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APPLIED PSYCHOLOGY: AN INTERNATIONAL REVIEW, 2006, 55 (1), 2751
Blackwell Publishing LtdOxford, UKAPPSApplied Psychology:an International0269-994X International Association for Applied Psychology, 2006January 2006551Original ArticleCORE SELF-EVALUATIONSDORMANN ET AL.
A State-Trait Analysis of Job Satisfaction:
On the Effect of Core Self-Evaluations
Christian Dormann*
Johann Wolfgang Goethe-University of Frankfurt, Germany
Doris Fay
Justus-Liebig-University of Giessen, Germany
Dieter Zapf
Johann Wolfgang Goethe-University of Frankfurt, Germany
Michael Frese
Justus-Liebig-University of Giessen, Germany
Une recherche rcente qui portait sur les fondements caractriels de la satis-faction au travail sest focalise sur le rapport entre la satisfaction profession-nelle observe et le noyau central des autovaluations (CSE). Cette tudesest occupe dune part de la relation entre la variance-trait de la satisfactionau travail et le CSE et dautre part de la structure des variables CSE. Enfaisant le choix dun modle de mesure longitudinal, nous avons dabordrecherch si le CSE tait suffisamment stable, cela partir dune analyse
* Address for correspondence: Christian Dormann, Johannes Gutenberg-Universitt Mainz,Staudingerweg 9, 55099 Mainz, Germany. Email: [email protected]
Doris Fay is now at Aston Business School, Aston University, Birmingham, UK.
The project AHUS (Aktives Handeln in einer UmbruchsituationActive actions in a radical
change situation) was supported by the Deutsche Forschungsgemeinschaft (DFG, No. Fr 638/
6-6) (principal investigator: Michael Frese). Thanks are due to the two firms Bayrische
Hypothekenund Wechselbank and Tobacco Reynolds, as well as the Hundertjahre Stif-
tung of the Ludwig-Maximilians-University in Munichthey all helped at the beginning of
the project. Other members of the project have been and are: Doris Fay, Harry Garst, Sabine
Hilligloh, Christa Speier, Thomas Wagner, and Jeannette Zempel, Giessen.
Other parts of this large-scale project were published by Frese, Kring, Soose, and Zempel
(1996), Frese, Fay, Hilburger, Leng, and Tag (1997), Speier and Frese (1997), Dormann and
Zapf (1999, 2002), Garst, Frese, and Molenaar (2000), Fay and Frese (2000a, 2000b, 2001),
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DORMANN ET AL.
secondaire de quatre priodes successives. Les rsultats montrent une fortestabilit du CSE (.87 sur deux ans). Nous avons ensuite opr une scissiontat-trait de la satisfaction professionnelle de faon dissocier la variance-
trait de la satisfaction au travail de la variance instable. Le facteur stable desatisfaction professionnelle fut mis en rapport, par rgression, avec les vari-ables CSE, en utilisant plusieurs modles de CSE (une sommation, un facteurlatent ou un concept global). Daprs les rsultats, il vaut mieux traiter lesvariables CSE comme une sommation, et cette srie rend compte de presquetoute la variance stable de la satisfaction professionnelle (84%). En outre,seuls laffectivit ngative et le locus of control interne avaient un impactsignificatif, alors que lestime de soi et lefficience personnelle nen avaientpas. On conclut que la conception actuelle du CSE comme concept supraor-donn englobant quatre dimensions est dfendable, mais trop gnrale pourles recherches sur la satisfaction professionnelle; il est plus satisfaisant etsuffisant danalyser la fois laffectivit ngative et le locus of control.
Recent research that looked into the dispositional base of job satisfactionfocused on relating observed job satisfaction to core self-evaluations (CSE).This study was concerned with (a) the relation between the trait variance ofjob satisfaction and CSE and (b) the structure of the CSE-variables. Using alongitudinal measurement model in a secondary analysis of four waves of alongitudinal study we first tested whether CSE are sufficiently stable overtime. Results indicate a high stability of CSE (.87 across 2 years). We thenperformed a state-trait decomposition of job satisfaction in order to separate
trait variance of job satisfaction from changing variance. The stable jobsatisfaction factor was regressed on CSE-variables, using different models ofCSE (a collective set, a latent factor, or an aggregate concept). Results werein favor of treating the CSE-variables as a collective set, and this setexplained almost all stable variance of job satisfaction (84%). Moreover, onlynegative affectivity and internal locus of control had a significant impact,whereas self-esteem and self-efficacy had not. It is concluded that currentconceptualisations of CSE as a superordinate concept underlying its fourdimensions is possible but overly broad in job satisfaction research; collectiveconsideration of LOC and NA is better and sufficient.
INTRODUCTION
For decades, job satisfaction has been one of the most extensively researched
concepts in work and organisational psychology. Job satisfaction is believed
to reflect an individuals affective and/or cognitive assessment of his or her
working conditions and job attributes (Weiss & Cropanzano, 1996); it has
been traditionally used to confirm the effectiveness of job redesign and
motivational conditions at work. Since the 1980s, however, an increasing
number of studies indicated that job satisfaction is influenced by personality
dispositions (e.g. Arvey, Bouchard, Segal, & Abraham, 1989; Staw & Ross,
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CORE SELF-EVALUATIONS
29
concluded that up to 35 per cent of the variance in job satisfaction might
reflect stable, unchangeable traits in contrast to changeable environmental
conditions (Dormann & Zapf, 2001). The second question pursued relates
to the type
of personality variables that could be the building blocks of a
trait-based part of job satisfaction. This research has primarily focused on
affective traits such as negative affectivity (NA) and positive affectivity (PA;
e.g. Brief & Roberson, 1989). Negative and positive affectivity are believed
to underlie job satisfaction as they decrease the threshold to experience
negative and positive emotions, respectively; and in fact, they do explain
considerable variance in job satisfaction (e.g. Thoresen, Kaplan, Barsky,
Warren, & de Chermont, 2003). A recent dispositional approach to job
satisfaction goes beyond affectivity: the model of core self-evaluations(CSE; Judge, Locke, Durham, & Kluger, 1998).
The present study aimed at advancing dispositional research on job sat-
isfaction in two respects. First, we investigated the impact of CSE on job
satisfaction. In contrast to previous research, we used a methodological
approach that allowed assessing the impact of the dispositional variables on
those aspects of job satisfaction that they theoretically seek to explain: the
variance in job satisfaction that is stable across time. Secondly, using a
framework provided by Edwards (2001), we analysed the structural relation
between CSE and job satisfaction in more detail, thereby addressing thequestion whether CSE shouldin job satisfaction researchbe conceptualised
as a set of first-order variables or as a higher-order construct.
Core Self-Evaluations
Core self-evaluations are an individuals conclusions about him- or herself.
They are based on ones fundamental standards, beliefs, and norms, which
determine the general level of well-being and self-worth. In the model of
Judge et al. (1998), CSE comprise self-esteem (which is related to PA),generalised self-efficacy, locus of control (LOC), and low neuroticism
(which is related to NA).
Core self-evaluations are likely to unfold their effect on job satisfaction
through at least two types of processes: first, CSE influence what types of
environment people seek and whether they successfully attain this environ-
ment (i.e. type or quality of job). This then leads to specific experiences at
work, which determine the level of job satisfaction. For instance, individuals
with an internal LOC get better jobs because they receive better evaluations
in personnel selection procedures (Cook, Vance, & Spector, 2000; Silvester,
Anderson-Gough, Anderson, & Mohamed, 2002). Second, CSE shape indi-
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DORMANN ET AL.
high in self-esteem are more likely to appraise critical events at work as a
challenge and experience less fear of failure (Locke, McClear, & Knight, 1996).
Judge and his colleagues (Judge et al., 1998; Judge, Bono, & Locke 2000)
repeatedly demonstrated that CSE and job satisfaction are significantly
related, and that the four CSE-concepts share a substantial amount of
variance. The meta-analysis by Judge and Bono (2001a) showed that the
estimated true score correlations with job satisfaction were .26 for self-esteem,
.45 for generalised self-efficacy, .32 for internal LOC, and
.24 for NA.
Open Questions
Considering these effect sizes and the maximum estimate for the disposi-tional variance in job satisfaction (about 35%; cf. Dormann & Zapf, 2001),
CSE may be a potent and parsimonious representation of the dispositional
part of job satisfaction.
Previous work related the personality variables investigated to the full
variance
of job satisfaction and not to its dispositional variance
. For example,
Judge and Bono (2001a) related the observed values of CSE to observed
values of job satisfaction, and Judge et al. (2000) used a latent factor of CSE
to predict the observed values of job satisfaction. To find out whether CSE are
a sufficient explanation ofstable variance
of job satisfaction, it is necessaryto first separate the stable from the variable part of job satisfaction (more
on this in the Results section). Only relating CSE to the stable
part of variance
will permit us to see whether CSE are really a parsimonious representation of
trait job satisfaction or whether additional personality variables are required
to understand trait job satisfaction. If, for example, CSE could explain only
50 per cent of job satisfactions trait variance, then other personality variables
should be explored to help to understand fully the trait variance.
Question 1
: To what extent do the personality factors that comprise the
CSEself-esteem, self-efficacy, neuroticism, and locus of controlexplain
the trait-like variance in job satisfaction?
The second question pursued in this paper relates to the structural relation
between CSE and the trait variance in job satisfaction. At least implicitly,
CSE has been thought to be a higher-order factor representing the shared
variance of its constituting variables. However, such a conceptualisation
may be unnecessarily complex and may not explain variance in job satisfaction
above and beyond, for example, neuroticism or locus of control. Recent
theorising on multi-dimensional constructs distinguished three different
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CORE SELF-EVALUATIONS
31
such a model, the four concepts that constitute the CSE (i.e. self-esteem,
self-efficacy, NA, and LOC) are directly related to the stable part of job
satisfaction. Second, CSE can be conceptualised as a superordinate con-
struct.
In this case, the four concepts serve as indicators of the latent factor
CSE. The third model conceptualises CSE as an aggregate construct
Then,
FIGURE 1. Conceptualisations of core self-evaluations as collective set, super-ordinate construct, and aggregate construct.
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32
DORMANN ET AL.
and job satisfaction. On the one hand, one could argue that the positive
self-evaluation inherent in all of the four concepts
represents the satisfaction
driver. This would speak for modeling CSE as a superordinate construct, in
which only the variance shared by the four constructs is related to job
satisfaction. Previous CFA yielded evidence for a one-factor structure of
CSE (Judge et al., 1998, 2000). Accordingly, Judge, Erez, Bono, and Thore-
sen (2002) stressed that one should conceptualise CSE in terms of the shared
variance of its dimensions (i.e. CSE as a superordinate construct).
On the other hand, the result that a one-factor model for the CSE yields
an excellent fit does not necessarily imply what the relationship of CSE with
other concepts
will look like. Meta-analyses showed that the strength of
relations between the individual CSE-variables and job satisfaction are notuniform (Judge & Bono, 2001a; Thoresen et al., 2003). The CSE-concepts
that are most strongly related to job satisfaction are not necessarily the
concepts that are most highly related to the higher-order factor CSE. Based
on Edwards (2001) framework, one plausible alternative could be to treat
the CSE-variables as four conceptually distinct variables, with varying degrees
of importance (e.g. regression weights) depending on the target variable
considered. Then, the four CSE-variables should be analysed collectively as a set.
A third possibility to conceptualise CSE in job satisfaction research is to
treat CSE as an aggregate construct. Unlike a superordinate latent con-struct, which represents the shared variance of its indicators, an aggregate
construct represents the weighted sum of its constituting variables. For
instance, the overall job performance of a person can be represented by a
weighted sum of his or her domain-specific performance scores. The weights
may vary depending on the conceptual domain. For example, they may be
different when predicting salary compared to predicting promotion. In a
similar vein, the contribution (weights) of the four CSE-variables to CSE as
an aggregate construct may be different in the domain of job satisfaction
compared to other domains such as job performance. Of course, determin-ing the optimal weights within a particular domain such as job satisfaction
research is a matter of empirical testing.
Question 2
: Should CSE be conceptualised as separate variables that affect
trait-job satisfaction collectively (collective set), or should they be more
parsimoniously conceived as indicators of a superordinate latent personality
trait, or as an aggregated construct?
METHOD
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CORE SELF-EVALUATIONS
33
between 1990 and 1995. Data presented here were from Waves 3 to 6, the
most recent waves (September 1991, September 1992, September 1993, and
September 1995). The general purpose of the panel was to analyse how
working conditions and job attitudes changed as a consequence of the
unification of West and East Germany in 1990.
Participants and Procedure
Participants were sampled using a random route method: streets were
randomly selected, and then every fourth apartment (in smaller houses
every third) in every third house was visited. The refusal rate was 33 per cent
(cf. Frese, Kring, Soose, & Zempel, 1996). Participants were assuredconfidentiality. Personal codes enabled us to handle the data anonymously,
where requested.
During the first wave of data collection, 463 subjects participated in the
study. At Wave 2, 202 additional participants were included. At subsequent
waves, all participants who were sampled at Wave 1 or Wave 2 were revisited
and asked to participate again. Between Waves 3 to 6 there were 478 to 503
individuals who participated. Since only four waves are required to estimate
a state-trait model, we analysed data from Waves 3 to 6.
We based the analyses on individuals that did not change their job in thefour years that we look at. The use of such a sample makes it more likely
to find a higher portion of trait-based variance in comparison to a sample
that change jobs, as they would be exposed to more variance in their work-
ing situation. Allowing for a relatively high proportion of trait variance to
emerge is a fairer test for Question 1 than a strategy that would keep the
proportion of trait variance small.
Participants indicated at each wave whether they still worked in the same
job or in the same organisation as at the time of the preceding survey. This
applied to 157 participants. Individuals were not included in the studypresented here when they changed their employerbecause they had been
given notice or voluntarily left the organisationwhen they became perma-
nently unemployed, when they retired, or when they were on parental leave,
which caused missing values at least at one measurement occasion. Among
the 157 participants selected for the present study, there was 1.07 per cent
missing data, which were accounted for by application of the expectation
maximisation approach using the EMCOV computer program (see Graham,
Hofer, & MacKinnon, 1996).
The participants were representative of the working population of
Dresden with respect to age, social class, and male/female percentage at
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DORMANN ET AL.
enterprises employed 30.9 per cent of the participants. There were 18.9 per
cent non-professional white-collar workers. Managers or professionals with
high qualification requirements formed 27.4 per cent of the sample. There
were 12.5 per cent higher-level public service employees mostly employed in
schools and universities, and 16.5 per cent skilled and 14.9 per cent unskilled
blue-collar workers, respectively.
We compared the job stayers analysed in the present study with those not
analysed (i.e. job changers) in all variables investigated in this study. There
were no significant differences between the two sub-samples in job satisfac-
tion, NA, self-esteem, self-efficacy, age, and gender; significant differences
emerged for socioeconomic status (SES) and LOC with the job stayers
having higher SES and higher LOC (all ps < .05). The differences can beaccounted for by involuntary job loss. People with a better education have
a lower likelihood of losing their job; and losing ones job causes a tempo-
rary dip in the perception of control.
Measures
The job satisfaction
scale was adopted from Warr, Cook, and Wall (1979).
Participants had to indicate how satisfied they were with respect to eight
aspects of their work, for example, Availability and condition of workingtools and resources which facilitate task accomplishment (properties,
devices, etc.). Responses were made on a 5-point scale that ranged from 1
(
very dissatisfied
) to 5 (
very satisfied
). The reliabilities for the 8-item scales
were .76, .79, .79, and .79 at Waves 3 to 6, respectively. To retain a favora-
ble ratio of parameter estimates to sample size in subsequent latent variable
modeling, we used item parcels instead of all available items. By randomly
distributing the eight items across two parcels (scales), we produced two
indicator variables for latent constructs. Allocation of items to parcels was
invariant across waves.
Locus of control
was measured with a scale labeled control appraisal by
Frese (1986). It captures individuals generalised belief in their ability to
control important things in life. The 4-item scale has been developed in
prior studies, starting with qualitative studies, several pilot studies, and then
two cross-sectional and two longitudinal studies (Greif, Bamberg, & Semmer,
1991). Participants were asked to indicate whether they could change or
organise things the way they want them to be and how much control they
have over several aspects of different domains of life. The items were:
Personally, my chance to influence political decisions at my place of resi-
dence is . . .; Personally, my chance to influence things at my work place
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CORE SELF-EVALUATIONS
35
body by law in German firms. Responses were made on a 4-point scale from
1 (
not at all good
) to 4 (
very good
).
Self-efficacy
was measured with the scale by Speier and Frese (1997),
which consists of general and work-related items. A sample item is: If I
want to achieve something, I can overcome setbacks without giving up my
goal. The scale consists of six items. Participants made their responses on
a 5-point scale ranging from 1 (
does not apply at all
) to 5 (
applies fully
).
Self-esteem
was measured with a scale by Mohr (1986), which was
adapted from Rosenberg (1965). The scale consists of eight items (e.g.
Sometimes, I feel pretty useless). A 5-point answer scale was used for
these items ranging from 1 (
does not apply at all
) to 5 (
applies fully
).
Negative affectivity
was measured with ten items from the PANAS-scaleof Watson, Clark, and Tellegen (1988). Participants were asked to indicate
on a 5-point scale ranging from 1 (
very little/not at all
) to 5 (
very much
) how
they felt on average with respect to the affects presented.
Each variable was measured at each wave except NA, which was only
available for the final two waves. For some of the later analyses, CSE-
variables were aggregated across all four waves with the exception of NA,
which was aggregated across the two final waves.
RESULTS
Structural equation modeling was used for all analyses. Before addressing
Questions 1 and 2, we performed two prerequisite analyses. We first ana-
lysed a longitudinal measurement model of CSE. Core self-evaluations have
been suggested to reflect a common factor that is stable over time. While
the stability of each of the CSE traits is well documented, the stability of
the higher-order CSE construct, both in terms of its structure and stability
over time, has not been explored yet. The longitudinal measurement model
tests both forms of stability. Results will show whether CSE indeed has thewidely presumed properties of a trait; while this is in general theoretically
important, it provides for this study specifically a justification for aggregating
the scores of the individual CSE-variables across the different waves.
In the next step, employing a state-trait approach, we separate the trait-like
variance of job satisfaction from the changing variance (more details below).
Then we approach Questions 1 and 2 by regressing the previously separated
trait variance of job satisfaction on different structural models of CSE.
Descriptive statistics of all study variables are presented in Table 1.
R lt f th L it di l M t M d l f CSE
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TABLE 1Descriptive Statistics of Study Variables (N
No. of
items M SD 1 2 3 4 5 6 7 8 9
1. Job satisfaction 1991 A 4 3.34 67 59
2. Job satisfaction 1991 B 4 3.09 72 70 62
3. Job satisfaction 1992 A 4 3.50 67 51 40 64
4. Job satisfaction 1992 B 4 3.24 70 35 48 69 65
5. Job satisfaction 1993 A 4 3.46 71 35 26 51 45 69
6. Job satisfaction 1993 B 4 3.23 69 30 37 45 58 73 61
7. Job satisfaction 1995 A 4 3.57 70 28 23 49 33 59 50 70
8. Job satisfaction 1995 B 4 3.27 70 30 36 42 45 49 63 70 61
9. Locus of control 1993 3 2.32 52 15 20 28 24 29 36 35 37 51
10. Locus of control 1995 3 2.37 56 05 07 29 26 21 26 26 26 79
11. Locus of control 9195 3 4 2.33 45 13 21 22 20 31 36 37 37 8212. Self-esteem 1993 8 3.99 46 19 17 28 23 22 15 20 13 28
13. Self-esteem 1995 8 4.05 44 17 14 25 19 17 12 19 08 27
14. Self-esteem 9195 8 4 3.97 36 10 11 29 24 16 11 17 12 18
15. Self-efficacy 1993 5 3.49 60 22 18 29 26 25 25 16 17 42
16. Self-efficacy 1995 5 3.49 64 27 19 32 25 25 28 16 18 40
17. Self-efficacy 9195 5 4 3.49 49 09 03 22 19 12 15 12 10 30
18. Negative affectivity 1993 10 1.77 57 28 28 30 31 31 34 29 38 15
19. Negative affectivity 1995 10 1.81 57 23 25 22 20 18 24 27 31 16
20. Negative affectivity 9395 10 2 1.79 49 24 23 29 33 34 33 22 34 10
Note: N= 157. Decimals omitted. Correlations exceeding .19 in absolute value are significant with p < .01; cor
p < .05 (one-tailed). Correlations appearing in the table were corrected for missing values using the expectatio
diagonal. Locus of control 9195, self-esteem 9195, and self-efficacy 9195 were aggregated across the fo
affectivity 9395 was aggregated across the final two waves of measurement.
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CORE SELF-EVALUATIONS 37
separated by a 2-year lag. We tested whether LOC, self-esteem, self-efficacy,
and NA confirm a longitudinal measurement model with two latent factors
(one for each measurement period) and autocorrelated errors over time. Thefactor loadings of the four scales (i.e. LOC, self-esteem, self-efficacy, and
NA) were constrained to be invariant over time. This represents a prerequi-
site for inferring that the substantive meaning of CSE did not change over
time (cf. Schaubroeck & Green, 1989). Also, a high stability of CSE over time
is a prerequisite for subsequent analyses, which assume that CSE represent
stable personality characteristics.
The model, displayed in Figure 2, showed a good fit (2= 21.17, df= 18,
p= .27, RMSEA = .03, CFI = .99). Compared to a model without constrained
factor loadings, the fit was not significantly worse (2= 5.73, df= 3,p= .13).
All coefficients were significant with p < .01 (one-tailed). The test-retest
FIGURE 2. Longitudinal measurement model of core self-evaluation.
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38 DORMANN ET AL.
State-Trait Analysis: Separating the Trait-Variance of JobSatisfaction from the Changing Variance
To obtain the stable, trait-based portion of variance in job satisfactionrelevant for Questions 1 and 2, we used a state-trait approach (e.g. Ormel
& Schaufeli, 1991). This allows us to estimate those parts of variance in job
satisfaction that are based on (a) dispositions and other stable factors that
remain constant over time (trait-factor); (b) occasion-specific factors that
are completely unstable such as rapidly changing mood states (Dwyer, 1983;
Zapf, Dormann, & Frese, 1996); and (c) changes in job satisfaction that
react to changes in job characteristics, in the organisation, and other situa-
tional variables that change over time to some degree (state-factors). Unlike
occasion-factors, state-factors are not completely unstable, and unlike trait-
factors, they are not completely stable.
The state-trait model, which is shown in the top part of Figure 3, included
a single trait-factor affecting latent job satisfaction at each wave of measure-
ment. For reasons of identification, the effects of the trait-factor were
assumed to be invariant across time. In addition, there are state-factors at
each wave. Their effects on latent job satisfaction were assumed to be
invariant, too, but their stabilities were estimated freely. There are also
effects of occasion-factors. Technically, occasion-factors correspond tothe amount of unexplained variance (latent disturbances) in latent job sat-
isfaction after measurement errors, uniqueness/specificity (by means of error
auto-correlations; see Edwards, 2001), the trait-factor, and state-factors are
accounted for.
The state-trait model showed a good model fit (2= 15.11; df= 17;p= .59,
RMSEA = .00, CFI = 1.00). As can be seen from Table 2, the state influences
accounted on average for 62.00 per cent of variance, which is 2.5 times the
variance explained by the trait-factor (24.25%). Occasion-factors explained
the smallest amount of variance (13.75%).
Analysing Questions 1 and 2
We then analysed to what extent CSE explain the trait variance in job
satisfaction (Question 1) and explored different types of structural relation-
ships of CSE with trait job satisfaction (Question 2). Both questions are
simultaneously dealt with. We proceeded as follows: the trait-factor of job
satisfaction obtained from the previously described state-trait model (cf.
Table 2, top Figure 3) was related to CSE. Thus, in contrast to Judge et al.
(1998), who estimated the effects of CSE on job satisfaction per se, we used
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CORE SELF-EVALUATIONS 39
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40 DORMANN ET AL.
four CSE-variables were very similar to those reported by Judge et al. (2002,
Study 3a, 3b, and 3c), and their order was the same as in their meta-analysis
(Study 1), albeit smaller because the meta-analysis yielded corrected corre-
lations. All CSE-variables were significantly correlated with the dispositional
part of job satisfaction.
In all analyses, we used the model shown in the top part of Figure 3 as a
submodel by fixing all its parameters to the values obtained from the prior
state-trait analysis. Then, only the residual variance of the trait-factor has
to be estimated freely instead of fixing it at 1.0. We used the scale scores
rather than a measurement model for each CSE-variable to keep the
TABLE 2Standardised Estimates of Structural Parameters Obtained from a State-Trait
Model of Job Satisfaction for N= 157 Job Stayers
Standardised
coefficient
% Explained variance
in job satisfaction
State-factors
Effects on Job Satisfaction
1991 .65** 42
1992 .81** 66
1993 .80** 64
1995 .87** 76
Average 62.00Stabilities
19911992 .56
19921993 .61**
19931995 .66**
Trait-factor
1991 .49* 24
1992 .51* 26
1993 .48* 23
1995 .49* 24
Average 24.25
Occasion-factors
1991 .33 33
1992 .09 9
1993 .13 13
1995 .00 0
Average 13.75
Note: **p < .01; *p < .05 (one-tailed). The chi-square value was 15.11, df= 17, p= .59.
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CORE SELF-EVALUATIONS 41
We distinguished three potential ways in which CSE might be structurally
related to the trait variance of job satisfaction (cf. Edwards, 2001): (1) CSE
affect job satisfaction collectively as a set; (2) CSE are conceptualised as a
superordinate construct; (3) CSE are modeled as an aggregate construct(cf. Figure 1).
First, we tested the relations of the four CSE-variables conceptualised as
a collective set, which is shown in the bottom part of Figure 3. The results
of the regression analysis are shown in the top panel of Table 4 (block C1).
There were two significant predictors. The strongest effect resulted for NA
(.65), followed by a similarly strong effect of LOC (.55). Controlling for
age and gender did not alter these effects much (block C2). Removing self-
esteem and self-efficacy from the analysis did not change the effects of NA
and LOC (block C3). The variables in this regression analysis explainedmost of the variance of the trait-factor underlying job satisfaction (84%).
Although LOC and NA correlated only moderately highly with job satisfac-
tionper se, these two variables were very closely connected to its underlying
trait-factor.
Then we tested models in which CSE was conceptualised as a super-
ordinate construct. The four concepts served as indicators of the latent factor
CSE. There are three variants of the superordinate construct: (1) the four
CSE-variables were modeled as parallel (equal loadings and error variances;
Table 4, block S1), (2) tau equivalent (equal loadings; Table 4, block S2),
and (3) congeneric (loadings and errors estimated freely; Table 4 block S3)
TABLE 3Correlations of the Trait-factor of Job Satisfaction Obtained from the State-Trait
Analysis and Core Self-Evaluation Variables for N= 157 Job Stayers
1 2 3 4 5 6 7
1. Job satisfaction trait-factor
2. Locus of control .65**
3. Self-esteem .47** .28**
4. Self-efficacy .52** .42** .68**
5. Negative affectivity .74** .15* .48** .42**
6. Gender .23 .21* .08 .22** .13
7. Age .19 .15 .01 .22** .03 .04
Note: ** p < .01; * p < .05 (one-tailed). Gender: 1 = male, 2 = female. Job satisfaction trait-factor = trait-
factor obtained from the state-trait model of job satisfaction (i.e. stable variance of job satisfaction); locus
of control, self-esteem, and self-efficacy were aggregated across all four waves, and negative affectivity was
aggregated across the final two waves.
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TABLE 4Regression of the Latent Trait-factor Obtained from the State-Trait Analysis
(N= 157 Job Stayers)
Predictor -> Criterion
Unstd.
coefficient T-value
Std.
Coefficient Mod
C1 4 CSE-variables as collecLocus of control -> trait-satisfaction 1.21 15.68 .55** 2= 46.22 (df
Self-esteem -> trait-satisfaction .04 .33 .02 CFI = 1.00; R
Self-efficacy -> trait-satisfaction .04 .46 .02
Negative affectivity -> trait-satisfaction 1.34 17.92 .65** R2= .84
C2 4 CSE-variables plus gender and ag
Gender -> trait-satisfaction .00 .02 .00
Age -> trait-satisfaction .03 2.34 .30* 2= 58.63 (df
Locus of control -> trait-satisfaction 1.24 4.31 .56** CFI = 1.00; R
Self-esteem -> trait-satisfaction .30 .63 .11
Self-efficacy -> trait-satisfaction .34 .91 .17
Negative affectivity -> trait-satisfaction 1.28 4.62 .63** R2= .92
C3 2 CSE-variables as collec
Locus of control -> trait-satisfaction 1.22 4.58 .55** 2= 26.44 (df
Negative affectivity -> trait-satisfaction 1.34 5.45 .65** CFI = 1.00; R
R2= .84
S1 CSE as superordinate construc
Latent CSE -> locus of control .28 12.57 .63** 2= 135.13 (df
Latent CSE -> self-esteem .28 12.57 .63** CFI = .93; RM
Latent CSE -> self-efficacy .28 12.57 .63**
Latent CSE -> negative affectivity .28 12.57 .63**
Trait-satisfaction locus of control .30 13.70 .59** 2= 101.90 (df
Latent CSE -> self-esteem .30 13.70 .80** CFI = .97; RM
Latent CSE -> self-efficacy .30 13.70 .67**
Latent CSE -> negative affectivity .30 13.70 .60**
Trait-satisfaction
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S3 CSE as superordinate construct Latent CSE -> locus of control .20 5.29 .44** 2= 81.19 (df
Latent CSE -> self-esteem .29 10.49 .80** CFI = .99; RM
Latent CSE -> self-efficacy .41 11.04 .83**
Latent CSE -> negative affectivity .27 6.97 .56**
Trait-satisfaction latent CSE .14f .18** 2= 66.59 (df
Self-esteem -> latent CSE .32f .34** CFI = 1.00; R
Self-efficacy -> latent CSE .34f .47**
Negative affectivity -> latent CSE .20f .29**Trait-satisfaction latent CSE 1.00f .34** 2= 59.00 (df
Self-esteem -> latent CSE 1.00f .27** CFI = 1.00; R
Self-efficacy -> latent CSE 1.00f .37**
Negative affectivity -> latent CSE 1.00f .37**
Trait-satisfaction latent CSE 1.00f .59** 2= 46.22 (df
Self-esteem -> latent CSE .03 .09 .02 CFI = 1.00; R
Self-efficacy -> latent CSE .04 .12 .02Negative affectivity -> latent CSE 1.11 3.19 .71**
Trait-satisfaction Criterion
Unstd.
coefficient T-value
Std.
Coefficient Mod
TABEL 4Continued
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44 DORMANN ET AL.
collective set does not make much sense, since the former always explains
less variance in the criterion than the latter (Edwards, 2001). Furthermore,
the power of this test (estimated using the procedure described in Jreskog
and Srbom, 1989) is very low because the number of degrees of freedom
of the model is very low (Marsh & Hau, 1999). For a probability level of
.05, power is only .004 (for p= .10 it is approximately .016). This can be
considered extremely low in view of Cohens (1988) recommendation for a
statistical power of .80. Thus, the differences in the amount of explained
variance between the two models should only be assessed at a descriptive
level, and the degrees of freedom saved with a superordinate structure
should be taken into account (Edwards, 2001).
Among the three superordinate models tested, the model with congenericdimensions fitted significantly better than the models with parallel and tau
equivalent dimensions (see Table 4, block S3). Hence, the four CSE dimen-
sions are not uniformly related to their superordinate common factor. Self-
esteem and self-efficacy were most strongly associated with the common
factor. Note that the previous analysis of CSE as a collective set has shown
these two dimensions to be weakly associated with the stable part of job
satisfaction. The amount of explained variance in the trait-factor explained
by superordinate CSE was .52, which compares low to the value of .84
previously obtained when the four constructs were analysed separately(block C1). Although the difference was expected, it was not significant
because of low statistical power (2= 2.17, df= 1,p > .15). Nevertheless,
we feel that the difference, which is 32 per cent in explained variance, is
quite large and it compares favorably to the loss of five degrees of freedom.
Further, all superordinate models fitted significantly worse compared to
Model C1 (collective set; see block C1). Thus, the superordinate CSE
model, which assumes that the four variables share a common base respon-
sible for the stable part of job satisfaction, seems to be overly broad; a
collective consideration of NA and LOC is sufficient.The third type of CSE model was an aggregate construct, with the four
CSE-variables as the causes of a latent factor. Again, there were three vari-
eties. Aggregate CSE was modeled as a sum of its four dimensions with
equal weights (Table 4, block A2), as a weighted sum with dimensions
weights proportional to principal component loadings (Table 4, block A1),
or as a sum with freely estimated weights (Table 4, block A3; for a detailed
description of the models see Edwards, 2001).
Models A1 and A2 can be statistically compared with Model A3, which
shows that Model A3 fits best. Also, Model A3 fits better than any super-
ordinate model. Models C1 and A3 show similar results because they are
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CORE SELF-EVALUATIONS 45
the four CSE variables the strongest and the only significant effects; also,
the explained variance is at .84 in both models comparatively high.
DISCUSSION
Previous research (e.g. Judge et al., 1998, 2000) looked at the relationship
of the CSE components with the observed, i.e.fullvariance of job satisfac-
tion, instead of the trait variance that it theoretically seeks to explain. We
built on this research with the goal of estimating the extent to which the
CSE-variables explain the trait-variance in job satisfaction (Question 1).
The second goal of this study was to gain a better understanding of the
appropriate structure of CSE-variables with job satisfaction (Question 2).Using a state-trait approach, we first separated the trait-variance of job
satisfaction from other types of variance and then regressed job satisfaction
on the CSE components. The two most important results will now be discussed
in turn: first, NA and LOC were the best predictors of job satisfaction;
second, results speak for a conceptualisation of the CSE-variables as an
aggregate construct or collective set rather than the suggested superordinate
construct.
Negative affectivity and LOC together explain 84 per cent of the trait
variance in job satisfaction. These two concepts represent a highly parsimo-nious set of dispositions, building the basis of trait job satisfaction. Of
course, other personality variables are still worth considering. They will either
explain the relatively small part of trait variance that remains unaccounted
for by NA and LOC (16% in the present study), or they will be strongly
correlated with NA or LOC to divert a bit of their explanatory value. For
example, reviews and meta-analyses identified PA to be more strongly related
to job satisfaction than other measures of affective disposition (Connolly &
Viswesvaran, 2000; Dormann & Zapf, 2001). Positive Affectivity is negat-
ively related to NA, and it is positively related to the other CSE-variables.Therefore, it is possible that including PA in addition to the CSE-variables
would show that PA has a high impact on job satisfaction. Since a measure
of PA was not available in the present study, it is left to future research to
address this issue.
Results on the structure of CSE raise an interesting question. The
longitudinal measurement model on the one hand suggests that the four
CSE-variables can be parsimoniously represented by a superordinate
common factor, which represents a very stable (2-year test-retest correlation
r= .87) disposition for positive self-evaluations. On the other hand, how-
ever, when the stable variance of job satisfaction was regressed on CSE, it
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46 DORMANN ET AL.
impact on job satisfaction, and the same applies to CSE as an aggregate
construct. This highlights an important discrepancy because in terms of
factor loadings, NA and LOC have weaker associations with latent CSE
compared to self-esteem and self-efficacy. This was also the case for all three
samples analysed by Judge et al. (1998; see also Judge, Erez, Bono, & Thoresen,
2003). Thus, even though CSE is well modeled as a superordinate concept
(characterised by properties of a trait), which is primarily characterised
by self-efficacy and self-esteem, it is rather NA and LOC which make a
significant contribution to job satisfaction. This result is also corroborated
by the fact that the pattern of associations among the CSE-variables was
very similar to patterns obtained by other authors (e.g. Judge et al., 2002).
Locus of control typically exhibits low correlations with core self-evaluations,and whether LOC belongs in core self-evaluations theory is an issue worthy
of further research (Judge et al., 2003, p. 325). The question then is what
is the meaning of CSE if LOC is removed? It may then be accurate and
parsimonious to conceptualise CSE as a broadened neuroticism concept,
including dysphoric beliefs about ones capabilities (Judge & Bono, 2001b).
This notion is empirically supported because NA and LOC exhibit the
clearest discriminant validity among the four CSE-variables (Judge et al.,
2002). Taken together, there is evidence suggesting that CSE has two main
elements, one closely related to LOC and the other to negative affect atwork. Hence, our findings underscore the Judge et al. model in some
respects; however, they challenge the current conceptualisation of CSE as a
superordinate latent conceptfor job satisfaction research. Research on CSE
in other areas such as work motivation, stress, and performance will
certainly benefit from following this analytical process.
We move on to discussing more specifically the results on NA and LOC.
Our results on NA emphasise the importance of analysing the trait variance
of job satisfaction instead of its observed (full) variance. Like previous
research (Dormann & Zapf, 2001; Connolly & Viswesvaran, 2000), we foundthe direct relationship between NA and observed job satisfaction to be moder-
ately high (see Table 1). Negative affectivity makes up an important part of
the stable variance in job satisfaction, but since the stable variance makes
up only a small portion in observed measures of job satisfaction (around
25%), the effect of NA on job satisfaction measurements is rather limited.
An important finding is that LOC represents a major dispositional cause
of job satisfaction. Control appears to be a vital antecedent for general well-
being (cf. Frese, 1989). White (1959) argued that there is a need for control.
When the need for control is not satisfied, humans tend to feel dissatisfied.
According to Miller (1979), perceived control represents a safety signal: a
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CORE SELF-EVALUATIONS 47
environment to react in a relatively relaxed manner to threatening situations
(cf. Glass & Singer, 1972). Perhaps individuals with an internal LOC even
spend less effort to impact on their environment than individuals with an
external LOC because individuals sometimes benefit from not investing
effort to control their situation. Schnpflug (1983) suggested that exerting
control, for example, in order to cope with unfavorable working conditions,
has its drawbacks. It requires and depletes mental resources (see also
Muraven & Baumeister, 2000), and a resources loss represents a psycholog-
ical threat in its own right (Hobfoll, 1989). Thus, an internal LOC may be
more important for individuals job satisfaction than actually available
control.
Another issue is whether the results may generalise to other cultures andlanguage areas. What speaks clearly for the generalisability is that some
patterns of results that emerged from this study are similar to other studies
and meta-analyses (Dormann & Zapf, 2001; Judge & Bono, 2001a; Judge
et al., 2002). Although the data were collected in East Germany following
the unification with West Germany, which created a volatile situation, the
situation quickly became more stabilised. Also, although the job stayers
analysed in the present study had to adapt to rapid changes in work organ-
isation and technology, and many of them can be characterised as survivors
of mass layoffs, we feel that this applies to more and more employees inWestern countries, too.
This study employed a measure of job satisfaction that captured different
specific facets; it is an open question whether our results extend to global
measures. On the one hand, it has been argued that averaging facet satisfac-
tions comes close to assessing global satisfaction (e.g. Wanous, 1974). On
the other hand, some facets may be more susceptible to trait influences than
others (cf. Arvey et al., 1989). Fisher (2000) has shown that global satisfaction
is more strongly affected by emotions than compounds of facet satisfaction,
suggesting that affective traits may be more relevant for global satisfactionthan for compounds of facets as used in our study. Thus, our approach may
have helped to detect more variance caused by the work environment than
a Kunin (faces) scale would have detected. Future research should, therefore,
consider other measures of job satisfaction, for example global measures to
validate the present findings.
Some methodological constraints may lead to a small overestimation of
the direct effects of the trait-factor on job satisfaction. A problem related to
the state-trait decomposition is that multiple traits and multiple situational
factors exist, which cannot be modeled appropriately. The trait-factor might
comprise several sources of stability in addition to personality variables. For
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48 DORMANN ET AL.
may in sum have stronger effects when accounted for separately. Also, one
might argue that the trait-factor may affect the state-factors. This cannot be
modeled because such a model is not identified (i.e. the structural equations
cannot be solved). If it were possible, such effects would reduce the direct
effect of the trait-factor and increase the direct effects of the state-factors.
Finally, state-trait interactions could not be modeled; however, we did a
series of simulations suggesting the potential bias to be very small.
We conclude with a remark on an issue that has re-emerged ever since
the onset of research on job satisfaction in terms of a trait. A question
relevant to both researchers and practitioners is to what extent observed job
satisfaction is based on a trait. If a high proportion of observed job satis-
faction were based on traits, the use of job satisfaction measures, for example,to evaluate working conditions or job redesign interventions would be
utterly useless. We believe that existing indirect approaches overestimated
the part of variance attributable to dispositions; we tried to obtain more
reliable estimates by partitioning the variance of job satisfaction into measure-
ment error, uniqueness, unstable occasion-factors, intermediately stable
situational factors, and stable trait-like causes. Results show that on average
24.25 per cent of the variance in job satisfaction is influenced by stable
variables such as dispositions, whereas 62.00 per cent is attributable to
changing factors in the environment. As previously described, the make-upof our sample should maximise the proportion of dispositional variance.
Estimation of less than 25 per cent of variance in job satisfaction being
dispositional clearly speaks for the usefulness of job satisfaction measures
to assess working conditions.
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