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Abstract

A vast number of published studies have suggested a link between job satisfaction levels and health. The sizes of the relationships reported vary widely. Narrative overviews of this relationship have been published, but no systematic meta-analysis review has been conducted. A systematic review and meta-analysis of 485 studies with a combined sample size of 267 995 individuals was conducted, evaluating the research evidence linking self-report measures of job satisfaction to measures of physical and mental wellbeing. The overall correlation combined across all health measures was r = 0.312 (0.370 after Schmidt-Hunter adjustment). Job satisfaction was most strongly associated with mental/psychological problems; strongest relationships were found for burnout (corrected r = 0.478), self-esteem(r = 0.429), depression (r = 0.428), and anxiety(r = 0.420). The correlation with subjective physical illness was more modest (r = 0.287). Correlations in excess of 0.3 are rare in this context. The relationships found suggest that job satisfaction level is an important factor influencing the health of workers. Organisations should include the development of stress management policies to identify and eradicate work practices that cause most job dissatisfaction as part of any exercise aimed at improving employee health. Occupational health clinicians should consider counselling employees diagnosed as having psychological problems to critically evaluate their work-and help them to explore ways of gaining greater satisfaction from this important aspect of their life.
ORIGINAL ARTICLE
The relationship between job satisfaction and health: a
meta-analysis
E B Faragher, M Cass, C L Cooper
...............................................................................................................................
See end of article for
authors’ affiliations
.......................
Correspondence to:
Dr E B Faragher,
Manchester Business
School, The University of
Manchester, Booth Street
West, Manchester M15
6PB, UK; brian.faragher@
manchester.ac.uk
Accepted
8 September 2003
.......................
Occup Environ Med 2005;62:105–112. doi: 10.1136/oem.2002.006734
Background: A vast number of published studies have suggested a link between job satisfaction levels and
health. The sizes of the relationships reported vary widely. Narrative overviews of this relationship have
been published, but no systematic meta-analysis review has been conducted.
Methods: A systematic review and meta-analysis of 485 studies with a combined sample size of 267 995
individuals was conducted, evaluating the research evidence linking self-report measures of job
satisfaction to measures of physical and mental wellbeing.
Results: The overall correlation combined across all health measures was r = 0.312 (0.370 after Schmidt-
Hunter adjustment). Job satisfaction was most strongly associated with mental/psychological problems;
strongest relationships were found for burnout (corrected r = 0.478), self-esteem(r = 0.429), depression
(r = 0.428), and anxiety(r = 0.420). The correlation with subjective physical illness was more modest
(r = 0.287).
Conclusions: Correlations in excess of 0.3 are rare in this context. The relationships found suggest that job
satisfaction level is an important factor influencing the health of workers. Organisations should include the
development of stress management policies to identify and eradicate work practices that cause most job
dissatisfaction as part of any exercise aimed at improving employee health. Occupational health clinicians
should consider counselling employees diagnosed as having psychological problems to critically evaluate
their work—and help them to explore ways of gaining greater satisfaction from this important aspect of
their life.
E
pidemiologists have long been aware that social and
environmental factors can contribute to the incidence of
many human diseases. Predictably, as the single activity
occupying most people’s waking time is work, pressures,
strains, and stresses within the workplace have been
identified as being a potentially important health factor.
Numerous theories now exist, developed from a wide range
of perspectives, postulating a direct link between organisa-
tional/workplace stress and wellbeing.
1
There is growing evidence that current trends in employ-
ment conditions may be eroding levels of job satisfaction—
and directly damaging the physical and mental health of
employees.
2
New working practices and rapid technological
advances are changing the nature of many jobs.
3
Employees
are regularly being required to work well beyond their con-
tracted hours, often unwillingly, as organisations struggle to
meet tight deadlines and targets.
4
Work practices are becom-
ing more automated and inflexible, leaving employees with
less and less control over their workload. Many organisations
are reducing their permanent workforce and converting to a
culture of short term contracts or ‘‘out-sourcing’’, increasing
feelings of job insecurity. These trends have undoubtedly
contributed considerably to the development of a ‘‘work-
aholic’’ culture throughout the UK and Europe—a climate
which is impacting negatively on the levels of enjoyment and
satisfaction employees gain from their work.
Governments in many developed industrial countries have
already drafted—or are drafting—legislation to make organisa-
tions accept greater responsibility for the impact of their work
practices on the health of their employees. Consequently, an
increasing number of employers are developing formal stress
intervention/management policies. Ideally, such policies should
be evidence based. However, in the past two decades alone,
considerable research effort has been directed at issues relating
to work related stress and health. The research database is
already enormous—and is growing at a considerable rate. Any
organisation starting to review the available evidence is likely to
be overwhelmed by the enormity of the task. Many different
workplace characteristics have been found to correlate sig-
nificantly with both stress levels and health—and equally many
organisational and environmental factors have been found that
potentially moderate the effects of stressors.
5
Furthermore, if
reviewed superficially, the evidence can easily appear to be both
contradictory and inconclusive.
Clearly, there is an urgent need for a systematic and
thorough review of the research evidence linking work
related stress factors with ill health. The HERMES (Health
and Employment Review: a MEta-Analysis Study) project
was instigated to start this task, funded initially by a grant
from the UK Health and Safety Executive. Of the factors
evaluated so far within this project, employee self-reported
job satisfaction has emerged as having by far the strongest
link with employee wellbeing. The findings of this part of the
HERMES project are reported below.
Previous meta-analyses have reported important relation-
ships between important life/work characteristics and job
satisfaction, most notably life satisfaction
6
and job perfor-
mance.
7
Using exactly the same statistical methodology—but
with a more comprehensive review of the literature using elec-
tronic methods not available to earlier authors and an extensive
search for ‘‘grey literature’ and unpublished reports—this
paper reports the finding of an exhaustive systematic review/
meta-analysis of the available research examining the relation-
ship between job satisfaction and both physical and mental
health.
METHODS
Rationale for the use of meta-analysis
As already stated, there are many, often apparently contra-
dictory, published studies linking job satisfaction to measures
105
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of health. Standard narrative literature review methods are
inadequate in this situation; more quantitative methods
are needed. The statistical methods of meta-analysis were
developed to meet this need. These enable the quantitative
results of several individual studies to be amalgamated and
provide a combined estimate of effect size (that is, in this
context, of the correlation between job satisfaction and
health). Meta-analytical statistics are based on much larger
sample sizes and yield weighted average effect size estimates
that are more accurate than those from individual studies—
and are sensitive to findings of different magnitude across
studies.
8
Most importantly, meta-analysis methods enable
researchers to determine the strength and direction of rela-
tionships after eliminating variance due to statistical artefacts
such as sampling and measurement error.
6
Measures
Job satisfaction
Some theorists view job satisfaction as being the positive
emotional reactions and attitudes an individual has towards
their job.
9
Others have viewed it as a bi-dimensional
construct consisting of ‘‘intrinsic’’ and ‘‘extrinsic’’ satisfac-
tion dimensions,
10
or alternatively of ‘‘satisfaction/lack of
satisfaction’’ and ‘‘dissatisfaction/lack of dissatisfaction’’
dimensions.
11
More recently, debate has arisen as to whether
job satisfaction is a global concept or is composed of facets of
satisfaction with various aspects of an individual’s job.
912
A
recent study
13
has suggested that the most important deter-
minants of job satisfaction are whether an employee finds
their job interesting, has good relationships with their
managers and colleagues, has a high income, is allowed to
work independently, and has clearly defined career advance-
ment opportunities.
Measures of job satisfaction tend to fall into two broad
types: single item global measures and composite measures
of satisfaction with various job components. Those most
commonly found in this review were the Warr Job Satis-
faction Questionnaire,
14
the Occupational Stress Indicator,
15
the Michigan Organisational Assessment Questionnaire,
16
the
Job Diagnostic Survey,
17
the Job Descriptive Index ‘‘work
itself’’ subscale,
18
the Minnesota Satisfaction Questionnaire,
19
and the Brayfield-Rothe Questionnaire.
20
All are self-report,
multi-item questionnaires.
Health outcome
Health outcome measures were divided into those evaluating
mental health and those relating to physical health. Mental
health scales measuring depression, anxiety, burnout, self-
esteem, and general mental health (predominantly hybrid
scales measuring elements of depression and anxiety) were
accepted; physical health scales were restricted to subjective
scales (mostly measuring a combination of ‘‘psychosomatic
complaints’’ such as headaches, dizziness, muscle pain, and
digestive problems), cardiovascular disease, and musculo-
skeletal disorders. The condition ‘‘strain’’ was created to
allow studies which combined mental and physical health
measures to be included.
Most self-rated health scales measure ill health, with high
scores denoting high levels of physical or mental disorder.High
levels of job satisfaction were hypothesised as being associated
with improved health. All correlations were converted to reflect
the extent to which they confirmed (or refuted) this hypothesis
(for example, if job satisfaction correlated negatively with a
measure of ill health, the correlation was evaluated as a positive
correlation between job satisfaction and good health).
Procedures
The procedures used to complete the systematic review and
meta-analysis were based on established best practice,
821
and
incorporated the relevant elements of the QUORUM state-
ment. The Cochrane Collaboration (http://www.cochrane.
org) was created in 1992 with the purpose of ‘‘preparing,
maintaining, and promoting the accessibility of systematic
reviews of the effects of healthcare interventions’’. Of its
many major contributions to the field of healthcare research,
one of its most important has been the creation of guidelines
for the execution and reporting of meta-analyses and
systematic reviews. While these were prepared primarily for
reviews of randomised controlled trials, many of the
principles enshrined in the guidelines hold also for the
combination of the results of observational/correlational
studies.
Formulation of the review question
The study objective was to systematically review the research
evidence linking job satisfaction to measures of health, and
to subject this evidence to a series of meta-analyses to obtain
combined estimates of the strengths of these statistical
relationships. The literature contains a number of studies
giving a narrative review of these relationships but, to our
knowledge, no systematic meta-analysis review has been
published.
Inclusion/exclusion criteria
Intervention and observational studies were accepted if:
N
An evaluation of the relationship between a measure of
job satisfaction and a relevant health measure was
reported, using one of the following effect-size statistics:
correlation coefficient, linear regression coefficient (pre-
ferably standardised), R
2
values from (multiple) regres-
sion analyses, odds-ratios/prevalence ratios, significant
test statistic (Student’s t, F ratio, or x
2
), group means (or
mean differences)
N
A normal working population was studied
N
Acceptably large samples (at least 30 respondents in total)
were evaluated
N
One of the following prospective or retrospective experi-
mental desi gns was used: cross -sectional/correlational
cohort, longitudinal cohort, case-control comparison,
rando mised control trial, group differences ba sed on
existing criteria
N
The study report was published after 1970.
Search for and location of eligible studies
An initial comprehensive electronic search for relevant
literature was carried out using the databases PsychInfo,
PubMed, Social Sciences Citation Index, Arts & Humanities
Citation Index, and ERIC. A list of all journals that might
potentially yield eligible studies (irrespective of publication
language) was then compiled and confirmation obtained that
all were included in the databases searched. Any remaining
journals were scanned using their individual websites or
other (non-electronic) databases.
Stress Medicine, Work & Stress, and the Journal of Occupational
and Organisational Psychology were then hand searched for
relevant articles published in the past five years. For every
paper found that had been missed by the electronic search,
the reasons were determined, a revised list of keywords
created, and the electronic searches repeated.
As not all research studies are published in mainstream
journals, the so-called ‘‘grey literature’’ was then searched,
including conference abstracts, thesis/dissertation abstracts,
non-refereed journals, government reports, and technical
reports. Finally, leading academics known to be actively
researching in this field were written to with a request for
details about any relevant unpublished studies owned by
106 Faragher, Cass, Cooper
www.occenvmed.com
them and for contact addresses for other researchers known
to them who might have unpublished studies.
Establishment of methodological criteria
Less methodologically rigorous studies are more likely to
produce inflated effect sizes (that is, to overestimate the
strengths of the relationships between job satisfaction and
health). Well established and agreed measures of methodo-
logical rigour have been established by the Cochrane Colla-
boration for combining randomised controlled trials in a
meta-analysis, but a thorough search failed to identify
criteria suitable for correlational studies. A measure of
methodological rigour was thus developed specifically for
this meta-analysis.
The literature was scanned for guidelines on research
procedures in organisational psychology. The methodological
criteria identified were added to those used for medical
studies, to create a list of 26 criteria potentially relevant for
assessing methodological quality in correlational studies. Six
experienced researchers in the field of organisational
psychology were asked to examine these criteria and to
rank in order the 10 they considered most important for
organisational psychology research. The following 10 criteria
were rated clearly higher than all others:
N
Sample representative of organisation(s) studied
N
Sample stratified for gender, age, level of seniority, ethnic
origin, or degree level
N
Summary statistics for gender, degree level, occupation,
industry, country, age
N
Response rate adequate
N
Sample size acceptable
N
Job satisfaction and health outcome measures acceptable
(that is, validated, reliable, internally consistent and either
continuous or ordinal)
N
Useable effect size statistic(s) reported (or obtainable from
authors)
N
Statistical analysis of study findings appropriate
N
Adjustment made for important confounding factors
N
Attrition rate adequate (if longitudinal design used).
Each study considered eligible for the meta-analysis was
rated according to these criteria (0 = unacceptable; 1 = accep-
table) and a summated ‘‘rigour’’ score computed (range 0–
10).
Statistical methods
Because of the nature of the studies being reviewed, the vast
majority of reports provided effect sizes in the form of
correlation coefficients or regression coefficients. In those
instances where other types of effect size statistic were
reported (for example, odds ratios, mean differences, etc),
these were converted to correlation coefficients using
standard formulae.
82122
For each study that passed the review quality criteria
detailed above, a correlation table was then constructed
containing the correlations between all of the measures, the
reliability alpha coefficients for each measure, and an
indicator of whether or not each correlation was in the
expected direction. This information was then entered
into the ‘‘Comprehensive Meta-Analysis (CMA)’’ computer
program.
23
Initially, the effects of sampling error were corrected for by
replacing the distribution of observed correlations with the
distribution of population correlations. The mean of the latter
distribution was estimated by the mean of the observed
correlations weighted by sample size; the variance was
estimated by subtracting the variance due to sampling error
from the variance of the observed correlations (this estimates
the actual variation in the study correlations without the
extra (nuisance) variance due to sampling error).
Correlation coefficients tend to be biased towards zero
because of artefacts. In most of the studies eligible for
inclusion in this review, job satisfaction and health were both
usually measured with imperfect statistical reliability (con-
ventionally measured using the Cronbach alpha coefficient).
This can attenuate the magnitude of the observed correla-
tions, so the Schmidt and Hunter correction formula
8
was
applied to each correlation to adjust for the reliability of the
measures used. Where reported, the actual study reliability
statistic was used; otherwise, the published ‘‘norm’’ relia-
bility was used.
A formal significance tests for heterogeneity between the
studies included in the meta-analysis was statistically
significant, indicating considerable study-to-study variation.
However, this test has low power and must be treated with
caution. There were no obvious technical or scientific reasons
for excluding any of the studies, so all were retained in the
analysis. The conclusions drawn are based on the random
effects model estimates of the combined correlations.
Two sensitivity analyses were carried out to investigate
the influence on the combined correlation estimates of
(a) the decade in which the study was published and
(b) the rigorousness of the study (as determined by the
‘‘rigour’’ score described above). Although there were many
other potential factors that could have increased the variation
between the studies evaluated (primarily, range restriction of
scales and differing validities), there was insufficient
information provided in the vast majority of reports to make
the necessary corrections. Similarly, too few studies provided
information on the characteristics of the study sample to
permit informative sensitivity analyses other than those
described above.
RESULTS
The expected relationship was that an increase in job
satisfaction would be associated with improved health. The
unadjusted and adjusted combined correlation estimates are
summarised in table 1; the corresponding Forrest plots are
shown in fig 1. The effects of both year of study and ‘‘rigour’’
rating are summarised in table 2.
The overall combined studies relationship found between
job satisfaction and (good) health was indeed positive
(r = 0.312, adjusted r (r
ˇ
) = 0.370). Job satisfaction was much
more strongly associated with mental/psychosocial problems
than with physical complaints. The largest relationship was
found for burnout (r = 0.409, r
ˇ
= 0.478); only five of the 62
studies into burnout identified failed to reach statistical
significance.
Job satisfaction also correlated positively, but slightly less
strongly, with the other mental health characteristics con-
sidered: depression (r = 0.366, r
ˇ
= 0.428); anxiety (r = 0.354,
r
ˇ
= 0.420); self-esteem (r = 0.351, r
ˇ
= 0.429); general mental
health (r = 0.318, r
ˇ
= 0.376). While the relationship between
job satisfaction and strain was also relatively high (r = 0.310,
r
ˇ
= 0.341), the correlation with subjective physical illness was
more modest (r = 0.235, r
ˇ
= 0.287).
The lowest correlations were found for the two physical
illnesses studied: cardiovascular disease (r = 0.113, r
ˇ
= 0.121)
and musculoskeletal disorders (r = 0.078, r
ˇ
= 0.079).
Studies published since 1990 tended to produce similar
or slightly smaller correlations than those completed before
that date, with the exception of general mental health (for
which the correlation increased slightly). The ‘‘rigour’’ rating
given to each study had a slightly stronger but inconsistent
influence; highly rated studies involving general mental
health tended to produce the highest correlations with job
Job satisfaction versus health 107
www.occenvmed.com
satisfaction, while correlations tended to decrease with
increased rating for physical illness, cardiovascular disease,
and strain.
DISCUSSION
This paper reports a meta-analysis of almost 500 studies of
job satisfaction, incorporating over 250 000 employees in a
large variety of different organisations based throughout the
world. The largest combined statistical correlations found
were between job satisfaction and measures of mental
health; smaller relationships were detected for measures of
physical health. All of the correlations were positive and
highly statistically significant. The authors of this paper
contend that the combined correlations were sufficiently
large numerically to be considered as being both strong and
extremely important.
The studies accepted for inclusion in the analysis were
predominantly cross-sectional and observational. Further-
more, the high levels of statistical significance obtained for
the correlation estimates were virtually inevitable given the
very large sample size represented by the combined studies
data set (the statistical significance level for a correlation
coefficient is related directly to the size of the sample from
which it is estimated). Thus, a causal relationship between
employee health and job satisfaction cannot be automatically
inferred directly from the statistical evidence. A consideration
of the psychological issues involved is also needed.
In the context of this review, however, causal inferences do
appear to be very plausible. The meta-analysis findings
indicate that, on average, employees with low levels of job
satisfaction are most likely to experience emotional burn-out,
to have reduced levels of self-esteem, and to have raised
levels of both anxiety and depression. Many people spend a
considerable proportion of their waking hours at work. If
their work is failing to provide adequate personal satisfac-
tion—or even causing actual dissatisfaction—they are likely
to be feeling unhappy or unfulfilled for long periods of each
working day. It seems reasonable to hypothesise that such
individuals are at increased risk of experiencing a lowering
of general mood and feelings of self-worth while at work,
culminating in mild levels of depression and/or anxiety. If
continued unresolved for any length of time, such emotions
could eventually lead to emotional exhaustion, particularly
if the individual is unable to prevent their feelings from
spilling over into their home/social life.
The numerical sizes of the relationships found between job
satisfaction and many of the mental health measures are also
noteworthy. Correlations involving measures of workplace
stress/health rarely exceed r = 0.333 (that is, R
2
= 10%). In
this meta-analysis, the corrected combined correlations
between job satisfaction and each of burnout, self-esteem,
depression, anxiety, and general mental health were well in
excess of this figure. The importance of such strong correla-
tions should not be underestimated. For example, the
corrected combined correlation between job satisfaction and
burnout was r
ˇ
= 0.478. As the measures were re-scaled for
the meta-analysis so that increases in job satisfaction were
related to improvements in wellbeing, a one standard
deviation decrease in job satisfaction level corresponds
statistically to an average increase in symptoms of burnout/
emotional exhaustion of almost one-half of one standard
deviation. A modest decrease in job satisfaction levels is
therefore associated with an increase in the risk of employee
burnout sufficiently large to be of considerable clinical
importance.
Interpreting the size of a correlation coefficient has always
caused difficulty. Several authors have attempted to provide
practical guidelines; the most commonly quoted are those
advocated by Cohen.
24
However, as Rosenthal and colleagues
state in their excellent discussion of this issue,
22
‘‘mechani-
cally labelling [correlation coefficients] automatically
as ‘small’, ‘medium’ and ‘large’ can lead to later difficulties
even ‘small’ effects can turn out to be practically important’’.
They then cite a number of published examples to prove their
case, the most celebrated of which is the major biomedical
study
25
that reported that regular use of aspirin significantly
reduced the risk of heart attack in the US population. The
findings were so dramatic that the aspirin part of the study
was terminated prematurely, with a statistical significance
level reported as p , 0.000001. Using the data in the study
Table 1 Effect-size summary statistics for relationship between job satisfaction and health measures
Health outcome
No. of
studies
Total sample
size
Combined correlation coefficient (95% CI)
Heterogeneity testFixed effects model Random effects model
Unadjusted
Anxiety 60 36443 0.322 (0.313 to 0.331) 0.354 (0.319 to 0.388) Q(59) =650.7, p,0.001
Burnout 62 19944 0.396 (0.385 to 0.408) 0.409 (0.378 to 0.439) Q(61) = 355.5, p,0.001
Cardiovascular disease 13 5303 0.147 (0.120 to 0.173) 0.113 (0.041 to 0.183) Q(12) = 55.0, p,0.001
Depression 46 38941 0.349 (0.341 to 0.358) 0.366 (0.310 to 0.421) Q(45) = 1553.1, p,0.001
General mental health 142 95814 0.331 (0.326 to 0.337) 0.318 (0.299 to 0.336) Q(141) = 1130.7, p,0.001
Musculoskeletal disorders 4 2442 0.078 (0.039 to 0.118) 0.078 (0.039 to 0.118) Q(3) = 2.3, p = 0.519
‘‘Other’’ illness 3 2124 0.315 (0.276 to 0.353) 0.251 (20.167 to 0.593) Q(2) = 124.4, p,0.001
Subjective physical illness 119 58762 0.228 (0.220 to 0.235) 0.235 (0.211 to 0.259) Q(118) = 937.8, p,0.001
Self-esteem 13 2529 0.345 (0.310 to 0.379) 0.351 (0.251 to 0.443) Q(12) = 80.9, p,0.001
Strain 23 5693 0.319 (0.295 to 0.342) 0.310 (0.230 to 0.385) Q(22) = 208.8, p,0.001
Combined 485 267995 0.310 (0.306 to 0.313) 0.312 (0.299 to 0.325) Q(487) = 6191.8, p,0.001
Schmidt-Hunter adjusted
Anxiety 60 36443 0.383 (0.374 to 0.392) 0.420 (0.379 to 0.459) Q(59) = 1051.7, p,0.001
Burnout 62 19944 0.463 (0.452 to 0.474) 0.478 (0.443 to 0.512) Q(61) = 534.7, p,0.001
Cardiovascular disease 13 5303 0.163 (0.136 to 0.189) 0.121 (0.043 to 0.197) Q(12) = 65.4, p,0.001
Depression 46 38941 0.412 (0.404 to 0.421) 0.428 (0.361 to 0.490) Q(45) = 2431.1, p,0.001
General mental health 141 95814 0.393 (0.388 to 0.399) 0.376 (0.353 to 0.397) Q(142) = 1778.8, p,0.001
Musculoskeletal disorders 4 2442 0.079 (0.039 to 0.118) 0.079 (0.039 to 0.118) Q(3) = 2.6, p = 0.452
‘‘Other’’ illness 3 2124 0.360 (0.323 to 0.397) 0.286 (20.201 to 0.660) Q(2) = 170.6, p,0.001
Subjective physical illness 119 58762 0.272 (0.265 to 0.280) 0.287 (0.255 to 0.319) Q(118) = 1886.3, p,0.001
Self-esteem 13 2529 0.439 (0.407 to 0.470) 0.429 (0.304 to 0.540) Q(12) = 144.2, p,0.001
Strain 24 5693 0.355 (0.333 to 0.377) 0.341 (0.250 to 0.426) Q(22) = 320.8, p,0.001
Combined 485 267995 0.367 (0.364 to 0.371) 0.370 (0.354 to 0.385) Q(484) = 10028.1, p,0.001
108 Faragher, Cass, Cooper
www.occenvmed.com
UpperLowerStdErEffecNTotYearDATCitation Jobsathk Healthhk Genderdist RigourgrouHealth –1.00 –0.50 0.50 1.000.00
–1.00 –0.50 0.50
Health is worse Health is better
1.000.00
5–70.4190.2130.0590.32029320000546 Lu OSI2 Job Satisfaction OSI2 Mental Health 40–60 pc males and femalesMental Health General/GHQ
5–70.264–0.1750.1140.0478019980547 Lau OSI2 Job Satisfaction OSI2 Mental Health greater than 60 pc maleMental Health General/GHQ
5–70.245–0.1090.0920.07012219960591 Siu OSI2 Job Satisfaction Mental Health dev for study 40–60 pc males and femalesMental Health General/GHQ
8–100.6020.2920.1010.46110119930613 Siu OSI Job Satisfaction OSI2 Mental Health 40–60 pc males and femalesMental Health General/GHQ
5–70.3650.0450.0860.21113819960701 Siu OSI2 Job Satisfaction OSI2 Mental Health greater than 60 pc maleMental Health General/GHQ
5–70.7170.5270.0800.63115819960701 Siu OSI2 Job Satisfaction OSI2 Mental Health greater than 60 pc maleMental Health General/GHQ
5–70.5000.3740.0400.43963419980802 Siu OSI2 Job Satisfaction OSI2 Mental Health 40–60 pc males and femalesMental Health General/GHQ
5–70.5830.3610.0740.48018719970805 Siu PMI PMI Mental Health 40–60 pc males and femalesMental Health General/GHQ
8–100.5330.3010.0730.42419319990844 Yeung Minnesota Satn Qn GHQ28 40–60 pc males and femalesMental Health General/GHQ
8–100.3380.1120.0610.22827219980877 CISMS Hong Kong Only OSI2 Job Satisfaction OSI2 Mental Health greater than 60 pc male
greater than 60 pc male
greater than 60 pc male
Mental Health General/GHQ
5–70.457–0.0040.1270.2406519960879 Ho OSI2 Job Satisfaction OSI2 Mental Health greater than 60 pc female
greater than 60 pc female
greater than 60 pc female
greater than 60 pc female
Mental Health General/GHQ
5–70.6880.4440.0940.57911719960880 Fung OSI2 Job Satisfaction OSI2 Mental HealthMental Health General/GHQ
5–70.4120.0050.1100.2188519990881 Wong OSI2 Job Satisfaction OSI2 Mental HealthMental Health General/GHQ
5–70.6260.2750.1150.4697819990881 Wong OSI2 Job Satisfaction OSI2 Physical HealthMental Health General/GHQ
0–40.4870.1590.0950.33311419960883 Yuen OSI2 Job Satisfaction OSI2 Mental HealthMental Health General/GHQ
5–70.4740.2290.0720.35819619970885 Chui Agho, Price, Mueller 92 Psychosom Distr InvMental Health General/GHQ
0.4070.3430.0190.3762833Mental Health General/GHQ (16)
0.4350.2800.0450.3602833Mental Health General/GHQ (16)
5–70.2300.0040.0590.11929320000546 Lu OSI2 Job Satisfaction OSI2 Physical Health 40–60 pc males and femalesPhysical Sub
5–70.275–0.1630.1140.0598019980547 Lau OSI2 Job Satisfaction OSI2 Physical Health greater than 60 pc male
greater than 60 pc male
greater than 60 pc male
greater than 60 pc male
greater than 60 pc male
greater than 60 pc male
greater than 60 pc male
greater than 60 pc male
greater than 60 pc female
greater than 60 pc female
greater than 60 pc female
greater than 60 pc female
greater than 60 pc female
greater than 60 pc female
Physical Sub
8–100.3770.0010.1010.19610119930613 Siu OSI Job Satisfaction OSI Physical Health 40–60 pc males and femalesPhysical Sub
5–70.4410.1360.0860.29613819960701 Siu OSI2 Job Satisfaction OSI2 Physical HealthPhysical Sub
5–70.7480.5710.0800.66815819960701 Siu OSI2 Job Satisfaction OSI2 Physical HealthPhysical Sub
5–70.3840.1150.0740.25418719970805 Siu PMI PMI Physical Health 40–60 pc males and femalesPhysical Sub
5–70.4570.3290.0390.39566619940834 Chiu Brayfield Rothe Steffy and JonesPhysical Sub
5–70.4250.2700.0450.35046719990835 Chiu Kalleburg 1997 Jones DuBoisPhysical Sub
8–100.3910.1720.0610.28527219980877 CISMS Hong Kong Only OSI2 Job Satisfaction OSI2 Physical HealthPhysical Sub
5–70.5040.0570.1270.2976519960879 Ho OSI2 Job Satisfaction OSI Physical HealthPhysical Sub
5–70.241–0.2040.1150.0197819990881 Wong OSI2 Job Satisfaction OSI2 Physical HealthPhysical Sub
5–70.5460.1780.1100.3778519990881 Wong OSI2 Job Satisfaction OSI2 Mental HealthPhysical Sub
0–40.5870.2920.0950.45211419960883 Yuen OSI2 Job Satisfaction OSI2 Physical HealthPhysical Sub
0.3610.2930.0190.3272734Physical Sub (13)
0.3960.2110.0520.3072734Physical Sub (13)
5–70.5410.2150.0990.39010619980860 Leung OSI2 Job Satisfaction Siu and CooperStrain
5–70.376–0.0600.1160.1667720000878 Cheng OSI2 Job Satisfaction OSI2 Combined MH PHStrain
5–70.245–0.0630.0800.09316019980884 Shaffer Hackman Oldman QES Combined MH PHStrain
5–70.226–0.0900.0820.07015319980884 Shaffer Hackman Oldman QES Combined MH PHStrain
0.2490.0760.0450.164496Strain (4)
0.3320.0150.0840.178496Strain (4)
0.3600.3150.0130.3386063Combined (33)
0.3740.2610.0320.3186063Combined (33)
Fixed
Rando
Fixed
Rando
Fixed
Rando
Fixed
Rando
Figure 1 Forrest plot of the Hunter-Schmidt corrected meta-analysis.
Job satisfaction versus health 109
www.occenvmed.com
report, the relevant correlation coefficient is very low
(r = 0.034). In most other contexts, this would be considered
much too small to be of any interest or importance. In fact,
this result has saved countless thousands of lives since its
publication and has passed into normal clinical practice. The
importance of a correlation coefficient is frequently context
dependent. We wish to argue that, in the context of
observational studies involving primarily ‘‘soft’’/subjective
measures, a combined (adjusted) correlation of 0.370 is, as
stated above, ‘‘sufficiently large numerically to be considered
as being both strong and extremely important’’.
Equally, the dangers of over-stating the extent of the
relationships found must also be avoided. Some researchers
26
argue cogently that standardised regression coefficients and
correlations are imperfect measures of effect-size; unfortu-
nately, the better alternatives suggested are reported for very
few studies, and they are not available in the widely used
meta-analysis software, so there is no real practical alter-
native. The vast majority of meta-analyses published combine
the results of comparative studies (usually randomised
controlled trials), so use odds ratios as the effect-size of
choice. However, the studies of interest to this review were
almost exclusively observational and so reported correlation/
regression coefficients. While the assumptions underpinn-
ing these coefficients can be problematic statistically (for
example, linear relationships usually have to be assumed),
the techniques for combing these are well developed.
While their average impact on individual employees may
be important, even very large coefficients account for only a
modest amount of overall variation in health levels. As stated
above, the corrected correlation between job satisfaction and
burnout was r
ˇ
= 0.478, corresponding to an R
2
value of just
22.8%. That is, less than one-quarter of the variation in
burnout scores is accounted for. While job satisfaction may
have a clearly discernible impact on this aspect of mental
health, there are also many other factors involved (at least
77.2% of the variation in burnout ratings remain unac-
counted for).
In common with other statistical methods, the results
obtained from a meta-analysis are subject to various potential
sources of bias. A combined effect-size estimate is only as
good as the individual values from which it is composed—
there are no standards widely adopted for the presentation of
observational studies, so the studies included were of very
variable quality. Some study reports indicated that measures
of both job satisfaction and health were included, but no
useable effect-size statistics were reported; this would
suggest that there may be a bias in favour of significant
correlations, inflating the combined estimate. However, a
funnel plot of the effect-sizes included in the meta-analysis
suggested that this was not a major problem. Finally, the
great majority of studies used self-report measures of both
job satisfaction and health, so there will have been some
inflation of the individual correlation coefficients due to
shared variance.
There are issues also with the concept of job satisfaction
itself. Job satisfaction is generally measured by asking
individuals to rate individual facets of their work and then
aggregating these into a single (global) score. The widely
used Job Descriptive Index
18
measures five facets of job
satisfaction, specifically satisfaction with the work itself, pay,
promotion, supervision, and co-workers. Working hours, job
security, supervisor support, and changes in job control levels
have also been related to individual job satisfaction levels.
27 28
Some critics argue, however, that facet measures of job
satisfaction should be separated from overall satisfaction, as
they are conceptually different.
29
Overall satisfaction is
essentially the global attitude or feeling an individual has
about their job as a whole and may be related to internal
Table 2 Fixed effects correlation coefficients for relationship between job satisfaction and health, by year of study and rigour rating of study
Health outcome
Year of study ‘‘Rigour’’ rating of study
Pre-1980 1980–89 1990+ 0–4 5–7 8–10
Anxiety 0.225 (0.156 to 0.291) 0.358 (0.341 to 0.375) 0.310 (0.299 to 0.321) 0.266 (0.210 to 0.321) 0.330 (0.320 to 0.339) 0.257 (0.224 to 0.290)
Burnout 0.435 (0.405 to 0.465) 0.379 (0.367 to 0.392) 0.372 (0.317 to 0.426) 0.389 (0.376 to 0.403) 0.382 (0.360 to 0.404)
Cardiovascular disease 0.049 (20.024 to 0.121) 0.137 (0.092 to 0.181) 20.037 (20.177 to 0.105) 0.126 (0.084 to 0.166) 0.110 (20.031 to 0.247)
Depression 0.434 (0.383 to 0.484) 0.446 (0.425 to 0.467) 0.316 (0.306 to 0.326) 0.209 (0.148 to 0.269) 0.397 (0.387 to 0.407) 0.165 (0.145 to 0.185)
General mental health 0.197 (0.153 to 0.240) 0.297 (0.279 to 0.314) 0.349 (0.342 to 0.355) 0.296 (0.275 to 0.317) 0.317 (0.308 to 0.326) 0.354 (0.346 to 0.362)
Physical illness 0.079 (0.042 to 0.117) 0.247 (0.227 to 0.268) 0.221 (0.212 to 0.230) 0.275 (0.227 to 0.322) 0.247 (0.238 to 0.256) 0.186 (0.171 to 0.200)
Strain 0.362 (0.333 to 0.390) 0.243 (0.203 to 0.283) 0.522 (0.383 to 0.638) 0.356 (0.325 to 0.386) 0.253 (0.215 to 0.291)
110 Faragher, Cass, Cooper
www.occenvmed.com
characteristics such as personality. Satisfaction levels can
vary markedly between different job facets—and these in
turn may be different from an employee’s overall feeling of
job satisfaction. Theoretically, an individual may have high
satisfaction with many facets of their job but still feel overall
job dissatisfaction (for example, they may enjoy the facets of
their job but be unhappy about the organisation they are
working in). Within the context of this study, however, such
a distinction may be largely academic: individuals with low
levels of satisfaction either with facets of their job or with
their job overall may be at increased risk of experiencing an
adverse effect on their mental health.
The papers accepted into this review mostly reported
studies carried out in Western Europe, the USA, and
Australasia, but many studies were included from other,
very different, geographical areas (for example, Asia). This
raises a problem of combining studies from different cultures,
where attitudes to and perceptions of job satisfaction may
differ considerably, and from a wide variety of occupations.
Certainly, measurement of very subjective concepts such as
job satisfaction should be tailored to deal with important
cultural and occupational differences—although most stu-
dies tended to use one of a small number of standard and
validated scales. However, the authors are unconvinced that
the relationship between job satisfaction and health will be
affected greatly by either cultural or occupational factors—
and no evidence of this was found. Lack of work satisfaction
is likely to have a negative impact on an individual’s feelings
about themselves and their life, leading to a reduction in
health (particularly mental health), irrespective of type of
work and culture.
Despite the reservations expressed above—and even
allowing for some bias leading to an inflated estimate of
the overall effect size/correlation—the emergence of job
satisfaction as by far the highest statistical correlate with
health of the workplace characteristics systematically
reviewed so far has important implications. Organisations
are being required to accept greater responsibilities for the
impact of their work practices on employee health levels.
Recent research suggests that effective stress intervention
policies require good communications between management
and employees.
30 31
Workplace policies aimed at improving
employee health should be developed through a meaningful
dialogue between employees and managers to identify those
facets of current work practices that are causing most stress.
This meta-analysis suggests that any changes implemented
should be monitored for their impact on job satisfaction as
well as on employee stress levels. Indeed, there may be a case
for identifying those aspects of work causing most job
dissatisfaction, and then implementing agreed changes
aimed at improving job satisfaction levels. Although organi-
sations may find it easier to influence facet elements of job
satisfaction, ways of improving global satisfaction (for
example, by changing management practices) need to be
explored. Importantly, changes implemented must be desired
by the employees and may need to be flexible. For example,
increasing job control levels may help some employees—but
might actually result in decreased job satisfaction in those
who prefer to have their work schedules rigidly defined.
Organisations should not assume that changes made to
work practices will be automatically effective. Rigorous
evaluation is needed using relevant organisational and
employee measures, with the latter including health factors
such as sickness and absenteeism rates. However, changes in
health levels may take some time (even years) to manifest,
whereas improvements in facet and/or global job satisfaction
levels may be more immediately discernible. A good job
satisfaction scale, developed for the specific needs of the
organisation if necessary, must be included as a measure of
the effects of the changes made to work practices. If the
hypothesised causal link does exist, changes in this scale
should predict later changes in overall employee health
levels.
Finally, there are equally challenging implications for
occupational health professionals. The wellbeing of employ-
ees—and in particular their mental health—may be compro-
mised if their work is causing them to experience high levels
of dissatisfaction. Thus, the extent to which individuals feel
satisfied with their work becomes an important (mental)
health issue. In addition to conventional therapeutic app-
roaches, employees diagnosed as having even a mild mental
health problem might usefully be counselled to take a hard
and critical look at how they feel about their work. If specific
tasks they are required to do as part of their job are identified
as causing particular dissatisfaction, the employee should be
assisted to appropriately change these (for example, by
discussing the issue with a manager or colleagues). If the root
of the dissatisfaction is more global, a change of job or
occupation may be warranted; however, this constitutes a
major and stressful life change in itself, so should be
considered very carefully.
The notion that health levels can be improved by making
changes to work practices intended primarily to enhance
facet and/or overall job satisfaction is compelling. Well
designed comparative longitudinal studies are now needed
in which the effects of workplace interventions on individual
employee job satisfaction and health levels are properly
monitored. In the meantime, there are good theoretical
reasons for hypothesising that job satisfaction is causal
linked to health—and particularly to mental wellbeing.
Organisations are urged to consider developing stress mana-
gement policies aimed at identifying and eradicating work
practices that cause most job dissatisfaction.
Conclusions
This large scale meta-analysis of almost 500 studies has
provided, for the first time, a clear indication of the immen-
sely strong relationship between job satisfaction and both
mental and physical health. The correlations identified are
numerically large and highly significant (in both the statis-
tical and clinical sense of the word). The relationships are
particularly impressive for aspects of mental health, specifi-
cally burnout, lowered self-esteem, anxiety, and depression,
where it can now be confirmed that dissatisfaction at work
can be hazardous to an employee’s mental health and
wellbeing. Importantly, the relationships found were much
greater than with any other work characteristic evaluated.
This has important health implications for the design and
delivery of employee health intervention programmes. The
results of this study allow us to conclude that risk assess-
ments of stress in the workplace should attempt to pinpoint
those aspects of work that are causing most dissatisfaction
among employees (for example, hours of work, organisa-
tional management style, workload, work control/autonomy,
etc) as these are likely to be also the factors causing raised
levels of stress. After meaningful consultation with employ-
ees, work practices should be changed appropriately—and
the impact of these measured both in terms of their effect on
stress levels and on job satisfaction. If the causal relationship
hypothesised holds, changes which have the greatest impact
on job satisfaction can be expected to produce the greatest
benefits to employee mental health (in particular, to reduce
levels of burnout/emotional exhaustion)—with a beneficial
knock-on effect for organisational health. Occupational
health clinicians should also consider counselling employees
diagnosed as having a stress related health problem to
critically evaluate their work and to explore ways of gaining
Job satisfaction versus health 111
www.occenvmed.com
greater satisfaction from this important and time consuming
aspect of their life.
ACKNOWLEDGEMENTS
We thank all members of staff in the School of Management at
UMIST who helped in any way with the considerable task of
collecting together the studies summarised in this report.
Authors’ affiliations
.....................
E B Faragher, M Cass, C L Cooper, BUPA Organisational Psychology
and Health Research Group, Manchester School of Management,
University of Manchester University of Science and Technology (UMIST),
UK
This project was funded by a grant from the UK Health & Safety
Executive
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This paper reports on the results of a study aimed at identifying the relative influence of generic and job-specific stressors experienced by a cohort of Australian managers. The results of a regression analysis revealed that both the generic components of the job strain model (JSM) and job-specific stressors were predictive of the strain experienced by participants. However, when looking at the total amount of variance that is explained by the predictor variables, the combined influence of job demand, job control and social support contributed 98 per cent of the explained variance in job satisfaction and 90 per cent of the variance in psychological health. The large amount of variance explained by the JSM suggests that this model provides an accurate account of the work characteristics that contribute to the strain experienced by managers and no augmentation is needed.
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Meta-analysis is arguably the most important methodological innovation in the social and behavioral sciences in the last 25 years. Developed to offer researchers an informative account of which methods are most useful in integrating research findings across studies, this book will enable the reader to apply, as well as understand, meta-analytic methods. Rather than taking an encyclopedic approach, the authors have focused on carefully developing those techniques that are most applicable to social science research, and have given a general conceptual description of more complex and rarely-used techniques. Fully revised and updated, Methods of Meta-Analysis, Second Edition is the most comprehensive text on meta-analysis available today. New to the Second Edition: * An evaluation of fixed versus random effects models for meta-analysis* New methods for correcting for indirect range restriction in meta-analysis* New developments in corrections for measurement error* A discussion of a new Windows-based program package for applying the meta-analysis methods presented in the book* A presentation of the theories of data underlying different approaches to meta-analysis
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A meta-analysis of single-item measures of overall job satisfaction (28 correlations from 17 studies with 7,682 people) found an average uncorrected correlation of .63 (SD = .09) with scale measures of overall job satisfaction. The overall mean correlation (corrected only for reliability) is .67 (SD = .08), and it is moderated by the type of measurement scale used. The mean corrected correlation for the best group of scale measures (8 correlations, 1,735 people) is .72 (SD = .05). The correction for attenuation formula was used to estimate the minimum level of reliability for a single-item measure. These estimates range from .45 to .69, depending on the assumptions made.
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A meta-analysis of single-item measures of overall job satisfaction (28 correlations from 17 studies with 7,682 people) found an average uncorrected correlation of .63 (SD = .09) with scale measures of overall job satisfaction. The overall mean correlation (corrected only for reliability) is .67 (SD = .08), and it is moderated by the type of measurement scale used. The mean corrected correlation for the best group of scale measures (8 correlations, 1,735 people) is .72 (SD = .05). The correction for attenuation formula was used to estimate the minimum level of reliability for a single-item measure. These estimates range from .45 to .69, depending on the assumptions made.
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Contrasts are statistical procedures for asking focused questions of data. Researchers, teachers of research methods and graduate students will be familiar with the principles and procedures of contrast analysis included here. But they, for the first time, will also be presented with a series of newly developed concepts, measures, and indices that permit a wider and more useful application of contrast analysis. This volume takes on this new approach by introducing a family of correlational effect size estimates. By returning to these correlations throughout the book, the authors demonstrate special adaptations in a variety of contexts from two group comparison to one way analysis of variance contexts, to factorial designs, to repeated measures designs and to the case of multiple contrasts.
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