job satisfaction and life satisfaction: a profile analysis

Job Satisfaction and Life Satisfaction: A Profile AnalysisAuthor(s): Janet P. Near, Robert W. Rice and Raymond G. HuntSource: Social Indicators Research, Vol. 19, No. 4 (Nov., 1987), pp. 383-401Published by: SpringerStable URL: http://www.jstor.org/stable/27520711 .

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JANET P. NEAR, ROBERT W. RICE AND RAYMOND G. HUNT

JOB SATISFACTION AND LIFE SATISFACTION:

A PROFILE ANALYSIS

(Received 15 June, 1986)

ABSTRACT. The form of the relationship between job satisfaction and life satisfaction was examined through a profile analysis of subgroups of respondents. Survey respon dents were classified as high on job satisfaction and life satisfaction, low on job satisfac tion and life satisfaction or in two intermediate categories (low on one, high on the

other). Results of discriminant analysis indicated that the four subgroups could be

systematically characterized by different profiles. Prediction of the subgroup in which an individual would be classified, based on the profile, was not highly reliable, however.

Recent reviews of the literature regarding the relationships between

work and nonwork domains of life have concluded that feelings and

activities associated with work life influence those in nonwork life;

likewise, feelings and activities off the job may affect life at work

(Champoux, 1981; Kabanoff, 1980; Near et al., 1980; Staines, 1980). Yet conflicting evidence is suggested by the finding that levels of job satisfaction and life satisfaction are not strongly correlated (Rice et al,

1980). Global analyses of the "average American" thus leave some doubt as

to how and why work life and nonwork life may be related. Without

such knowledge, the true relationship between Quality of Work Life

(QWL) and Quality of Life (QOL) cannot be understood (Near, 1983). Evidence of a relationship between QWL and QOL would certainly add to the importance of managerial QWL programs. Thus both theory and practice would benefit from this understanding. As a result, this

study focuses on specific subgroups of the population, profiling the

characteristics of survey respondents who say they feel similarly pleased or displeased with life and work.

Social Indicators Research 19 (1987) 383?401. C 1987 by D. Reidel Publishing Company.



384 JANET P. NEAR, ETAL.

COMPENSATION AND SPILLOVER

The Issue of Intention

Some years ago Wilensky (1960) postulated that people may relate

their work and nonwork lives in one of two ways: they may allow

positive or negative experiences from one domain to generalize or

spillover to the other or they may compensate in one domain for

disappointments experienced in the other. Evans and Bartolom? (1980)

argued that spillover and compensation may occur with regard to

activities on and off the job or with feelings on and off the job. An

example of spillover of activities is provided by the individual who

reads a great deal on and off the job; compensation would involve the

avoidance of reading off the job in favor of some more active behavior.

An example of spillover of feelings might be that an individual hates the

job (i.e., negative feeling) and therefore feels likewise alienated from

family (negative feeling); in contrast, this individual might compensate

by drawing greater satisfaction from involvement in family life (positive

feeling). Studies of spillover and compensation have thus focused on

both feelings and activities.

By and large, however, such studies have not distinguished between

the similarity of activities or feelings between work and nonwork and

the meaning of the similarity. As Kando and Summers (1971) pointed out, just because an individual engages in dissimilar activities on and off

the job does not mean that he/she has sought these dissimilar activities

intentionally in order to compensate for dislike of one set of activities.

In other words, a negative correlation between types of activities on and

off the job does not necessarily indicate that an intentional strategy of

compensation has been adopted by workers; the negative correlation

may exist for a variety of reasons, only one of which is the intent or

strategy of the worker. In fact, survey results suggest that other factors

may well be involved in this correlation (Anthony and Rice, 1982). If compensation and spillover imply conscious strategies adopted by

workers to negotiate the relationships between their work and nonwork

lives, then other terms are needed to connote the simple statistical

association between work and nonwork activities and feelings. Kahn

(1981) has referred to a positive association between work and non



PROFILE ANALYSIS 385

work activities or feelings as "consistency," a term which implies no

deliberate strategy of the worker to create spillover between these

domains.

These individuals who show contrasting patterns of life and job

satisfaction (one low, the other high) represent a group who show

"complementarity'* according to Kahn (1981). Some of these individuals

may be actively compensating in one domain for disappointments in the

other; again, this cannot be determined from our data. What can be

assessed is the degree to which our respondents show contrasting or

complementary patterns of job and life satisfaction.

Profiles of Satisfaction

Focusing for the moment on attitudes toward life and work, rather than

behaviors associated with both domains, people may be classified in

one of four subgroups, the first two representing consistent patterns and the second two complementary patterns: satisfied with job and life,

dissatisfied with job and life, satisfied with job but dissatisfied with life, and satisfied with life but dissatisfied with job. Those who are satisfied

with both job and life presumably differ in some significant ways from individuals falling into one of the other three categories.

Examination of correlational relationships between job and life

satisfaction does not permit determination of the real patterns of

relationships between variables. For example, the low positive correla

tion found in most studies (Rice et al, 1980) probably indicates an

amalgamation of individuals in each of the three main categories:

consistency, complementarity and segregation (Dubin, 1956, 1976) or

lack of relationship between the two domains.

The purpose of this study is to conduct a profile analysis of the

characteristics common to members within each of the four subgroups which allows a determination of the nature of differences between

subgroups. Earlier studies have not addressed this question specifically, however, their findings serve as a basis for educated guesses about the

characteristics of individuals in at least two of the four categories. Marx's classic discussion (Marx and Engels, 1939) of the concept of

alienation has been interpreted in many ways. At a most simplistic level




he seemed to be saying that workers alienated from their jobs would

become alienated from family and friends, and finally themselves. In

effect, feelings of dissatisfaction and displeasure with the job have

spread to other areas of life. If Marx's alienated worker were to

participate in an attitude survey, it seems likely that he/she would

report less than average levels of satisfaction with job and life.

Blood and Hulin (1967), studying the worker who seemed to be alienated from middle-class norms (which they termed "anomic,"

following Durkheim, 1947), found that workers so classified were most

likely to reside in urban areas. More recent studies have indicated that

dissatisfied and possibly alienated workers were also more likely to

have grown up in urban areas (Ruh et ai, 1975; Wanous, 1974). Kabanoff and O'Brien (1981), investigating the activities of workers

on and off the job, found that workers involved in dull, repetitive activities on the job were most likely to engage in likewise passive,

repetitive activities off the job. Similar results have been reported by other researchers; for example, alienated workers are less likely to

participate in political activities off the job (reviewed by Near et aL,

1980). Kabanoff and O'Brien took their analysis a step further, how

ever, finding that workers who engaged in similar and negative activities

on and off the job were more likely than others to be young, male,

poorly educated and earning a low income.

Studies of workers who associate positive activities or feelings with

both job and life have been even more limited in number. Champoux

(1981) found that workers in positions offering high job scope (i.e., skill

variety, autonomy, task identity, task significance and feedback) were

more likely to perceive their jobs to be similar to their lives away from

the job. Evans and Bartolom? (1980), studying European managers, found that high levels of job stress and job satisfaction were also

associated with the spillover of feelings between work and nonwork; in

other words, these managers saw their work world as being closely linked to their lives off the job.

These studies seem to indicate two conclusions. Similar but negative

feelings and activities associated with work and nonwork may be most

likely among individuals who reside in an urban area or grew up in an

urban area, are young, male, relatively uneducated and relatively poor. In contrast, similar and positive feelings and activities associated with




work and nonwork are more likely to be reported by individuals who

are in challenging but stressful occupations.

HYPOTHESES

Extensions of these descriptions are plausible; for example, if people feel negatively about both their jobs and lives, it seems likely that they

will express negative attitudes about other aspects of thier lives. Other

demographic characteristics may also be related to these general

expressions of satisfaction. It could be predicted, for example, that

minorities would be less satisfied with job and life than whites. Like

wise, marital status may play a part; married individuals may be more

likely to be satisfied with work and life than unmarried individuals.

These predictions are obviously speculative, based on results from a

limited number of studies or on assumptions about what constitutes the

"good life." These ideas, albeit exploratory, may be stated as formal

hypotheses which predict a package of characteristics, or profile,

thought likely to be related to negative feelings regarding job and life.

Thus:

HI Demographic Variables. The subgroup of respondents with low

job satisfaction and low life satisfaction is more likely than

other subgroups to be: male, nonwhite, unmarried, young and

low on education.

H2 Geographic Variables. The subgroup of respondents with low


other subgroups to have grown up in an urban area or to

currently reside in an urban area.

H3 Socioeconomic Status. The subgroup of respondents with low


other subgroups to have a low income and to reside in poorly

kept housing.

H4 Job Variables. The subgroup of respondents with low job satisfaction and low life satisfaction is more likely than other

subgroups to be unemployed or to have jobs of lower task




scope, indicated by low occupational prestige with shorter

tenure on the job.

H5 Positive Attitudes. The subgroup of respondents with low job satisfaction and low life satisfaction is less likely than other

subgroups to rate favorably other aspects of the living situation

(e.g., housing, health, and life over past years).

METHOD

Respondents

Interviews were completed in 1975 with 1041 respondents from

western New York, through the Erie-Niagara Area Survey (due to

missing data, 389 responses were analyzed). This omnibus survey covered many topics, including data reported here, and was sponsored

by investigators who participated on a time-sharing basis.

The respondents represent a stratified random sample of residents in

the Buffalo, New York SMSA, clustered by blocks. Comparison of this

local sample to recent national samples showed few differences in terms

of demographic characteristics. Despite the fact that the data are now

over ten years old, it seems likely that the relationships being examined

would not have changed greatly. That is, there is no theoretical reason

to believe that the relationships between variables would be different in

data collected more recently.

Measures

The indicants of job satisfaction and life satisfaction are, respectively,

single-item measures of job satisfaction and life satisfaction (in this

sample, r ?0.30).

The job satisfaction item required respondents to rate their present or most recent job on a four-point scale (1

? very satisfied, 4 ?

very

dissatisfied). The question asked "On the whole, how satisfied (are/

were) you with the work you (do-did on your last job)? Would you say

you (are/were..."

The life satisfaction item was rated on a five-point scale (1 ?




extremely satisfied, 5 ? not satisfied). The question was worded as:

"Taking everything into consideration, how satisfied are you with life in

general at the present time? Would you say you are..."

These items (and distributions of responses to them) are very similar

to those used in previous studies of overall job and life satisfaction.

Andrews and Withey (1976), for example, measured life satisfaction

with an item very similar to ours; their primary measure of subjective

well-being was the average response to such an item asked twice in the

interviews. Campbell et al (1976) likewise used a similar life satis

faction question; responses to this question composed half of their

overall Index of Weil-Being (the other half was the average of eight semantic differential items). Campbell et al (1976) also measured job satisfaction with a single survey item. It may have been preferable to

use measures composed of multiple items, but there is precedence for using items similar to the ones used in the present study. More

importantly, our measures displayed empirical patterns similar to those

reported in earlier studies of job satisfaction and life satisfaction.

The remaining variables fell into five categories: (1) demographic ?

race, sex, marital status, age and years of education; (2) geographic ?

grew up in or currently lives in rural area, urban area or suburbs; (3) socioeconomic status ? household income in dollars and interviewer's

ratings on ten-point scale of appearance of respondent's home and

neighborhood (i.e., how well-kept) (4) job variables ? employment

status, occupational prestige using Duncan's index (Duncan, 1961), and number of years of tenure on the job; (5) other attitudes ?

respondent's ratings of his/her health (five-point scale), neighborhood

(four-point scale) and life satisfaction over time (three-point scale with

alternative responses reflecting that life has gotten better, worse, or

stayed the same).

Procedures

Respondents were classified in terms of their relative levels of job satisfaction (JS) and fife satisfaction (LS). Four subgroups were created:

those found to be above the median on job satisfaction and life satisfac

tion (high JS/high LS); those below the median on both job satisfaction

and life satisfaction (low JS/low LS); those below the median on job




satisfaction but above the median on life satisfaction (low JS/high LS); and those above the median on job satisfaction but below the median

on life satisfaction (high JS/low LS).1 Subgroup membership was thus

based on the respondent's scores on the measures of job satisfaction

and life satisfaction.

Differences among subgroups were tested with multiple discriminant

analysis. This method allowed comparison of responses from four

subgroups of respondents: those high on both job satisfaction and life

satisfaction (high JS/LS), those low on both job satisfaction and life satisfaction (low JS/LS) and those low on one but high on the other.

RESULTS

The form of the relationship between JS and LS, specified as subgroup

membership, is taken as the classification variable in the analysis.

Discriminating variables included those measures described above.

Intercorrelations among these variables are shown in Table I.

A few variables did not enter the discriminant analysis because their

tolerance level was too low (e.g., sex). Three significant functions

emerged (Table H).

The Discriminant Functions

The variables with strongest coefficients on the first function were

respondent's rating of health and neighborhood (inversely related). On

the second function, the strongest coefficients were represented by life

satisfaction over time, respondent's rating of neighborhood, whether the

respondent lived in a suburban or a rural area (both inversely related) and being widowed. Variables most strongly associated with the third

function were age (inversely related), health, being nonwhite, education

and growing up in the suburbs (inversely related).

Subgroup Classification

Table HI list the group centroids for each subgroup on each discriminant

function. The first group, low JS/low LS loads negatively on the first

function. The second group, low JS/high LS, loads negatively and




moderately on the second and third functions. The high JS/low LS

group receives a high positive score on the second function. Finally, the

high JS/high LS group receives a moderate and positive score on the

first function.

Inspection of the strongest discriminators indicates different charac

terizations of each of the four groups. The results show the high JS/high LS subgroup to be characterized by a positive score on the first

function, indicating a group of people who are in good health and living in a good neighborhood.

Perhaps not surprisingly, the subgroup of people that is characterized

by low JS/low LS scored negatively on this first function, and thus

reflects a mirror opposite to the first group. They are in poor health

and living in a poor neighborhood.

People who are high JS/low LS (Group 3) show low life satisfaction

over time, live in the city in a poor neighborhood and are widowed.

Their low levels of life satisfaction seem to be accounted for by factors

other than the job, especially living space (i.e. poor, urban neighbor

hood), marital status (i.e., widowed) and chronic dissatisfaction (i.e., low

life satisfaction over time). A negative score on a second function is received by the subgroup

that is low JS/high LS. They also score negatively on the third dis

criminant function. Neither score is very large, and the fact that they score on two functions may make this group most difficult to distinguish; at the very least, it seems to fall into two subcategories.

The first of these two subcategories, by virtue of its negative score

on the second discriminant function, could be characterized as: non

widowed respondents high on life satisfaction over time and living in a

good neighborhood in the suburbs or in a rural area.

The second subcategory of people who are low JS/high LS are old, in good health, living in poor neighborhoods (as rated by interviewer)

not in the suburbs; they are white, low in education, and grew up in the

suburbs. They thus differ from the other subcategory in terms of age,

health, neighborhood, race and education.

Patterns of Relationships

Most people fall into subgroups showing consistency or spillover




TABLE I

Means, standard deviations and correlations among variables*

Variables SD 1 10

0.25 -

0.18 -0.01 -

0.16 -0.28 -0.07 -

0.18 -0.24 -0.01 0.65

(1) Life satisfaction over rime: poor 00 1.00 ?

(2) R's rating of health:

poor 0.00 1.00 0.20

(3) Occupational prestige (Duncan's SES) 42 22.46 -0.06

(4) Job tenure 9 8.93-0.03

(5) Is rating of home:

poor 0.00 1.00 0.20

(6) Ts rating of neighbor 0.00 1.00 0.18 hood: poor 0.00 1.00

(7) R*s rating of neighbor hood: poor 0.00 1.00-0.08-0.13 0.06 0.09-0.27

(8) Lives in rural area 0.11 0.31 0.00 0.02-0.02 0.03 0.03

(9) Uves in suburbs 0.48 0.50 -0.17 -0.15 0.21 -0.02 -0.31

(10) Lives in city 0.41 0.49 0.17 0.14-0.20 0.01 0.29

(11) Grew up in rural area 0.18 0.38-0.01 0.14-0.08 0.09 0.07

(12) Grew up in suburb 0.12 0.33-0.07-0.17 0.03-0.16-0.05

(13) Grew up in city 0.69 0.46 0.06 0.01 0.05 0.05-0.02

(14) Education 0.12 3.05 -0.08 -0.32 0.52 -0.18 -0.18

(15) Household income

(dollars) 13,682 850 -0.21 -0.26 0.30 0.09 -0.24

(16) Age (year) 44 19 0.04 0.37 0.00 0.64-0.11

(17) Sex(1-female, 2-male) 1.54 0.50 0.11 0.09 0.09-0.21-0.02

(18) Race(1-white, 2-nonwhite) 1.06 0.23 0.02 0.12-0.15-0.05 0.29

(19) Registered voter

(1-yes,2-no) 1.27 0.44 0.09 0.02 0.15-0.24 0.15

(20) Marital status: married 0.66 0.47 -0.16 -0.04 0.11 0.25 -0.12

(21) Marital status: divorced 0.03 0.18 0.14 0.02 -0.01 -0.03 0.06

(22) Marital status:

separated 0.02 0.15 0.10 0.09 -0.06 -0.01 0.13

(23) Marital status: widowed 0.09 0.29 0.17 0.17 -0.03 0.02 0.02

(24) Marital status:

single 0.19 0.40 -0.04 -0.12 -0.09 -0.27 0.06

(25) Employment status: retired 0.11 0.31 0.02 0.18 0.00 NA -0.06

(26) Employment status: disabled 0.03 0.18 0.10 0.32 -0.07 NA 0.08

(27) Employment status: housewife 0.15 0.35 0.03 0.04 -0.01 NA -0.05

(28) Employment status: student 0.02 0.12 -0.02 -0.07 -0.05 NA -0.05

-0.36 -

-0.03 0.04 -

-0.40 0.18 NA -

0.42 -0.20 NA NA -

0.10 -0.04 0J0 -0.15 -0.04 -0.08 0.01 0.01 0.21 -0.21 -0.02 0.02 -0.25 -0.04 0.19 -0.22 0.02 -0.01 0.21 -0.20

-0.23 0.06 0.01 0.23 -0.25 -0.06 0.13 0.01 0.00-0.01

-0.02 -0.04 -0.03 -0.01 0.03

0.36 -0.20 -0.09 -0.23 0.29

0.12-0.11 0.02-0.08 0.07

-0.09 0.06 0.00 0.13 -0.13

0.08 -0.05 -0.01 -0.08 0.09

0.09 -0.06 -0.01 -0.08 0.09

0.04 0.07 0.00-0.08 0.08

0.01 -0.08 0.01 -0.03 0.02

-0.03 0.10 0.00 0.00 0.00

0.09 -0.05 -0.03 -0.09 0.11

-0.03 -0.01 0.07 0.00 -0.04

-0.01 -0.01 -0.04 0.00 0.02

* r > 0.07 is significant, p < 0.01. r > 0.06 is significant, p < 0.05.

NA ? not applicable, due to calculation of dummy variables. N ?

513, due to missing data.




TABLE I

Means, standard deviations and correlations among variables*

11 12 13 14 IS 16 17 18 19 20 21 22 23 24 25 26 27 28

NA -

NA NA -

-0.17 0.12 0.06 -

-0.03 0.12 -0.06 0.37 -

0.11 -0.22 0.05 -0.34 -0.22 -

-0.04 -0.15 0.06 -0.08 -0.13 0.03

0.08 -0.09 0.00 -0.15 -0.14 -0.05 0.04 -

0.06 0.02-0.07-0.07-0.11-0.26 0.02 0.04

0.09 -0.08 -0.01 0.07 0.13 0.04 -0.09 -0.10 -0.10 -

-0.03 -0.04 0.06 0.01 -0.12 -0.04 0.09 0.08 0.04 NA -

0.00 -0.02 0.02 -0.06 -0.12 0.00 0.08 0.10 0.03 NA NA -

0.00 -0.09 0.06 -0.24 -0.25 0.43 0.15 0.04 -0.06 NA NA NA -

-0.09 0.19-0.05 0.11 0.12-0.34-0.08 0.02 0.14 NA NA NA NA -

0.06 -0.07 -0.01 -0.19 -0.22 0.54 -0.07 -0.03 -0.07 -0.12 -0.05 -0.01 0.30 -0.04 -

0.01 -0.04 0.02 -0.15 -0.17 0.15 0.10 0.04 -0.01 -0.13 0.02 0.12 0.18 -0.02 NA -

0.05 -0.06 -0.01 -0.07 -0.05 -0.04 0.38 -0.06 0.07 0.18 -0.04 -0.05 0.01 -0.19 NA NA -

-0.02 0.03 -0.01 0.09 -0.01 -0.12 0.05 0.10 -0.01 -0.02 0.00 0.01 -0.04 0.05 NA NA NA




TABLE n

Results of discriminant analysis

Variables Standardized discriminant

function coefficients

Life satisfaction over time: low

Age (years) R's rating of health: poor Lives in suburbs

Lives in rural area

R's rating of neighborhood: poor Race (1

? white, 2 ?

nonwhite) Education (years) Job tenure (years) Household income (dollars) R is registered voter (1

? yes, 2 ?

no) Marital status: married

Marital status: widowed

Marital status: divorced

Marital status: separated Fs rating of neighborhood: poor Fs rating of home: poor

Employment status

Occupational prestige (Duncan's SES) Grew up in rural area

Grew up in suburbs

-0.24

-0.25

-0.52

0.08

-0.07

-0.34

-0.18

-0.22

0.29

0.23

-0.16

0.19

0.10

0.08

0.08

0.03

-0.04

0.05

0.04

0.11

0.24

0.62

-0.07

0.14

-0.33

-0.33

0.31

-0.23

-0.27

0.10

0.22

0.26

0.16

0.32

0.00

0.05

0.06

-0.02

-0.08

0.20 0.27

-0.15

-0.05

-0.46

0.26

0.56

-0.19

0.13

0.48

0.34

-0.15

-0.03

0.05

-0.04

-0.13

0.21

-0.16

-0.24

0.19

0.08

-0.10

0.07

-0.33

N ? 380 (due to missing data).

between job satisfaction and life satisfaction (N ?

634) although sizeable minorities show complementarity or compensation (N

? 355).

Among those subgroups high on job satisfaction the only consistent

pattern seems to be favorable ratings of housing and neighborhood. The subgroup which shows consistency (i.e., high JS/high LS) also rates

its health positively while the subgroup that shows complementarity

(i.e., high JS/low LS) rates its long-term life satisfaction poorly. Such

findings are neither particularly surprising nor illuminating. The other subgroup showing complementarity (i.e., low JS/high LS)

is also concerned with neighborhood, as members of this group live in

the city. One subcategory within this group actually rates its neighbor hood positively and shows high long-term life satisfaction. The other

subcategory is older, white and not highly educated. Again, there seem



PROFILE ANALYSIS

TABLE HI

Group centroids

Groups Discriminant functions

1

Group 1: low JS/low LS

Group 2: low JS/high LS

Group 3: high JS/low LS

Group 4: high JS/high LS

-0.71

0.16

-0.06

0.54

-0.10

-0.40

0.85

0.01

0.17

-0.41

0.28

-0.28

Function

Characteristics of Functions

Eigenvalue

Percent

variance

Cannonical

correlation

Wilks lambda

0.25

0.15

0.09

51.50

30.46

18.05

0.45

0.36

0.28

0.64

0.80

0.92

to be few common denominators here. The final consistent subgroup

(i.e., low JS/low LS) of course, shows characteristics opposite to those

of the high JS/high LS subgroup. The tendencies associated with each group should be viewed with

caution, however, as only 49% of all cases could be correctly classified

on the basis of knowledge of these selected measures (see Table TV). Correct predictions of actual group membership were least difficult to

obtain for the high JS/low LS and high JS/high LS subgroups but even here the odds of predicting correctly are not much better than even.

Thus, although there appear to be some differences among the four

subgroups which can be characterized rather systematically, these

differences represent only tendencies which are not strongly associated

with membership in any subgroup.

Further, computation of a coefficient of discriminatory power

(Tatsuoka, 1970) indicated that only 36% of the variability in these

variables could be attributed to subgroup membership. Thus, subgroup




TABLE IV

Classification of respondents into subgroups

Actual group membership * Predicted group membership

1

Group 1: low JS/low LS

Group 2: low JS/high LS

Group 3: high JS/low LS

Group 4: high JS/high LS

46% 21% 16% 16%

16% 44% 26% 14%

14% 13% 53% 16%

14% 17% 21% 53%

* 49% of all cases correctly classified.

membership may not be reliably associated with these variables, in

other samples.

The Findings

It was predicted that members of the low JS/low LS subgroup would

show particular demographic characteristics. In fact, age, sex, race,

education and marital status were unrelated to membership in this

subgroup, contrary to prediction. The second hypothesis argued that members of the low JS/low LS

subgroup would be more likely to have grown up or live currently in an

urban area. Neither prediction was upheld. It was expected that members of the low JS/low LS subgroup would

have lower socioeconomic status. This hypothesis was not supported. The fourth hypothesis predicted that this subgroup would have poor

working conditions. Partial support was indicated in that members of

the low JS/low LS subgroup had low job tenure. This subgroup was no

more likely to be unemployed or to have low occupational prestige, however.

Finally, it was expected that these interviewees would rate their

situations less favorably. This hypothesis was supported: they rated less

favorably their neighborhoods and their health. Yet, life satisfaction

over time was unrelated to membership in this subgroup.




DISCUSSION

Results of discriminant analysis suggest that there are few characteristics

on which people who show how levels of satisfaction with job and life

differ from others. The strongest predictors of membership in this

subgroup are the interviewees' own ratings of their health and neigh

borhoods, two of the three attitudes expressed by each interviewee.

Thus, their overall view of life does not seem to be very favorable and,

further, shows consistency of feelings and conditions in work and

nonwork situations.

Predictive Power

The findings do support the expectation that people differ in the ways

they use to relate work and nonwork lives. About twice as many

respondents show similar levels of job satisfaction and life satisfaction

as are high on one but low on the other; these results support earlier

findings regarding the relative proportions falling into such categories

(Evans and Bartolom?, 1980). Across all subgroups, accurate prediction of membership may be

made for nearly half (49%) of the respondents. With improved measure

ment of more characteristics of work and nonwork life, the predictive

power of this equation might be increased substantially. Subgroup

memberships explains over one third of the variance in measures of

working and living conditions used here.

Such results suggest that different types of people tend to use

different mechanisms for integrating their work and nonwork lives.

Overlap among the characteristics, however, implies that use of dif

ferent mechanisms is not a stable characteristic but varies with living and working situations. For example, respondents rating their neigh borhood favorably may be either high JS/high LS or low JS/high LS, possibly indicating that they move between these two subgroups,

depending perhaps on situational variables. Thus, like Kabanoff and

O'Brien (1980), our results suggest that characteristics of the four

subgroups overlap somewhat. This overlap suggests that the subgroup

membership of the individual varies considerably with the situation.

Further, this encourages speculation that reactions to work and non




work are not totally stable characteristics, but vary over time if a person encounters new workplace and/or living conditions.

Our results seem to indicate some real differences between individ

uals with positive consistency between work and nonwork attitudes and

those with negative consistency. Our inability to accurately predict which of these groups an individual will fall into suggests that their

boundaries are not totally clear, however. Perhaps consistency and

complementarity are patterns observable in most peoples' livess at

different times and depending on the situation. If so, then further

studies should be directed at the assessment of situational changes which may cause changes in patterns of relationships between work and

nonwork lives.

Limitations of the Study

Implicit in the current study is the notion that work and other domains

of life can be separated or related, just as work and particular domains

(e.g., leisure) of life can be separated or related. Yet, from another

perspective, job satisfaction and life satisfaction must logically be

interrelated as a part-whole phenomenon. That is, job satisfaction must

logically constitute some part of the broader construct of life satisfac

tion; thus, the question regarding spillover between the two must be

"how much" not "whether." From this perspective, consistency or

complementarity between job and life satisfaction represents a some

what different type of phenomenon than consistency or complementarity between ? for example

? job satisfaction and leisure satisfaction. Our

analyses here thus depart from the more traditional analyses of

consistency and complementarity between work and other specific domains (e.g., Kabanoff and O'Brien, 1980).

Yet, from another perspective, job and life satisfaction do exert

influence over each other, that is, the whole (i.e., life satisfaction) does

influence the part (i.e., job satisfaction). Evidence of a reciprocal

relationship (Schmitt and Bedeian, 1982), though made murky by

conflicting results (Near, 1985; Orpen, 1978; Schmitt and Mellon,

1980), suggests that any interrelationship between the two variables

may be two-way. From this view, then, it is as logical to examine the

relationship between job satisfaction and life satisfaction as between




job satisfaction and any other domain satisfaction. This is an issue

which requires further theoretical consideration as well as empirical

investigation.

Beyond this, the results of the present study are clearly disappointing. It would be surprising indeed if individual differences do not explain

patterns of relationship between job satisfaction and life satisfaction.

Further research ? with a more extensive list of individual differences

variables ? is needed to verify (and hopefully overturn!) the results

obtained here (Rice et al, in press).

Implications

Given that job satisfaction spills over to life satisfaction and that life

satisfaction spills over to job satisfaction for at least some of the people some of the time, it seems that performance on the job may be at least

partly related to events and feelings associated with life outside the

workplace. Although this a truism that managers have acknowledged for many years, it is not clear that they have actually done anything about it. Informally, managers may need to be more aware of their

subordinate's lives off the job, so as to try to prevent negative spillover in either direction. Formally, they may have to consider changes in

organizational structure that help employees cope with pressures off the

job (e.g., day care centers) lest these pressures reduce performance on

the job. It has been argued that one factor in the success of Japanese

management is the emphasis on employees as people with personal life

demands as well as demands in work life; the holistic concern with

employees is argued to make those employees feel, in return, greater commitment to the firm (e.g., Ouchi and Price, 1978). Perhaps it is time

for American managers as well to recognize the forces of compensation and spillover and their potential impact on life at work.

ACKNOWLEDGEMENT

The authors would like to thank Boris Kabanoff, Dennis W. Organ, and Donald A. Wood for comments on earlier drafts of this paper. Requests for reprints should be sent

to Janet P. Near, Department of Management, School of Business, Indiana University,

Bloomington, IN 47405.




NOTE

1 The analyses were repeated using the mean to classify respondents into subgroups.

The results were virtually identical to those produced when respondents were classified

on the basis of median scores.

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School of Business,

Indiana University,

Bloomington, IN47405,

U.S.A.



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