The moderating effects of occupation, age, and urbanization on the relationship between job satisfaction and life satisfaction

Journal of Vocational Behavior 17, 106-123 (1980)

The Moderating Effects of Occupation, Age, and Urbanization on the Relationship between Job Satisfaction and Life Satisfaction PAUL J. BAMUNDOAND RICHARD E. KOPELMAN Baruch College,

The City University

of New York

A considerable body of research has accumulated concerning the strength of the relationship between job and life satisfaction. However, very few studies have examined the possible moderating effects of other variables. The present study, using a sample of 911 heads of households, examined the moderating effects of seven variables related to occupation, age, and urbanization. As hypothesized, education and income positively, and strongly, moderated the job satisfaction-life satisfaction relationship. Self-employment (vs non-self-employment) also had a significant impact; occupation, though, had only a modest effect. Age and job longevity exhibited strong, curvilinear effects. Urbanization did not attenuate the relationship. In view of national work force trends toward increased education, professionalization, income, and age, the relationship between job and life satisfaction will likely become stronger and more relevant over time.

Three views have been advanced concerning the nature of the relationship between job satisfaction and life satisfaction: (a) that there is a positive relationship (the generalized or “spillover” model); (b) that there is a negative relationship (the compensatory model); and (c) that there is no relationship (the segmentation model). To date, the evidence strongly supports the first view. In our review of the literature, we have found 16 studies reporting correlations between job and life satisfaction (or related indices such as work and nonwork satisfaction). The median correlation was r = .37. Although it has been said that the relationship is “much weaker than we might have thought” (Near, Rice, & Hunt, 1978, p. 249) the relationship, nonetheless, is substantially stronger and more reliable than most social science findings. This paper is based on the first author’s doctoral dissertation, done at Baruch College under the supervision of Dr. Mahmoud A. Wahba. An earlier and abbreviated version of this paper was presented at the 1979 meeting of the Eastern Academy of Management; a brief write-up appeared in the Proceedings. Address reprint requests to Richard Kopelman, Management Department, Baruch College, City University of New York, 17 Lexington Avenue, New York, NY 10010.

OOOl-8791/80/040106-18$02.00/O Copyright @ 1980 by Academic Press, Inc. All rights of reproduction in any form reserved.

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Of greater importance, perhaps, than the judged magnitude of the job satisfaction-life satisfaction relationship, is the question of boundary conditions. Under what conditions might the relationship between job and life satisfaction be enhanced or attenuated? Additionally, if there are conditions which make a difference, what are the implications of projected trends in these moderating conditions? If, for example, trends point toward a strengthening of the relationship between job and life satisfaction, then it might be inferred that job satisfaction will become increasingly important to employees. However, it has been found that the more important an outcome, the steeper the slope of the relationship between outcome attainment and outcome satisfaction (Locke, 1976). Hence, managers of organizations might want to be increasingly attentive to job satisfaction as a criterion variable. Occupation-Related Variables A comprehensive literature review by Kaufman (in press) indicates very clearly that higher occupational groups (e.g., professionals, managers) are more highly job involved and view their work as more central to their lives than do lower occupational groups. Hence, it follows that the relationship between job and life satisfaction should be stronger among higher occupation groups in comparison to lower occupation groups (Hypothesis 1). To date, two studies have directly tested this proposition, with somewhat inconclusive results. Dowel1 (1975) found that nonwork satisfaction contributed more than work satisfaction to the life satisfaction of nonmanagers and bottom-level supervisors (the mean nonwork satisfaction-life satisfaction correlation was r = .42; the mean work satisfaction-life satisfaction correlation was r = .19). In contrast, among managers work satisfaction tended to contribute more to life satisfaction than did nonwork satisfaction (r = .35 vs r = .20). Although the JS-LS (job satisfaction-life satisfaction) correlation among managers was greater than the JS-LS correlation among nonmanagers and bottom-level supervisors, a secondary analysis of the data indicates that the difference was not significant (r = .35 vs r = .19, t = 1.29, p < .lO). The second relevant study, by Kavanagh and Halpern (1977), unexpectedly found a negative relationship between occupation level and the strength of the JS-LS relationship. However, this finding was based on averaging results using combinations of specific and general satisfaction measures. Using only general satisfaction measures, no significant differences were found. Because education is positively related to occupation level, it was hypothesized that education would positively moderate the JS-LS relationship (Hypothesis 2). Similarly, income is also positively related to occupation level: accordingly, it was posited that income would positively

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moderate the JS-LS relationship (Hypothesis 3). However, not only do income differences reflect occupation level differences, they also reflect individual performance differences within an occupation. Thus, if Hall (1971) and Hall and Foster (1977) are correct, income may serve as feedback regarding performance which affects psychological success/ failure, which in turn affectsjob involvement. It would appear, therefore, that income may have a dual impact on the JS-LS relationship. No prior research has examined the possible moderating effects of either education or income on the JS-LS relationship. A fourth occupation-related moderator was also examined: the effect of being self-employed versus non-self-employed. Under the assumption that being in business for one’s self would tend to heighten job involvement-after all, both income and assets are involved-it was hypothesized that among self-employed individuals the JS-LS correlation would be stronger than among individuals who were not self-employed (Hypothesis 4). Age-Related

Variables

There is abundant evidence that age is positively related to job involvement. For example, in Hall and Mansfield’s (1975) study of engineers and scientists, the single variable most related to age was job involvement, r = .40 (p < .OOl).Indeed, the median age-job involvement correlation in nine studies was r = .24 (Rabinowitz & Hall, 1977). Furthermore, looking at the “other side of the coin,” Schesta (1975) examined the relationship between age and the desire for leisure. He found that older individuals commit themselvesmore to the job and seek less identity through leisure, regardless of job satisfaction level. Thus, in view of the accumulated evidence, it was posited that age would positively moderate the JS-LS relationship (Hypothesis 5). No prior research, however, has directly examined this proposition. Job longevity is highly related to age. In a sample of 3085 public sector employees, Katz (1978)found an age-job longevity correlation of r = .47. Hence, based on the demonstrated relationship between age and job involvement, it was posited that job longevity would positively moderate the JS-LS relationship (Hypothesis 6). Urbanization

A number of researchers have used the variable “residence” (e.g., urban vs rural) as a moderator in examining reactions to job characteristics. Used as a proxy for the psychological construct “alienation from middle class norms,” the effects of residence have been mixed. In some studies positive job scope-job satisfaction relationships were found for

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“rurals” and negative relationships were found for “urbans”; in other studies stronger positive relationships were found for “rurals” than for “urbans” (cf. Stone, 1976). In either case it would seem to follow that residential city size (urbanization) should negatively moderate the JS-LS relationship (Hypothesis 7). METHODOLOGY Sample Population

and Procedure

The sample was drawn from a nationwide consumer panel of approximately 2200 households, used by a major advertising agency for marketing research. The total panel was almost perfectly representative of the national population on various demographic variables (Bamundo, 1977). A 209-item questionnaire was mailed to the homes of all potential respondents, along with a stamped and addressed return envelope. A cover letter, signed by a representative of the agency with whom the panel was familiar, requested participation and gave assurances that responses would, as usual, be confidential. Three weeks later a follow-up postcard was sent to nonrespondents to the first mailing. A total of 911 usable responses was obtained. All respondents were heads of households, defined as the person with the largest personal income in the household. In some respects the obtained sample was representative of the national population: the median age was 35.8 versus the national work force median of 36.0; 66% of the respondents were males compared to 60% of all workers (not necessarily heads of households) nationwide. In other respects, though, the sample reflected a systematic self-selection bias: the median family income of respondents was $15,000 whereas the median family income of nonrespondents was $9208; similarly, the median individual income of respondents was $12,475 versus $6743 for employed individuals nationwide. There was also overrepresentation among respondents living in large metropolitan areas (30% of respondents versus 20% of nonrespondents), and also overrepresentation among those with college degrees (26% of the sample versus 18% nationwide of all workers). Nevertheless, selection bias was not a material problem in the present research because the concern was for differences in job satisfaction-life satisfaction correlations across moderator conditions; the purpose was not to determine the true national average level of correlation. It was, of course, necessary that there be adequate diversity among respondents with respect to the several moderator conditions; however, examination of Tables 2 through 8 indicates that the sample did meet this requirement. Measures

A global measure of general life satisfaction was employed: “In general, how satisfying do you find the way you’re spending your life these

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days? Would you call it completely satisfying, pretty satisfying, or not very satisfying ?” This measure has been used repeatedly by the Survey Research Center. Converse and Robinson (1972) reported a test-retest reliability of .59 (Kendall’s T) after an interval of 4 to 6 months. Similarly, Wilson (1960), cited in Robinson and Shaver (1973), found test-retest reliabilities of .70 and .67. A global facet-free job satisfaction measure was employed that was taken from the Survey Research Center’s “Quality of Employment Survey” (Quinn & Shepard, 1974). Specifically, the following question was asked: “All in all, how satisfied would you say that you are withyourjob? Would you say you are very satisfied, somewhat satisfied, not too satisfied, or not at all satisfied?” Facet-specific satisfaction was measured using the JDI (Smith, Kendall, & Hulin, 1969). Indicative of the convergent validity of the global satisfaction measure, it correlated significantly with all five JDI facet satisfaction scores, the largest correlation being with work satisfaction, r = .43. Additionally, the global satisfaction measure was negatively related in the present study to a measure of work alienation (r = - .44), and negatively related to Patchen’s Stress scales B and C (r = - .35 and r = - .33, respectively). All seven moderator variables utilized cut points that were based on absolute distinctions (e.g., degree level), rather than on relative scores (e.g., highest 20% on variable X). Hence, the problems of relativism and nonreplicability (cf. Korman & Tanofsky, 1975) did not arise in the present research. Data Analysis

Two methods were employed to determine whether the hypothesized moderator effects emerged: moderated multiple regression and subgroup analysis. Moderated multiple regression examines the increase in explained variance attributable to the introduction of an interaction term (independent variable x moderator). Subgroup analysis partitions the sample into k subgroups based on scores on a moderator variable, and correlations are compared across subgroups. Moderated multiple regression has three principal advantages in comparison to subgroup analysis: (1) it offers greater statistical power, since all of the data are utilized; (2) it eliminates the possibility of spurious results arising from the arbitrary selection of cut points; and (3) it allows for the examination of nonlinear moderator effects, whereas subgroup analysis tends to minimize nonlinear effects. In the present research, though, subgroup sample sizes were generally large, and cut points for most of the moderators were nonarbitrary. Hence, the principal advantage of moderated multiple regression was the third one-the capability of detecting nonlinear moderator effects. It has recently been suggested (Peters & Champoux, 1979) that the

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nature of the research question should determine the type of moderation technique employed. Where the focal issue is one of differential responding, moderated regression is suggested; where the focal issue is one of differential prediction or differential validity, subgroup correlations are more appropriate. With respect to the correlation between job satisfaction and life satisfaction, it is clear that differential prediction is involved. Therefore, the principal moderation technique employed in the present research was subgroup analysis. Moderated multiple regressions were performed for each moderator variable, primarily, though, to identify nonlinear moderator effects. Statistical Independence of Moderators Intercorrelations among the study variables are reported in Table 1. The seven potential moderators were generally independent of the predictor and criterion variables (job satisfaction and life satisfaction), a necessary condition for the investigation of moderator variables (Zedeck, 1971). Correlations between the seven moderators and job satisfaction ranged from .Ol to .14, the median being .07. Similarly, correlations between the moderators and life satisfaction ranged from - .03 to . 10, the median being .04. The maximum proportion of shared variance between a moderator and either job satisfaction or life satisfaction was .0192. RESULTS AND DISCUSSION Hypothesis 1: Occupation Level Correlations between job and life satisfaction as moderated by occupation level are presented in Table 2a. Occupation level categories were created by collapsing the original 1I-catego’ry scheme borrowed from the Survey Research Center (Quinn & Shepard, 1974) into three levels based on income and education (cf. Form & Huber, 1976). Testing for whether correlation coefficients from k different distributions are equal (Marascuilo, 1971), the results were not supportive of Hypothesis 1. Although there were not significant differences across all subgroups, it was predicted a priori that in the three “low” occupational groups (clerical, operative, and service workers) the JS-LS correlation would be lower than in the two “high” occupational groups (professional/technical, and managerial/administrative). In all six comparison cases the magnitudes of the correlations were as predicted, and in two cases differences were significant at p < .05 (clerical vs professional/technical, and clerical vs managerial/administrative). Moderated multiple regression analysis (Table 2b) indicated that there was a significant occupation level x job satisfaction interaction (r, < .Ol), but the magnitude of the effect was not sizable. The proportion of explained variance increased from .1250 to .1355, an increase of 8.4%, after

Occupation level Education Income Self-employment0 Ave Job longevity Residential city size Job satisfaction Life satisfaction

.36*** .32*** - .07* - .08** - .06* - .14*** .02 -.Ol

1 .29*** -.08** -.23*** -.14*** -.09** .Ol .04

2

- .03 -.08* .18*** - .22*** .08* .05

3

.08** .t0*** .13*** .07* .Ol

4

.40*** .oo .14*** .05

5

.06* .14*** .10**

6

.Ol -.03

7

Note: N varies from 771 to 907 due to missing vahtes; the number of cases with scores on these nine variables ranged from 823 to 907. a 1 = Not self-employed; 2 = self-employed. * p < .05. ** p < .ot. *** p < ml.

1. 2. 3. 4. 5. 6. 7. 8. 9.

Variables

TABLE 1 Intercorrelations among Variables

.35***

8

E z

zz 8

i? u

3

5

g

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TABLE 2 The Moderating Effect of Occupation Level on the JS-LS Relationship 2a: Subgroup analysis Occupation High level Professional/technical Managerial/administrative Middle level Sales Low level Service Operative Clerical

n

JS-LS correlation

I56 248

-39 .40

49

.45

152 54 129

.37 .28 .20

2b: Moderated regression analysis Cumulative Cumulative Variable entered Job satisfaction (JS) Occupation level Occupation level x JS

R

R'

.3532 .3535 .3680

.I247 .I250 .1355

Amount of Percentage increment increment in RI in Rz .ooo3 .OlOS

0.24 8.40

F

(3,881) 137.90* .73 10.70’

Note. In 2a, N = 788; I5 cases were lost due to missing values; I08 cases were excluded due to small numbers of cases in 5 of the original I1 occupation categories.

* p < .Ol. ** p < .ool.

the introduction of the interaction term. As Champoux (Note 1) noted, the statistical significance of an interaction indicates the reliability of the finding, not its magnitude. In short, both the subgroup analysis and the moderated regression analysis yielded modest support for occupation level as a moderator of the JS-LS relationship. Thus, the present results are consistent with the findings of prior research. Hypothesis 2: Education The moderating effect of education on the JS-LS relationship is shown by subgroup analysis in Table 3a. Correlations were substantially different across education levels (x* = 15.10, p c .Ol), and the a priori contrast between grammar school respondents and those with a graduate school degree was striking (r = .07 vs r = S8, t = 3.57, p < .OOl). Consistent with these results, the education x job satisfaction interaction (Table 3b) increased the proportion of explained variance from .1258 to .1679, an increase of 33.5%. Clearly, education had a far greater effect on the JS-LS relationship than did occupation. It might be speculated that

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BAMUNDO AND KOPELMAN TABLE 3 The Moderating Effect of Education on the JS-LS Relationship 3a: Subgroup analysis n

JS-LS correlation

65 369 148 93

.07 .32 .44 .58

Education Grammar school only High school only College degree only Graduate degree

3b: Moderated regression analysis Cumulative Cumulative Variable entered Job satisfaction (JS) Education Education x JS

R

R2

.3532 .3647 .4097

.1247 .1258 .1679

Amount of Percentage increment increment in Re in R2 .ooll .0421

0.88 33.47

(3,:88) 177.69* 2.27 44.89*

Note. In 3a, N = 675; 19 cases were lost due to missing values; 217 cases were excluded because individuals fell in between categories (e.g., some college). * p ==c.ool.

it is the direct investment a person makes in self-development that affects the JS-LS relationship, rather than the more general level of occupational attainment. Hypothesis

3: Individual

Income

Correlations between job and life satisfaction across different income level groups are shown in Table 4a. As hypothesized, correlations were significantly different across subgroups, indicating a positive moderating effect (x2 = 13.49,~ < .05). The impact of individual income on the JS-LS relationship was also examined after partialing out the effects of age. The resulting first-order correlations were virtually identical to the zero-order ones: - .Ol , .27, .33, .46, .40, .32, and S7, respectively. Results were also examined using total family income, rather than individual income, as the moderator variable. (Because all respondents were heads of households, the two variables were highly correlated, r = .83.) On psychological grounds it would be expected that family income would less strongly moderate the JS-LS relationship than would individual income. This in fact was the case: x2 = 12.59, p < .05. The results of moderated multiple regression analysis (Table 4b) were entirely consistent with the subgroup analysis findings. Introduction of the income x job satisfaction interaction term yielded a substantial

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TABLE 4 The Moderating Effect of Individual Income on the JS-LS Relationship 4a: Subgroup analysis Individual income

n

JS-LS correlation

Under $4,000 $4,000 to $7,999 $8,000 to $11,999 $12,000 to $14,999 $15,000 to $19,999 $20,000 to $24,999 $25,008 and over

71 147 171 129 149 78 74

.09 .29 .34 .46 .41 .31 .55

4b: Moderated regression analysis Cumulative Cumulative Variable entered Job satisfaction (JS) Individual income Individual income x JS

R

R=

.3532 .3536 .3938

.1247 .1251 .1551

Amount of Percentage increment increment iti R2 in R2 .0004 .0300

0.32 23.98

F (3,805) 145.70* .31 28.59*

Note. In 4a, N = 819; 92 cases were lost due to missing values. * p < .OOl.

23.98% increase in the proportion of explained variance-from .1555. Hypothesis

.1251 to

4: Self-Employment

It was hypothesized that the JS-LS correlation would be stronger among self-employed individuals in comparison to those who were not self-employed. Results of the subgroup analysis, presented in Table 5a, were supportive of this hypothesis (t = 1.70, p < .05). Similarly, the moderated regression analysis (Table 5b) yielded results of comparable strength: the proportion of explained variance increased from .1249 to .1477, a gain of 18.25%. Hypothesis

5: Age

Table 6a presents subgroup analysis data pertaining to the moderating effect of age on the JS-LS relationship. Correlations were not significantly different across all subgroups; hence Hypothesis 5 was not supported. Two observations, though, might be made about the data in Table 6a. First, although the 60-69 vs 20-29 contrast was not significant, the 60-69 vs 30-39 contrast was significant (even though the JS-LS correlation was identical in the two youngest groups= 1.92, p < .05. A

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BAMUNDO AND KOPELMAN TABLE 5 The Moderating Effect of Self-Employment on the JS-LS Relationship Sa: Subgroup analysis

Employment category Self-employed Not self-employed

n

JS-LS correlation

159 723

.46 .34

5b: Moderated regression analysis Cumulative Cumulative Variable entered

R

R'

Job satisfaction Self-employment Self-employment

3532 .3534 .3843

.1247 .1249 .1477

x

JS

Amount of Percentage increment increment in R* in R2 .0002 .0228

0.16 18.25

F (3,880) 152.20* .13 23.54*

Note. In 5a, N = 828; 29 cases were lost due to missing values. *p < ml.

second observation is that the JS-LS relationship appeared to be nonlinear when moderated by age. Indicative of a curvilinear moderation effect, the age x job satisfaction interaction term-increasedthe proportion of explained variance by 50.48%from .1248to .1878(Table 6b). Moreover, when the variable age* was added to the regression analysis, thereby allowing for a parabolic relationship 0, = a + bx + cx *), the proportion of explained variance increased by 81.57%, from .1248 to .2266. A highly speculative explanation of the high JS-LS correlation among individuals in their forties, is that it relates to the “Mid-life Transition” (cf. Levinson, 1978). According to Levinson, the ages 40 to 45 are typically a turbulent developmental period during which time individuals reassesstheir life choices and often make life changes. (It should be noted that Levinson’s model only applies to males.) Perhaps, therefore, the job attitudes (of males) are particularly salient to life satisfaction during the decade of the forties. Hypothesis 6: Job Longevity

Hypothesis 6 predicted a positive moderating effect of job longevity on the JS-LS relationship. Subgroup analysis results, presented in Table 7a, were not supportive: 2 = 7.21, p = .14. Rather than exhibiting a linear moderating effect, the JS-LS correlation increased with job longevity up until the 6- to IO-year period, and then declined. Consistent with this pattern were the results of moderated regression analysis (Table 7b). Introduction of the job longevity x job satisfaction term increased the proportion of explained variance from .I270 to .1626, an increase of

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TABLE 6 The Moderating Effect of Age on the JS-LS Relationship 6a: Subgroup analysis Age category 20-29 30-39 40-49 so-59 60-69

n

JS-LS correlation

68 190 210 241 130

.38 .28 .45 .30 .43

6b: Moderated regression analysis Cumulative Cumulative Variable entered Job satisfaction (JS) Age Age x JS

R

RP

.3532 .3532 .4334

.1247 .1248 .1878

Amount of Percentage increment increment in Rz in RP .OOOl .0630

0.08 50.48

F (3,826) 187.92* .oo 64.13*

Note. In 6a, N = 839; 72 cases were lost due to missing values. * p < .OOl.

TABLE 7 The Moderating Effect of Job Longevity on the JS-LS Relationship 7a: Subgroup analysis Job longevity Less than 1 year 1 to 5 years 6 to 10 years 11 to 15 years 16 or more years

n

JS-LS correlation

81 227 164 138 278

.22 .30 .49 .36 .36

7b: Moderated regression analysis Cumulative Cumulative Variable entered Job satisfaction Job longevity Job longevity x JS

R

R*

.3532 .3563 4033

.I247 .1270 .1626

Amount of Percentage increment increment inRP in Rx .0023 .0356

Note. In 7a, N = 888; 23 cases were lost due to missing values. *p<.OO1.

1.84 28.03

F (3,876) 160.55* 1.93 37.31

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BAMUNDO AND KOPELMAN

28.03%. Furthermore, the inclusion of a squared interaction term (iob longevity 2 x job satisfaction) provided an even better fit: the proportion of explained variance increased to .1980, an increase of 55.91%. A post hoc explanation of this pattern can be derived from Katz’ (1978) study of job longevity and reactions to job characteristics. Katz identified three separate stages of reactions: (a) the learning stage (up to 3 months), during which period reactions to most job characteristics are weak; (b) the responsive stage (4 months to 10 years), during which time reactions to most job characteristics are strong; and (c) the unresponsive stage (lo+ years), during which period reactions to all job characteristics are weak. Katz presented data showing that this pattern is independent of age. (Similarly, in the present research the first-order JS-LS correlations, partialing out age, were virtually identical to the zero-order correlations when analyzed across job longevity categories.) Thus, extending Katz’ and the present findings somewhat, it might be reasoned that people-for a limited period of time-become increasingly job involved as they gain experience and anticipate personal growth and promotion to a higher level job. However, with the passage of time on a given job, many individuals may recognize the limits to their upward mobility, and become more involved in nonwork activities such as family or civic affairs. Indeed, this explanation is entirely consistent with the longitudinal results of Bray, Campbell, and Grant (1974) and Kopelman (1977). Hypothesis

7: Urbanization

The seventh hypothesis was that residential city size (urbanization) would be inversely related to the strength of the JS-LS correlation. Subgroup analysis results, presented in Table 8a, indicate that Hypothesis 7 was not confirmed. The JS-LS relationship was virtually unaffected by city size, the one possible exception being individuals who reside in nonmetropolitan areas (< 50,000 population). For people in such areas (the “rurals”), the JS-LS relationship was rather low. A post hoc comparison (Marascuilo, 1971) was performed comparing the “rural” subgroup with all other subgroups combined, and the comparison only approached being significant (p < . 10). Results of the moderated multiple regression analysis (Table 8b) were similarly unsupportive of Hypothesis 7. Thus, the present results suggest that there is not a stronger JS-LS relationship among “rurals” in comparison to “urbans.” Indeed, if there is a relationship, it may be that the JS-LS relationship is weaker among people living in rural areas. CONCLUSION

Summarizing results, there was clear evidence that education and income positively moderated the JS-LS relationship; however, there was only modest support for occupation level as a moderator. Self-

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TABLE 8 The Moderating Effect of Urbanization on the JS-LS Relationship 8a: Subgroup analysis Residential city size 2,000,000 or more 1,ooo,ooo to 2,000,000 500,ooo to 1,000,000 250,000 to 500,ooo 50,ooo to 250,000 Under 50,000

n

JS-LS correlation

267 126 83 92 80 252

.3-l .45 .47 .40 .47 .23

8b: Moderated regression analysis

Variable entered Job satisfaction (JS) Residential city size Residential city size x JS

Cumulative Cumulative R R2 .3532 .3546 .3556

.I247 .1257 .1264

Amount of Percentage increment increment in RP in Rz .OOlO .0007

0.08 0.06

(3,&!6) 127.36* .58 .73

Note. In 8a, N = 900; 11 cases were lost due to missing values. *p<.oo1.

employment (vs non-self-employment) exhibited a significant moderator effect. Age and job longevity demonstrated strong curvilinear moderation effects on the JS-LS relationship: the JS-LS relationship was strongest among individuals in their forties and in their sixties, and among individuals with job tenure of 6-10 years. Contrary to expectation, individuals living in rural areas did not exhibit a stronger JS-LS relationship than was the case for individuals living in cities. Three competing hypotheses were examined as alternative explanations for the present results. First, the subgroup analysis results might simply reflect differences in the variability of job satisfaction and life satisfaction scores across subgroups. Fortunately, a method exists for the detection of this possible artifact, namely: the comparison of unstandardized regression weights (b-weights) across subgroups, rather than the comparison of correlation coefficients (James, Coray, Homick, & Demaree, Note 2). James et al. (Note 1) note that b-weights; unlike correlations, do not necessarily differ across populations simply because of differences in variances. Accordingly, a supplementary analysis was performed by which the variances in job and life satisfaction scores were removed from the subgroup correlations, leaving b-weights. The pattern of results using b-weights was virtually identical to the pattern of results obtained using correlation coefficients. Correlations between b-weights and correlation coefficients (within moderators) ranged from r = 37 to r =

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1.00, the median being Y = .97. Hence, it is safe to conclude that the subgroup results were not an artifact of differences in the variability of job and life satisfaction scores across subgroups. A second possible explanation for the present results is that they represent an artifactual by-product of socioeconomic differences. It might be argued that people with higher incomes and higher occupation levels are more conscientious in completing a questionnaire, and hence their responses are more reliable. In order to explore the validity of this alternative hypothesis, the following analysis was performed. Individuals in the lowest two income categories (under $8000 per year) were compared to individuals in the highest two income categories (over $20,000 per year) in terms of internal consistency reliabilities on two multi-item scales: the IO-item Rosenberg self-esteem scale, and the g-item Survey Research Center job tension scale. (Unfortunately, it was not possible to examine the internal consistency reliability of variables used in the present study because none employed a multi-item scale.) On the self-esteem scale Cronbach a!values were .83 and .82, respectively; on the job tension scale Cronbach (Yvalues were .87 and .87. Similarly, internal consistency reliabilities were compared for individuals with low and high occupation levels. On the self-esteem scale Cronbach (Yvalues were .83 and $3; on the job tension scale Cronbach (Y values were .86 and .86. Third, and in more general terms, there is the potential problem of artificially elevated results arising from common method variance. That is, for each sampling unit all data were provided by one source (the respondent), at one point in time, in response to a single, general methodology (a questionnaire.) Of course, this problem is a common one in attitudinal research: how else can we measure job satisfaction and life satisfaction without asking the individual? Yet, interestingly, in the present research the effects of common method variance (if any) wouId have been to produce findings contrary to those hypothesized. Common method variance would spuriously elevate all correlations (e.g., due to response set bias); however, it was hypothesized that there would be significant differences in correlations. The present findings seem particularly relevant when viewed in light of demographic trends and projections. Kaufman (1974) noted that the knowledge sector of the U.S. economy-those jobs which produce and distribute information and ideas-grew from 25% of GNP in 1955 to 33% in 1965, and projections are for this sector to reach 50% by 1980. Thus, in one generation the U.S. economy will have been transformed from a goods-producing economy to a knowledge-producing economy. Coincident with this transformation are the concomitant increases in the proportion of working people holding bachelors degrees, the growing proportion of managerial and professional jobs, the increased urbanization of the work force, and, beginning in 1980, the increased age of the work force

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(U.S. Department of Commerce, 1977). These demographic trends, when coupled with the results of the present research, suggest that the relationship between job satisfaction and life satisfaction will likely become stronger over time. It is, therefore, reasonable to infer that job satisfaction will become increasingly important to a growing proportion of the work force-e.g., fewer and fewer people will experience their jobs as a trivial part of their lives. A related but different trend has received even more attention: the revolution of rising expectations. In this regard Ginzberg (1975) asserted that the more that young people invest in preparing for work and careers, the higher their expectations and the greater their potential source of dissatisfaction if their goals are not fulfilled. More recently, Yankelovich (1978, p. 50) has written about the “New Breed” of worker whose hallmark is a “preoccupation with self.” Along these lines, Renwick and Lawler (1978) have noted that workers today are increasingly selforiented. There is a “turning inward that is taking place in the nation as a whole. Americans today seem to have less interest in social reform than they do in securing a satisfying job for themselves. . . . People seem to believe again in the value of hard work and in developing themselves at the workplace. On the other hand, they are not likely to be easy to satisfy or retain as employees. They are likely to demand a great deal, and, if they don’t receive it, will look elsewhere” (Renwick & Lawler, 1978, p. 65). The combined effects of (a) work becoming more important, and (b) the rising expectations (demands) from the world of work, would appear to be potentially profound. Locke (1976) has noted that every emotional response (e.g., job satisfaction) reflects a dual judgment: the discrepancy between what is wanted and what is obtained, and the importance of what is wanted. More specifically, among people for whom job satisfaction is highly important, there will be a steep slope in the functional relationship between the perceived value-outcome discrepancy (what is wanted compared to what is obtained) and experienced job satisfaction. Further, to the extent that growing numbers of people (including women and minorities) want a lot from their jobs, it will be all the more difficult to satisfy what is wanted. Thus the overall future effect of these two trends may be an unpleasant multiplicative interaction. All told, with an increasingly educated, professional, higher paid, urbanized, and older employee population, the relationship between job and life satisfaction will likely strengthen over time; hence, job satisfaction will likely become more important to more people. Given the growing number of workers with high expectations, job satisfaction will probably become increasingly relevant to employing organizations. Perhaps this accounts, in part, for the growing organizational interest in the quality of work life and in career development programs.

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REFERENCES Bamundo, P. J. The relationship

between job satisfaction and life satisfaction: An empirical test of three models on a national sample. Unpublished Ph.D. dissertation, Baruch

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Renwick, P. A., & Lawler, E. E. What you really want from your job, Psychology Today, 1978, 11, 53-65, 118. Robinson, J. P., & Shaver, P. R. Measures of social psychological attitudes. Ann Arbor, Mich.: University of Michigan, 1973, rev. ed. Schesta, J. F. Leisure: Compensation for job dissatisfaction. Unpublished Ph.D. dissertation, University of Missouri at Columbia, 1975.

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Smith, P. C., Kendall, L. M., 8z Hulin, C. L. The measurement ofsatisfaction in work and retirement. Chicago: Rand McNally, 1%9. Stone, E. F. The moderating effect of work-related values on the job scope-job satisfaction relationship. Organizational Behavior and Human Performance, 1976, 15, 147-167. U.S. Department of Commerce, Statisticnl abstract ofthe United States. Washington, D.C.: U.S. Gov. Printing Office, 1977. Wilson, N. An attempt to determine some correlates and dimensions of hedonic tone. Unpublished Ph.D. dissertation, Northwestern University, 1960. Yankelovich, D. The new psychological contracts at work. Psychology Today, 1978, 11, 4650. Zedeck, S. Problems with the use of “moderator” variables. Journal ofApplied Psychology, 1971, 76, 295-310.

REFERENCE

NOTES

1. Champoux, J. E. The moderating effect of work context satisfactions on the job scopeafictive response relationship. Paper presented at ‘the 39th national meeting of the Academy of Management, 1979. 2. James, L. R., Coray, K. E., Homick, C. W., & Demaree, R. G. Moderation analysis based on subgroupings: Some potential pitfalls. Paper presented at the 86th national meeting of the American Psychological Association, 1978. Received: September 9, 1979