not reported). In the majority of the studies reviewed, therefore, the idea that personality
characteristics may make individuals more vulnerable to, or more resistant to, the adverse
effects of psychosocial stressors was apparently not addressed.
This finding is surprising as several of the personality variables concerned (e.g. NA, locus
of control, sense of coherence) have been shown to act as moderators of relations between
psychosocial stressors and health outcomes37,56,58,89,90. Failure to consider possible
interactions can lead to potentially significant and revealing moderating effects remaining
concealed, while weak and/or non-significant main effects are reported. This problem is
especially relevant in relation to ‘cross-over’ interactions in which the main effects of both
interacting variables may be non-significant.
It is also noteworthy that in the relatively few studies in which moderating effects were
reported, the majority (75%) found significant results for one or more interactions. It is
possible that this finding reflects a reporting bias, that is, the tendency for significant
findings to be reported while non-significant findings are disregarded. Moreover, two of
the three studies that reported non-significant interactions were based on the ERI model in
which OC, regarded as an intrinsic personality moderator variable, is an inherent
component, thus making it more likely that interactions will be tested and reported, even
if non-significant.
An additional problem in drawing general conclusions from the present review was that
the 33 studies that met the selection criteria proved to be very heterogeneous, ranging
from epidemiological research primarily concerned with identifying psychosocial risk
factors in large population samples to studies that used structural models to evaluate the
21
magnitude and direction of causal pathways among measures of personality, psychosocial
stressors, and health in particular employee groups. A marked feature of the
epidemiological research was the tendency to dichotomise (or otherwise sub-divide)
continuous variables, thus losing information from the original scales. Also,
epidemiological studies were more likely to present separate data analyses for men and
women, rather than to treat gender, and interactions with gender, as factors in a
multivariate model.
Although all the studies were prospective with initial baseline data collection and one or
more follow-up assessments, they also varied widely in the nature and size of the samples,
the follow-up duration, the number of waves of data collection, the psychosocial measures
used, and the types of outcomes assessed. These differences in research aims and
methodology limit direct comparisons across studies. Instead, as a framework for drawing
together the main findings from the review as a whole, a series of general questions is
considered below with reference to the findings of the papers reviewed.
2.5.1
Do personality characteristics act as independent risk factors?
This question addresses the issue of the independent additive effects of personality and
work-related psychosocial factors on mental and physical health. Two personality
variables identified in the present review, over-commitment (OC) and neuroticism (NA)
stood out as being significant, consistent, and moderately strong risk factors for adverse
mental health and job-related affective outcomes, and significant (although smaller) risk
factors for CHD incidence. NA was a significant predictor in 9 of the 11 studies in which
it was assessed, while OC was significant in 7 out of 8 studies.
Independently of psychosocial exposures, NA was a significant predictor not only for selfreported mental health, job-related distress and incident CHD but also, among women, for
objectively recorded sickness absences. Thus, as a risk factor, the independent effects of
NA were apparent in relation to psychosocial risk factors represented in the DCS model,
and a range of non-theoretically based measures, and in relation to each different type of
health outcome.
The finding that NA is associated with a range of adverse health effects in a variety of
different contexts, is consistent with evidence summarised in a recent review article91.
This article describes the many mental and physical health disorders, the impaired quality
of life, and the high usage of health care services, associated with high NA. The author
concludes that there is ‘growing evidence that neuroticism is a psychological trait of
profound public health significance’91, and recommends that understanding the nature and
origins of neuroticism, and the mechanisms which link it to mental and physical disorders,
should be a research priority.
In the studies reviewed, the OC measure was used primarily in relation to general mental
health and cardiovascular disease outcomes; in each of these studies, OC was a significant
independent predictor in multivariate analyses including psychosocial work exposures
22
(ERI measures). In addition, one study reported non-significant effects of OC in relation to
objective measures of physician visits. However, none of the studies included measures of
both NA and OC; thus, the extent to which NA and OC contribute independently to
observed outcomes when both measures are included in the predictive model could not be
established. Evidence from two cross-sectional studies92,93 suggests that NA and OC are
significantly correlated, and that if both NA and OC are included in multivariate models
predicting health outcomes, the magnitude of the effect of OC is reduced, in some cases,
to non-significance, while NA remains significant.
Whilst findings for other personality variables considered in the present review were less
clear than those for NA and OC, three other measures (hostility/Type A, locus of control,
and sense of coherence), each used in at least five of the studies concerned, also
contributed additively to multivariate models. In particular, ‘sense of coherence’ (SOC)
which is conceptualised as a positive personality resource associated with adaptive coping
behaviours, was a significant predictor of favourable health outcomes in two studies.
Hostility showed significant and adverse main effects in four studies, but it also interacted
with psychosocial work dimensions to predict outcomes.
2.5.2 Do personality measures act as confounding variables?
There was little evidence to suggest that personality variables acted as confounders of
relations between work-related psychosocial factors and health outcomes. In one study,
control for personality variables reduced the effects of work demand to non-significance,
but other studies specifically noted the absence of personality confounding effects. It is
relevant that the studies reviewed included control for the baseline level of the dependent
variable, and some screened baseline ‘cases’ out of the analysis sample. Consequently,
variance associated with personality and other confounding factors would be largely
accounted for by the dependent variable as assessed at baseline, thereby reducing potential
confounding effects in longitudinal analyses. Thus, although NA tends to act as a
confounding variable in cross-sectional studies of psychosocial stressors, particularly in
relation to self-reported health outcomes94, in the prospective studies reviewed here,
confounding effects of NA were not markedly apparent.
2.5.3 Does personality moderate relations between psychosocial stressors and health?
In the 12 studies that tested interactions between personality and psychosocial stressors,
hostility stood out as showing the most consistent moderating effects. Thus, in significant
interactions, hostility was a risk factor for poor self-rated health among unemployed, but
not employed, men; it interacted with procedural justice to predict sickness absence, but
only among men; and, during a period of down-sizing, it interacted with job control, also
to predict sickness absence. In addition, in an exploratory test of multiple interactions,
hostility interacted with job control; high-hostile men appeared to benefit more from high
job control (in terms of reduced depression) than low-hostile men.
23
These findings highlight the particularly adverse effects observed when high hostile
individuals are exposed to stressful work conditions, including job insecurity or actual job
loss, lack of procedural justice at work (i.e. lack of fairness in organizational policies and
practices), and low control at work. It is also noteworthy that the present review included
only two studies in which sickness absence was the outcome measure, and in both these
studies, hostility moderated the effect of work conditions on absence. It is possible that
high hostility leads to lack of work-related social support, and difficult interpersonal
relations with co-workers, accentuating negative responses to stressful conditions,
including withdrawal from the work situation.
NA was found to be a significant moderating factor in two studies, both of which showed
that high NA individuals were more vulnerable to work-related stressors (high workload
in one study and, in the other, lack of relational justice, among men). Although
interactions were also reported in other studies (e.g. SOC moderated the effects of high
strain conditions on health outcomes; LOC moderated the effects of iso-strain on job
dissatisfaction; and self-efficacy moderated the effects of role ambiguity and workload on
job satisfaction), only for NA and hostility was there evidence from more than one study
of significant findings for predicted interactive effects. The small number of studies that
tested interactions precluded identification of particular psychosocial stressors and
personality variables that combine synergistically to predict specific types of outcomes.
The practical implication of interactions between psychosocial risk factors and
personality, as noted by Houkes et al.78, is that organizations wishing to reduce ill-health
and distress among employees should consider both work characteristics and individual
factors. A similar conclusion was reached by Ferguson et al.73 from meta-analytic
structural equation modelling of ‘perceived negative job characteristics’ (PNJC) and
‘negatively oriented personality’ (NOP) as predictors of concurrent and future symptom
reporting in longitudinal studies. In this meta-analysis, it was found that a model based
solely on NOP offered a more parsimonious account of baseline and future symptom
reporting than did PNJC. In view of the importance of NOP, the authors concluded that
interventions should focus on individuals and organizations, rather than targeting
interventions solely at the organizational level.
2.5.4
Are the effects of personality mediated through psychosocial stressors?
Three studies examined mediating effects using structural modelling analyses. In one of
these studies, a general measure of work stress was found to mediate the path between the
personality measure of self-efficacy and the health measure of burnout39; thus, in this
model, self-efficacy influenced perceptions of work stress which in turn influenced
burnout. This path represents the ‘normal’ theoretically predicted causal direction in which
a personality trait influences work perceptions, which in turn influence health. There was
no evidence of reverse causation in this analysis. In contrast, no support for the ‘normal’
causal direction was reported by Mäkikangas et al.95; rather one reverse causal path was
24
found in which affective response at follow-up predicted baseline organizational climate.
Also, in this study, SOC did not act as a mediator.
Two other studies of mediation adopted a different model, in which the personality
variable was treated as the mediator between psychosocial stressors and health. First, over
a one-year period, a good organizational climate and low job insecurity at Time 1 was
positively related to high levels of SOC, which in turn predicted affective well-being at
Time 243; in this study, changes in organizational climate were related to changes in wellbeing through changes in SOC. Second, also using latent growth curve modelling, the
effects of level and change in job control on health measures over a ten-year period were
found to be mediated by level and change in personal control (a measure of LOC)85.
Both these studies demonstrated that personality measures changed in response to
changing work conditions. Whilst measures of personality would normally be regarded as
relatively stable, individual levels of SOC42 are considered to be malleable and shaped not
only by early childhood but also by adult work and life experiences43. The role of SOC as
a mediator variable is consistent with this interpretation. In this case, work experiences
gave rise to change in SOC over a time period of only one year, which accords with
findings suggesting that the SOC measure has a significant state component96, rather than
representing a stable global orientation. Evidence that personal control (LOC) acted as a
mediator variable related to a longer time period (10 years)85 but similar arguments apply,
and are consistent with earlier findings suggesting that LOC is reciprocally related to work
conditions97,98.
2.5.5
Implications
Several general conclusions can be drawn from the results outlined above. First, it is clear
that personality plays significant roles in the paths by which work-related psychosocial
stressors are associated with adverse mental and physical health outcomes, although
findings across different studies are not entirely consistent. Second, these paths are not
necessarily in the theoretically-predicted directions, nor are they necessarily unidirectional, although the relatively small sample of studies reviewed does not allow clear
conclusions about the relative importance of the different mechanisms.
Third, the findings tentatively suggest that different personality traits may act through
different pathways. For instance, negative affectivity and over-commitment showed strong
additive effects, largely independent of work-related psychosocial risks, hostility acted as
a vulnerability factor interacting with psychosocial risks, and mediator or mediated effects
were most apparent for the dimensions of locus of control and sense of coherence.
However, any such conclusions must be qualified by the fact that in many studies only
additive effects were considered, and thus more complex mechanisms may have been
overlooked.
The relevance of examining the various mechanisms by which personality is implicated in
work stress processes depends on the focus, aims and methods of any particular study.
However, it is clear that the importance of possible interactive effects has been
disregarded in much research. In particular, interactions reveal ways in which a particular
25
personality trait coupled with a particular psychosocial work exposure may lead to poor
mental or physical health while either factor alone would not have done so. Such
information can potentially guide more effective intervention strategies to enhance wellbeing at work. From both research and applied viewpoints, it would be valuable if future
research gave greater attention to predicting and testing interactive effects, using
appropriate statistical methods, and reporting the findings irrespective of whether or not
they are significant. Prospective studies of mediating effects, and particularly the possible
role of psychosocial work exposures in bringing about changes in personality, would also
contribute to a better understanding of work stress processes.
Issues such as these have been widely recognized in the research literature. For instance, it
has been suggested that one reason the success of organisational attempts to modify job
strain have so far been relatively modest may be that the effects of personality are largely
ignored when work stress is considered to originate mainly from work conditions99.
Similarly, Wilhelm et al.100 note the need for “more longitudinal studies and consideration
of factors which the worker brings to the workplace (psychosocial issues, personality
traits), as well as interpersonal issues and consideration of systemic, organizational,
political and economic factors, including leadership styles”. Other researchers have also
recommended that more attention be given to personality in work stress research89,101-103.
More generally, over the past two decades, industrial changes (including organizational restructuring, down-sizing, privatisation, out-sourcing and contract work) associated with
economic globalisation have increased job insecurity and led to changes in work processes
and management behaviour. Such changes may adversely affect employees; for instance,
in a recent survey, 85% of the 86 studies examined reported adverse health and safety
effects of down-sizing104. These changes in work conditions have led to recognition of the
need to expand traditional work stress models to include new work dimensions, such as
employment uncertainty, irregular/atypical schedules, emotional work demands, and the
quality of social relationships at work105. Innovative models which incorporate not only
novel work dimensions but also take into account the role of personality in coping and
adaptation are needed to fully address these concerns.
26
Table 2.2
Summary table of the 33 studies reviewed
Author
Sample
Follow-up
duration
Dependent
measure (s)
Control for
initial/
baseline level
of dependent
measure(s)
Exposure
measure(s)
Personality
measures
Confounding
/control
variables
included in
analysis model
Main effects of
personality
Tests of
personality x
psychosocial
work factors
interactions
reported ?
Significant interactions
between personality x
work factors
☺☺ indicates initial
hypotheses predicted
specific personality x work
moderating effects
☺ Exploratory analyses
of moderating effects
MENTAL HEALTH OUTCOMES
Bridger et al
(2009)
791
UK naval
personnel
319 females
472 males
1 yr
3 waves
of data
collection
Strain defined
as
GHQ-12
‘caseness’,
cut point ≥ 4
Chronic strain
group (‘cases’
at each
assessment,
n=78),
compared
with ‘strainfree’ group
(n=345)
Effort/reward
(ERI measures)
Overcommitment
(OC)
Anxiety and
depression
(HADS scales)
Life satisfaction
Yes
Work hours
Overcommitment
(OC)
1
BuddebergFischer et al
(2008)
433
Swiss
medical
residents
(physicians)
2 yrs
(T2 and T3
from
prospective
study)
2
Burgard et al
(2009)
Good study
3
Two
samples of
employed
U.S. adults
N= 1507
age 25+ yr
1986-1989
N=1216, age
25-74 yrs
1995-2005
3 / 10 yrs
2 waves
of data
collection
ERI measures
of effort and
reward
Physical/mental
well-being
Self-rated
health (SRH)
Depressive
symptoms
Telephone
interview
assessments
Yes
Perceived job
insecurity
Objective job
insecurity
(actual
experience of
unemployment
for one yr prior
to each data
collection wave
Neuroticism
Over-commitment:
(β=.32***), and
ERI (β=.19*) were
both significant in the
logistic model.
Gender
Self-reported
health at
baseline
Gender, age,
race,
employment
history,
education,
income, blood
pressure, etc
Significant additive
effects of ERI and OC
on health outcomes
(p<.0001)
In adjusted model,
neuroticism was a
risk factor for SRH
B= -0.076 ** (s.e. 0.026);
and for depressive
symptoms
B=0.067 *** (s.e. 0.012)
Persistent perceived
job insecurity was a
risk factor for poor
SRH (2 samples) and
depression (1 sample)
No
___
Yes
☺☺
ERI x OC interactions
were non-significant for
all health outcome
measures
No
_____
27
Author
Godin et al
(2005)
Sample
1986
Employees
from four
Belgium
companies
Follow-up
duration
1 yr
Dependent
measure (s)
Depression
Anxiety
Somatisation
(Symptom
Check List
SCL90)
Chronic fatigue
Drug use
Control for
initial/
baseline level
of dependent
measure(s)
Exposure
measure(s)
Participants
with mental
health
problems at
Time 1 were
excluded,
reducing the
analysis
sample by
25%
ERI measures
of effort and
reward
Personality
measures
Overcommitment
(OC)
Confounding
/control
variables
included in
analysis model
Main effects of
personality
Age
Education
Threat from
global
economy
Job
dissatisfaction
Workplace
instability
In men, high OC
predicted three
outcomes, over and
above ERI:
4
Kivimaki et al
(2003)
Tests of
personality x
psychosocial
work factors
interactions
reported ?
No
N=311,
cohort born
in1959
87%
response at
follow-up
27 yrs
Self-rated
health (5=very
good to 1=very
poor)
n/a
Employment
status
Unemployed vs.
not unemployed
at age 36 yrs
Hostility
(rated by
teacher at age
8 yrs)
Parental SES
Current
financial strain
Alcohol use
Main effects of
hostility and
unemployment on
health were nonsignificant
Yes
6,359
employees
working at
least 15 hrs
per week
Mean age 37
yrs at
baseline
7 yrs;
First episode of
psychological
distress
(Composite
International
Diagnostic
Interview)
Yes
Skill utilisation
Decision
authority
Physical and
psychological
demands
Social support,
Job insecurity
Self-esteem
(Rosenberg
scale)
Internal locus
of control
(LOC)
(Pearlin &
Schooler)
SOC
(Antonovsky)
Age and gender
Family
characteristics
Lifestyle
factors
Childhood
stressful events
Social network
In adjusted model:
LOC (β=-.069**)
OR = .93**(.91-.96)
Yes
6
4 cycles of
assessment
Response
rates 8586% at
each cycle
hypotheses predicted
specific personality x work
moderating effects
☺ Exploratory analyses
of moderating effects
___
Depression
OR=2.4(1.4–4.1)
Anxiety
OR=2.5 (1.5–4.4)
Somatisation
OR= 1.8 (1.1–3.1)
In women, OC only
predicted depression
OR=1.8 (1.0–3.0)
5
Marchand et al
(2005)
Significant interactions
between personality x
work factors
☺☺ indicates initial
SOC (β=-.065**)
OR= .94 (.93-.95)
Self-esteem
(β= -.015, ns)
OR = .99 (.95-1.02)
Control for
personality reduced
the effects of work
demands to ns
☺☺
Significant interaction
between hostility and
employment status in
men (β= -0.21, p< 0.04),
but not among women.
Unemployment was a
health risk only among
men high in hostility.
☺
None significant; details
not reported
Non-linear relationships
of work exposures with
outcome were also nonsignificant
28
Author
Melchior et al
(2007)
Sample
Follow-up
duration
Dependent
measure (s)
Control for
initial/
baseline level
of dependent
measure(s)
Exposure
measure(s)
Personality
measures
Confounding
/control
variables
included in
analysis model
891 men and
women (NZ
birth cohort)
assessed
every 2 yrs
from age 3
yrs. In
employment
at age 32yrs
(response
rate, 96%)
Baseline
psychiatric
assessment,
ages
11 yrs
Major
depressive
disorder (MDD)
or
generalized
anxiety disorder
(GAD) assessed
by Diagnostic
Interview
Schedule, and
diagnosed
according to
DSM-IV
criteria
Only new
onset cases
included.
Participants
with prior
psychiatric
diagnosis
were excluded
DCS model:
Job demands
Work decision
latitude
Social support
Physical job
demands
NA (rated by
interviewer
from Big Five
Inventory
items)
SES
Internalizing/
externalizing
disorders, age
11 yrs
Work decision
latitude
Social support
Physical job
demands
1392
Japanese
civil
servants
1 yr
Subjective
perceptions of
quality of life in
four domains:
Baseline
measures
included in
the analysis
model
Karasek job
strain measures
Job demand
Job control
SOC
13-item
translated
version
Gender
Age
Employment
category
7
Nasermoaddeli
et al (2003)
Physical health;
psychological
health, social
relationships,
environment
8
(WHO/QOL26)
Negative
affectivity
(NA)
(Affect
balance scale)
Main effects of
personality
NA was a significant
predictor of MDD-orGAD in women:
RR=1.55 (1.33–1.81)
and men:
RR= 1.98 (1.66–2.36)
it partially accounted
for the effect of high
job demands. In men,
the effect of high job
demand
RR =2.00 (1.13–3.56)
reduced to
RR= 1.84 (1.09–3.11)
with NA controlled.
In women, the
corresponding RR
values were:
RR= 1.90 (1.22–2.98)
reduced to
RR= 1.79 (1.16–2.76)
SOC was significant
for each health
outcome:
Physical health
(β = .21**)
Psychological health
(β = .12***)
Social relationships
(β = .13***)
NA predicted selfrated physical health
(β= -.05*)
Tests of
personality x
psychosocial
work factors
interactions
reported ?
Significant interactions
between personality x
work factors
☺☺ indicates initial
hypotheses predicted
specific personality x work
moderating effects
☺ Exploratory analyses
of moderating effects
No
___
No
___
29
Author
Niedhammer et
al (2004)
Sample
Follow-up
duration
Dependent
measure (s)
Control for
initial/
baseline level
of dependent
measure(s)
Exposure
measure(s)
Personality
measures
Ota et al (2009)
11
Tests of
personality x
psychosocial
work factors
interactions
reported ?
Self-reported
health (SRH)
Analysis
restricted to
those who
reported
‘good’ SRH
at baseline
ERI measures
of effort and
reward
Overcommitment
(OC)
Demographics
Occupation
Stressful events
Chronic
conditions
Depression
Lifestyle
factors
High OC was a
significant predictor
of poor SRH among
men, OR=1.67 (1.322.12), but not among
women. ERI was not
significant when
controlled for OC
No
Employees
aged 19-56
yrs (Swedish
Level of
Living
survey)
T1 response
rate 76.3%
Panel
analysis,
N=2246
9 yrs
‘Excellent’
health
contrasted with:
psychological
distress;
musculoskeletal pain;
poor self-rated
health
(all derived
from a single
measure)
Longitudinal
analysis was
restricted to
those with
adverse work
conditions at
baseline
Job demand
Job control
Physical/
ergonomic work
demands
Abbreviated
SOC measure
3 items
Age, health
status, gender,
marital status,
and work hours
at baseline
Not reported
(SOC was only
considered to be a
potential moderator)
Yes
730 middleaged
Japanese
workers
2 yrs
Onset of
insomnia
(defined with
reference to
(DSM-IV)
Analysis
excluded
those with
insomnia at
baseline
ERI measures
of effort and
reward
Overcommitment
(OC)
Sex, age,
illness,
occupational
conditions
(manager, shiftwork,
overtime)
drinking and
smoking status
at baseline
High OC (upper
tertile) was significant
risk factor for onset of
insomnia:
No
9
10
Main effects of
personality
1 yr
GAZEL
cohort
(France)
4475 men
1811 women
Olsson et al
2009
Confounding
/control
variables
included in
analysis model
DCS model
OR=1.75 (1.16-2.66)***
Significant interactions
between personality x
work factors
☺☺ indicates initial
hypotheses predicted
specific personality x work
moderating effects
☺ Exploratory analyses
of moderating effects
____
☺
When exposed to high
strain conditions, high
(relative to low) SOC
increased the probability
of ‘excellent’ health
(OR = 2.35**), and
reduced the probability
of musculo-skeletal
problems (OR=.34**)
___
30
Author
Paterniti et al
(2002)
Sample
GAZEL
cohort
(France)
Analysis
sample
N = 10159
Follow-up
duration
3 yr
Dependent
measure (s)
Depression
(CES-D selfreport scale,
20-item)
Control for
initial/
baseline level
of dependent
measure(s)
Exposure
measure(s)
Analysis of
change in
CES-D scores
over 3 yr
follow-up,
adjusted for
baseline level
Decision
latitude
Job demand
Social support
(16-item scale)
Personality
measures
Hostility
Self-esteem
Type A
(Bortner
scale)
Confounding
/control
variables
included in
analysis model
Main effects of
personality
Demographics
Stressful
personal events
Chronic
diseases
Physical
workload
Lifestyle
factors
Risk factors for
increased depression:
Self-esteem (low)
B= -0.13 (0.01)*** M
B= -0.17 (-0.03)*** F
Hostility (high)
B = 0.07 (0.01)*** M
B = 0.11 (0.02)***F
(Type A, ns)
Manual
workers
N=343
Professionals
N=658
3 waves
of data
collection
over 15
months
Mental strain
(GHQ-12
measure)
Control for
baseline
mental strain
14
1365
White collar
employees
in a
Japanese
company
ERI model
Effort / reward
components
ER imbalance
1 yr
5 samples
in
successive
years.
Response
rates 5068%
Onset of major
depression
(Zung Self-rating
Depression Scale,
and DSM-IV
diagnostic
criteria)
First onset
cases
compared
with never-ill
cases
Perceived job
stress
3 specific workrelated events
Significant interactions
between personality x
work factors
☺☺ indicates initial
hypotheses predicted
specific personality x work
moderating effects
☺ Exploratory analyses
of moderating effects
☺
Exploratory tests of
multiple interactions:
Among men, two
interactions were found:
Decision latitude x
hostility (B= -0.009**
(s.e.= 0.003)
Social support x selfesteem (B= -0.01**
(s.e.= 0.004)
No interactions reported
for women
Overcommitment
(OC)
None
OC contributed
significantly to
mental strain among
professionals (β =
.14*), but not among
manual workers, over
and above ERI and
DCS measures
No
____
Neuroticism,
extraversion,
openness
(from the
Five-Factor
NEO
Inventory)
Non-significant
factors dropped
from final
model
Neuroticism was a
significant risk factor,
OR=3.59 (2.06-6.26)
Final model also
included significant
effects of work events
OR =1.50 (1.18–1.90)
and lack of social
support
OR= 2.55 (1.58–4.10)
No
_____
DCS model
Demand
Control
Support
13
Tokuyama et al
(2003)
Yes
Personality and
psychosocial stressors
were independent
predictors of change
in depression.
12
Rydstedt et al
2007
Tests of
personality x
psychosocial
work factors
interactions
reported ?
31
Author
Wickrama, et al
(2008)
Sample
318 U.S.
men (from
Iowa
Mid-life
Transition
study)
employed
throughout
the followup years
Follow-up
duration
10yrs
4 waves
of data
collection
(1991,
1992,
1994,
2001)
Dependent
measure (s)
Depressive
symptoms from
SCL-90-R scale
Control for
initial/
baseline level
of dependent
measure(s)
Exposure
measure(s)
Baseline data
included in
analysis
model
Work control
Personality
measures
Personal
control (LOC)
Tests of
personality x
psychosocial
work factors
interactions
reported ?
Significant interactions
between personality x
work factors
☺☺ indicates initial
Confounding
/control
variables
included in
analysis model
Main effects of
personality
Education
Latent growth curve
modeling
Higher levels of work
control (1991) and
increase in work
control (1991-94)
were negatively
related to poor health
and depressive
symptoms (2001).
These effects were
fully mediated by
level of personal
control (1991) and
change in personal
control (1991-94)
No
_____
Structural model:
T2 NA and T2
workload predicted
T2 emotional
exhaustion, after
control for
T1 NA and T1
workload
Yes
☺☺
LISREL sub-group
analyses
(Pearlin,1981,
Mastery scale)
Poor self-rated
physical health
(mean of 2
items)
15
hypotheses predicted
specific personality x work
moderating effects
☺ Exploratory analyses
of moderating effects
JOB-RELATED AFFECTIVE WELL-BEING
Houkes et al
(2003)
16
338 Bank
employees
/teachers,
mean age 44
yrs. 55%
response rate
at baseline,
30% for
two-wave
panel data
1 yr
Emotional
exhaustion
(Dutch version
of MBI)
Work
motivation
Turnover
intention
Yes
Task
characteristics
NA (from
Dutch version
of PANAS)
Workload
Social support
Upward
striving (US)
Growth need
strength
(GNS)
Gender
Age
NA x Workload
predicted emotional
exhaustion in both
synchronous and crosslagged T1/T2 analysis
NA x social support
interaction was ns.
US and GNS measures
did not show moderating
effects
32
Author
Jimmieson et al
(2004)
Sample
Follow-up
duration
213
Government
employees
(Australia)
2 yrs
457 Finnish
employees
from a
random
sample of
the working
age
population
45%
response rate
at T1
1 yr
409 Finnish
health care
personnel
46%
response at
baseline
2 yrs
Dependent
measure (s)
Control for
initial/
baseline level
of dependent
measure(s)
Exposure
measure(s)
Psychological
well-being and
client
engagement
(MBI)
Job satisfaction
(4 items)
Yes
Workload
Job satisfaction
Emotional
exhaustion
(MBI)
Mental distress
(GHQ-12)
Physical
symptoms (10
items)
Yes
Work
engagement
(absorption,
vigor,
dedication)
Yes
Role ambiguity
during work relocation/reorganization
Personality
measures
Tests of
personality x
psychosocial
work factors
interactions
reported ?
Confounding
/control
variables
included in
analysis model
Main effects of
personality
Self-efficacy
(related to the
organizational
change
process)
No
T1 Self efficacy
predicted T2 client
engagement
Yes
Optimism
(Life
Orientation
Test)
Age
Education
Leadership
position
Self-esteem and
optimism were both
significant negative
predictors of mental
distress (β= -.21**)
and emotional
exhaustion (β= -.19**)
among men, but not
among women
Yes
Self-esteem did not
predict any of the
outcome scales after
control for baseline
levels.
In the fully adjusted
model, job insecurity
(negatively) and job
control (positively)
predicted dedication.
No
Significant interactions
between personality x
work factors
☺☺ indicates initial
hypotheses predicted
specific personality x work
moderating effects
☺ Exploratory analyses
of moderating effects
☺☺
Self-efficacy moderated
the effects of T1 role
ambiguity
(β = .14, p < .05)
and T1 workload
(β= .16, p < .01
on T2 job satisfaction
17
Makikangas &
Kinnunen
(2003)
18
Mauno et al
(2007)
19
Time pressure
Lack of control
Job insecurity
Poor
organizational
climate
Workload
Job insecurity
Work-family
conflict
Job control
Management
quality
Self-esteem
(Rosenberg
scale)
Self-esteem
related to the
organization
Demographics
Structural work
variables
☺
3/16 two way
interactions work
stressor x self-esteem
were significant for
men, 0/16 for women
3/16 two-way
interactions work
stressor x optimism
were significant for
women. 0/16 for men
___
33
Author
Michielsen at al
(2004)
Sample
Follow-up
duration
325
participants
randomly
chosen by
phone
contact,
working at
least 20 hrs
per week
2 yrs
542
1 yr
Dependent
measure (s)
Emotional
exhaustion
(MBI)
Control for
initial/
baseline level
of dependent
measure(s)
Exposure
measure(s)
Yes
Workload
Personality
measures
Main effects of
personality
Gender, age
children
Employment
contract
(permanent/
temporary)
Marital status
Number of
work
hours/week
Reduced
structural model
Hardiness
(β = - 0.10*)
predicted T2
emotional exhaustion,
but not T2 fatigue
after control for T1
levels.
Other personality
variables were ns in
initial model and were
dropped from the
analysis
No
Locus of
control (LOC)
(measure
based on
Spector Work
Locus of
Control scale)
Age
Gender
Main effect of LOC
was non-significant.
Yes
Self-efficacy
(Latent
variable with
two
indicators)
Age
Type A (JAS)
Hardiness
FFPI
measures
Extraversion
General fatigue
Agreeableness
Conscientious
-ness
Emotional
stability (NA)
Autonomy
20
Rodriguez et al
(2001)
European
data
processing
operators
171 men
371 women
21
Age 18-36
yrs, mean
22.6 yrs, at
baseline
Schwarzer et al
(2008)
458 German
teachers
22
2nd and 3rd
waves of a
3-wave, 3yr study
1 yr
Job
dissatisfaction
(assessed by
standardized
interview)
Yes
Burnout (MBI)
Yes
Job demands
Job control
Social support
(non-standard
DCS measures)
Job stress
(15-item scale)
Tests of
personality x
psychosocial
work factors
interactions
reported ?
Confounding
/control
variables
included in
analysis model
Significant main
effects of support and
demands.
Structural modeling
analyses:
T1 self efficacy
predicted T2 burnout;
no evidence of
reverse causation.
Relationship between
self-efficacy and
burnout was fully
mediated by work
stress
No
Significant interactions
between personality x
work factors
☺☺ indicates initial
hypotheses predicted
specific personality x work
moderating effects
☺ Exploratory analyses
of moderating effects
___
☺☺
Significant demand x
control x social support
x locus of control
interaction.
Job demand x control
interaction was
significant only for
internal LOC
participants under
conditions of high
support.
____
34
Author
Tyssen et al
(2005)
Sample
371 hospital
medical
interns
Follow-up
duration
1 yr
Dependent
measure (s)
Perceived job
stress
Control for
initial/
baseline level
of dependent
measure(s)
Exposure
measure(s)
Perceived
stress at
medical
school
Work hours
Sleep hours
Organizational
climate
Personality
measures
Neuroticism
Extraversion
Compulsiveness/control
Confounding
/control
variables
included in
analysis model
Age
Gender
23
Feldt et al
(2000)
25
In adjusted model:
NA was significant,
B = 1.7*** (.94-2.95)
Other personality
variables, all ns.
Sleep hours and
organizational climate
were significant.
Tests of
personality x
psychosocial
work factors
interactions
reported ?
Significant interactions
between personality x
work factors
☺☺ indicates initial
hypotheses predicted
specific personality x work
moderating effects
☺ Exploratory analyses
of moderating effects
No
(interactions
with gender
were tested)
(NA interacted
significantly with gender)
219 Finnish
employees in
health care
and service
organization
159 women
60 men
1 yr
2-wave
panel
study
Emotional
exhaustion
(MBI)
Psychosomatic
symptoms
Yes
Job insecurity
Influence at
work
Leadership
relations
Organizational
climate
Sense of
coherence
SOC
Age
Education
Gender
Structural equation
modeling (LISREL)
SOC mediated the
relationship between
level of job insecurity
and levels of
psychosomatic
symptoms and
emotional exhaustion.
Intra-individual change
in SOC mediated the
effects of changes in
organizational climate
and leadership relations
on both outcomes
No
_____
615 Finnish
employees,
(trades union
members)
94% men
Response
rates
64% (T1)
70% (T2)
3 yrs
2 wave
panel
study
Anxiety
Comfort
Depression
Enthusiasm
(4 factors from
Warr scale of
job-related
affective wellbeing
Yes
Job control
Supportive
organizational
climate
Sense of
coherence
SOC
Gender
Age
Managerial
level
Structural equation
modeling (LISREL)
Direct and reverse
causal models were
tested. T2 outcomes
were not predicted by
SOC, but weak
evidence of one reverse
causal path was found
(T1 ‘comfort’ factor to
T2 organizational
climate). Curvilinear
paths between work
measures and affective
outcomes were ns.
No
____
24
Makikangas et
al (2007)
Main effects of
personality
35
Author
Sample
Follow-up
duration
Dependent
measure (s)
Control for
initial/
baseline level
of dependent
measure(s)
Exposure
measure(s)
Personality
measures
Confounding
/control
variables
included in
analysis model
Main effects of
personality
Tests of
personality x
psychosocial
work factors
interactions
reported ?
Significant interactions
between personality x
work factors
☺☺ indicates initial
hypotheses predicted
specific personality x work
moderating effects
☺ Exploratory analyses
of moderating effects
JOB-RELATED BEHAVIOURAL OUTCOMES
Cavanaugh et al
(2000)
26
Elovainio et al
(2003)
‘High-level’
managers
(U.S.) 91%
were male.
Analysis
sample
N=671
(19% response
1 yr
Voluntary
turnover
n/a
Job stress
measures:
Challenge stress
(6 items)
Hindrance stress
(5 items)
NEO fivefactor model
Neuroticism,
Extraversion
Conscientious
-ness
Gender
2 yr
Sickness
absence
(Formal records
of self-certified
sickness)
Yes
Measures of
relational and
procedural
organizational
justice
Hostility
Neuroticism
Age
Salary
Baseline
sickness
absence
28
Poisson regression
model.
Hostility had main
effects on absence for
men (β= .15***) and
for women (β=.03**).
Yes
260 working
adults in two
companies
Mean age
36.9 yrs
Response
rate 60%
1 yr
Health care
usage assessed
by records of
costs and claims
Yes
Stressful work
events
Social support
Hardiness
Type A
behaviour
____
Gender
Age
Ethnicity
Education
Exercise
Quality of life
Personality measures
and psychosocial
variables were ns.
Only ‘stressful work
events’ predicted
health care costs
☺☺
Two interactions were
significant for men, but
not women:
Relational justice x
neuroticism (β= -.08**)
Neuroticism had main
effects on absence for
men (β= .08*) and
women (β= .05***)
27
Fusilier &
Manning
(2005)
No
Extraversion (β=.04*)
Hindrance-related job
stress (β=.74**)
Neuroticism and
conscientiousness (ns)
at T1, and
45% response
from T1
respondents at
T2)
Hospital
employees
506 men
3570 women
In maximum
likelihood model
predicting voluntary
turnover:
Procedural justice x
hostility (β= -.08*)
No
___
36
Author
Ostry et al
(2004)
Sample
1886
Canadian
sawmill
workers
62.8%
response
Follow-up
duration
1 yr
Dependent
measure (s)
Physician visits
for mental
health reasons,
or 30+ visits for
any reason
(from ‘Linked
Health Data
Base’)
Control for
initial/
baseline level
of dependent
measure(s)
Exposure
measure(s)
Participants
with mental
health visit in
previous year,
or
hospitalized in
previous year
were excluded
Job strain
model
Job control
Job demand
Personality
measures
Overcommitment
(OC)
Confounding
/control
variables
included in
analysis model
Main effects of
personality
Sociodemographic
variables
No significant effects
of OC
Job strain and ERI
were ns in relation to
physician visits for
mental health reasons.
Nonpsychosocial
work
conditions
variables
ERI model
Effort / reward
components
ER imbalance
30
No
Significant interactions
between personality x
work factors
☺☺ indicates initial
hypotheses predicted
specific personality x work
moderating effects
☺ Exploratory analyses
of moderating effects
____
Low ER status control
1.32* (1.01 – 1.82), and
Low ER esteem reward,
1.63** (1.19 – 2.23)
were risk factors for
30+ physician visits
29
Vahtera et al
(2000)
Tests of
personality x
psychosocial
work factors
interactions
reported ?
757
municipal
employees
(response
rate, 77%)
Mean age
41 yrs
6 yrs
(3 yrs
during
downsizing, and
3 yrs
following
downsizing)
Sickness
absence rates
derived from
municipal
records
Control for
baseline
health status
Down-sizing
Job control (18
item scale)
Hostility
High versus
low
Age
Gender
Income
Significant overall
effects of hostility
during and after
down-sizing.
Rate ratios relative to
low hostility reference
group:
High hostility during:
1.21 (1.06–1.39)
High hostility after:
1.38 (1.22–1.55)
Yes
☺
Significant interaction
(p<.05) between job
control (good vs poor)
and hostility (high vs
low) in predicting sick
leave rates only during
down-sizing.
After down-sizing, the
job control x hostility
interaction was nonsignificant
37
Author
Sample
Follow-up
duration
Dependent
measure (s)
Control for
initial/
baseline level
of dependent
measure(s)
Exposure
measure(s)
Personality
measures
Confounding
/control
variables
included in
analysis model
Main effects of
personality
Tests of
personality x
psychosocial
work factors
interactions
reported ?
Significant interactions
between personality x
work factors
☺☺ indicates initial
hypotheses predicted
specific personality x work
moderating effects
☺ Exploratory analyses
of moderating effects
CORONARY HEART DISEASE OUTCOMES
Kuper, SinghManoux, et al
(2002)
Whitehall II
6895 male
and 3413
female civil
servants at
baseline
11 yrs
(mean)
Phase 1
baseline
to
Phase 5
Validated
incident
coronary heart
disease events.
Incident cases
ERI model
(proxy scales
derived from
other measures)
Effort, reward
ERI
OC (single
item proxy
measure, 5point scale
dichotomized
for analysis)
Age
Gender
Coronary risk
factors
High OC was a risk
factor for incident
CHD at follow-up in
adjusted model.
OR= 1.26(1.09 –1.46)
Highest quartile ERI
relative to lowest
quartile:
OR=1.26 (1.03–1.55)
Yes
Whitehall II
10308 UK
civil servants
12.5 yrs
(mean)
Phase 1
(baseline)
to Phase 7
Fatal CHD
(from NHS
records)
Non-fatal
myocardial
infarction,
angina
(self-report,
clinically
verified)
Incident cases
DCS model
Job demand
Job control
Social support
NA
PA
Affect balance
Age
Gender
Ethnicity
Employment
grade
Smoking,
Alcohol, and
other health
behaviors/lifestyle factors
Hypertension
Highest tertile of NA
was a significant risk
factor for incident
coronary heart
disease, HR = 1.32
No
☺
No evidence of
significant ERI x OC
interaction.
For High ERI quartile in
adjusted model:
High OC:
OR=1.39 (1.05–1.85)
Low OC:
OR=1.31 (0.98–1.75)
31
Nabi et al
(2008)
6895 men
3413
women
Cholesterol
32
(1.09 to 1.60),
in model adjusted for
demographic factors.
Control for job strain,
and other potential
confounders did not
substantially change
this finding. No
significant effects of
PA or affect balance
_____
38
Author
Nabi, SinghManoux, et al
(2008)
Sample
Whitehall II
6396 civil
servants
4453 men
1943 women
Follow-up
duration
11.1yrs
(mean)
Phases 1-2
baseline,
follow-up
Phases 3-7
Dependent
measure (s)
Coronary heart
disease (CHD)
Fatal CHD from
NHS records;
non-fatal
myocardial
infarction and
angina was
determined
from medical
records
Control for
initial/
baseline level
of dependent
measure(s)
Exposure
measure(s)
Free of
clinically
validated
CHD at start
of follow-up
period
DCS model
Job demands
Decision
latitude
Social support
at work
Personality
measures
NA (average
of Phase I and
Phase 2
assessments)
Confounding
/control
variables
included in
analysis model
Main effects of
personality
Age
Gender
Ethnicity
Employment
grade
Smoking
Alcohol, and
other health
behaviors/lifestyle factors
Hypertension
NA was a risk factor
for CHD incidence,
adjusted for all
confounders including
psychosocial stress
(iso-strain) at work:
Cholesterol
33
RII=1.64(1.17-2.32)**
Control for
inflammatory markers
did not attenuate this
risk; therefore,
inflammatory markers
did not mediate the
direct NA→ CHD link
* p<.05 ** p<.01 ***p<.001
SOC Sense of coherence
LOC Locus of control
NA Negative affectivity/neuroticism
PA Positive affectivity
MBI Maslach Burnout Inventory
GHQ General Health Questionnaire
SRH Self-reported health
OC Over-commitment
DCS Demand-Control-Support model
ERI Effort-Reward Imbalance model
CHD Coronary heart disease
NHS National Health Service (UK)
Tests of
personality x
psychosocial
work factors
interactions
reported ?
No
Significant interactions
between personality x
work factors
☺☺ indicates initial
hypotheses predicted
specific personality x work
moderating effects
☺ Exploratory analyses
of moderating effects
_____
39 References list for studies included in Table 2.2
1.
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2007. Occupational Medicine, 58, 534-539.
2.
Buddeberg-Fischer, B., Klaghofer, R., Stamm, M., et al. (2008). Work stress and reduced health in young
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3.
Burgard, S.A., Brand, J.E. & House, J.S. (2009). Perceived job insecurity and worker health in the United
States. Social Science and Medicine, 69, 777-785.
4.
Godin, I., Kittel, F., Coppieters, Y. & Siegrist, J. (2005). A prospective study of cumulative job stress in
relation to mental health. BMC Public Health, 5.
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5.
Kivimäki, M., Elovainio, M., Kokko, K., et al. (2003). Hostility, unemployment and health status: Testing
three theoretical models. Social Science and Medicine, 56, 2139-2152.
6.
Marchand, A., Demers, A. & Durand, P. (2005). Do occupation and work conditions really matter? A
longitudinal analysis of psychological distress experiences among Canadian workers. Sociology of Health and
Illness, 27, 602-627.
7.
Melchior, M., Caspi, A., Milne, B.J., et al. (2007). Work stress precipitates depression and anxiety in young,
working women and men. Psychological Medicine, 37, 1119-1129.
8.
Nasermoaddeli, A., Sekine, M., Hamanishi, S. & Kagamimori, S. (2003). Associations between sense of
coherence and psychological work characteristics with changes in quality of life in Japanese civil servants: A
1-year follow-up study. Industrial Health, 41, 236-241.
9.
Niedhammer, I., Tek, M.L., Starke, D. & Siegrist, J. (2004). Effort-reward imbalance model and self-reported
health: Cross-sectional and prospective findings from the GAZEL cohort. Social Science and Medicine, 58,
1531-1541.
10. Olsson, G., Hemström, O. & Fritzell, J. (2009). Identifying Factors Associated with Good Health and Ill Health
- Not Just Opposite Sides of the Same Coin. International Journal of Behavioral Medicine, 1-8.
11. Ota, A., Masue, T., Yasuda, N., et al. (2009). Psychosocial job characteristics and insomnia: A prospective
cohort study using the Demand-Control-Support (DCS) and Effort-Reward Imbalance (ERI) job stress models.
Sleep Medicine, 10, 1112-1117.
12. Paterniti, S., Niedhammer, I., Lang, T. & Consoli, S.M. (2002). Psychosocial factors at work, personality traits
and depressive symptoms: Longitudinal results from the GAZEL Study. British Journal of Psychiatry, 181,
111-117.
13. Rydstedt, L.W., Devereux, J. & Sverke, M. (2007). Comparing and combining the demand-control-support
model and the effort reward imbalance model to predict long-term mental strain. European Journal of Work
and Organizational Psychology, 16, 261-278.
14. Tokuyama, M., Nakao, K., Seto, M., et al. (2003). Predictors of first-onset major depressive episodes among
white-collar workers. Psychiatry and Clinical Neurosciences, 57, 523-531.
15. Wickrama, K.A.S., Surjadi, F.F., Lorenz, F.O. & Elder Jr, G.H. (2008). The influence of work control
trajectories on men's mental and physical health during the middle years: Mediational role of personal control.
Journals of Gerontology - Series B Psychological Sciences and Social Sciences, 63, S135-S145.
16. Houkes, I., Janssen, P.P.M., De Jonge, J. & Bakker, A.B. (2003). Personality, work characteristics, and
employee well-being: A longitudinal analysis of additive and moderating effects. Journal of Occupational
Health Psychology, 8, 20-38.
40 17. Jimmieson, N.L., Terry, D.J. & Callan, V.J. (2004). A Longitudinal Study of Employee Adaptation to
Organizational Change: The Role of Change-Related Information and Change-Related Self-Efficacy. Journal
of Occupational Health Psychology, 9, 11-27.
18. Mäkikangas, A. & Kinnunen, U. (2003). Psychosocial work stressors and well-being: Self-esteem and
optimism as moderators in a one-year longitudinal sample. Personality and Individual Differences, 35, 537557.
19. Mauno, S., Kinnunen, U. & Ruokolainen, M. (2007). Job demands and resources as antecedents of work
engagement: A longitudinal study. Journal of Vocational Behavior, 70, 149-171.
20. Michielsen, H.J., Willemsen, T.M., Croon, M.A., et al. (2004). Determinants of general fatigue and emotional
exhaustion: A prospective study. Psychology and Health, 19, 223-235.
21. Rodriguez, I., Bravo, M.J., Peiro, J.M. & Schaufeli, W. (2001). The Demands-Control-Support model, locus of
control and job dissatisfaction: A longitudinal study. Work and Stress, 15, 97-114.
22. Schwarzer, R. & Hallum, S. (2008). Perceived teacher self-efficacy as a predictor of job stress and burnout:
Mediation analyses. Applied Psychology, 57, 152-171.
23. Tyssen, R., Vaglum, P., Grønvold, N.T. & Ekeberg, Ã. (2005). The relative importance of individual and
organizational factors for the prevention of job stress during internship: A nationwide and prospective study.
Medical Teacher, 27, 726-731.
24. Feldt, T., Kinnunen, U. & Mauno, S. (2000). A mediational model of sense of coherence in the work context:
A one-year follow-up study. Journal of Organizational Behavior, 21, 461-476.
25. Mäkikangas, A., Feldt, T. & Kinnunen, U. (2007). Warr's scale of job-related affective well-being: A
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26. Cavanaugh, M.A., Boswell, W.R., Roehling, M.V. & Boudreau, J.W. (2000). An empirical examination of
self-reported work stress among U.S. managers. Journal of Applied Psychology, 85, 65-74.
27. Elovainio, M., Kivimäki, M., Vahtera, J., et al. (2003). Personality as a moderator in the relations between
perceptions of organizational justice and sickness absence. Journal of Vocational Behavior, 63, 379-395.
28. Fusilier, M. & Manning, M.R. (2005). Psychosocial predictors of health status revisited. Journal of Behavioral
Medicine, 28, 347-358.
29. Ostry, A.S., Hershler, R., Chen, L. & Hertzman, C. (2004). A longitudinal study comparing the effort-reward
imbalance and demand-control models using objective measures of physician utilization. Scandinavian Journal
of Public Health, 32, 456-463.
30. Vahtera, J., Kivimäki, M., Uutela, A. & Pentti, J. (2000). Hostility and ill health: Role of psychosocial
resources in two contexts of working life. Journal of Psychosomatic Research, 48, 89-98.
31. Kuper, H., Singh-Manoux, A., Siegrist, J. & Marmot, M. (2002). When reciprocity fails: Effort-reward
imbalance in relation to coronary heart disease and health functioning within the Whitehall II study.
Occupational and Environmental Medicine, 59, 777-784.
32. Nabi, H., Kivimäki, M., De Vogli, R., et al. (2008). Positive and negative affect and risk of coronary heart
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33. Nabi, H., Singh-Manoux, A., Shipley, M., et al. (2008). Do psychological factors affect inflammation and
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41
3. Personality change over the life course
Personality traits are considered to be ‘relatively enduring’ patterns of thoughts, feelings
and behaviours that distinguish individuals from one another. However, this definition
does not imply that personality traits do not change over the adult life span, although the
time course and extent of change has been disputed. Thus, some researchers argue for a
‘plaster’ theory, claiming that there is little change in personality beyond the age of about
30 years106,107; others, endorsing a ‘plasticity’ theory, consider that personality traits
continue to change in middle and old age108,109.
The two main approaches to studying personality change and continuity over the life
course are evaluation of mean-level change and evaluation of individual differences in
patterns of change. Mean-level change refers to increases or decreases in specific
personality traits over a specified period in the life course for a population of individuals;
individual differences in patterns of change reflect deviations from these normative meanlevel trends110. Individual differences in the nature and extent of personality change over
time affect the relative rank-order placement of individuals within the group, designated
consistency. Thus, rank-order consistency can be maintained even when mean-level
changes occur; similarly, rank-order can change without necessarily bringing about a
change in mean level.
In the following sections, patterns of mean-level personality change are described, and
individual differences in personality change across the life course, and the factors
b
associated with such changes, are then examined .
3.1
Mean-level changes in personality across the life course
Mean-level change in personality is usually regarded as representing normative change,
which occurs when most people change in the same way during a specific period within
the life course. The term maturity principle has been used to describe this normative
pattern of increasing psychological maturity from adolescence to middle age111. Although
normative, mean-level personality changes occur predominantly in young adulthood (2040 years), some change may continue throughout the life course.
3.1.1
Mean-level change in personality traits assessed by the Five Factor Inventory
The Five Factor Inventory (e.g. NEO-FFI)87, which assesses the personality characteristics
of extraversion, neuroticism, conscientiousness, openness, and agreeableness, has been
extensively used to study mean-level patterns of personality change across the lifespan in
both cross-sectional and longitudinal samples112-114, 115. A meta-analysis of 92 longitudinal
studies covering the age range from 10 to 101 yrs demonstrated a clear pattern of
normative change in NEO-FFI personality traits across the life course109,110.
b
The papers cited in this section were located by a search of computer databases, focusing primarily on
literature published in the past 10 years.
42
In general, with increasing age, people became more conscientious and emotionally stable.
The most marked changes occurred in young adulthood, but different facets of personality
showed different profiles across age groups and some changes continued into middle and
old age. Thus, conscientiousness and agreeableness tended to increase steadily throughout
the life course, while emotional stability increased relatively little beyond the age of 40
years, and openness to experience decreased in the 50+ age groups. Extraversion showed
more complicated patterns; one component, social dominance, increased with age, while a
second component, social vitality, showed small increases in adolescence and small
decreases late in life110.
A recent study of students in transition from school to college over a two-year period also
found mean-level changes consistent with the maturity principle; scores on agreeableness,
conscientiousness, and openness increased over time, whereas neuroticism decreased116.
A separate study found that the rank-order consistencies of the major dimensions and most
trait facets of the NEO-FFI were unrelated to age, although some scales showed increasing
stability after age 30 years107. Whilst poorer psychometric properties of personality
inventories in young people might account for the apparent lesser stability of traits in
younger groups, evidence from a psychometric evaluation showed that measurement
unreliability did not explain the findings; rather, the underlying traits were found to be less
stable in young adults117.
In two further studies, confirmatory factor modelling methods were used to analyse crosssectional and longitudinal datasets from a Dutch sample of middle-aged and older
adults118,119. This study used the FFPI inventory 120 which assesses traits similar to those
covered by the NEO-FFI, except that a measure of autonomy replaces the NEO-FFI
openness measure. The results showed a particularly marked increase in conscientiousness
across the age range of 16-70+ years, smaller increases in agreeableness and emotional
stability, a decrease in extraversion and, from age 60 yrs, an abrupt decrease in autonomy.
In general, men and women show similar profiles of mean-level change although some
evidence suggests that increases in emotional stability with age are greater among women
than among men. Thus, in a large cross-sectional study of U.S. and Canadian individuals
(N=132,515, age range 21-60 yrs), recruited through the Internet, emotional stability
continued to increase across the age span among women whereas there was little change in
scores either below or above the age of 30 years among men108.
3.1.2
Mean-level change in ‘sense of control’
The NEO-FFI personality inventory, on which most of the findings related to mean-level
change over time are based, does not include a measure of locus of control. However,
‘sense of control’ refers to a similar personality characteristic (i.e. the extent to which
individuals regard themselves as responsible for their own successes and failures rather
than believing that outcomes are due to luck or chance, and unrelated to ability or effort),
and stability and change in this measure has been examined in several studies.
43
Two large national surveys of adults showed that mean levels of sense of control remained
stable and relatively high in the age range 18-50 years, followed by progressive decreases
in older age groups121. The pattern of age-related change was not markedly altered by
controlling for potential confounders122; this analysis also showed that the generally lower
sense of control among women as compared with men was more marked in older age
groups. Over time, women's sense of control declined more sharply than that of men.
Differences in education, personal employment history, household income, and physical
functioning accounted for some of the age-related effects of gender.
The role of education in promoting a sense of control is particularly important; among
those with higher levels of education, sense of control tends to increase for longer, rise
higher, and decrease more slowly with age123. Factors found to underlie the link between
education and sense of control include more challenging and interesting work, greater
control over schedules, and greater economic rewards and security, which are associated
with higher status occupations124. Among older, chronically ill adults, sense of control was
found to decline gradually over six successive bimonthly assessments; aside from age at
baseline, the only other major predictor was mental health125.
3.1.3
Mean level change in ‘Sense of Coherence’
Several studies have examined changes over time in the personality dimension of ‘sense of
coherence’(SOC)126. SOC describes a generalised tendency to view the world and the
individual environment as comprehensible, manageable, and meaningful; it is
conceptualised as a measure of ‘salutogenic’ personal resources, which have a positive
influence on health. Antonovsky42 regarded SOC as a ‘dispositional orientation’ rather
than a personality trait; he considered that an individual’s SOC should be fully developed
by the age of 30 yrs and that, although change might occur subsequently, the stable level
would return after the cause of the change had dissipated.
The stability or otherwise of SOC measures, and the extent of change with age, has been
much debated in the light of mixed findings. In one study, for instance, SOC was not
found to be more stable in an older group compared with the younger group; age did not
play any role in the stability of, level of, or mean changes in sense of coherence127. In
contrast, a systematic review concluded that SOC does tend to increase with age128.
Further, and more consistent evidence, demonstrates that SOC is more stable over time
among those with high SOC levels as compared with those with low SOC levels129,130.
An intervention designed to boost re-employment among unemployed individuals131
provided evidence SOC can change over relatively short time periods. The intervention
significantly increased SOC scores, and participants who found employment showed the
largest changes. Of the SOC subscales, comprehensibility and manageability showed
significant overall increases, while meaningfulness increased only among those who
gained employment. However, younger participants (< 30 yrs) did not show more benefit
than those who were older.
44
3.2
Individual differences in patterns of personality change
Life experiences, particularly in work and relationship roles, contribute to individual
patterns of personality change which deviate from normative mean-level changes in
personality across the life course111,132,133, possibly even running counter to these normal
trends134. Findings from several longitudinal studies demonstrate that work conditions,
and occupational factors more generally, contribute to individual personality change over
extended periods of time.
3.2.1
Individual personality change: The roles of work and occupational factors
Work stress. Evidence suggests that perceived work stress is associated with adverse
changes in personality. In a 3-year longitudinal study of personality in relation to midlife
parental concerns135, personality was assessed at the first (T1) and third (T3) waves of data
collection while aspects of midlife functioning, including work stress and life satisfaction,
were assessed at the second wave (T2). Analyses predicting T3 personality measures,
controlled for T1, showed that measures of midlife functioning explained significant
variance (albeit, only 1 - 4%) in personality change.
In particular, among men, work stress was associated with decreases in emotional
stability, extraversion and, marginally, agreeableness. In relation to emotional stability, the
negative effect of work stress (β = -.20***) was more marked than the positive effect of
life satisfaction (β =.12**). However, among women, increases in conscientiousness,
openness and agreeableness were positively associated with life satisfaction; work stress
was not a significant factor for women.
Job loss. As part of a study of heart disease, a large sample of men and women in their
40’s completed the NEO-PI (a version of the Five Factor inventory) at baseline and again
6-9 years later. At follow-up, they also responded to items asking whether they had
experienced changes in family, work, health and economic circumstances since baseline.
As compared with those who were promoted, those who were fired showed significant
decreases in emotional stability and conscientiousness over the follow-up years106.
De-investment in work. In a study carried out over 8 years with young adults, deinvestment in work (i.e. engaging in counter-productive behaviours, such as fighting with
co-workers, malingering, breaking safety rules) was associated with personality changes
between the ages of 18 and 26 years that ran counter to normative trends, i.e. with increase
in negative emotionality (e.g. aggression and alienation), and with no increase in traits
reflecting constraint and self-control134.
Work control. A recent longitudinal study showed that work conditions (specifically, work
control assessed by the Karasek measure1) contributed to individual differences in
personal control (assessed by the ‘Mastery’ scale 82), and that personal control was related
to health measures. In this study, based on data from middle-aged men in a rural U.S.
state, Wickrama et al.85 used latent growth curve analysis to test a theoretical model that
included both initial level (1991) and rate of change (from 1991 to 1994) in work control
and personal control as predictors of physical health and depressive symptoms in 2001
(controlled for 1991 level).
45
Over the three-year period, 1991-1994, a small overall increase in work control was found
but there was significant variability across individuals. Over the same period, there was a
decreasing trend in personal control but inter-individual variability was relatively small.
Significant positive paths were found from level of work control to level of personal
control, and from change in work control to change in personal control; moreover, initial
level of personal control, and change in personal control, fully mediated the relationships
between level and change in work control and health outcomes.
Thus, these findings demonstrate that work experiences and changes in work conditions
contribute to changes in personal control, which in turn have implications for health
change over an extended period. The findings are also consistent with the emphasis placed
on the job control dimension in the demand-control model1, although this study extends
the model by demonstrating the meditational role of personal control in the relationship
between work control and health.
Work role quality. Changes in neuroticism and in extraversion were examined in relation
to work satisfaction and relationship satisfaction in a five-wave, 8-year longitudinal
study136. Latent growth curve modelling was used to test whether neuroticism decreased as
work became more satisfying, and whether the relationship varied by age group. The
results showed that, as work satisfaction increased, neuroticism decreased, but the
relationship did not change across age groups. Similarly, as work satisfaction increased
over time, so did extraversion; again, age was not a significant factor. An earlier study
carried out over a 30-year period, found similar results; positive role-quality was
associated with increases in measures of effective functioning and well-being and
decreases in measures of anxiety and neuroticism137.
3.2.2
Reciprocal relations between personality change and work exposures
In summary, the studies outlined above show that adverse occupational experiences are
associated with negative personality changes. In particular, traits associated with
neuroticism increase and conscientiousness decreases; conversely, favourable work
experiences are associated with positive personality changes. However, in spite of the
longitudinal nature of these studies, researchers are cautious about making causal
inferences, noting for instance that “we can conclude only that change in personality is
associated with work experiences”133. Underlying this caution is evidence that personality
plays a role in determining the work situations that individuals select; people actively seek
out environments that fit with their personality133,138. Thus, as has been demonstrated in
several studies, relationships between organizational experiences and personality are
reciprocal, rather than uni-directional.
For instance, among young adults entering employment, personality at age 18 yrs
predicted objective and subjective work experiences at age 26 yrs139. Conversely, work
experiences were related to changes in personality between 18 and 26 yrs; the traits that
selected people into specific work experiences were the same traits that changed in
response to these experiences. Higher status, more satisfying jobs, and gaining financial
security, were associated with decreases in negative emotionality and increases in
46
sociability, positive dispositional characteristics, and self-esteem. In contrast, those who
scored high on negative emotionality at age 18 years experienced difficult transitions into
employment; by age 26 years they occupied lower prestige jobs, were less satisfied with
their jobs, and reported financial difficulties.
More recent findings also illustrate reciprocal transactions between work experiences and
personality140. In this study, personality characteristics, and extrinsic (income and
occupational prestige) and intrinsic (job satisfaction) measures of career success, were
assessed at the start and end of a 10-yr interval. Cross-lagged analyses tested whether
personality predicted change in career success over the 10-year interval and, likewise,
whether markers of career success predicted change in personality.
The main findings were that, among younger participants, baseline income predicted
decreases in neuroticism over the follow-up period, and baseline extraversion predicted
increases in income, but these findings did not hold for older participants, nor did they
apply to the other occupational measures. Whilst not directly concerned with work
conditions, there is also evidence of reciprocal relationships over a 2-3 yr period between
life stressors and personality, specifically neuroticism and anxious depression141.
Although Wickrama et al 85 examined only uni-directional paths between work control,
personal control and health, evidence from previous studies indicates that there are
reciprocal relationships between locus of control and work experiences. Thus, in a sample
of middle-aged males, analysis of survey data spanning a two-year interval, demonstrated
that locus of control systematically influenced work success and, conversely, success at
work enhanced internal locus of control97. Similar findings were reported for school
leavers entering private-sector employment, but in public-sector employment the opposite
was true, career progress leading to increased externality, although this effect was of only
marginal significance142.
A more extensive study98, in which baseline and 10-yr follow-up data were analysed using
structural equation modelling, also demonstrated reciprocal relations between personality
and job conditions. In particular, ‘self-directed orientation’ (a dimension similar to locus
of control), ideational flexibility (a measure of problem-solving ability), and occupational
self-direction (substantively complex work) were found to act as a ‘mutually reinforcing
triumvirate’, each influencing the others over time.
3.3
Implications
Findings relating work experiences to individual patterns of personality change over the
life course highlight the importance of promoting work conditions that reinforce favorable,
health-promoting personality changes. In particular, research demonstrating that adverse
work situations (e.g. work stress, job loss, low job control) are associated with increases in
neuroticism raises serious concerns in view of evidence that high neuroticism at baseline
and an increase in neuroticism over subsequent years are independent predictors of
mortality risk143.
47
From a methodological viewpoint, the reciprocal nature of relations between personality
traits and work experiences suggests that, in longitudinal studies of work-related
psychosocial risk factors and health, salient personality characteristics should be measured
on more than one occasion, and appropriate bi-directional paths incorporated into the
causal models tested. Latent growth curve models offer a valuable method of analyzing
such data.
Moreover, there is a need for long-term cohort studies; as Wickrama et al.85 concluded,
‘an investigation of health outcomes in the middle years calls for a “long view” so that
health consequences of adverse work conditions in early midlife can be documented later,
as they emerge in later midlife’. Moreover, such work calls for collaboration among
medical scientists, epidemiologists, and social scientists if research resources are to be
used to best effect to increase understanding of the complex patterns of change over time
in work conditions, individual characteristics, and health.
48
4. Psychometric issues: Reliability and validity of personality tests
The research cited in this report identifies a range of personality attributes implicated in
the processes by which work-related psychosocial risks are linked to adverse health
outcomes. Interpretation of this research requires an understanding of the psychometric
properties of personality measures concerned. This section outlines methods used in the
development and evaluation of personality measures, and examines evidence of the
psychometric properties of several scales widely used in psychosocial research.
4.1
Scale development
In the development of a personality test, a large number of items relevant to the
personality attribute of interest are generated for initial data collection purposes; factor
analysis methods are then used to analyse the data matrices and determine the underlying
factor structure. Initial exploratory factor analyses allow identification of the minimum
number of dimensions that account for scores on the test items, and facilitate rejection of
ambiguous or unsuitable items that do not fit the hypothesised model.
Confirmatory factor analysis methods, applied to data from a new sample, can then be
used to evaluate the goodness-of-fit of the factor solution derived from the initial
exploratory analysis. Hence, it is possible to create scales to assess separate personality
characteristics or to identify sub-scales representing different facets of a single personality
attribute. The psychometric properties of these scales can then be evaluated. The stages
involved in the development of personality measures, and the problems and pitfalls that
may be encountered, have been detailed in a review by Furnham144.
In recent years, the development of a new statistical approach, ‘Item Response Theory’
(IRT)145, which models the relationship between latent traits and responses to test items,
has brought greater sophistication and flexibility to psychometric methods, leading to
improved reliability of assessment.
4.2
Reliability and validity
In selecting a test to assess any particular personality attribute, issues of reliability and
validity are important considerations. The methods traditionally used to assess reliability
and validity in the development and evaluation of personality tests are outlined below.
4.2.1
Reliability
Reliability refers to the consistency of a test; a test is reliable to the extent that whatever it
measures, it measures it consistently. There are several ways of assessing reliability. The
two most important are (i) internal consistency which measures homogeneity, that is, the
degree to which the items jointly measure the same construct, and (ii) ‘test-retest’
reliability which measures stability over repeated administrations of the same test. The
most frequently reported measure of reliability is ‘coefficient alpha’ (a measure of internal
49
consistency). The minimum acceptable value of alpha is normally considered to be .70146.
For research purposes, reliability should generally be greater than .80, while standardized
tests used for clinical or educational purposes should have alpha values greater than .90147.
The maximum possible value of a correlation between two variables is inherently
attenuated by reliability; low reliability also lessens power against Type II (false negative)
errors148. Thus, the reporting of effect sizes requires that the reliability of the test scores is
known, and that effect sizes are interpreted in the light of the reliability estimates.
Moreover, as different samples, test conditions, and other factors can affect observed
scores and hence reliability estimates, reliability should be reported for the actual scores
obtained in the study, rather than relying on estimates from prior studies or test manuals.
4.2.2 Validity
Validity can be defined as the agreement between a test score and what it is intended to
measure. Validity can be assessed in several different ways.
•
Construct validity. There are two aspects of construct validity. Convergent validity
refers to whether a test correlates well with other tests that measure the same construct,
or with which it should theoretically correlate. Discriminant validity requires that a
test has low correlations with tests of unrelated constructs, i.e. it measures something
different from other scales designed to measure different conceptual variables. To
establish construct validity, both convergent and discriminant validity are necessary.
•
Concurrent and predictive validity. If a test correlates with a criterion measure which
is known to be valid and assessed at the same time point, it is said to have concurrent
validity. When information about the criterion measure is collected at a later time
point, the aim is to establish predictive validity.
These two aspects of validity are the focus of the present review. In addition, two further
aspects of validity are concerned with the actual content of the items:
•
Face validity refers to whether the content of the test items appears to be reasonably
related to the perceived purpose of the test.
•
Content validity refers to the adequacy of the representation of the domain (e.g.
extraversion) that the test is designed to cover. It requires ensuring that the items are a
fair and representative sample of the potential content, and appropriately worded for
the population or group concerned.
4.3
Fairness in psychometric testing
In recent years, issues relating to the fairness, appropriateness and validity of tests and test
procedures have been subject to increased scrutiny, particularly in the context of education
and employment-related testing. These concerns have given rise to formal guidelines, such
as the American Psychological Association document149, covering legal, professional, and
ethical issues in the construction and application of psychometric tests.
50
5. Psychometric properties of specific personality measures
The following sections summarise information about the psychometric properties of
several well-established personality measures relevant to studies of work-related
psychosocial risks and health. Particular attention is given to personality characteristics
that significantly predicted mental and physical health outcomes in the prospective studies
reviewed in Section 2. In each case, evidence of test reliability and validity is summarised
with reference to literature findings. Examples of the tests described, when available, are
shown in the Appendix.
c
5.1
The NEO Five-Factor personality measures
The NEO Five-Factor Model is currently the most widely accepted model of personality
structure; it has generated a vast literature (to date, the manual for the NEO personality
measures87 has received more than 4000 citations). The ‘Revised NEO Personality
Inventory’ (NEO-PI-R) assesses 30 trait measures that define five basic domains of
normal personality, Neuroticism, Extraversion, Openness to Experience, Agreeableness,
and Conscientiousness. Each of the NEO-PI-R trait measures has 8-items; each of the five
domains has 48 items (240 items in total). The traits that make up the five personality
domains are listed below:
•
•
•
•
•
Neuroticism: anxiety (N1), anger–hostility (N2), depression (N3), selfconsciousness (N4), impulsiveness (N5) and vulnerability (N6).
Extraversion: warmth (E1), gregariousness (E2), assertiveness (E3), activity (E4),
excitement-seeking (E5) and positive emotions (E6).
Openness to experience: fantasy (O1), aesthetics (O2), feelings (O3), actions (O4),
ideas (O5) and values (O6).
Agreeableness: trust (A1), straightforwardness (A2), altruism (A3), compliance
(A4), modesty (A5) and tender mindedness (A6).
Conscientiousness: competence (C1), order (C2), dutifulness (C3), achievement
striving (C4), self-discipline (C5) and deliberation (C6).
In addition to the NEO-PI-R and the shorter 60-item ‘NEO Five-Factor Inventory’ (NEOFFI)87,150, a simpler version (NEO-PI-3) has been produced to meet criticisms that the
NEO items were not always readily understood151. Other authors have described
alternative measures (some of which are much shorter than the NEO scales) which assess
c
The ‘NEO Five-Factor Inventory’ (NEO-FFI) and the ‘Revised NEO Personality
Inventory’ (NEO-PI-R) are copyright protected; the test materials for paper-and-pencil or
computer-based administration are available from Personality Assessment Resources,
PAR Inc., 16204 North Florida Avenue, Lutz, FL 33549, USA, and from European
suppliers.
51
the same personality domains120,152,153. Validated translations of the NEO measures have
also been reported154,155. The NEO personality measures have been extensively used in
academic and applied research, including organizational and vocational research140,156,157
and, more specifically, studies of work-related psychosocial risks and health158-160.
5.1.1 Reliability
Internal consistency. The NEO-PI-R domain scales show high levels of internal
consistency; coefficient alpha values for the self-report scales range from .86 to .92, while
for observer ratings, the values range from .89 to .93156. Other authors have also reported
good internal consistency for the NEO-PI-R self-report scales160-163.
Test-retest reliability. The five NEO scales also show good stability over time; thus, testretest reliability over periods of 6-9 years were found to be above .60106, and a metaanalysis demonstrated a high level of rank order stability over 6.7 years164. In a six-wave
study, 6-month test-retest reliabilities for the five scales ranged from .80 to .87, while
longer-term reliabilities ranged from .73 to .86162. Agreeableness tended to show the
lowest stability, while Openness to Experience had the highest stability. In a further study,
over an average 8-year follow-up, NEO-PI-R scores showed high levels of stability in the
sample as a whole, although individuals who had experienced a recent traumatic event
showed increased Neuroticism (specifically, the ‘angry hostility’ trait)165.
5.1.2
Construct validity
Convergent validity. The convergent validity of the NEO scales is demonstrated by the
correlations between NEO domain/trait scores and measures of similar constructs. For
instance, Costa156 reports correlations of r=.55 and r=.54, respectively, between measures
of the NEO anxiety trait (N1) and two other well-established measures of trait anxiety, the
Spielberger State-Trait Anxiety Inventory, and the ‘tension’ measure of the Profile of
Mood States. Similarly, the NEO trait ‘trust’ (A1) was negatively related (r = -.46) to the
suspicion scale of the Buss-Durkee Hostility scale.
At the level of higher-order constructs, Rossier et al.166 found that four out of the five
NEO-PI-R domains were associated with the corresponding dimensions of the 16
Personality Factors scale (16 PF5); only Agreeableness was not represented in the 16 PF5
item set166. Close convergence between the NEO Neuroticism and Extraversion scales and
the corresponding scales of the Eysenck Personality Questionnaire167 has also been
reported168,169.
Discriminant validity. As evidence of discriminant validity of the NEO measures, Costa156
highlights the contrasting patterns of correlates of different NEO traits in relation to the
Jackson Personality Research Form. The discriminant validity of the NEO traits measures
has also been evaluated in relation to concurrent and prospective measures of dysfunction
in social, work, and recreational domains of life in a large, longitudinal clinical sample.
52
Consistent with theoretical predictions, NEO Neuroticism was broadly related to
dysfunction across all domains, Extraversion was primarily related to social and
recreational dysfunction, Openness to Experience to recreational dysfunction,
Agreeableness to social dysfunction, and Conscientiousness to work dysfunction170.
5.1.3
Concurrent and predictive validity
Vocational, behavioural, and psychosocial research has provided evidence of the validity
of the NEO measures. Some examples are outlined below.
Vocational settings. Several studies demonstrating the concurrent and predictive validity
of NEO measures in vocational settings, including selection, training and job performance,
are reviewed by Costa156. In a study of the predictive validity of the NEO scales in higher
education, Conscientiousness was found to predict academic achievement, while
Neuroticism was positively related to performance only under low-stress conditions171.
Behavioural studies. NEO traits show the expected correlations with daily behaviours
reported over a 30-day period172, and with self-reports of general behaviour patterns173. In
a further study, behavioural indicators were assigned a priori to the Big Five dimensions
on the basis of theory and expert ratings. These indicators were then assessed in a student
sample in a variety of social situations; each of the five personality dimensions predicted
actual behaviours in line with the model174.
Psychosocial research. Significant associations have been found between NEO scores and
work-related psychosocial factors; thus, low Neuroticism together with high Extraversion
and high Conscientiousness predicted lower stressor exposure, better physical health, and
lower job dissatisfaction175. NEO measures are also important correlates of subjective
well-being; Extraversion and Openness to Experience are associated with positive affect,
while Neuroticism is strongly linked to negative affect163. In two longitudinal studies,
NEO Neuroticism scores significantly predicted the onset of major depression9,159.
5.2
Over-commitment
The personality characteristic of over-commitment (OC) describes individual attitudes,
behaviours and emotions reflecting excessive work-related striving combined with a need
for approval and esteem. This personal coping pattern forms the intrinsic component of the
Effort-Reward Imbalance (ERI) model2. The OC dimension was originally designated
‘need for control’, and assessed by means of a 29-item scale with four sub-scales.
However, in further studies176,177, the factorial structure of the scale was not successfully
replicated, and the scale was unduly long for epidemiological research. Subsequently,
exploratory and confirmatory factor analyses were carried out to develop a shorter 6-item
scale focusing on items concerned with ‘inability to withdraw from work obligations’75.
This OC scale is now routinely used to assess the intrinsic component of the ERI model.
53
The factorial stability of the ERI measures, including the 6-item OC scale, has also been
examined; the authors concluded that researchers utilizing the ERI scales “can feel
confident that self-reported changes are more likely to be due to factors other than
structural change of the ERI scales over time, which has important implications for
evaluating job stress and health interventions”76.
5.2.1
Reliability
Internal consistency. Coefficient alpha values for the 6-item OC measure were reported
from a study of five diverse European samples75. In these samples (ranging in size from
N=960 to N=10,174), coefficient alpha was in the range .79 to .82 in both male and female
groups, with the exception of one low value (.64) relating to a small group of female
transport workers. Other researchers, working with different occupational groups, and
using the OC scale in several different European languages, have found values of
coefficient alpha in the range .72 - .8123,76,92,178 although occasional higher179 or lower180
values have been reported.
Test/retest reliability. De Jonge76 reported test/retest reliability for the OC scale for two
samples with different time lags. Over a one-year interval, test/re-test stability was .53,
reducing to .45 over two years. The authors note that the test/re-test coefficients assume
stability in the work situation, which is rarely the case over extended periods of time.
5.2.2 Validity
Convergent validity. Several studies have found significant correlations (in the range .30
to .38) between OC and measures of neuroticism/NA23,92,93. OC was also found to
correlate .28 with Type D personality (a combination of high NA and high social
inhibition)181. As OC would be expected to include an element of negative affectivity,
these findings provide some evidence of the convergent validity of the OC measure.
Discriminant validity. Correlations between OC and NEO Five-Factor measures
demonstrate discriminant validity; thus, in a cross-sectional sample, OC was found to
correlate only weakly with Extraversion (r=.03), Openness (r=.06) and Conscientiousness
(r=-.12), both of which represent personality characteristics conceptually very different
from OC. Siegrist75 refers to a different form of discriminant validity in noting that mean
OC scores differ significantly across major socio-demographic groups.
Concurrent and predictive validity. High OC is conceptualised as a maladaptive coping
response to demanding work conditions; cross-sectional relationships with measures of
impaired health therefore provide indicators of concurrent validity. There is evidence that
high OC scores are associated with a wide range of adverse health outcomes, including
low self-rated health75, poor psychological well-being and intention to quit among
teachers179, adverse physiological responses in men35; exhaustion and impaired health
54
(although not absenteeism)181; high levels of fatigue182, and musculoskeletal pain92. These
relationships remained significant when potential confounding variables were controlled.
Longitudinal research demonstrates the predictive validity of OC in relation to adverse
health outcomes; for instance, high OC predicted poor self-rated health over a one-year
period183, the onset of insomnia over a two-year period184, and mental strain among
professionals over a 15-month period185. Thus, in relation to health outcomes, the
concurrent and predictive validity of the OC scale appears to have been well established.
5.3
Negative affectivity
Stable and pervasive individual differences in the tendency to experience negative
emotional states, including anxiety, anger, guilt and distress are assessed by measures of
neuroticism and negative affectivity. For present purposes, the general term ‘negative
affectivity’ (NA) will be used for this personality trait. High NA is associated with
emotional vulnerability, pessimism, and a general disposition to react negatively to life
and work stressors186; such individuals tend to be worried, easily upset, often moody or
depressed, and focused on negative aspects of self, other people, and the world in general.
The tendency to focus on negative aspects of self is also reflected in the significant
correlations found between NA and self-reported mental and physical health problems,
and frequency of health-service use91,94. High NA also predicts long-term ill-health,
including all-cause mortality and cardiovascular disease, over extended follow-up
periods30,187. Self-report measures of psychosocial stressors, including work-related
factors, are also related to NA, leading to suggestions that NA acts as a ‘nuisance’ factor
potentially confounding relations between self-reports of psychosocial factors and health
outcomes44,45. However, other researchers point to possible substantive mechanisms that
could underlie the pattern of relationships shown by NA34,188.
5.3.1
Measures of NA
Several measures are currently used for assessing NA traits. They include the Neuroticism
measure of the NEO Five-Factor Inventory87 (see Section 4.5), the Eysenck Personality
Questionnaire167,189, and the trait NA measure from the State-Trait Anxiety Inventory
(STAI)190. These measures are highly inter-correlated168. Two other measures, the
Bradburn Affect Balance scale191 and the Positive and Negative Affect Scales (PANAS)192
assess both positive (PA) and negative (NA) affectivity.
These two measures differ conceptually. The Bradburn scale consists of five PA items and
five NA items, scored dichotomously; positive and negative affect are treated as opposing
poles of a single dimension, and the overall ‘affect balance’ score is difference between
the PA and NA totals. In contrast, the PANAS scales treat PA and NA as two orthogonal
dimensions. In this model, high PA is associated with experiencing positive feelings such
as enthusiasm and alertness, whereas low PA is related to feelings of lethargy and
sluggishness. Similarly, high levels of NA are associated with negative feelings such as
55
guilt, fear, anxiety, and nervousness, whereas low NA is related to feelings such as
serenity and calmness192.
The PANAS scales were derived from an initial set of 60 descriptive adjectives193. Starting
from this item set, Watson et al. identified and validated 10-item PA and NA scales, made
up of relatively ‘pure’ items that had a substantial factor loadings on one factor, but nearzero loadings on the other. The advantages of the PANAS scales (assessment of NA and
PA, simplicity, relatively short length, and possibility of using them to assess either traits
or short-term states) have led to their widespread application. This section therefore
focuses primarily on the reliability and validity of the PANAS scales.
5.3.2
Reliability of the PANAS scales
Coefficient alpha values for the PANAS scales were found to range from .84 to .87 ( NA)
and from .86 to .90 (PA); in both cases, the alpha values were not markedly affected by
the time frame of the assessment192. When administered in the ‘trait’ form (i.e.
respondents were asked to report how they generally felt), the NA and PA scales also
showed good test-retest reliability (.71 and .68 respectively) over an eight-week interval.
More recent data confirm the high reliability of PA and NA194,195, and a shortened version
with 5-item scales was also found to show good reliability196.
5.3.3
Validity of the PANAS scales
Convergent and discriminant validity. Watson et al.192 reported relatively low intercorrelations of the NA and PA, ranging from -.12 to -.23 across different time frames,
although a later confirmatory factor analysis indicated a correlation of -.30 between the
latent factors for NA and PA194. Both the PANAS scales demonstrated good convergent
validity with similar measures devised in the Netherlands197.
In relation to other trait measures, the PANAS scales were found to correlate with the
measures from the NEO Five-Factor Inventory; PA correlated .61 with extraversion and
.55 with neuroticism, while NA correlated -.72 with neuroticism and -.31 with
extraversion195. In this study, NA and PA also showed patterns of significant relationships
with other personality variables. In particular, PA was strongly positively related to selfesteem (.75) and self-efficacy (.65), while NA was negatively related (-.50 and -.48,
respectively) to these traits, thus raising some issues of discriminant validity.
Concurrent and predictive validity. Several studies have demonstrated the validity of the
trait form of the PA and NA measures in relation to health and work-related outcomes:
•
PA was positively related to job satisfaction, both concurrently (r = .45) and
predictively over a six-month period (r = .34); NA was significantly negatively related
to job satisfaction (r = -.20, concurrently, and r=-.24, predictively)195.
56
•
Denollet et al.197 found significant negative correlations between NA and measures of
mental health, physical health, social relationships, and personal competence, together
with significant positive correlations of NA with perceived stress, depressive
symptoms, emotional exhaustion, and fatigue. PA showed relationships in the opposite
direction and generally smaller in magnitude. Similar findings have been reported
from a separate study198.
•
A meta-analytic study of PA and NA in work settings demonstrated that, as predicted,
NA was positively related to withdrawal behaviours, counterproductive work
behaviours, and occupational injury, while PA was a positive predictor of
organizational citizenship behaviours and task performance199. This study also showed
that extraversion and neuroticism measures did not contribute to explaining task
performance over and above the effects of NA and PA.
•
Using a Dutch version of the PANAS scale, and a two-wave panel design, Houkes et
al. found that NA predicted increase in emotional exhaustion over a one-year period78.
•
In two prospective studies, trait NA (assessed by the ‘Bradburn Affect Balance’ scale)
predicted incident coronary heart disease over a mean follow-up period of 11-12 years
after control for a range of demographic, physiological, and lifestyle factors200,201.
5.4
Hostility
Type A behaviour pattern (TABP) has long been recognized as a risk factor for coronary
heart disease and other health problems; however, of the two main components that make
up TABP (time urgency, and aggression/hostility), evidence suggests that hostility is the
‘toxic’ factor underlying health risks29,66,202. This review therefore focuses on the
reliability and validity of hostility measures, rather than measures of overall TABP.
Hostility is a multi-faceted construct, incorporating an affective component (anger,
annoyance, resentment and contempt), a cognitive component (cynicism and negative
beliefs about human nature in general), and a behavioural component (aggression,
antagonism, and uncooperativeness). However, these components are not always
distinguished clearly in measures of hostility, leading to ambiguity in interpreting scale
scores. Nonetheless, hostility has been identified as an important risk factor for a range of
physical health problems and for all-cause mortality79,202,203.
The main features and psychometric properties of two widely-used measures of hostility
(the Buss-Durkee scale, and the Cook-Medley Scale) together with a short 3-item scale
(Finnish Twin Study scale) are summarised below.
57
5.4.1
Buss-Durkee Hostility Inventory
The Buss-Durkee Hostility Inventory (BDHI)204 is the longest-established measure of
hostility; it also has the most items (75 true/false statements) from which eight subscales
(‘assault’, ‘verbal hostility’, ‘indirect hostility’, ‘irritability’, ‘negativism’, ‘suspicion’,
‘resentment’ and ‘guilt’) are derived. The BDHI scale has been used in a variety of
studies101,205,206, but it is too long for most epidemiological research. It has also been
criticised on psychometric grounds; although the total score has an acceptable internal
consistency (mean alpha value = .82 ± .11)207, the subscales tend to have low alpha
values205,208. The test-retest reliability over a two-week period was found to be .64208.
Convergent and discriminant validity. Factor analysis of the BDHI items has identified
two separate sub-scales, ‘expressive hostility’ (or ‘overt aggression’), and ‘neurotic
hostility’ (or ‘covert aggression’), respectively209,210, both of which have good
reliability209. In relation to the NEO Five-Factor Personality Inventory, ‘neurotic hostility’
is strongly related to Neuroticism, r = .66 (men) and r = .63 (women), while ‘expressive
hostility’ is negatively related to Agreeableness, r = -.57 (men) and -.51 (women)211. The
correlations of the hostility factors with NEO scores for Openness and Extraversion were
low and non-significant. Thus, there is evidence for the convergent and discriminant
validity of the two factors of the BDHI.
Concurrent and predictive validity. Evidence of the concurrent and predictive validity of
the BDHI scales in relation to physical illness, including cardiovascular disease, is
reviewed by Smith29. Other relevant findings include significant cross-sectional
relationships between specific BDHI subscales and symptoms of anti-social and
borderline personality disorders in non-clinical groups205. Prospectively, the BDHI
‘neurotic hostility’ factor predicted all-cause mortality over a 12-year period in a fullyadjusted model, including control for other personality factors212, and the BDHI total
predicted increase in depression over a three-year period101.
5.4.2 Cook-Medley scale
The Cook-Medley hostility scale (Ho)213 is a 50-item true/false scale derived from the
Minnesota Multiphasic Personality Inventory (MMPI); it is generally regarded as a
measure of ‘cynical hostility’ assessing anger-proneness, resentment, cynicism, and
mistrust towards others. The original Ho scale has acceptable internal consistency
(reported alpha values of .77208 and .8446), but the relatively high correlation between Ho
scores and NA may confound interpretation of findings. For instance, control for NA
markedly reduced the correlation between Ho scores and psychosocial measures46. These
findings suggest that NA should be controlled when using the Ho scale in psychosocial
research.
Factor analyses of the Cook-Medley scores revealed a strong single factor, but also
evidence of additional small, unstable factors214. Using a more sophisticated form of factor
analysis, Strong et al.215 identified 17 items that reflected hostile attitudes and expectations
58
of a hostile world. The 17-item scale total correlated highly with the 50-item Ho scale, and
(unlike the 50-item scale) was not differentially influenced by gender. The 17-scale has
been reported to have good reliability (α = .82), to correlate with other measures of
hostility, and to predict responses to a laboratory stressor216. High scores on the scale are
associated with depressive symptoms and negative affect but not with positive affect216.
Thus, there is some evidence of convergent and discriminant validity for the 17-item
Cook-Medley scale.
There is also evidence of the predictive validity of the 50-item Cook-Medley total score.
In a large sample of women, those with scores in the top quartile of the scale had
significantly higher rates of heart disease at 8-year follow-up relative to those in the lowest
quartile217 and, in a separate 8-year follow-up study, total scores were predictive of poorer
pulmonary function and more rapid rates of decline among older men.
5.4.3
Finnish Twin Study Scale of Hostility
Reliability. The ‘Finnish Twin Study Scale of Hostility’218 has only three items. Short
scales tend to have lower reliability, and the value of coefficient alpha (.63) for this scale77
falls below the normally acceptable level. Nonetheless, the brevity of the scale offers
advantages in large-scale survey research. A test-retest correlation of .57 over a nine-year
period indicates that the scale has satisfactory temporal stability219.
Construct validity. The scale has shown convergent validity in significant correlations
with Spielberger’s Trait-Anger scale (r=.62 for women, r = .71 for men)219, and it has
shown predictive validity in relation to sickness absence, hospitalization, coronary heart
disease, and mortality218-220.
5.5
Sense of coherence
‘Sense of Coherence’ (SOC) is the central construct in Antonovsky’s salutogenic theory
which seeks to understand “how people manage stress and stay well”126. SOC is defined as
“A global orientation that expresses the extent to which one has a pervasive, enduring
though dynamic feeling of confidence that (1) the stimuli deriving from one’s internal and
external environments in the course of living are structured, predictable, and explicable;
(2) the resources are available to one to meet the demands posed by these stimuli; and (3)
these demands are challenges, worthy of investment and engagement”42.
These three components of SOC reflect individual beliefs about the ‘comprehensibility’,
‘manageability’, and ‘meaningfulness’ of events and experiences in life. Although the
three components reflect different aspects of the SOC orientation, the scale was designed
to assess a global orientation, rather than three subscales42. The original SOC scale had 29
items, but a 13-item version has also been developed42,221. The SOC scales have been
widely used, although the majority of published work originates from Finland and other
Scandinavian countries. Consistent with Antonovsky’s theory, SOC measures have
primarily been applied in studies of stress, coping and health.
59
5.5.1
Reliability
Internal consistency. In an exhaustive review of SOC measures, coefficient alpha was
reported to range from .70 to .95 for the 29-item scale, and from .70 to .92 for the 13-item
scale128. More recent work also finds coefficient alpha values in these ranges86,131. Thus,
internal consistency values are good or very good. A 3-item scale, with one item for each
of the SOC components, has also been reported222,223, but there appears to be no
information about alpha values for this scale.
Test-retest reliability. As reviewed by Eriksson and Lindström128, test-retest reliabilities
for the 29-item SOC scale range from .92 (over one week) to .77 (over six months). Over
similar time periods, the 13-item SOC had slightly lower values. Longer time periods are
associated with lower test-retest values; for instance, .63 over a three-year period224.
Antonovsky considered that an individual’s level of SOC would be fully developed and
stable by age 30 years42. Some findings support the view that the temporal stability of
SOC scores is greater among those age 30+ years as compared with a younger groups225
although, in an earlier study, age was not found to play any role in the stability of SOC127.
Moreover, other evidence suggests that SOC tends to increase with age throughout the
whole life span128. The many discrepant findings have led to extensive debate about the
temporal stability of the SOC scale. In a further contribution, Smith et al. suggested that
SOC measures incorporate both a stable ‘trait’ component and a changeable ‘state’
component, and cautioned against using SOC to represent a stable disposition96. This view
is consistent with findings indicating that SOC acts as a mediator of relations between
psychosocial stressors and health outcomes43.
5.5.2
Construct validity
Convergent and discriminant validity. The role of SOC as a health-promoting individual
characteristic suggests that convergent validity should be shown by positive correlations
between SOC scores and personality measures reflecting adaptability and good coping
skills, and negative correlations with measures of neuroticism/NA. Some relevant
evidence has been reported. For instance, SOC scores are positively correlated with a
combined measure of autonomy, adaptability, and psychological functioning226, and with
frustration tolerance227. Strong positive correlations with self-esteem and optimism have
also been reported128.
The strongly negative association of SOC scores with trait anxiety (-.85)221, and with
neuroticism (-.85)86, provides further evidence of convergent validity. Several other
researchers have also drawn attention to the high negative relationships between SOC and
measures of negative affectivity227,228. These findings raise questions about discriminant
validity of SOC relative to NA, and the extent to which SOC and NA contribute
independently to the prediction of measures of mental and physical health.
60
The relatively modest correlations between SOC scores and measures of extraversion86,227,
and other NEO Five Factor scales86 provide some evidence of discriminant validity.
However, Frenz et al.221 noted that SOC scores were significantly positively correlated
with social desirability, and concluded that evidence for the discriminant validity of the
SOC measures was mixed. In a further study229, in which social desirability was
controlled, SOC scores were found to show significant partial correlations with
dispositional measures and with measures of adaptive coping behaviours. Thus, the
discriminant validity of the SOC scale is less well-established than its convergent validity.
Concurrent and predictive validity. SOC scores are associated, both concurrently and
predictively, with a wide range of health-related outcomes. For instance, SOC scores are
higher in non-clinical as compared with clinical groups221; SOC correlates negatively with
depression (r=−0.49) and anxiety (r=−0.52)226; and, in employed groups, high SOC scores
are related to lower rates of sickness absence230, and to greater ability to cope with job
demands231. High SOC is also associated, in favourable directions, with a range of health
behaviours such as diet, exercise, and smoking habits232,233.
Prospective studies demonstrate the predictive validity of SOC scores in relation to health.
For instance, baseline SOC predicted all-cause mortality at 6-year follow-up, independent
of age, sex, and chronic disease; this result remained significant after control for other
variables, including hostility and NA223. Mastery and SOC scores were also found to
contribute independently to the prediction of mortality234. Over a shorter follow-up period
(one year), SOC predicted physical and mental health outcomes after control for
demographic factors and initial health status235. In both these studies, the effect of SOC
was significant over and above the effects of NA. Other prospective studies227,236,237 also
illustrate the role of SOC as a protective factor that mitigates the adverse effects of life and
work stress.
5.6
Locus of control
The locus of control (LOC) construct refers to the extent to which individuals believe that
outcomes are determined by personal effort and ability (internal control) rather than by
outside influences such as fate, chance, and powerful others (external control). The
original LOC measure, devised by Rotter238, was intended for use with students. Scales
which are more suited to general population samples, have better psychometric properties,
and may incorporate several subscales, have been developed subsequently82,239,240; other
LOC scales focus on specific life domains, such as work83. The LOC construct has also
been incorporated into measures assessing broader personality constructs, such as
hardiness (challenge, commitment, and control)241 and, more recently, ‘Core Self
Evaluations’242. A 1993 critique of some 50 different LOC measures reviews conceptual
and methodological issues in the assessment of control beliefs, including dimensionality,
domain specificity, and scale reliabilities243.
61
In general, internal LOC is positively associated with adaptive personality traits, such as
extraversion, sense of coherence, self-esteem, and self-efficacy, and negatively associated
with neuroticism and other NA measures244,245; internal control is also associated with use
of adaptive coping responses246-248, with more effective work functioning249 and, in a 7year prospective study, with lower risk of incident psychological distress250.
5.6.1
‘Spheres of Control’ scale
The ‘Spheres of Control’ scale (SC)240 provides an example of the development and
psychometric evaluation of a multi-dimensional LOC measure. The 30-item scale assesses
control beliefs in three activity domains240,251: in the non-social environment, in the form
of efficacy and achievement (personal control), in dyadic relationships and social groups
(interpersonal control), and in society more generally (socio-political control). The three
domains are seen as conceptually independent; empirically, the scale inter-correlations are
low to moderate244,252,253.
Reliability. For the interpersonal and socio-political scales, alpha values are generally in
the acceptable range251,254. However, the original personal control scale had low
reliability251,252, and was subsequently revised251. Test-retest reliability for the scale total
was found to be .80 after a four-week interval, and .60 after six months251.
Convergent and discriminant validity. The three SC scales show good convergent validity
with similar constructs assessed by other LOC measures; for instance, the personal control
scale is related to other measures of internality and self-efficacy; the interpersonal scale is
related to interpersonal competence, and the socio-political scale is related to a measure of
a ‘politically responsive world’251. The subscales are also correlated in predictable ways
with trait measures, such as extraversion, self-esteem, and neuroticism244,252.
Concurrent and predictive validity. Several studies have demonstrated the concurrent
validity of the SC scales in psychosocial research. Thus, external interpersonal control was
associated with role ambiguity254; external personal control was significantly related to
self-reports of worry about life stressors255; and internal personal and interpersonal control
predicted life satisfaction256. In work contexts, internal personal control was found to be
negatively related to voluntary union membership257, and positively related to willingness
to act as a mentor258. Also, in a longitudinal diary study, interpersonal control was found
to predict adaptive coping behaviours and emotional reactions to conflict259.
5.7
Core Self Evaluations scale
‘Core Self Evaluations’ (CSE) is described as a higher-order construct representing an
individual’s fundamental beliefs about his or her own competence and self-worth242. The
CSE measure combines four traits, emotional stability, locus of control, general selfefficacy, and self-esteem; although these traits have traditionally been regarded as
separate, evidence suggests that they can be represented by a single latent factor260. The
development of this scale is relatively recent and it was not used in any of the studies
62
reviewed in Section 2, but it has been shown to have potential application in work stress
research and its psychometric characteristics are therefore briefly considered here.
The development and validation of a 12-item CSE measure, and its relationships to other
personality constructs, has been reported by Judge et al.242,260,261. The alpha reliability
value was found to be .84, and test-retest reliability over three months was .81242. The
CSE total correlates highly with measures of each of the component traits, particularly the
Neuroticism measure from the NEO Five Factor inventory. Other NEO scales, are
moderately (Conscientiousness and Extraversion) or weakly (Agreeableness and
Openness) correlated with CSE scores242. Thus, the CSE scale shows convergence with
related traits, and divergence from measures of different constructs.
Substantial evidence, including longitudinal findings262-265 has accumulated to
demonstrate that individuals with high CSE scores perceive fewer stressors and use more
effective coping strategies265,266, are less likely to experience burnout in stressful work
environments267, are more successful at work263,268, report higher job satisfaction269, and
are more able to recover from job loss264.
These results suggest that the CSE measure has good concurrent and predictive validity in
occupational settings. However, in relation to measures of stress, strain and depression,
neuroticism was found to account for significant incremental variance (10%) over and
above the CSE measure260. Thus, although CSE is seen as representing a broad, inclusive
measure of emotional stability270, in the context of stress research, use of a specific NA
trait measure potentially provides additional information over and above that attributable
to CSE.
The CSE scale has also been used with measures from the job strain and effort-reward
models to predict health-related outcomes. CSE was found to be a stronger predictor of
reported job stress than the demand, control, and support variables of the job strain
model271 and, in a longitudinal study based on the ERI model, CSE was a significant
predictor of emotional exhaustion over and above the effects of baseline measures and
ERI at follow-up262. The CSE measure has been described as particularly well-suited to
stress research265,271. The findings outlined above (good reliability and validity, relatively
short length, and its significant role in relation to stress, coping and health) tend to confirm
this view, although the role of NA in explaining incremental variance, over and above the
CSE measure, in predicting stress-related outcomes should not be disregarded.
5.8
General issues
The sections above summarise the psychometric properties of a range of measures that
assess personality characteristics known to be implicated in the pathways by which workrelated psychosocial risk factors give rise to adverse health outcomes. There are of course
other personality factors not included in the present review that also play significant roles
in the stress process. However, the five personality variables considered in detail here
(over-commitment, negative affectivity, hostility, sense of coherence, and locus of
63
control), together with the NEO Five-Factor model dimensions, accounted for the great
majority of the significant findings in the systematic review of prospective studies
described in Section 2. Several general points emerge from the material considered:
•
Whilst most of the measures described have adequate internal consistency, as assessed
by coefficient alpha, there is a trade-off between scale length and coefficient alpha
values. Longer scales tend to have higher alpha values, but they are less suitable for
large-scale survey research because of the time required to complete the items. Thus,
short 3-item versions of longer scales (e.g. hostility and SOC) were successfully used
by some researchers in place of the original measures, in spite of the relatively low
alpha values for these scales. Irrespective of the particular scale selected, it is
important that coefficient alpha values are reported for the data set concerned.
•
Evidence of the convergent and discriminant validity of the scales reviewed shows
that over-commitment, hostility, sense of coherence (SOC) and locus of control are
significantly correlated (either positively or negatively) with NA. The negative
correlation with NA is particularly high for the SOC measure. These findings raise
questions about the extent to which other personality measures, moderately or highly
correlated with NA, contribute independently to health-related outcomes when
evaluated jointly with NA in simultaneous analysis models.
The prospective studies reviewed in Section 2 provide little information on this point.
Although nine studies included one or more personality variables in addition to a
measure of NA, there were only three instances in which the other personality
variables contributed significant incremental variance. Thus, over and above the
effects of NA, hostility predicted sickness absences77; SOC predicted physical
health235, and extraversion predicted voluntary turnover272. In addition, research not
included in the present review provides some further evidence of the independent
effects of SOC and NA on health outcomes223.
•
As discussed in Section 3, there is evidence that not only mean-level change in
personality occurs over the life-course, but also that work experiences may result in
personality change. Thus, personality measures cannot be regarded as entirely stable
over extended periods of time, or even in some instances (e.g. SOC) over time periods
as short as one year. The implication is that in prospective studies including
personality variables, the measures should be repeated at each wave of data collection,
rather than only used at baseline. If repeated assessments are made, personality
change across time can be taken into account in structural modelling analyses.
•
Structural modelling methods also allow pathways between latent variables (rather
than observed measures) to be evaluated thus removing the effects of measurement
error. These methods are increasingly being applied in studies of work stress,
personality and health, including several of the studies reviewed in Section 2 and
Section 3.
64
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80
Appendix
To illustrate the nature of the measures described in this report, some of the scales
reviewed are reproduced here, with details of scoring systems, when available.
Over-commitment (OC) scale
1.
I get easily overwhelmed by time pressures at work.
2.
As soon as I get up in the morning I start thinking about work problems.
3.
When I get home, I can easily relax and ‘switch off’ work.
4.
People close to me say I sacrifice too much for my job.
5.
Work rarely lets me go, it is still on my mind when I go to bed.
6.
If I postpone something that I was supposed to do today I’ll have
trouble sleeping at night.
Items are scored on a 1-4 Likert scale, ranging from 1= ‘Strongly disagree’ to 4 =
‘Strongly agree’. The six items are summed to form a total score ranging from 6 - 24. The
higher the score, the more likely a subject is to experience over-commitment at work.
Source: Siegrist et al.(2009)273
81
Positive and negative affect scales (PANAS)
Source: Watson et al. (1988)192
This scale consists of a number of words that describe different feelings and emotions.
Read each item and then mark the appropriate answer in the space next to that word.
Indicate to what extent (INSERT APPROPRIATE TIME INSTRUCTION HERE). Use the
following scale to record your answers:
1 = very slightly or not at all
2 = a little
3 = moderately
4 = quite a bit
5 = extremely
___________ interested*
___________ irritable
___________ distressed
___________ alert*
___________ excited*
___________ ashamed
___________ upset
___________ inspired*
___________ strong*
___________ nervous
___________ guilty
___________ determined*
___________ scared
___________ attentive*
___________ hostile
___________ jittery
___________ enthusiastic*
___________ active*
___________ proud*
___________ afraid
* Items marked with an asterisk assess PA; the remaining items assess NA
PANAS has been used with the following time instructions:
Moment
(you feel this way right now, that is, at the present moment)
Today
(you have felt this way today)
Past few days
(you have felt this way during the past few days)
Week
(you have felt this way during the past week)
Past few weeks
(you have felt this way during the past few weeks)
Year
(you have felt this way during the past year)
General
(you generally feel this way, that is, how you feel on average)
82
Eysenck Personality Questionnaire, EPQ-R
Source: Eysenck, Eysenck & Barrett (1985)167
Instructions. Please answer each of the questions below by putting a circle around the
"YES" or the "NO to indicate the response which is true, or most often true, of yourself.
There are no right or wrong answers, and no trick questions. Work quickly and do not
think too long about the exact meaning of the questions.
N
Does your mood often go up and down?
YES
NO
E
Are you a talkative person?
YES
NO
N
Do you ever feel 'just miserable' for no reason?
YES
NO
E
Are you rather lively?
YES
NO
N
Are you an irritable person?
YES
NO
E
Do you enjoy meeting new people?
YES
NO
N
Are your feelings easily hurt?
YES
NO
E
Can you usually let yourself go and enjoy yourself at a party?
YES
NO
N
Do you often feel 'fed up'?
YES
NO
E
Do you usually take the initiative in making new friends?
YES
NO
N
Would you call yourself a nervous person?
YES
NO
E
Can you easily get some life into a rather dull party?
YES
NO
N
Are you a worrier?
YES
NO
E
Do you tend to keep in the background on social occasions?
YES
NO
N
Would you call yourself tense or 'highly-strung'?
YES
NO
E
Do you like mixing with people?
YES
NO
N
Do you worry too long after an embarrassing experience?
YES
NO
E
Do you like plenty of bustle and excitement around you?
YES
NO
N
Do you suffer from nerves?
YES
NO
E
Are you mostly quiet when you are with other people?
YES
NO
N
Do you often feel lonely?
YES
NO
E
Do other people think of you as being very lively?
YES
NO
N
Are you often troubled about feelings of guilt?
YES
NO
E
Can you get a party going?
YES
NO
KEY
E = Extraversion items
N = Neuroticism items
(Items on other EPQ-R scales are not included in the list above)
The bold response for each item is scored 1. Other responses are scored 0.
83
Cook-Medley Hostility scale
17-item version. Source: Strong et al. (2005)215
Items are scored on a true/false basis
People use unfair means for gainsb
I’m on guard with friendly people
I am more competent than my bossb
Pleased when others punished
Many are guilty of bad conductb
Boss takes all credit and no blame
I am impatient when interruptedb
Often disappointed by others
Difficult to convince others of the truthb
My behaviour is often misunderstood
Inwardly dislike helping othersb
Go out of my way to win an argument
Question motives of helpful people
Others are honest to avoid punishmentb
Others can tell what I’m thinking
Friends are only there to be usedb
I enjoy keeping others guessing
Others are jealous of my ideasb
b
These items form an alternative 9-item scale
Finnish Twin Study Scale of Hostility
Source: Elovainio et al, (2003)77
Instructions. Answer according your real self-opinions, not according your ideal or
other peoples opinion. Use the extremes when appropriate, and circle the middle line only
in a case when it really best indicates your character.
Scoring
1
2
3
4
5
6
7
Seldom get into
into arguments
—
—
—
—
—
—
—
Prone to get
into arguments
easily
Do not get angry
easily
—
—
—
—
—
—
—
Get angry
easily
Get irritated easily
—
—
—
—
—
—
—
Do not get
irritated easily
The hostility score is the average of the three item scores. Coefficient alpha =.63
84
Sense of coherence scale (SOC)42
13-item version. Source: Smith et al (2003)96
Items are scored on a 7-point Likert scale; response scales are shown for each item.
1. How often do you have a feeling that you don’t really care about what goes on around
you? (1=very seldom or never, 7=very often)*
2. How often in the past were you surprised by the behaviour of people whom you
thought you knew well? (1=never happened, 7=always happened)*
3. How often have people you counted on disappointed you? (1=never happened,
7=always happened)*
4. How often do you have the feeling you’re being treated unfairly? (1=very often,
7=very seldom or never)
5. How often do you have the feeling you are in an unfamiliar situation and don’t know
what to do? (1=very often, 7=very seldom or never)
6. How often do you have very mixed up feelings and ideas? (1=very often, 7=very
seldom or never)
7. How often do you have feelings inside that you would rather not feel? (1=very often,
7=very seldom or never)
8. Many people – even those with a strong character – sometimes feel like losers in
certain situations. How often have you felt this way in the past? (1=very seldom or
never, 7=very often)*
9. How often do you have the feeling that there’s little meaning in the things you do in
your daily life? (1=very often, 7=very seldom or never)
10. How often do you have feelings that you’re not sure you can keep under control?
(1=very often, 7=very seldom or never)
11. Until now has your life had no clear goals or purpose, or has your life had very clear
goals and purpose? (1=no clear goals and purpose, 7=very clear goals and
purpose)
12. When something happens, you generally find that you overestimate or underestimate
its importance or do you see things in the right proportion? (1=overestimate or
underestimate its importance, 7=see things in the right proportion)
13. Is doing the things you do every day a source of great pleasure and satisfaction or a
source of pain and boredom? (1=a great sense of pleasure and satisfaction, 7=a
source of pain and boredom)*
* Five of the 13 questions are reversed-scored, giving a total score ranging from 13 to 91;
the higher the score, the greater the sense of coherence.
85
Sense of Coherence (3-item version)
Source: Lundberg et al. (1995)222
Manageability
(1)
Do you usually see a solution to problems and difficulties that other people find
hopeless?
Yes, usually
Yes, sometimes
No
2
1
0
Meaningfulness
(2)
Do you usually feel that your daily life is a source of personal satisfaction?
Yes, usually
Yes, sometimes
No
2
1
0
Comprehensibility
(3)
Do you usually feel that the things that happen to you in your daily life are hard to
understand?
Yes, usually
Yes, sometimes
No
2
1
0
86
Personal Control / Mastery scale
Source: Pearlin et al. (1981)82
1=strongly
agree
5=strongly
disagree
How strongly do you agree or disagree that:
1.
I have little control over the things that happen to me
1
2
3
4
5
2.
Sometimes I feel that I’m being pushed around in life
1
2
3
4
5
3.
There is really no way I can solve some of the
problems I have
1
2
3
4
5
4*. I can do just about anything I really set my mind to
1
2
3
4
5
1
2
3
4
5
1
2
3
4
5
1
2
3
4
5
5.
I often feel helpless in dealing with the problems of life
6*. What happens to me in the future mostly depends on me
7.
There is little I can do to change many of the important
things in my life
* Indicates a reverse-scored item
Version used by Marchand et al (2005) 250 and by Wickrama et al. (2009)85
87
Core Self Evaluations Scale
Source: Judge et al. (2003)242
Instructions: Following are several statements about which you may agree or disagree.
Using the response scale provided, indicate you agreement or disagreement with each
item by putting the appropriate number on the line preceding that item.
1 = Strongly disagree
2 = Disagree
3 = Neutral
4 = Agree
5 = Strongly agree
_______ I am confident I get the success I deserve in life
_______ Sometimes I feel depressed **
_______ When I try, I generally succeed
_______ Sometimes when I fail, I feel worthless **
_______ I complete tasks successfully
_______ Sometimes I do not feel in control of my work **
_______ Overall, I am satisfied with myself
_______ I am filled with doubts about my competence **
_______ I determine what will happen in my life
_______ I do not feel in control of my success in my career **
_______ I am capable of coping with most of my problems
_______ There are time when things look pretty bleak and hopeless to me **
** These items are reverse scored