PENTRU vBIBLIO Job Satisfaction and Life Satisfaction

Job satisfaction and life satisfactionrevisited: A longitudinal test of anintegrated modelJoseph C. Rode

A B S T R AC T Research indicates that job satisfaction is significantly related to life

satisfaction. However, previous studies have not included variables

that may confound the relationship. Furthermore, 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 are

discussed.

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

Results of more than three decades of research have led researchers toconclude that job satisfaction is significantly related to, or ‘spills over’ into,overall attitudes towards life, or life satisfaction (see Rain et al., 1991 andTait et al., 1989 for reviews). We would expect the two to be related becauseof the amount of time spent at work by full-time employees, and also

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Human Relations

DOI: 10.1177/0018726704047143

Volume 57(9): 1205–1230

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because, for most people, work is a central life activity (Dubin, 1956).Empirical studies have consistently reported moderate correlations betweenjob 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 observedzero-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 lifesatisfaction 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 domainsaccounts 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 thetwo variables is ‘top-down’ (Diener, 1984), or that life satisfaction influencesjob satisfaction (Judge & Watanabe, 1993; Schmitt & Bedeian, 1982). Theinfluence of life satisfaction on job satisfaction represents a dispositionaleffect (Staw et al., 1986), whereby the positive affect associated with lifesatisfaction results in the recall of a greater number of positive job eventsand more positive interpretations of job conditions, which leads to higherjob satisfaction (Bower, 1981; Judge & Hulin, 1993). Probably the strongestempirical support for this perspective comes from Judge and Watanabe(1993) who found that life satisfaction had a stronger relationship to jobsatisfaction over a 5-year period than job satisfaction had on life satisfactionover the same period.

The third perspective has not been previously explicated, but issuggested by the results of previous studies. It may be that much of therelationship 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 andsatisfaction with nonwork domains are influenced by many of the sameenvironmental variables (e.g. job income), and as a result, satisfaction withnonwork domains may confound the relationship between job satisfactionand 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.

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The second possibility is suggested by Judge et al. (1997) who proposeda broad personality construct called ‘core self-evaluations’, which they devel-oped specifically to explicate the process by which disposition may influenceboth job satisfaction and life satisfaction. Unlike earlier top-down theories,Judge et al. proposed that life satisfaction is not a dispositional variable perse, but that it is influenced by disposition, as is job satisfaction. Empiricalresearch has found core self-evaluations to be related to both job and lifesatisfaction, suggesting that it may be at least partially responsible for thebivariate relationship between the two constructs (Heller et al., 2002; Judgeet al., 1998). To date, the possible confounding effects of both core self-evaluations and satisfaction with nonwork domains on the relationshipbetween job satisfaction and life satisfaction have not been simultaneouslytaken into account.

Irrespective of the underlying theoretical perspective, the vast majorityof the empirical research on job and life satisfaction has utilized a cross-sectional design. Although the use of longitudinal data in a non-experimentalstudy does not necessarily establish causality, it does provide strongersupport for causal relationships than can be inferred from analysis of cross-sectional data (Menard, 1991). This is particularly relevant to studies of joband life satisfaction, given that the perspectives described earlier are basedon differing causal assumptions. In this study, I tested a comprehensive modelthat enabled me to examine the relationship between job and life satisfactionover time, while taking into account the possible confounding influences ofnonwork satisfaction, personality (i.e. core self-evaluations), and a set ofenvironmental 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 thatsatisfaction 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 domainsatisfactions and life satisfaction as shown in Figure 1. I incorporated thedispositional 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 thebottom-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 dispositionalin 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 indirectrelationships contained in the model, but I do take into account thesemediated 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 one’s job are basedon how one evaluates one’s pay, supervisor support, working conditions, etc.relative to desired levels of these variables. The direction of influence isassumed to be from the specific to the general, in this case from satisfactionwith specific life domains (e.g. work) to overall life satisfaction. From thisperspective, satisfaction with a life domain represents the aggregate evalu-ations of the domain’s salient aspects that are taken into account whenmaking 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 lifesatisfaction (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 domainsatisfactions on life satisfaction appear to be additive, with the most salientlife domains explaining the most variance; there is little evidence of inter-action effects between life domains (Andrews & Withey, 1976; Campbell etal., 1976). For parsimony, I classified satisfaction with all nonwork domainsas ‘nonwork’ satisfaction. The hypotheses also consider the effects of coreself-evaluations, which, as described below, is also proposed to be related tolife 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 satisfactionvariables

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 versusdescriptions), (iii) ‘fundamentality’ to more specific surface traits (Cattell,1965), and (iv) breadth or scope (Allport, 1961). Judge et al. identified fourexisting constructs meeting these criteria: self-esteem, generalized self-efficacy, locus of control, and neuroticism. However, these were not proposedas comprehensive or exclusive measures of the construct.

Judge et al. (1997) proposed that core self-evaluations affected theperception of objective conditions and events and the norms against whichperceived conditions and events are appraised, which in turn influenced satis-faction judgments. For example, persons who consider themselves to befundamentally incompetent may experience little satisfaction with a given setof 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 mayexperience greater satisfaction with the same set of working conditionsbecause they are more confident in their ability to take advantage of thoseconditions. Additional support for this notion comes from the finding thatindividuals 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 greaterincidences of perceived failure and lower satisfaction (Brockner, 1988).

Several studies provide empirical support for the proposed relationshipbetween 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 nonworklife domains as well. The hypotheses reflect the inclusion of a set of environ-mental and demographic control variables, which, as described later, havebeen 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 thatthe 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 theself, it should influence overall life satisfaction evaluations in the samemanner 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 conditionson overall life satisfaction, Campbell et al. (1976) concluded that both therealities of life and the perception of those realities influenced overall lifesatisfaction, but that the effects were almost entirely mediated by satisfactionwith life domains. Subsequent studies found that some environmentalconditions 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 ofthe control variables to job and nonwork satisfaction, as the effects ofenvironmental 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 environmentalconditions and satisfaction judgments), but I included these variables for tworeasons. First, I wanted to provide a more comprehensive test of the bottom-up causal flow assumption inherent in my model. Second, including thesevariables allowed me to assess the relationship between core self-evaluationsand domain satisfactions, after controlling for the effects of a set of environ-mental conditions and demographics, which has not been done in previousstudies involving core self-evaluations. I was limited to the measures includedin the data set, which, admittedly, do not represent all the environmentalconditions that could potentially influence satisfaction judgments. Still, thevariable set is reasonably comprehensive, compared with that utilized insimilar studies examining the relationships between environmentalconditions and satisfaction judgments (Andrews & Withey, 1976; Campbellet 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 Institutefor Social Research at the University of Michigan (House, 1997). Data werecollected in two waves, 3 years apart, from the same cohort. The survey wasdesigned to provide a wide range of data on sociological, psychological, andmedical 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. Residentsresiding in households on military bases, in group quarters, or in institutionswere excluded. The survey design specified a two to one oversampling ofinterviewees 60 years of age or older and of African Americans aged 25–59,and a four to one oversampling of African Americans 60 years of age orolder. The overall response rate for wave 1 was 67 percent. The attrition ratefor the follow-up survey was 20.7 percent. Subsequent analysis revealed nosignificant differences in response rates across demographic variables, exceptthat 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 theinterviewee’s place of residence. All cases were anonymous, identified withonly 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 whohad worked fewer than 1500 hours in the year preceding either adminis-tration. I also deleted 15 cases that showed strong evidence of containingresponse sets (details available from the author) for a final n = 892. Tocompensate for the oversampling of African Americans and people over 60years old, each case was assigned a weight based on the sampling method-ology employed: non-Blacks between 25 and 60 years old received asampling 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 yearsold received a sampling weight of 1. After applying this weighting scheme tothe data set (sample size was set to n = 892 for all statistical analysesdescribed later), the final sample was fairly representative of the overall USworkforce at the time, within three percentage points of the overall work-force in terms of occupational categories, gender, ethnicity and marital status.

Measures

Measures available in this data set were not always as extensive as I wouldhave liked. However, possible measurement limitations not withstanding, theACL database provided the opportunity to examine a large, random nationaldata set, whose results could generalize to the population in much the sameway that presidential polls normally predict voting behavior. These data alsohad two data collection points (referred to hereafter as Time 1 and Time 2),thereby permitting longitudinal analyses, which is very rare in this field andan important focus of this study. Unless otherwise noted, all multiple itemmeasures were modeled as latent variables in the structural equationmodeling (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 2data. The first item was, ‘overall, how satisfied would you say you are withyour life these days?’, rated on a 7-point scale, ranging from 1 (completelysatisfied) to 7 (completely dissatisfied). This item is practically identical tothe single life satisfaction item used by Campbell et al. (1976). The seconditem, ‘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 beenused extensively in studies of subjective well-being, which is conceptually andempirically very similar to life satisfaction (Diener, 1984). I recoded bothitems so that higher scores corresponded to higher life satisfaction. Descrip-tive statistics were calculated based on the average of the standardized valuesof both items, because of the differing response formats.

Job satisfaction

I measured job satisfaction with two items (� = .78 and .79, at Time 1 andTime 2, respectively): ‘how satisfied are you with your job?’ and ‘how muchdo you enjoy your work?’ each recoded so that the rating scale ranged from1 (low) to 5 (high). Descriptive statistics were calculated using the averageof the two items.

Nonwork satisfaction

I created a composite measure of nonwork attitudes consisting of five itemsrelated 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 one’s jobthrough job income, satisfaction with finances was included in the nonworkdomain because it is influenced by contextual factors beyond job income (e.g.spousal income, investment activity, material desires, current and futureperceived financial demands, etc.), which fall within the realm of nonworklife. I do consider the influence of job salary on both job satisfaction andnonwork satisfaction in the SEM models described later.

I modeled nonwork satisfaction as a formative variable in the SEManalyses because the individual items were not intended to measure the sameunderlying construct, but were designed to measure separate aspects ofnonwork life. I was interested in the variance contributed by the items intotal, which is best accomplished by utilizing a single composite (i.e.observed) variable in SEM analyses (Kline, 1998). I did not attempt to modelerror variance in the SEM analyses, nor did I calculate internal reliabilitystatistics because these analyses assume the underlying construct to be unidi-mensional, which was not the case here, given that the items focused ondiffering, and largely unrelated, aspects of nonwork life. This consolidationmethodology is similar to that utilized in other studies employing generalnonwork satisfaction constructs (e.g. Hart, 1999; Near et al., 1984). Irecoded the items so that higher scores indicated higher satisfaction, and Istandardized 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 thefollowing: 3 items from Rosenberg’s (1965) 10-item self-esteem scale, 3 itemsfrom Pearlin and Schooler’s (1978) 7-item mastery scale (described below),and 5 items from Eysenck and Eysenck’s (1968) 12-item neuroticism scale.An example of a self-esteem item was ‘I take a positive attitude towardmyself.’ An example of a mastery item was ‘there is really no way I can solvethe problems I have.’ An example of a neuroticism item was ‘are you aworrier?’, with yes and no response categories. All mastery and self-esteemitems were scored on a 4-point scale with 1 = strongly agree and 4 =strongly disagree. Items were recoded so that higher scores indicated highermastery and self-esteem levels. All neuroticism items were worded such thata yes response indicated higher levels of neuroticism. In their original form,yes responses were assigned a value of 1, and no responses were assigned avalue of 5. Also, in a few cases a neuroticism item had an original value of3, indicating that the interviewee was unable to select yes or no and hadverbally 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), masteryappears to fit well with the core self-evaluations conceptualization in that itis both fundamental and wide in scope (it refers to the self in general) andcontains a strong evaluative component. According to Pearlin et al. (1981:340), mastery represents ‘the extent to which people see themselves as beingin control of the forces that importantly affect their lives.’ This definition isvery similar to Rotter’s (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 tocomplement self-esteem as a self-evaluations construct (Pearlin et al., 1981).

Unfortunately, although the neuroticism scale showed adequateinternal reliability (� = .69), the abbreviated self-esteem (� = .53) and mastery(� = .39) scales each had unacceptably low internal reliability coefficients. Inan attempt to construct core self-evaluations measures with greater constructvalidity and higher levels of reliability, the items from all three scales weresubjected to a common factor analysis with orthogonal rotation. Factorswith 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 wasretained in factor two, because dropping the item would have significantlyreduced the internal reliability of the scale created from the items loading onfactor two.

The first factor contained two items from the abbreviated self-esteemscale and two items from the abbreviated mastery scale. The four items loadingonto this factor were averaged to create a new scale which demonstrated anacceptable internal reliability (� = .68). This new scale was labeled ‘self-esteemand 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 notdisplay acceptable internal reliability (� = .45), so these items were droppedfrom 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 latentvariable, 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 .14Pushed around in life (mastery) .57 .12 .05Feel I am a failure (self-esteem) .52 .09 .10No way to solve problems (mastery) .48 .13 .11Often feel fed up (neuroticism) –.40 –.38 .04Am a nervous person (neuroticism) –.03 –.74 –.14Am tense/high strung (neuroticism) –.12 –.53 –.05Am a worrier (neuroticism) –.21 –.51 –.14Mood goes up and down (neuroticism) –.35 –.38 .03Can do anything (mastery) .08 .05 .64Positive 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 containedin 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 basedon Likert scales (i.e. perceptions of workplace autonomy). Despite its percep-tual underpinnings, autonomy has been shown to be empirically distinctfrom work attitudes (Glick et al., 1986). I measured autonomy with twoitems (� = .64 and � = .69 in Time 1 and Time 2, respectively), which weresimilar 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 sayabout what happens in my work’ and ‘I decide how to do my work’ ratedon a 4-point agreement scale. Items were recoded so that higher scores corre-sponded to higher levels of autonomy. I included four other workingconditions measures: annual salary (corrected for non-normal distributionswith 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) basedon 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 currentlyliving in the household; (ii) household income, measured as the midpointvalues on an 8-point interval scale (which resulted in a normal distributionthat did not require any transformation); (iii) social integration, measured asthe arithmetic mean of the standardized values of three items related tofrequency of attendance at formal social gatherings (five categories), andparticipation in informal social gatherings with friends and relatives (sixcategories); (iv) health conditions, measured as a count of 10 chronic healthconditions such as heart disease and arthritis; and (v) care hours, measuredas the number of hours spent in the past year (indicated by the midpointvalues of two separate 5-point interval scales) caring for non-householdpersons who either had significant long-term health problems or who experi-enced serious injury, illness, or personal crises. Social integration wasmodeled as formative variable in the SEM analyses. I also controlled fordemographic variables, including age, gender (1 = female, 0 = male), maritalstatus (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 modelusing confirmatory factor analysis prior to estimation of the hypothesizedmodels to prevent interpretational problems inherent in the simultaneousestimation of measurement and hypothesized models (Andersen & Gerbing,1988). Second, I developed two structural models (described later) to test thedirect relationships included in the hypothesized model. I estimated allmodels 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 thegoodness-of-fit index (GFI), comparative fit index (CFI), and the root meansquared 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 metaanalysis by Tait et al. (1989).

To confirm the factor structure of the latent variables (job satisfactionin both Time 1 and Time 2, life satisfaction in Time 2, core self-evaluationsin 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 respectivelatent 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. Allindicators had loadings of .50 or higher. Examination of the correlationresiduals and modification indices and did not find evidence of significantcross-loadings of indicators, suggesting discriminant validity. Furthermore,the average variance explained across the indicators of each latent variablewas higher than the variance shared by any two latent variables, therebypassing 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 measuredduring Time 1, except life satisfaction, which was measured at Time 2. Thismodel 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 20 21 22 23 24 25 26 27 28 29 30

1 Life satisfaction T2 0.00 0.90 752 Nonwork satisfaction T1 .00 0.61 423 Nonwork satisfaction T2 .01 0.62 57 584 Job satisfaction T1 4.11 0.80 24 30 30 785 Job satisfaction T2 4.04 0.79 30 31 37 49 796 Self-esteem and mastery T1 3.34 0.59 27 29 22 25 24 687 Neuroticism T1 .32 0.26 –28 –32 –28 –18 –20 –39 698 Job complexity T1 4.88 2.07 03 06 04 10 06 16 –079 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 6911 Self-employed T1 3% 07 03 04 12 09 04 00 15 07 1512 Self-employed T2 3% 02 07 02 05 07 01 00 10 03 12 4013 Salary T1a 9.95 .67 11 15 15 11 05 23 –12 44 25 23 08 0614 Salary T2a 10.18 .64 09 07 09 10 04 20 –12 37 21 19 06 03 8615 Work hours T1 46.43 10.72 08 09 08 14 14 08 00 18 18 18 10 09 33 2416 Work hours T2 46.19 10.08 06 09 07 09 14 12 –06 13 16 19 1 11 16 21 5217 Household size T1 2.86 1.37 02 07 00 –02 06 –04 –02 –10 04 03 04 00 04 03 00 –0318 Household size T2 2.85 1.35 04 04 –03 –07 00 –03 –03 –08 04 02 01 01 01 03 03 02 7319 Family income T1 36725 24011 09 18 14 07 07 21 –13 34 21 24 12 12 62 46 25 14 10 0820 Family income T2 44323 27766 11 16 14 09 07 19 –12 39 22 24 06 05 67 78 21 19 06 10 7321 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 0522 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 02 4023 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 –14 –04 –0724 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 –09 –03 –04 6725 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 01 11 09 13 1026 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 05 14 11 08 13 2727 Married T1 64% 08 24 11 02 05 00 –09 05 06 11 08 05 11 10 09 07 48 42 31 27 –01 –15 –01 –01 –02 0028 Married T2 66% 17 19 22 03 04 01 –10 07 06 12 07 05 14 20 10 10 38 46 27 35 05 –17 –04 –04 –03 –01 8029 Age T1 42.67 12.41 04 14 15 21 16 06 –08 00 11 07 05 01 04 03 03 –07 –21 –33 11 04 –04 –09 36 41 05 02 05 –0130 Female 42% –09 –13 –08 01 00 –08 15 –05 –06 –11 09 04 –28 –25 –23 –24 –06 –14 –18 –18 09 09 14 17 11 13 –26 –31 0231 White 79% 06 06 05 10 –01 08 –03 26 15 18 12 05 16 16 12 08 –10 –09 15 15 03 09 –06 –07 01 –02 08 10 –01 –09

Note. Cronbach’s 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 significant, p < .01.a Salary variables have been subjected to natural log transformations.

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self-evaluations, job satisfaction, and nonwork satisfaction on life satis-faction over time. In the second model, core self-evaluations measures weretaken from Time 1, and all other variables were measured at Time 2. Thismodel was designed to asses the longitudinal relationship of core self-evaluations on job satisfaction, while controlling for environmental conditionsat 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: thepaths from each of the control variables (i.e. environmental conditions anddemographics) to both job satisfaction and nonwork satisfaction, the pathsfrom 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 disturbanceterms (i.e. the variance not accounted for by the respective exogenous vari-ables) of job satisfaction and nonwork satisfaction to be freely estimated, aswell as the correlations among the exogenous variables. I did not initiallyinclude paths between any of the control variables and life satisfaction in theinitial models because theoretically I expected that the effects of these vari-ables on life attitudes would be indirect, mediated by job satisfaction and/ornonwork 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 Model1, and �2(81, n = 892) = 144.82 (p < .01), GFI = .98, CFI = .98, RMSEA = .03for 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 onlife satisfaction had been taken into account. In neither model did the modifi-cation indices indicate the presence of additional potentially significant pathsto life satisfaction. For presentation simplicity, I excluded variables whosepath coefficients were not significant at p < .05 (e.g. gender) from Figures 2and 3. In Model 1, three of the five working conditions measures, one of thefive nonworking conditions measures, and three demographic variables weresignificantly related to one or more dependent variables. In Model 2, threeworking conditions, three nonworking conditions, and two demographicmeasures 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 inHypothesis 2, the standardized path coefficient between nonwork satis-faction and life satisfaction was significant in both Model 1 (.32, p < .01)


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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 nonworksatisfaction (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 lifesatisfaction in both models (standardized path coefficients = .33, p < .01, and.29, p < .01, for Models 1 and 2, respectively), as predicted by Hypothesis5. Thus, the relationships posited in the hypothesized model were allsupported, except that no significant relationship was observed between jobsatisfaction and life satisfaction.

I also performed two hierarchical multiple regressions in an attempt tovalidate 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 thestructural 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)

Core Self-Evaluations

.24

.33

.44

White

.07*

.19

Work Hours.11*

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

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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 nonworksatisfaction from Time 2 entered at step 1, core self-evaluations from Time1 entered at step 2, and job satisfaction from Time 2 entered at step 3. Asshown in Table 2, the results indicated that in both models the incrementalvariance 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 samevariables over time. If core self-evaluations is dispositional, it should be morestable than satisfaction measures, which are conceputuized not as disposi-tional, but as influenced by dispositions as well as other factors, includingenvironmental conditions. Unfortunately, it was not possible to makedetailed 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)

Core Self-Evaluations

.32

.29

.33

HoursWorked

.10*

Salary .12

Care Time

SocialIntegration

.10

–.07*

.30

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

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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 .04Autonomy T1 .02Autonomy T2 .02Self-employed T1 –.05Self-employed T2 .00Salary T1a .01Salary T2a .02Work hours T1 –.03Work hours T2 –.03Household size T1 .02Household size T2 .04Family income T1 .01Family income T2 .07*Social integration T1 .08**Social integration T2 .04*Health conditions T1 .03Health conditions T2 –.03Care hours T1 –.04Care hours T2 –.04Married T1 .07*Married T2 .12**Age –.02 –.03Female –.02 –.01White .02 .02Nonwork 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 1.61Job satisfaction T1 .05Job 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.

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self-evaluations and satisfaction variables over time, because neuroticism wasnot measured during Time 2, and also because no comparable life satisfactionmeasure was administered in Time 1. The correlation between the self-esteem/mastery scale measured at Time 1 and Time 2 was r = .59, which wasgreater than the correlation between job satisfaction at Time 1 and Time 2(r = .49), and similar to the correlation between nonwork satisfaction at Time1 and Time 2 (r = .58). Thus, while these results with respect to nonworksatisfaction are unclear, the finding that a core self-evaluations measure wasmore stable than a satisfaction measure over time lends some support to theconceptual 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 nonworksatisfaction, working conditions, nonworking conditions and demographicmeasures. I discuss the major findings here.

Relationship between core self-evaluations and job satisfaction

Job satisfaction was found to be significantly related to a personalityvariable measured 3 years earlier, after controlling for a number of working,nonworking and demographic variables measured concurrently with jobsatisfaction. This finding lends support to the ‘dispositional’ theory of jobsatisfaction 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 alongitudinal design. To my knowledge, only Judge et al. (2000) havespecifically examined the effects of core self-evaluations on job satisfactionover time. My findings largely replicate those reported by Judge et al.(2000), but with a more comprehensive list of control variables (Judge etal. controlled only for the census occupational code based measure of jobcomplexity), and a nationally representative sample that can be generalizedto the US population. Thus, my results indicate that core self-evaluationsmay 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 controllingfor core self-evaluations and nonwork satisfaction. This result is notablebecause previous studies have found significant relationships between jobsatisfaction and life satisfaction, even after controlling for nonwork satis-faction (Hart, 1999; Near et al., 1983, 1984). In those studies, the zero-ordercorrelations between job satisfaction and life satisfaction were very similarto that found in this sample, suggesting that my finding is not due to anyanomaly in the data, but rather to the inclusion of a broad personalityvariable (core self-evaluations) that was not included in those studies. Theresults imply that job satisfaction and life satisfaction may not be directlyrelated, but that the variance shared between the two variables is the resultof a common predictor variable (core self-evaluations) and the fact that bothare correlated with nonwork satisfaction. Why job satisfaction was notsignificantly related to life satisfaction, whereas satisfaction with nonworkdomains showed relatively strong relationships with life satisfaction ispuzzling. It may be that for most people work is not a central life activity asproposed by Dubin (1956). It may be that most Americans work primarilyto support their nonwork lives (George & Brief, 1990; Seeman, 1967), andthat the effects of job satisfaction on life satisfaction are simply too weak todetect 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 jobsatisfaction and nonwork satisfaction (i.e. portion of variance not accountedfor by core self-evaluations, environmental conditions, and demographics)were significantly correlated, suggesting that the exogenous variables did notaccount for all the variance shared between the two variables. This impliesthat job satisfaction and nonwork satisfaction are either influenced bycommon variables not included in the model (e.g. additional environmentalor personality variables), or that a causal relationship exists between the twovariables. 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


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satisfaction is a central component; Diener, 1984; Diener et al., 1999) maybe related to other important variables as well. For example, empiricalevidence suggests that life satisfaction may be directly related to in-roleperformance (Rode et al., 2003), and also to supervisor ratings of employeeperformance even when job satisfaction is not related to such ratings (Wright& Cropanzano, 2000). Also, subjective well-being is associated with theonset of heart disease (Booth-Kewley & Friedman, 1987), which has obviousimplications for employee absenteeism and healthcare costs.

The results of this study imply that organizations wishing to increaseemployee life satisfaction may wish to consider at least two possibilities.First, they could implement policies and benefits that allow employees toattend to, and further develop, the nonwork domains of their lives (e.g.flexible working arrangements, employee assistance programs for personalissues, and other ‘family-friendly’ work practices), given that nonwork satis-faction was strongly related to life satisfaction. Conversely, interventionsdesigned to increase job satisfaction, although certainly desirable, may havenegligible impacts on life satisfaction.

Second, the results indicated that core self-evaluations had significantdirect and indirect effects on life satisfaction. Although personality traits, bydefinition, demonstrate stability across time, an encouraging body ofevidence suggests that some dispositions can change as a result of environ-mental conditions and learning, particularly those related to evaluationsregarding 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 senseof control in both work and nonwork domains may lead to higher core self-evaluations which the results of the current study suggest may facilitatehigher 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; thisappears to be a potentially rich area for research.

Limitations and conclusions

At least four limitations should be noted. First, the data set contained alimited number of measurement items for several key constructs, which mayhave impacted the reliability, and by extension, the validity of thoseconstructs. Specific measurement issues existed with respect to the job satis-faction, life satisfaction, and core self-evaluations variables. Both the jobsatisfaction and life satisfaction variables were measured using two items,instead of more robust scales which generally include four or more items to


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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 satisfactionand life satisfaction at Time 2 was nearly identical to the average unadjustedcorrelation reported in the meta analysis by Tait et al. (1989), which providessome, but certainly not conclusive, support for the validity of the abbrevi-ated measures. Core self-evaluations was measured using 9 items taken fromthree different scales, in contrast to previous studies which have utilized asmany as 45 items from four scales to measure the construct. The limitednumber of items required that I model the construct as a first-order latentvariable, instead of a higher order factor, as in previous studies. Althoughthe 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 extentto which the use of fewer indicators impacted the results. The strength of theresults in both of the tested models with respect to core self-evaluationsprovides some confidence in the measure, but they do not provide con-clusive proof of validity.

A second potential limitation concerns the possible effects of commonmethod variance, as both the independent and dependent variables weresubjective measures derived from survey instruments. However, the effects ofcommon method variance may have been limited in this study for at leasttwo reasons. First, the longitudinal data set provided 3-year lags betweenmany of the subjective measures (precisely which measures varied somewhatby model) which removed common method variance resulting from moodand situational cues. Second, in two similar studies involving core self-evaluations and satisfaction measures, Judge et al. (1998, 2000) found onlyminor 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 significantlyaffected by common method variance.

Third, the generalizability of the results may be impacted somewhat bythe oversampling of African Americans and individuals over the age of 60.While weighting schemes designed to compensate for oversampling arecommonly used in large-scale survey research, it is impossible to assess theeffectiveness of the weighting strategy employed in this particular sample onvariables other than readily available demographics.

Finally, some authors have noted high correlations between autonomyand job satisfaction (e.g. Fried, 1991), and have suggested that modelingautonomy as a predictor of job satisfaction may partial out true variance injob satisfaction. In the current study, autonomy was modeled as a predictor


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of job satisfaction, but not of life satisfaction. Thus, any true variancepartialled out of the job satisfaction measure would have provided for a moreconservative test of the predictors of job satisfaction (i.e. core self-evaluations), but would not have affected the relationship between jobsatisfaction and life satisfaction.

Obviously, the finding that job satisfaction and life satisfaction maynot be directly related has important implications for both theory andpractice, especially given that research indicates that life satisfaction may berelated to several outcomes of interest to managers (Booth-Kewley &Friedman, 1987; Wright & Cropanzano, 2000) beyond the desirability of lifesatisfaction as an end in and of itself. Overall, the results indicated thatpaying greater attention to employee’s personal characteristics and nonworklives may yield important benefits, for both managers and organizationalbehavior researchers alike, that may not be realized by focusing only onemployee 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. Hereceived his PhD in organizational behavior and human resources fromIndiana University, Bloomington in 2002. His research interests includethe relationships between work and nonwork domains and the effects ofindividual differences on human performance.[E-mail: [email protected]]

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PENTRU vBIBLIO Job Satisfaction and Life Satisfaction