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Job satisfaction and life satisfactionrevisited: A longitudinal test of an

integrated model

Joseph C. Rode

A B S TRA C T Research indicates that job satisfaction is significantly related to life

satisfaction. However, previous studies have not included variables

that may confound the relationship. Fur thermore, the vast majority

of studies have relied on cross-sectional data. I tested a compre-

hensive model that examined the relationship between job and life

satisfaction and a broad personality construct called core self-

evaluations, as well as nonwork satisfaction and environmental vari-

ables, using a nationally representative (US), longitudinal data set.

Results indicated that core self-evaluations was significantly related

to both job satisfaction and life satisfaction over time, and that the

relationship between job satisfaction and life satisfaction was not

significant after taking into account the effects of core self-evaluations

and nonwork satisfaction. Implications for theory and practice arediscussed.

K E Y W O R D S job life nonwork personality satisfaction

Results of more than three decades of research have led researchers to

conclude that job satisfaction is significantly related to, or spills over into,overall attitudes towards life, or life satisfaction (see Rain et al., 1991 and

Tait et al., 1989 for reviews). We would expect the two to be related because

of the amount of time spent at work by full-time employees, and also

1 2 0 5

Human Relations

DOI: 10.1177/0018726704047143

Volume 57(9): 12051230

Copyright 2004

The Tavistock Institute

SAGE Publications

London, Thousand Oaks CA,

New Delhi

www.sagepublications.com
http://www.sagepublications.com/http://www.sagepublications.com/


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because, for most people, work is a central life activity (Dubin, 1956).

Empirical studies have consistently reported moderate correlations between

job and life satisfaction, an average of .31 or .44 when corrected for atten-

uation (Tait et al., 1989).

However, the relationship is more complex than these numbers imply,

and at least three theoretical perspectives could account for the observed

zero-order correlations between job and life satisfaction. The first perspec-

tive, sometimes referred to as the bottom-up perspective (Brief et al., 1993;

Diener, 1984), proposes that job satisfaction has a casual influence on life

satisfaction because it is part of life satisfaction (Andrews & Withey, 1976;

Cambell et al., 1976; Rice et al., 1985). Life satisfaction is conceptualized,

in part, as the result of satisfaction with various life domains such as work,family, health, etc., and the effects of environmental conditions on life satis-

faction are assumed to be largely mediated by satisfaction with life domains.

Research indicates that satisfaction with work and nonwork domains

accounts for about 50 percent of the variance in life satisfaction (Andrews

& Withey, 1976; Cambell et al., 1976; Hart, 1999; Near et al., 1984).

The second perspective argues that the causal relationship between the

two variables is top-down (Diener, 1984), or that life satisfaction influences

job satisfaction (Judge & Watanabe, 1993; Schmitt & Bedeian, 1982). The

influence of life satisfaction on job satisfaction represents a dispositionaleffect (Staw et al., 1986), whereby the positive affect associated with life

satisfaction results in the recall of a greater number of positive job events

and more positive interpretations of job conditions, which leads to higher

job satisfaction (Bower, 1981; Judge & Hulin, 1993). Probably the strongest

empirical support for this perspective comes from Judge and Watanabe

(1993) who found that life satisfaction had a stronger relationship to job

satisfaction over a 5-year period than job satisfaction had on life satisfaction

over the same period.The third perspective has not been previously explicated, but is

suggested by the results of previous studies. It may be that much of the

relationship between job satisfaction and life satisfaction is spurious, result-

ing from common influences. The literature presents two possibilities. First,

research following the bottom-up tradition indicates that job satisfaction and

satisfaction with nonwork domains are influenced by many of the same

environmental variables (e.g. job income), and as a result, satisfaction with

nonwork domains may confound the relationship between job satisfaction

and life satisfaction. In fact, the percent of variance in life satisfactionuniquely attributed to job satisfaction often falls to 5 percent or lower

(Andrews & Withey, 1976; Campbell et al., 1976; Hart, 1999; Near et al.,

1984) when the effects of satisfaction in nonwork domains are controlled.

Human Relations 57(9)1 2 0 6


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The second possibility is suggested by Judge et al. (1997) who proposed

a broad personality construct called core self-evaluations, which they devel-

oped specifically to explicate the process by which disposition may influence

both job satisfaction and life satisfaction. Unlike earlier top-down theories,

Judge et al. proposed that life satisfaction is not a dispositional variable per

se, but that it is influenced by disposition, as is job satisfaction. Empirical

research has found core self-evaluations to be related to both job and life

satisfaction, suggesting that it may be at least partially responsible for the

bivariate relationship between the two constructs (Heller et al., 2002; Judge

et al., 1998). To date, the possible confounding effects of both core self-

evaluations and satisfaction with nonwork domains on the relationship

between job satisfaction and life satisfaction have not been simultaneouslytaken into account.

Irrespective of the underlying theoretical perspective, the vast majority

of the empirical research on job and life satisfaction has utilized a cross-

sectional design. Although the use of longitudinal data in a non-experimental

study does not necessarily establish causality, it does provide stronger

support for causal relationships than can be inferred from analysis of cross-

sectional data (Menard, 1991). This is particularly relevant to studies of job

and life satisfaction, given that the perspectives described earlier are based

on differing causal assumptions. In this study, I tested a comprehensive modelthat enabled me to examine the relationship between job and life satisfaction

over time, while taking into account the possible confounding influences of

nonwork satisfaction, personality (i.e. core self-evaluations), and a set of

environmental and demographic variables. I tested the model using a nation-

ally representative, longitudinal sample of US workers.

Theoretical model and hypotheses

My model integrates the bottom-up perspective and Judge et al.s (1997)

dispositional perspective. Following bottom-up theorists, I propose that

satisfaction with specific life domains (i.e. work and nonwork in this study),

mediates the relationship between environmental conditions and life satis-

faction (e.g. Andrews & Withey, 1976; Campbell et al., 1976; Rice et al.,

1985). I also propose that core self-evaluations is related to both domain

satisfactions and life satisfaction as shown in Figure 1. I incorporated the

dispositional view proposed by Judge et al. over the dispositional proposi-tion that life satisfaction influences job satisfaction for two reasons. First,

Judge et al.s approach offered greater conceptual congruence with the

bottom-up approach which forms the basis of my model. Second, a growing

Rode Job and life satisfaction 1 2 0 7


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body of research supports the idea that life satisfaction is not dispositional

in and of itself, but that it is influenced by disposition (Brief et al., 1993;

Diener et al., 1999). For simplicity, I do not explicitly address the indirect

relationships contained in the model, but I do take into account these

mediated effects in the structural equation modeling analysis described later.

Relationships between domain satisfactions and life satisfaction

Research indicates that life satisfaction can be viewed as the result, in part,

of satisfaction with various life domains (Andrews & Withey, 1976;

Campbell et al., 1976). This notion is based on the assumption that indi-

viduals evaluate the details of experience when making overall satisfactionjudgments (Rice et al., 1985). For example, evaluations of ones job are based

on how one evaluates ones pay, supervisor support, working conditions, etc.

relative to desired levels of these variables. The direction of influence is

assumed to be from the specific to the general, in this case from satisfaction

with specific life domains (e.g. work) to overall life satisfaction. From this

perspective, satisfaction with a life domain represents the aggregate evalu-

ations of the domains salient aspects that are taken into account when

making overall life satisfaction evaluations.

Empirically, satisfaction with major life domains (e.g. family, work,health, and leisure) explains about 50 percent of the variance in overall life

satisfaction (e.g. Andrews & Withey, 1976; Campbell et al., 1976; Hart,

1999), with the remaining 50 percent presumably the result of measurement


Controls

Working

Conditions Nonworking

Conditions Demographics

+

JobSatisfaction

NonworkSatisfaction

LifeSatisfaction

+

+

+

+

Core Self-Evaluations

Figure 1 Hypothesized model


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error and individual differences (e.g. personality). The effects of domain

satisfactions on life satisfaction appear to be additive, with the most salient

life domains explaining the most variance; there is little evidence of inter-

action effects between life domains (Andrews & Withey, 1976; Campbell et

al., 1976). For parsimony, I classified satisfaction with all nonwork domains

as nonwork satisfaction. The hypotheses also consider the effects of core

self-evaluations, which, as described below, is also proposed to be related to

life satisfaction. Formally stated:

Hypothesis 1: Job satisfaction is positively related to life satisfaction,

controlling for nonwork satisfaction and core self-evaluations.

Hypothesis 2: Nonwork satisfaction is positively related to life satis-

faction, controlling for job satisfaction and core self-evaluations.

Relationships between core self-evaluations and satisfaction

variables

Core self-evaluations are basic conclusions or bottom-line evaluations

(Judge et al., 1997) people hold regarding their selves and their capabilities.Judge et al. proposed four criteria to determine the degree to which disposi-

tional traits were indicative of core self-evaluations: (i) reference to the self,

(ii) evaluation focus (i.e. the extent to which traits involve evaluations versus

descriptions), (iii) fundamentality to more specific surface traits (Cattell,

1965), and (iv) breadth or scope (Allport, 1961). Judge et al. identified four

existing constructs meeting these criteria: self-esteem, generalized self-

efficacy, locus of control, and neuroticism. However, these were not proposed

as comprehensive or exclusive measures of the construct.Judge et al. (1997) proposed that core self-evaluations affected the

perception of objective conditions and events and the norms against which

perceived conditions and events are appraised, which in turn influenced satis-

faction judgments. For example, persons who consider themselves to be

fundamentally incompetent may experience little satisfaction with a given set

of working conditions because they conclude their incompetence will eventu-

ally lead to failure, demotion, and disgrace when they do not perform up

to expectations. Conversely, persons with high core self-evaluations may

experience greater satisfaction with the same set of working conditionsbecause they are more confident in their ability to take advantage of those

conditions. Additional support for this notion comes from the finding that

individuals with low self-esteem (which is considered an indicator of core



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self-evaluations; Judge et al., 2002) tend to have unrealistically high perform-

ance level and influence expectations of themselves, which leads to greater

incidences of perceived failure and lower satisfaction (Brockner, 1988).

Several studies provide empirical support for the proposed relationship

between core self-evaluations and job satisfaction (e.g. Judge et al., 1998,

2000). Judge et al. (1997) did not explicitly discuss nonwork life domains,

but the same linking mechanisms should apply to satisfaction with nonwork

life domains as well. The hypotheses reflect the inclusion of a set of environ-

mental and demographic control variables, which, as described later, have

been associated with job and nonwork satisfaction. Formally stated:

Hypothesis 3: Core self-evaluations is positively related to job satis-faction, controlling for environmental conditions and demographics.

Hypothesis 4: Core self-evaluations is positively related to nonwork satis-

faction, controlling for environmental conditions and demographics.

Both Andrews and Withey (1976) and Campbell et al. (1976) proposed that

the self qualifies as a life domain in the same manner as work, family, health,

etc. Campbell et al. (1976) found that a measure of personal competence,

which they argued was closely related to satisfaction with the self, was signifi-cantly related to life satisfaction after controlling for other life domains.

Thus, to the extent that core self-evaluations represents satisfaction with the

self, it should influence overall life satisfaction evaluations in the same

manner as satisfaction with other life domains (Andrews & Withey, 1976;

Campbell et al., 1976). Formally stated:

Hypothesis 5: Core self-evaluations is positively related to life satis-

faction, controlling for job satisfaction and nonwork satisfaction.

Relationships between control variables and domain satisfactions

In an exhaustive study of the effects of working and nonworking conditions

on overall life satisfaction, Campbell et al. (1976) concluded that both the

realities of life and the perception of those realities influenced overall life

satisfaction, but that the effects were almost entirely mediated by satisfaction

with life domains. Subsequent studies found that some environmental

conditions are significantly associated with both interdomain and cross-domain satisfaction (e.g. working conditions affecting nonwork satisfaction;

Near et al., 1983, 1984), which may account for some of the observed corre-

lations between job satisfaction and satisfaction in nonwork domains.



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I do not explicitly address the theoretical mechanisms linking each of

the control variables to job and nonwork satisfaction, as the effects of

environmental and demographic variables were not the focus of the study

(see Campbell et al., 1976; Diener et al., 1999; Rice et al., 1985; and Spector,

1997 for detailed discussions of the relationships between environmental

conditions and satisfaction judgments), but I included these variables for two

reasons. First, I wanted to provide a more comprehensive test of the bottom-

up causal flow assumption inherent in my model. Second, including these

variables allowed me to assess the relationship between core self-evaluations

and domain satisfactions, after controlling for the effects of a set of environ-

mental conditions and demographics, which has not been done in previous

studies involving core self-evaluations. I was limited to the measures includedin the data set, which, admittedly, do not represent all the environmental

conditions that could potentially influence satisfaction judgments. Still, the

variable set is reasonably comprehensive, compared with that utilized in

similar studies examining the relationships between environmental

conditions and satisfaction judgments (Andrews & Withey, 1976; Campbell

et al., 1976; Near et al., 1983, 1984).

Methods

The data for the study came from the first and second waves of the Ameri-

cans Changing Lives (ACL) survey, which were collected by the Institute

for Social Research at the University of Michigan (House, 1997). Data were

collected in two waves, 3 years apart, from the same cohort. The survey was

designed to provide a wide range of data on sociological, psychological, and

medical aspects of Americans daily lives.

Sample

The study population for the survey included the US household population,

aged 25 or older, exclusive of residents of Alaska or Hawaii. Residents

residing in households on military bases, in group quarters, or in institutions

were excluded. The survey design specified a two to one oversampling of

interviewees 60 years of age or older and of African Americans aged 2559,

and a four to one oversampling of African Americans 60 years of age or

older. The overall response rate for wave 1 was 67 percent. The attrition ratefor the follow-up survey was 20.7 percent. Subsequent analysis revealed no

significant differences in response rates across demographic variables, except

that Blacks had a slightly higher response rate than the overall sample (71



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percent; House, 1997). Trained interviewers conducted both surveys in the

interviewees place of residence. All cases were anonymous, identified with

only a random identification number.

Because I wanted to focus on full-time employees, I excluded inter-

viewees who were unemployed during either survey administration, or who

had worked fewer than 1500 hours in the year preceding either adminis-

tration. I also deleted 15 cases that showed strong evidence of containing

response sets (details available from the author) for a final n = 892. To

compensate for the oversampling of African Americans and people over 60

years old, each case was assigned a weight based on the sampling method-

ology employed: non-Blacks between 25 and 60 years old received a

sampling weight of 4; non-Blacks over 60 years old and Blacks between 25and 60 years old received a sampling weight of 2; and Blacks over 60 years

old received a sampling weight of 1. After applying this weighting scheme to

the data set (sample size was set to n = 892 for all statistical analyses

described later), the final sample was fairly representative of the overall US

workforce at the time, within three percentage points of the overall work-

force in terms of occupational categories, gender, ethnicity and marital status.

MeasuresMeasures available in this data set were not always as extensive as I would

have liked. However, possible measurement limitations not withstanding, the

ACL database provided the opportunity to examine a large, random national

data set, whose results could generalize to the population in much the same

way that presidential polls normally predict voting behavior. These data also

had two data collection points (referred to hereafter as Time 1 and Time 2),

thereby permitting longitudinal analyses, which is very rare in this field and

an important focus of this study. Unless otherwise noted, all multiple itemmeasures were modeled as latent variables in the structural equation

modeling (SEM) analyses described later, with each item serving as an indi-

cator of the latent construct.

Overall life satisfaction

I used two items ( = .75) as indicators of overall life attitudes for Time 2

data. The first item was, overall, how satisfied would you say you are with

your life these days?, rated on a 7-point scale, ranging from 1 (completelysatisfied) to 7 (completely dissatisfied). This item is practically identical to

the single life satisfaction item used by Campbell et al. (1976). The second

item, overall, how happy would you say you are these days?, measured on



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a 3-point scale ranging from 1 (very happy) to 3 (not too happy), has been

used extensively in studies of subjective well-being, which is conceptually and

empirically very similar to life satisfaction (Diener, 1984). I recoded both

items so that higher scores corresponded to higher life satisfaction. Descrip-

tive statistics were calculated based on the average of the standardized values

of both items, because of the differing response formats.

Job satisfaction

I measured job satisfaction with two items ( = .78 and .79, at Time 1 and

Time 2, respectively): how satisfied are you with your job? and how much

do you enjoy your work? each recoded so that the rating scale ranged from1 (low) to 5 (high). Descriptive statistics were calculated using the average

of the two items.

Nonwork satisfaction

I created a composite measure of nonwork attitudes consisting of five items

related to satisfaction with various nonwork domains, including health,

marriage, family, finances, and home, all rated on either a 5- or 7-point scale,

with 1 = completely satisfied. Although finances are linked to ones jobthrough job income, satisfaction with finances was included in the nonwork

domain because it is influenced by contextual factors beyond job income (e.g.

spousal income, investment activity, material desires, current and future

perceived financial demands, etc.), which fall within the realm of nonwork

life. I do consider the influence of job salary on both job satisfaction and

nonwork satisfaction in the SEM models described later.

I modeled nonwork satisfaction as a formative variable in the SEM

analyses because the individual items were not intended to measure the sameunderlying construct, but were designed to measure separate aspects of

nonwork life. I was interested in the variance contributed by the items in

total, which is best accomplished by utilizing a single composite (i.e.

observed) variable in SEM analyses (Kline, 1998). I did not attempt to model

error variance in the SEM analyses, nor did I calculate internal reliability

statistics because these analyses assume the underlying construct to be unidi-

mensional, which was not the case here, given that the items focused on

differing, and largely unrelated, aspects of nonwork life. This consolidation

methodology is similar to that utilized in other studies employing generalnonwork satisfaction constructs (e.g. Hart, 1999; Near et al., 1984). I

recoded the items so that higher scores indicated higher satisfaction, and I

standardized the scores before averaging the items, because of the differing



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response formats. The resulting composite variable was used in all subse-

quent analyses.

Core self-evaluations

Core self-evaluations measures contained within the survey included the

following: 3 items from Rosenbergs (1965) 10-item self-esteem scale, 3 items

from Pearlin and Schoolers (1978) 7-item mastery scale (described below),

and 5 items from Eysenck and Eysencks (1968) 12-item neuroticism scale.

An example of a self-esteem item was I take a positive attitude toward

myself. An example of a mastery item was there is really no way I can solve

the problems I have. An example of a neuroticism item was are you aworrier?, with yes and no response categories. All mastery and self-esteem

items were scored on a 4-point scale with 1 = strongly agree and 4 =

strongly disagree. Items were recoded so that higher scores indicated higher

mastery and self-esteem levels. All neuroticism items were worded such that

a yes response indicated higher levels of neuroticism. In their original form,

yes responses were assigned a value of 1, and no responses were assigned a

value of 5. Also, in a few cases a neuroticism item had an original value of

3, indicating that the interviewee was unable to select yes or no and had

verbally indicated that the correct response was maybe. These values wererecoded with yes responses = 1, no responses = 0, and maybe responses =

1.5.

Although not specifically mentioned by Judge et al. (1997), mastery

appears to fit well with the core self-evaluations conceptualization in that it

is both fundamental and wide in scope (it refers to the self in general) and

contains a strong evaluative component. According to Pearlin et al. (1981:

340), mastery represents the extent to which people see themselves as being

in control of the forces that importantly affect their lives. This definition isvery similar to Rotters (1966) well-established locus of control concept,

which Judge et al. (1997) specified as a potential measure of core self-

evaluations. Moreover, the mastery construct was developed specifically to

complement self-esteem as a self-evaluations construct (Pearlin et al., 1981).

Unfortunately, although the neuroticism scale showed adequate

internal reliability ( = .69), the abbreviated self-esteem ( = .53) and mastery

( = .39) scales each had unacceptably low internal reliability coefficients. In

an attempt to construct core self-evaluations measures with greater construct

validity and higher levels of reliability, the items from all three scales weresubjected to a common factor analysis with orthogonal rotation. Factors

with eigenvalues > 1, before rotation, were retained. As shown in Table 1,

the results indicated the presence of three separate underlying factors. The



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one item that displayed similar loadings on both factors one and two was

retained in factor two, because dropping the item would have significantly

reduced the internal reliability of the scale created from the items loading on

factor two.

The first factor contained two items from the abbreviated self-esteem

scale and two items from the abbreviated mastery scale. The four items loading

onto this factor were averaged to create a new scale which demonstrated an

acceptable internal reliability ( = .68). This new scale was labeled self-esteem

and mastery. The second factor consisted of the five neuroticism items. Unfor-

tunately, the scale formed by the two items loading on the third factor did not

display acceptable internal reliability ( = .45), so these items were dropped

from further analysis. In the SEM analyses, core self-evaluations was modeledas a latent variable, with each of the two derived scales serving as indicators.

This methodology is similar to that used in earlier studies (e.g. Judge et al.,

1998, 2000) which have modeled core self-evaluations as a higher order factor.

However, in this study core self-evaluations was modeled as a first-order latent

variable, because modeling higher order factors requires a minimum of

three first order factors (i.e. scales). Descriptive statistics for the two core


Table 1 Results of core self-evaluations factor analysis

Item (original scale) Factor 1: Factor 2: Factor 3:

Self-Esteem Neuroticism Self-Esteem

and Mastery 1 and Mastery 2

Think I am no good (self-esteem) .60 .12 .14

Pushed around in life (mastery) .57 .12 .05

Feel I am a failure (self-esteem) .52 .09 .10No way to solve problems (mastery) .48 .13 .11

Often feel fed up (neuroticism) .40 .38 .04

Am a nervous person (neuroticism) .03 .74 .14

Am tense/high strung (neuroticism) .12 .53 .05

Am a worrier (neuroticism) .21 .51 .14

Mood goes up and down (neuroticism) .35 .38 .03

Can do anything (mastery) .08 .05 .64

Positive attitude toward self (self-esteem) .30 .25 .38

Eigenvalue 1.61 1.51 .65Percent variance explained 14.67 13.69 5.89

Note: bold indicates the factor on which items were retained for subsequent scale development.


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self-evaluation scales were calculated using the average of the items contained

in each scale.

Controls

Most of the measures of environmental conditions were objective and quan-

titative (e.g. salary or household income), but one was perceptual and based

on Likert scales (i.e. perceptions of workplace autonomy). Despite its percep-

tual underpinnings, autonomy has been shown to be empirically distinct

from work attitudes (Glick et al., 1986). I measured autonomy with two

items ( = .64 and = .69 in Time 1 and Time 2, respectively), which were

similar to two of the items included in the widely used three-item scale devel-oped by Hackman and Oldham (1975). The items were I have a lot of say

about what happens in my work and I decide how to do my work rated

on a 4-point agreement scale. Items were recoded so that higher scores corre-

sponded to higher levels of autonomy. I included four other working

conditions measures: annual salary (corrected for non-normal distributions

with log transformations), average number of hours worked per week, self-

employment status (1 = self-employed, 0 = not self-employed), and an objec-

tive measure of job complexity derived by Roos and Treiman (1980) based

on the three-digit 1970 US Census occupation code. Unfortunately, the datawere classified by the 1980 Census occupation code for the follow-up survey,

so no job complexity measure was available at Time 2.

Nonworking conditions control variables were: (i) household size,

measured as the number of individuals related to the interviewee currently

living in the household; (ii) household income, measured as the midpoint

values on an 8-point interval scale (which resulted in a normal distribution

that did not require any transformation); (iii) social integration, measured as

the arithmetic mean of the standardized values of three items related tofrequency of attendance at formal social gatherings (five categories), and

participation in informal social gatherings with friends and relatives (six

categories); (iv) health conditions, measured as a count of 10 chronic health

conditions such as heart disease and arthritis; and (v) care hours, measured

as the number of hours spent in the past year (indicated by the midpoint

values of two separate 5-point interval scales) caring for non-household

persons who either had significant long-term health problems or who experi-

enced serious injury, illness, or personal crises. Social integration was

modeled as formative variable in the SEM analyses. I also controlled fordemographic variables, including age, gender (1 = female, 0 = male), marital

status (1 = married, 0 = not married), and ethnicity (1 = White, 0 = non-

White).



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Analysis

I tested the above model in two steps. First, I tested the measurement model

using confirmatory factor analysis prior to estimation of the hypothesizedmodels to prevent interpretational problems inherent in the simultaneous

estimation of measurement and hypothesized models (Andersen & Gerbing,

1988). Second, I developed two structural models (described later) to test the

direct relationships included in the hypothesized model. I estimated all

models using covariance structural equation modeling (SEM) with AMOS 5,

which is comparable (Hox, 1995) to other SEM programs (e.g. EQS,

LISREL). Owing to the large sample size I gauged model fit through the

goodness-of-fit index (GFI), comparative fit index (CFI), and the root mean

squared error of approximation (RMSEA) measure, as well as traditional chi-square test results. All SEM analysis was performed using the maximum like-

lihood procedure.

Results

Intercorrelations and descriptive statistics are shown in Table 2. The corre-

lation between job satisfaction and life satisfaction at Time 2 (r = .30) wasvery close to average unadjusted correlation (r = .31) reported in the meta

analysis by Tait et al. (1989).

To confirm the factor structure of the latent variables (job satisfaction

in both Time 1 and Time 2, life satisfaction in Time 2, core self-evaluations

in Time 1, and autonomy in Time 1 and Time 2), I performed a confirma-

tory factor analysis with each indicator constrained to load on its respective

latent variable and the correlations between the latent variables uncon-

strained. The overall measurement model provided a good fit to the data:2(39, n = 892) = 159.57 (p < .01), GFI = .97, CFI = .96, RMSEA = .05. All

indicators had loadings of .50 or higher. Examination of the correlation

residuals and modification indices and did not find evidence of significant

cross-loadings of indicators, suggesting discriminant validity. Furthermore,

the average variance explained across the indicators of each latent variable

was higher than the variance shared by any two latent variables, thereby

passing a very stringent test of discriminant validity among the latent vari-

ables (Fornell & Larcker, 1981).

To test the relationships contained in the proposed model, I developedtwo separate structural models. In the first model all variables were measured

during Time 1, except life satisfaction, which was measured at Time 2. This

model was designed to assess the relative strength of the effects of core



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Table 2 Means or percentages, standard deviations, reliabilities, and intercorrelations (n = 892)

Mean/% SD 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

1 Life satisfaction T2 0.00 0.90 75

2 Nonwork satisfaction T1 .00 0.61 42

3 Nonwork satisfaction T2 .01 0.62 57 58

4 Job satisfaction T1 4.11 0.80 24 30 30 78

5 Job satisfaction T2 4.04 0.79 30 31 37 49 79

6 Self-esteem and mastery T1 3.34 0.59 27 29 22 25 24 68

7 Neuroticism T1 .32 0.26 28 32 28 18 20 39 69

8 Job complexity T1 4.88 2.07 03 06 04 10 06 16 07

9 Autonomy T1 3.33 0.64 13 16 16 38 27 25 12 34 64

10 Autonomy T2 3.34 0.65 16 13 17 22 37 21 13 30 51 69

11 Self-employed T1 3% 07 03 04 12 09 04 00 15 07 15

12 Self-employed T2 3% 02 07 02 05 07 01 00 10 03 12 40

13 Salary T1a 9.95 .67 11 15 15 11 05 23 12 44 25 23 08 06

14 Salary T2a 10.18 .64 09 07 09 10 04 20 12 37 21 19 06 03 86

15 Work hours T1 46.43 10.72 08 09 08 14 14 08 00 18 18 18 10 09 33 24

16 Work hours T2 46.19 10.08 06 09 07 09 14 12 06 13 16 19 1 11 16 21 52

17 Household size T1 2.86 1.37 02 07 00 02 06 04 02 10 04 03 04 00 04 03 00 03

18 Household size T2 2.85 1.35 04 04 03 07 00 03 03 08 04 02 01 01 01 03 03 02 73

19 Family income T1 36725 24011 09 18 14 07 07 21 13 34 21 24 12 12 62 46 25 14 10 08

20 Family income T2 44323 27766 11 16 14 09 07 19 12 39 22 24 06 05 67 78 21 19 06 10 73

21 Social integration T1 0.03 0.93 10 07 08 06 10 03 04 13 13 09 00 01 04 06 08 03 00 03 03

22 Social integration T2 0.04 0.90 09 06 09 07 08 07 00 13 13 06 07 08 04 04 01 03 14 14 04

23 Health conditions T1 0.69 0.95 09 17 13 02 01 12 14 10 07 06 05 01 15 19 00 05 09 12 07

24 Health conditions T2 0.77 1.06 14 17 13 03 02 14 16 08 00 08 07 03 14 11 02 06 09 12 06

25 Care hours T1 33.28 62.44 04 05 07 00 07 04 04 02 02 03 05 01 03 01 01 01 01 03 02

26 Care hours T2 40.30 70.61 00 03 06 00 05 01 02 02 07 01 06 02 01 00 01 08 06 05 04

27 Married T1 64% 08 24 11 02 05 00 09 05 06 11 08 05 11 10 09 07 48 42 31

28 Married T2 66% 17 19 22 03 04 01 10 07 06 12 07 05 14 20 10 10 38 46 27

29 Age T1 42.67 12.41 04 14 15 21 16 06 08 00 11 07 05 01 04 03 03 07 21 33 1130 Female 42% 09 13 08 01 00 08 15 05 06 11 09 04 28 25 23 24 06 14 18

31 White 79% 06 06 05 10 01 08 03 26 15 18 12 05 16 16 12 08 10 09 15

Note. Cronbachs alpha appears on the diagonal; decimals not shown for correlations, to save space; all correlation values >.06 are significant, p < .05; all correlation values >.08 are signia Salary variables have been subjected to natural log transformations.


15/26

self-evaluations, job satisfaction, and nonwork satisfaction on life satis-

faction over time. In the second model, core self-evaluations measures were

taken from Time 1, and all other variables were measured at Time 2. This

model was designed to asses the longitudinal relationship of core self-

evaluations on job satisfaction, while controlling for environmental conditions

at the time of the job satisfaction measure, and also to examine the relation-

ship between job satisfaction and life satisfaction when the effects of core self-

evaluations on the two variables were subject to the same time lag.

Both models allowed the following paths to be freely estimated: the

paths from each of the control variables (i.e. environmental conditions and

demographics) to both job satisfaction and nonwork satisfaction, the paths

from core self-evaluations to job satisfaction, nonwork satisfaction, and lifesatisfaction, and the paths from both job satisfaction and nonwork satis-

faction to life satisfaction. I allowed the correlations between the disturbance

terms (i.e. the variance not accounted for by the respective exogenous vari-

ables) of job satisfaction and nonwork satisfaction to be freely estimated, as

well as the correlations among the exogenous variables. I did not initially

include paths between any of the control variables and life satisfaction in the

initial models because theoretically I expected that the effects of these vari-

ables on life attitudes would be indirect, mediated by job satisfaction and/or

nonwork satisfaction (Andrews & Withey, 1976; Campbell et al., 1976; Riceet al., 1985).

The models both displayed a good fit with the data; 2(86,

n = 892) = 174.59 (p < .01), GFI = .98, CFI = .98, RMSEA = .03 for Model

1, and 2(81, n = 892) = 144.82 (p < .01), GFI = .98, CFI = .98, RMSEA = .03

for Model 2. I examined the modification indices for the presence of poten-

tially significant paths (i.e. modification indices > 4) between the control vari-

ables and life satisfaction to ensure that all potentially significant effects on

life satisfaction had been taken into account. In neither model did the modifi-cation indices indicate the presence of additional potentially significant paths

to life satisfaction. For presentation simplicity, I excluded variables whose

path coefficients were not significant atp < .05 (e.g. gender) from Figures 2

and 3. In Model 1, three of the five working conditions measures, one of the

five nonworking conditions measures, and three demographic variables were

significantly related to one or more dependent variables. In Model 2, three

working conditions, three nonworking conditions, and two demographic

measures were significantly related to one or more dependent variables.

Contrary to the prediction made in Hypothesis 1, job satisfaction wasnot significantly related to life satisfaction in either model. As predicted in

Hypothesis 2, the standardized path coefficient between nonwork satis-

faction and life satisfaction was significant in both Model 1 (.32, p < .01)



16/26

and Model 2 (.54,p < .01). However, core self-evaluations was also signifi-

cantly related to job satisfaction (standardized path coefficients = .24,

p < .01, and .32,p < .01, for Models 1 and 2, respectively) and to nonwork

satisfaction (standardized path coefficients = .44,p < .01 and .33,p < .01,for Models 1 and 2, respectively), as predicted by Hypotheses 3 and 4,

respectively. Finally, core self-evaluations was significantly related to life

satisfaction in both models (standardized path coefficients = .33,p < .01, and

.29,p < .01, for Models 1 and 2, respectively), as predicted by Hypothesis

5. Thus, the relationships posited in the hypothesized model were all

supported, except that no significant relationship was observed between job

satisfaction and life satisfaction.

I also performed two hierarchical multiple regressions in an attempt to

validate the unexpected finding that job satisfaction was not significantlyrelated to life satisfaction, using alternative statistical analysis. The hier-

archical multiple regression models followed the same time lag logic as the

structural equation models described above. Model 1 included all the control


LifeSatisfaction

(.32)

JobSatisfaction(.40)

.32

.49

JobComplexity

.11*

Age

.14

HealthConditions

.14

Time 1 Time 2

Married.22

Autonomy

.13

NonworkSatisfaction

(.31)


.24

.33

.44

White

.07*

.19

Work Hours .11*

Figure 2 Results of path analysis of overall life attitudes, Model 1

Note: n = 892; standardized path coefficients are indicated by lines with arrows; correlation

between job satisfaction and nonwork satisfaction error terms is indicated by curved line;

*denotes path coefficients significant at p < .05, all other path coefficients significant at p < .01;

numbers in parentheses represent variance explained. Circles/ovals represent latent constructs.

Rectangles represent indicators.


17/26

variables and nonwork satisfaction from Time 1 entered at step 1, core self-

evaluations from Time 1 entered at step 2, and job satisfaction from Time 1entered at step 3. Model 2 included all the control variables and nonwork

satisfaction from Time 2 entered at step 1, core self-evaluations from Time

1 entered at step 2, and job satisfaction from Time 2 entered at step 3. As

shown in Table 2, the results indicated that in both models the incremental

variance predicted by job satisfaction entered in step 3 was not significant.

Thus, these results were consistent with those obtained in the SEM analyses.

Finally, I compared the relative strengths of the correlations of the same

variables over time. If core self-evaluations is dispositional, it should be more

stable than satisfaction measures, which are conceputuized not as disposi-tional, but as influenced by dispositions as well as other factors, including

environmental conditions. Unfortunately, it was not possible to make

detailed comparison of the relative strengths of the correlations of all core


LifeSatisfaction

(.50)

JobSatisfaction

(.40)

.54

.46

Age

.14

HealthConditions

.10

Time 1 Time 2

Married.19

Autonomy

.10*

NonworkSatisfaction

(.21)


.32

.29

.33

Hours

Worked.10*

Salary .12

Care Time

SocialIntegration

.10

.07*

.30

Figure 3 Results of path analysis of overall life attitudes, Model 2

Note: n = 892; standardized path coefficients are indicated by lines with arrows; correlationbetween job satisfaction and nonwork satisfaction error terms is indicated by curved line;

*denotes path coefficients significant at p < .05, all other path coefficients significant at p < .01;

numbers in parentheses represent variance explained. Circles/ovals represent latent constructs.

Rectangles represent indicators.


18/26


Table 3 Results of hierachical multiple regression (n = 892)

Model 1 Model 2

Independent variables Beta R2 F value Beta R2 F value

Step 1: Control variables .20 26.14** .36 61.43**

Job complexity T1 .04

Autonomy T1 .02

Autonomy T2 .02

Self-employed T1 .05

Self-employed T2 .00

Salary T1a .01

Salary T2a .02

Work hours T1 .03

Work hours T2 .03

Household size T1 .02

Household size T2 .04

Family income T1 .01

Family income T2 .07*

Social integration T1 .08**

Social integration T2 .04*

Health conditions T1 .03

Health conditions T2 .03

Care hours T1 .04

Care hours T2 .04

Married T1 .07*

Married T2 .12**

Age .02 .03

Female .02 .01

White .02 .02

Nonwork satisfaction T1 .34**

Nonwork satisfaction T2 .51**

Step 2: Core self-evaluations .04 35.19** .02 29.70**

Self-esteem/mastery .13** .13**

Neuroticism .12** .10**

Step 3: Job satisfaction .00 1.83 .00 1.61

Job satisfaction T1 .05

Job satisfaction T2 .03

Overall F 27.18** 56.04**

Note: Standardized regression weights are for the full model. Both models included life satisfaction from Time2 as the dependent variable. Model 1 included all predictor variables from Time 1. Model 2 included all predic-

tor variables from Time 2 except core self-evaluations, which was from Time 1.aSalary variables have been subjected to natural log transformations.

* p < .05; ** p < .01.


19/26

self-evaluations and satisfaction variables over time, because neuroticism was

not measured during Time 2, and also because no comparable life satisfaction

measure was administered in Time 1. The correlation between the self-

esteem/mastery scale measured at Time 1 and Time 2 was r = .59, which was

greater than the correlation between job satisfaction at Time 1 and Time 2

(r = .49), and similar to the correlation between nonwork satisfaction at Time

1 and Time 2 (r = .58). Thus, while these results with respect to nonwork

satisfaction are unclear, the finding that a core self-evaluations measure was

more stable than a satisfaction measure over time lends some support to the

conceptual underpinnings of the model.

Discussion

This study examined the longitudinal relationships among a broad person-

ality construct (i.e. core self-evaluations), job satisfaction, and life satis-

faction, within a theoretically derived model that also included nonwork

satisfaction, working conditions, nonworking conditions and demographic

measures. I discuss the major findings here.

Relationship between core self-evaluations and job satisfaction

Job satisfaction was found to be significantly related to a personality

variable measured 3 years earlier, after controlling for a number of working,

nonworking and demographic variables measured concurrently with job

satisfaction. This finding lends support to the dispositional theory of job

satisfaction popularized by Staw and his colleagues (Staw et al., 1986; Staw

& Ross, 1985). Although research on the relationships between disposi-

tional traits and job satisfaction has increased significantly over the pastdecade (see Judge & Larsen, 2001 for a review), very few have utilized a

longitudinal design. To my knowledge, only Judge et al. (2000) have

specifically examined the effects of core self-evaluations on job satisfaction

over time. My findings largely replicate those reported by Judge et al.

(2000), but with a more comprehensive list of control variables (Judge et

al. controlled only for the census occupational code based measure of job

complexity), and a nationally representative sample that can be generalized

to the US population. Thus, my results indicate that core self-evaluations

may be partially responsible for the surprising stability of job satisfactionover time observed in previous studies (e.g. Staw et al., 1986; Staw & Ross,

1985).



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Relationship between job satisfaction and life satisfaction

As predicted, nonwork satisfaction was significantly related to life satis-

faction in both models. However, contrary to prediction, job satisfaction wasnot significantly related to life satisfaction in either model, after controlling

for core self-evaluations and nonwork satisfaction. This result is notable

because previous studies have found significant relationships between job

satisfaction and life satisfaction, even after controlling for nonwork satis-

faction (Hart, 1999; Near et al., 1983, 1984). In those studies, the zero-order

correlations between job satisfaction and life satisfaction were very similar

to that found in this sample, suggesting that my finding is not due to any

anomaly in the data, but rather to the inclusion of a broad personality

variable (core self-evaluations) that was not included in those studies. Theresults imply that job satisfaction and life satisfaction may not be directly

related, but that the variance shared between the two variables is the result

of a common predictor variable (core self-evaluations) and the fact that both

are correlated with nonwork satisfaction. Why job satisfaction was not

significantly related to life satisfaction, whereas satisfaction with nonwork

domains showed relatively strong relationships with life satisfaction is

puzzling. It may be that for most people work is not a central life activity as

proposed by Dubin (1956). It may be that most Americans work primarilyto support their nonwork lives (George & Brief, 1990; Seeman, 1967), and

that the effects of job satisfaction on life satisfaction are simply too weak to

detect after taking into account life domains that are of greater importance

(e.g. family, health, self).

It should be noted that in both models the disturbance terms of job

satisfaction and nonwork satisfaction (i.e. portion of variance not accounted

for by core self-evaluations, environmental conditions, and demographics)

were significantly correlated, suggesting that the exogenous variables did not

account for all the variance shared between the two variables. This impliesthat job satisfaction and nonwork satisfaction are either influenced by

common variables not included in the model (e.g. additional environmental

or personality variables), or that a causal relationship exists between the two

variables. The theoretical model utilized in this study suggests the former,

although this is certainly an area for future research.

Implications

Although life satisfaction is certainly a desirable outcome by itself, the

positive organizational behavior view recently explicated by Luthans

(2002) suggests that life satisfaction or subjective well-being (of which life



21/26

satisfaction is a central component; Diener, 1984; Diener et al., 1999) may

be related to other important variables as well. For example, empirical

evidence suggests that life satisfaction may be directly related to in-role

performance (Rode et al., 2003), and also to supervisor ratings of employee

performance even when job satisfaction is not related to such ratings (Wright

& Cropanzano, 2000). Also, subjective well-being is associated with the

onset of heart disease (Booth-Kewley & Friedman, 1987), which has obvious

implications for employee absenteeism and healthcare costs.

The results of this study imply that organizations wishing to increase

employee life satisfaction may wish to consider at least two possibilities.

First, they could implement policies and benefits that allow employees to

attend to, and further develop, the nonwork domains of their lives (e.g.flexible working arrangements, employee assistance programs for personal

issues, and other family-friendly work practices), given that nonwork satis-

faction was strongly related to life satisfaction. Conversely, interventions

designed to increase job satisfaction, although certainly desirable, may have

negligible impacts on life satisfaction.

Second, the results indicated that core self-evaluations had significant

direct and indirect effects on life satisfaction. Although personality traits, by

definition, demonstrate stability across time, an encouraging body of

evidence suggests that some dispositions can change as a result of environ-mental conditions and learning, particularly those related to evaluations

regarding personal control (e.g. Brockner, 1988; Markus & Kunda, 1986),

which is a central component of core self-evaluations. Thus, designing jobs,

organizational structures, and human resource policies to facilitate a sense

of control in both work and nonwork domains may lead to higher core self-

evaluations which the results of the current study suggest may facilitate

higher levels of job satisfaction, nonwork satisfaction, and also life satis-

faction. However, very little is known about the existence or the strength ofthe relationship between workplace variables and core self-evaluations; this

appears to be a potentially rich area for research.

Limitations and conclusions

At least four limitations should be noted. First, the data set contained a

limited number of measurement items for several key constructs, which may

have impacted the reliability, and by extension, the validity of those

constructs. Specific measurement issues existed with respect to the job satis-faction, life satisfaction, and core self-evaluations variables. Both the job

satisfaction and life satisfaction variables were measured using two items,

instead of more robust scales which generally include four or more items to



22/26

measure these constructs. However, a meta-analysis by Wanous et al. (1997)

reported minimum reliability levels to be at least .70 for the included single-

item job satisfaction measures. Also, the correlation between job satisfaction

and life satisfaction at Time 2 was nearly identical to the average unadjusted

correlation reported in the meta analysis by Tait et al. (1989), which provides

some, but certainly not conclusive, support for the validity of the abbrevi-

ated measures. Core self-evaluations was measured using 9 items taken from

three different scales, in contrast to previous studies which have utilized as

many as 45 items from four scales to measure the construct. The limited

number of items required that I model the construct as a first-order latent

variable, instead of a higher order factor, as in previous studies. Although

the items utilized in the current study fit well with the conceptual definitionof the construct, and the factor structure of the latent variables were vali-

dated by confirmatory factor analysis, it is impossible to determine the extent

to which the use of fewer indicators impacted the results. The strength of the

results in both of the tested models with respect to core self-evaluations

provides some confidence in the measure, but they do not provide con-

clusive proof of validity.

A second potential limitation concerns the possible effects of common

method variance, as both the independent and dependent variables were

subjective measures derived from survey instruments. However, the effects ofcommon method variance may have been limited in this study for at least

two reasons. First, the longitudinal data set provided 3-year lags between

many of the subjective measures (precisely which measures varied somewhat

by model) which removed common method variance resulting from mood

and situational cues. Second, in two similar studies involving core self-

evaluations and satisfaction measures, Judge et al. (1998, 2000) found only

minor differences in their results when measures where taken from a combi-

nation of self-reports and other reports versus all self-reports, suggesting thatthe primary variables of interest to this study may not be significantly

affected by common method variance.

Third, the generalizability of the results may be impacted somewhat by

the oversampling of African Americans and individuals over the age of 60.

While weighting schemes designed to compensate for oversampling are

commonly used in large-scale survey research, it is impossible to assess the

effectiveness of the weighting strategy employed in this particular sample on

variables other than readily available demographics.

Finally, some authors have noted high correlations between autonomyand job satisfaction (e.g. Fried, 1991), and have suggested that modeling

autonomy as a predictor of job satisfaction may partial out true variance in

job satisfaction. In the current study, autonomy was modeled as a predictor



23/26

of job satisfaction, but not of life satisfaction. Thus, any true variance

partialled out of the job satisfaction measure would have provided for a more

conservative test of the predictors of job satisfaction (i.e. core self-

evaluations), but would not have affected the relationship between job

satisfaction and life satisfaction.

Obviously, the finding that job satisfaction and life satisfaction may

not be directly related has important implications for both theory and

practice, especially given that research indicates that life satisfaction may be

related to several outcomes of interest to managers (Booth-Kewley &

Friedman, 1987; Wright & Cropanzano, 2000) beyond the desirability of life

satisfaction as an end in and of itself. Overall, the results indicated that

paying greater attention to employees personal characteristics and nonworklives may yield important benefits, for both managers and organizational

behavior researchers alike, that may not be realized by focusing only on

employee job satisfaction.

Acknowledgements

This article is based on my dissertation for which I would like to thank my disser-

tation committee. I would also like to thank Janet Near and three anonymousreviewers for their helpful comments on an earlier draft.

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Joe Rode is an Assistant Professor of Management in the Richard T.

Farmer School of Business at Miami University in Oxford, Ohio. He

received his PhD in organizational behavior and human resources fromIndiana University, Bloomington in 2002. His research interests include

the relationships between work and nonwork domains and the effects of

individual differences on human performance.

[E-mail: [email protected]]