A StaTrait Analysis of Job Satisfaction

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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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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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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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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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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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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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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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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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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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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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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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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




Trait-satisfaction


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2006

The

Authors.Journalcompilation

2006

InternationalAssoc

iation

forApplied

Psychology.

S3 CSE as superordinate construct Latent CSE -> locus of control .20 5.29 .44** 2= 81.19 (df




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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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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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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Documents

A StaTrait Analysis of Job Satisfaction