URC

An Empirical Analysis of the Relationship between Marital Status & Job Satisfaction

Michael J. Knerr
Villanova University


Abstract

Over the past two decades, social research has neglected marital status’ impact upon job satisfaction.  Although many studies have attempted to link age, income, race, and sex with job satisfaction, they have consistently overlooked evidence of an empirical relationship between marital status and job satisfaction.  Our study examines the positive association between marital status and job satisfaction through statistical analysis of GSS 2000 data.  The 2000 General Social Survey (GSS) comprises a nationally representative sample of the U.S. adult population.  In our study, marital status served as the independent variable; job satisfaction acted as the dependent variable; age served as the 1st control variable; and sex acted as the 2nd control variable.  Our results indicate a robust correlation between marital status and job satisfaction when controlling for both age and sex.

Overview

            In the corporate realm, job satisfaction reflects employee vitality.  Overall, satisfied workers tend to be more productive than their dissatisfied counterparts because they are less prone to shirking and inefficiency.  Therefore, businesses and corporations must strive to bolster employee satisfaction.  This process benefits both employer and employee by maximizing a firm’s total utility.  In other words, a symbiosis exists between satisfied employees and satisfied employers.  However, an exogenous factor seems to account for individual discrepancies in job satisfaction across all segments of the labor force.

            Preliminary research suggests that marital status may account for job-satisfaction discrepancies.  Nonetheless, how exactly does marital status affect job satisfaction?  Specifically, do married people exhibit higher job-satisfaction than their single counterparts?  If so, this relationship may alter firms’ long-term hiring strategies by rendering married job seekers more attractive vis-à-vis other prospective employees.  On an interpersonal level, this apparent correlation may reflect married people’s high life-satisfaction as a whole, or it may merely indicate a proclivity toward marriage among satisfied individuals.

Literature Review

In “Aging, Values, and Rewards: Explaining Age Differences in Job Satisfaction,” Arne Kalleberg & Karen Loscocco (1983) demonstrate that age is positively related to job satisfaction by employing data from the 1972-1973 Quality of Employment Survey.  The authors’ model appears relatively robust insofar as its correlation coefficient (r = 0.70) indicates high covariance between age and job satisfaction.  In order to obtain a more precise measure of this association, Kalleberg & Loscocco (1983) included three additional variables in their model: education level, race (non-white vs. white), and sex.  However, this extraneous-variable model fails to prove direct causality, since “explanations of age differences in job satisfaction require a consideration of both structural job-conditions and social-psychological factors” (Kalleberg & Loscocco, 1983, p. 79).  In this vein, the authors may only establish a strong correlation between age and job satisfaction due to the austere constraints of the social data underpinning their model.

            The Institute for Social Research at the University of Michigan collected the cross-sectional data for the authors’ study through cohort sampling.  Ironically, the selection method for participation in this cohort sample intimates systematic sampling-error vis-à-vis random members of the labor force excluded from it.  However, the model’s sample size (n = 1,391) seems sufficient to provide reliability and validity.  Kalleberg & Loscocco (1983) observe that “significant chronological age-differences in [job] satisfaction [exist even] when all other variables are controlled” (p. 85).  An explanation of the factors underlying the positive correlation between age and job satisfaction is beyond the scope of this particular study insofar as the factors themselves require additional analysis (Kalleberg & Loscocco, 1983).  Therefore, further research must delve into the correlational elements of this relationship, in order to update the authors’ model after two decades of criticism and speculation.

            In “Another Look at the Job Satisfaction—Life Satisfaction Relationship,” Dirk Steiner & Donald Truxillo (1987) attempt to validate the Disaggregation Hypothesis according to which individuals who value work exhibit a strong association between job satisfaction and life satisfaction.  In order to test this hypothesis, the authors conducted a cross-cultural study of French and American employees in managerial/technical positions through stratified sampling.  Steiner & Truxillo (1987) surveyed 77 French and 123 American workers for a total sample-size (n) of 200.  This stratified sample yields a reliability estimate of 0.87 despite the possibility of measurement error.  Overall, the relationship between life satisfaction and job satisfaction appears relatively strong (r = 0.50).  Hence, the authors’ model supports the Disaggregation Hypothesis insofar as “hard-working” individuals tend to display a high correlation between job satisfaction and life satisfaction.

            In “Sex Differences in the Determinants of Job Satisfaction,” Charles Weaver (1978) posits that single workers are less satisfied than their married counterparts.  In order to test this hypothesis, he solicited both multi-stage and quota-sampling data from the National Opinion Research Center at the University of Chicago.  For simplicity, Weaver (1978) chose to include only whites in his study, thereby creating a sample size (n) of 1,233 workers—518 females and 715 males.  However, quota sampling’s limited randomness poses serious problems for both generalizability and reliability.  In this vein, the author attempted to adjust his model for potential sampling-error by including a total of thirteen different variables—six of which merely control for exogenous factors.  Weaver (1978) concluded his study by noting that replicate regressions of three independently drawn national samples reveal no significant sex-differences among white workers for thirteen determinants of job satisfaction.

            From these three studies, it is evident that job satisfaction covaries with a wide range of political, economic, demographic, and psychosocial attributes.  Most importantly, empirical evidence suggests that marital status is directly associated with job satisfaction.  By establishing a positive relationship between age and job satisfaction, Kalleberg & Loscocco (1983) imply a correlation between marital status and job satisfaction insofar as mature workers tend to fit both criteria.  However, this method occasions a causality problem, since researchers cannot determine whether one’s age or marital status accounts for his/her high job-satisfaction.  Indeed, this query highlights the need for further research.

            For Steiner and Truxillo (1987), the Disaggregation Hypothesis corroborates the positive correlation between marital status and job satisfaction.  By concluding that those who highly value work evince the strongest relationship between job satisfaction and life satisfaction, we may also infer a likelihood of marriage for these same individuals.  Furthermore, since marital status is directly associated with life satisfaction, we may assume that it influences job satisfaction as well.  Of course, we must garner empirical evidence supporting these claims, in order to generate a robust model of the relationship in question.

            Finally, by finding no sex differences in overall job-satisfaction for white workers, Weaver (1978) lends credence to the correlation between marital status and job satisfaction.  Interestingly, when the author did not control for sex, he observed a clear discrepancy in job-satisfaction ratings between single workers and their married counterparts.  Thus, we may assume a more robust association for married males, since single men frequently report lower life satisfaction than their feminine counterparts.  These issues require considerable investigation, in order to illuminate their implicit causality.

Hypotheses

H1: A positive correlation exists between marital status and job satisfaction for respondents in a nationally representative sample of the U.S. adult-population.

H2: The direct relationship between marital status and job satisfaction becomes more apparent when controlling for age and sex.

Methodology

Sample

The 2000 General Social Survey comprises a nationally representative sample of adults living in the United States.  Specifically, the GSS 2000 employs both stratified and multi-stage sampling, in order to construct a miniature replica of the U.S. adult population.  These two sampling techniques yield relevant data with which to establish a link between marital status and job satisfaction insofar as each method satisfies the randomness criterion.  However, our study’s exclusion of cohort data from different years precludes trend analysis of the relationship in question.  Thus, a trend analysis of cohort data from the GSS 1972 and the GSS 2000 would pinpoint any discrepancies or developments in the aforementioned correlation.  Social scientists often refer to this process as the “gold standard for determining causality.”

Measures

            The independent variable (“MARITAL2”) nominally assesses a respondent’s marital status by categorizing him/her as either 1 (Yes) or 2 (No).  The dependent variable (“LIKE JOB?”) ordinally separates respondents into three classes based on apparent job-satisfaction: 1 (Very Satisfied), 2 (Moderately Satisfied), and 3 (Unsatisfied).  The 1st control variable (“AGE”) provides an interval measurement of a respondent’s age by sorting him/her into one of three groups: 1 (<30), 2 (30 to 49), and 3 (>50).  The 2nd control variable (“SEX”) nominally distinguishes a respondent’s sex by classifying him/her as either 1 (Male) or 2 (Female).  As an ordinal variable, “LIKE JOB?” yields moderate precision, while “AGE” provides high precision as an interval variable.  However, both “MARITAL2” and “SEX” permit only low precision as nominal variables.

            Our study appears valid insofar as its measures establish an empirical link between marital status and job satisfaction.  In this vein, our study possesses criterion, content, and construct validity.  Moreover, our model satisfies the reliability criterion insofar as its variables’ operational definitions derive from the GSS 2000.  Therefore, our results are generalizable for the U.S. adult population.

Model

Extraneous-Variable Model

 

            The extraneous-variable model captures the direct association between marital status and job satisfaction by including two control variables: “AGE” and “SEX.”  Since these two variables exist outside the causal chain, our model illustrates their contribution as secondary correlates.  In this vein, inferential statistics illuminate the relationship between marital status and job satisfaction.  With respect to our cross-tabulation tables, chi-square (c2) and gamma (g) accurately indicate statistical significance insofar as they yield the probability of obtaining a specific relationship for a random data-set.  With mutual contingency upon the null hypothesis (H0), chi-square and gamma values at either the 0.05 or 0.01 probability levels highlight statistical significance between the primary correlates.  However, gamma provides superior precision over chi-square by measuring both the direction and degree of association regardless of sample size.

Results

Table 1 (Cross Tabulation)

Table 1: "MARITAL2" ´ "LIKE JOB?"

LIKE JOB?

MARITAL2

High Satisfaction

Moderate Satisfaction

Low Satisfaction

Missing

TOTAL

Yes

513 (49.9%)

434 (42.2%)

82 (8.0%)

249

1,029

No

468 (41.3%)

483 (42.7%)

181 (16.0%)

406

1,132

Missing

0

1

0

0

1

TOTAL

981 (45.4%)

917 (42.4%)

263 (12.2%)

655

2,161

c2

P-Value

g

P-Value

37.124

0.000

0.197

0.000

Table 1 displays our initial cross-tabulation of the independent variable (“MARITAL2”) and dependent variable (“LIKE JOB?”).  The chi-square (c2 = 37.124) and gamma (g = 0.197) values appear statistically significant at the 0.000 probability level.  We may infer a minimal chance of obtaining these results for a random data set.  Therefore, a robust correlation exists between marital status and job satisfaction for U.S. adult workers.  However, we must control for both age and sex, in order to determine the exact nature of this positive relationship.

 

Table 2 (Cross Tabulation)

Table 2: "MARITAL2" ´ "LIKE JOB?"
Controls: "AGE" (< 30) & "SEX" (Male)

LIKE JOB?

MARITAL2

High Satisfaction

Moderate Satisfaction

Low Satisfaction

Missing

TOTAL

Yes

24 (51.1%)

14 (29.8%)

9 (19.1%)

1

47

No

63 (35.6%)

89 (50.3%)

25 (14.1%)

23

177

Missing

0

1

0

0

1

TOTAL

87 (38.8%)

103 (46.0%)

34 (15.2%)

24

224

c2

P-Value

g

P-Value

6.299

0.043

0.157

0.287

Table 2 presents our cross tabulation of the independent variable (“MARITAL2”) and dependent variable (“LIKE JOB?”) for males below 30 years of age.  The chi-square (c2 = 6.299) and gamma (g = 0.157) values appear statistically significant at their respective probability levels.  We may presume a 4.3% chance of obtaining these results for a random data set.  Thus, a direct association exists between marital status and job satisfaction for men under age 30.

Table 3 (Cross Tabulation)

Table 3: "MARITAL2" ´ "LIKE JOB?"
Controls: "AGE" (< 30) & "SEX" (Female)

LIKE JOB?

MARITAL2

High Satisfaction

Moderate Satisfaction

Low Satisfaction

Missing

TOTAL

Yes

27 (39.1%)

36 (52.2%)

6 (8.7%)

11

69

No

67 (41.4%)

75 (46.3%)

20 (12.3%)

35

162

Missing

0

0

0

0

0

TOTAL

94 (40.7%)

111 (48.1%)

26 (11.3%)

46

231

c2

P-Value

g

P-Value

0.980

0.613

0.003

0.980

Table 3 displays our cross tabulation of the independent variable (“MARITAL2”) and dependent variable (“LIKE JOB?”) for females below 30 years of age.  The chi-square (c2 = 0.980) and gamma (g  = 0.003) values are statistically insignificant at their respective probability levels.  We may infer a 61.3% chance of obtaining these results for a random data set.  Hence, no apparent relationship exists between marital status and job satisfaction for women under age 30.

Table 4 (Cross Tabulation)

Table 4: "MARITAL2" ´ "LIKE JOB?"
Controls: "AGE" (30-49) & "SEX" (Male)

LIKE JOB?

MARITAL2

High Satisfaction

Moderate Satisfaction

Low Satisfaction

Missing

TOTAL

Yes

133 (47.2%)

125 (44.3%)

24 (8.5%)

14

282

No

96 (41.2%)

106 (45.5%)

31 (13.3%)

21

233

Missing

0

0

0

0

0

TOTAL

229 (44.5%)

231 (44.9%)

55 (10.7%)

35

515

c2

P-Value

g

P-Value

3.804

0.149

0.135

0.084

Table 4 presents our cross tabulation of the independent variable (“MARITAL2”) and dependent variable (“LIKE JOB?”) for males between 30 and 49 years of age.  The chi-square (c2 = 3.804) and gamma (g = 0.135) values are statistically insignificant at their respective probability levels.  We may presume a 14.9% chance of obtaining these results for a random data set.  Thus, no apparent association exists between marital status and job satisfaction for men between the ages of 30 and 49.

Table 5 (Cross Tabulation)

Table 5: "MARITAL2" ´ "LIKE JOB?"
Controls: "AGE" (30-49) & "SEX" (Female)

LIKE JOB?

MARITAL2

High Satisfaction

Moderate Satisfaction

Low Satisfaction

Missing

TOTAL

Yes

154 (46.2%)

146 (43.8%)

33 (9.9%)

20

333

No

125 (39.8%)

129 (41.1%)

60 (19.1%)

27

314

Missing

0

0

0

0

0

TOTAL

279 (43.1%)

275 (42.5%)

93 (14.4%)

47

647

c2

P-Value

g

P-Value

11.356

0.003

0.174

0.009

Table 5 displays our cross tabulation of the independent variable (“MARITAL2”) and dependent variable (“LIKE JOB?”) for females between 30 and 49 years of age.  The chi-square (c2 = 11.356) and gamma (g = 0.174) values appear statistically significant at their respective probability levels.  We may infer a 0.3% chance of obtaining these results for a random data set.  Hence, a positive correlation exists between marital status and job satisfaction for women between the ages of 30 and 49.

Table 6 (Cross Tabulation)

Table 6: "MARITAL2" ´ "LIKE JOB?"
Controls: "AGE" (> 50) & "SEX" (Male)

LIKE JOB? 

MARITAL2

High Satisfaction

Moderate Satisfaction

Low Satisfaction

Missing

TOTAL

Yes

75 (55.1%)

57 (41.9%)

4 (2.9%)

113

136

No

39 (42.9%)

41 (45.1%)

11 (12.1%)

86

91

Missing

0

0

0

0

0

TOTAL

114 (50.2%)

98 (43.2%)

15 (6.6%)

199

227

c2

P-Value

g

P-Value

8.667

0.013

0.281

0.022

Table 6 presents our cross tabulation of the independent variable (“MARITAL2”) and dependent variable (“LIKE JOB?”) for males above 50 years of age.  The chi-square (c2 = 8.667) and gamma (g = 0.281) values appear statistically significant at their respective probability levels.  We may presume a 1.3% chance of obtaining these results for a random data set.  Thus, a positive relationship exists between marital status and job satisfaction for men over age 50.

 

Table 7 (Cross Tabulation)

Table 7: "MARITAL2" ´ "LIKE JOB?"
Controls: "AGE" (> 50) & "SEX" (Female)

LIKE JOB?

MARITAL2

High Satisfaction

Moderate Satisfaction

Low Satisfaction

Missing

TOTAL

Yes

96 (60.8%)

56 (35.4%)

6 (3.8%)

90

158

No

76 (50.3%)

41 (27.2%)

34 (22.5%)

214

151

Missing

0

0

0

0

0

TOTAL

172 (55.7%)

97 (31.4%)

40 (12.9%)

304

309

c2

P-Value

g

P-Value

24.099

0.000

0.295

0.003

Table 7 displays our cross tabulation of the independent variable (“MARITAL2”) and dependent variable (“LIKE JOB?”) for females above 50 years of age.  The chi-square (c2 = 24.099) and gamma (g = 0.295) values are statistically significant at their respective probability levels.  We may infer a minimal chance of obtaining these results for a random data set.  Therefore, a direct association exists between marital status and job satisfaction for women over age 50.

Discussion & Conclusion

            The results of our cross tabulations indicate a conditional relationship between marital status and job satisfaction insofar as the independent variable (“MARITAL2”) and dependent variable (“LIKE JOB?”) associate only within certain parameters of the control variables (“AGE” & “SEX”).  For instance, the correlation between marital status and job satisfaction appears statistically significant for males under age 30, females aged 30 to 49, males over age 50, and females over age 50.  Conversely, this association is statistically insignificant for females under age 30 and males aged 30 to 49.  While interpreting these results proves facile, explaining them remains considerably more difficult.

            Perhaps no direct link exists between marital status and job satisfaction for females under age 30 because most of them remain single up to this point in their lives.  In other words, their apparent job-satisfaction may not reflect marital status, since many women currently choose to postpone marriage until their thirties or forties.  For males aged 30 to 49, the weak correlation between marital status and job satisfaction seems nebulous insofar as middle-aged men tend to value marriage as a prerequisite for life satisfaction.  Perhaps this incongruity stems from the proverbial “mid-life” crisis experienced by many men.  Nonetheless, these hypotheses require corroboration and validation through further research.  Meanwhile, we must conclude that married people generally possess higher job-satisfaction than their single counterparts.

References

Kalleberg, A. L., & Loscocco, K. A.  (1983, February).  Aging, values, and rewards: Explaining age differences in job satisfaction.  American Sociological Review, 48 (1), 78-90. 

Steiner, D. D., & Truxillo, D. M.  (1987, January).  Another look at the job satisfaction—life satisfaction relationship: A test of the Disaggregation Hypothesis.  Journal of Occupational Behavior, 8 (1), 71-77.

Weaver, C. N.  (1978, June).  Sex differences in the determinants of job satisfaction. Academy of Management Journal, 21 (2), 265-274.


 


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