role of social support in the experience of stress at work

7/28/2019 Role of Social Support in the Experience of Stress at Work

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Journal of Applied ftychology1986, Vol. 71. No. 1, I02-HO Copyright 1986 by the American Psychological Association, Inc.Q02I-90K>/86/$W.75

Role of Social Support in the Experienceof Stress at WorkDaniel C. Ganster

Department ofManagementUniversity of NebraskaLincoln

Marcelline R. FusilierDepartment of Management

University of NebraskaOmahaBronston T. Mayes

Department ofManagementCalifornia State UniversityFullerton

It has been hypothesized that the positive relation between stress and strain responses is stronger forindividuals who have lowlevelsof social support than for thosewho have high levels of support. Thishypothesis that social support buffers (moderates) the negativeeffects ofstresshasbeen tested extensivelyin a variety of settingwith highly conflictingresults. Some theorists have recently proposed that themoderatingeffect ofsocial support isitself buffered byothervariables such as sex or social class. Thepresent study was designed to examine the role ofsocial support in the experience of workstresswitha sample large enough to provide statistically powerful tests of models of social support that specifytwo-way and three-way interactions. No support for higher order interactive models was found. Inaddition, no evidence emerged demonstrating any buffering effect for social support. Argumentsareadvanced for a parsimonious model in which social support has a modest direct effect of loweringexperienced strain.

Researchers in the social sciences have sought to identify factorsreducing or eliminating negative effects of stress in the worksetting (Ganster, Mayes, Sime, & Tharp, 1982). The primarysocial factor hypothesizedto mitigate these effects, or strains, isthe degreeof social support that an individual receives. The na-ture of the effect ofsocial support onstrains, however, ispresentlyunclear (see Gore, 1981;House, 198l;Kessler,Price, &Wortman,1985;Leavy, l983;Thoits, 1982; for reviews). This lack ofclarityhas implications for the developmentand refinement of modelsof the phenomenon of stress as well as for managers and coun-selors seeking methods for coping with the problem. Until therole of social support in work stress is identified, its potentialbenefits cannot be fully used. The present study, therefore, at-tempts to delineate and investigate the relations amongstressors,strains, and social support in the work setting.

Thedominant social support hypothesishas been that it buffersthe impact of stressors on manifestations of strain. However, agreat deal of confusion and imprecision has accompanied theuse of this term. Most authors have used the term in a wayconsistent with the explicit definition of LaRocco, House, andFrench (1980). Social support is hypothesized to interact withstressorssuch that the relation between stressand strainisstrongerfor persons with lowlevels of social support than for those withhigh levels of support. In other words, social support moderates

This research wassupported by agrant from the National Institute ofMental Health (1-R01-MH34408), Daniel C. Ganster, Principal Inves-tigator. We would like to thank Mary Barton and Pamela Perrewe fortheir assistance in data collection and coding. We would also like to ac-knowledge the helpful comments of Robert Guion and two anonymousreviewers.

Correspondence concerning this article should be sent to Daniel C.Ganster, Department of Management, University of Nebraska, Lincoln,Nebraska 68588.

the stress-strain relation. It is important todistinguish betweenthe terms moderate and mediate, because both have been usedin reference to the buffering effect. Following James and Brett(1984), the buffering role ofsocial support refers to amoderatingeffect; the terms buffering and moderating will be used inter-changeably.

Findings have been inconsistent. Several studies report evi-dence of the moderatingeffect (Abdel-Halim, 1982; Gore, 1978;House, McMichael, Wells, Kaplan, & Landerman, 1979; Kar-asek, Triantis, & Chaudry, 1982; Kobasa & Puccetti, 1983;LaRocco et a]., 1980;Lefcourt, Martin, &Saleh, 1984; Sandier& Lakey, 1982; Seers, McGee, Serey, & Graen, 1983;Wilcox,1981). Many of these investigations, however, did not find con-sistent effects across different (a) stressors and indexes of strain,(b) sources of support, and (c) personal characteristics of thesubjects. For example, LaRocco et al. (1980) reported that socialsupport moderated the effects of stressors on health outcomessuch as depression and somatic complaints, but they found noevidence of the effect on job-related strains such as job dissat-isfaction and boredom. Kobasa and Puccetti (1983) reportedthat support from the bossbuffered the effect of critical lifeeventson illness symptoms but that support from the family did not.Sandier and Lakey (1982) found social support buffered the im-pact ofcritical life events on depressionand anxiety for personswith an internal locusofcontrol but not forthose withanexternallocus of control. In sum, the evidence of moderating effects isequivocal, suggesting that their existence may depend on thesource of support, the recipients, and the stressors and strainsbeingexamined.

Inaddition to the studies that to someextent support bufferingeffects, other investigationshave not (Aneshensel &Stone, 1982;Blau, 1981; Ganellen & Blaney, 1984; Lin, Simeone, Ensel, &Kuo, 1979; Turner, 1981). Still others have reported what mightbe termed opposite buffering effects. That is, social support ap-

102


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SOCIAL SUPPORT 103

peared to exacerbate the effects of stressors on strains. For ex-ample, Beehr (1976) noted that work group support tended toincrease the impact of role ambiguity on job dissatisfaction.Similar opposite buffering findings were reported by Abdel-Halim (1982) and Kobasa and Puccetti (1983).

In summary, the literature is unclear about the generality ofa buffering effect ofsocial support on stress. Reasonsand researchstrategies are discussed below.

Methodological IssuesSeveral recent reviews of the social support literature have

suggested that some of the discrepant results may be accountedfor by methodological shortcomings (Gore, 1981; House, 1981;Thoits, 1982). To provide a rigorous examinationof the role ofsocial support in the workplace, the present study was designedto meet three methodological concerns that generally have notreceived adequate treatment in past research.

The first issue concerns the construct of social support itself.Many operationalizations have been used in the literature, dif-fering inobjectivity, dimensionality, and meaning. Social supportcan bebroadly defined as "the availabilityofhelping relationshipsand the quality of those relationships" (Leavy, 1983, p. 5). Thisdefinition connotes social ties of a positive nature. Although ob-jectiveindicators such as marital status, the size of the individual'ssocial network, and the number of social contacts have all beenused to make inferences about the amount of social support onereceives, such measures are deficient because they omit assess-ment of the quality ofsuch relations. Also neglected isassessmentof social support dimensionssuch as who provides the supportand what form it takes. Sources of support include co-workers,supervisors, friends, and families. Support might take the formof emotional reassurance or assistance in meeting goals. Aknowledge of the effects of support dimensions may have im-plications for structuring the work environment as well as forbetter understanding the dynamics of social support in the stressprocess. The present study uses a measurement approach thatattempts to assess directly individuals' perceptions of variousforms of positive support from three different sources.

Second, much of the research investigating social suport atwork has relied on a limited number of stress measures (e.g.,role conflict and ambiguity); clearly, other organizational andjob characteristics may be related to subsequent strains. Fur-thermore, social support has been found to act as a moderatorfor some strains but not others (LaRocco et al., 1980). In par-ticular, it appears to matter whether the strains reflect healthoutcomes, such as somatic complaints, or whether they are ex-plicitlyjob-related orattitudinal. The present study is multivariateregarding both stressors and strains.

Finally, size and heterogeneity of the sample are importantmethodological issues. To obtain statistical power sufficient todetect interactionsof typically small effect size, a large sampleis needed. Large samples have not been the norm in the socialsupport literature (see Thoits, 1982), so much of the apparentvariability in reported findings might be a function of samplingerror (see Hunter, Schmidt, & Jackson, 1982). In addition, anideal samplewould represent a range ofpersonal and worksettingcharacteristics. Such a sample would allowthe testingof higherorder interactive effects as are now frequently being proposed

(Mitchell &Trickett, 1980). For example, social support mightbuffer the stress-strain relation in the presence of some worksettingcharacteristics but not others.

Such characteristics could be involved in a three-way inter-action with social support and stressors. Caplan, Cobb, French,Harrison, and Pinneau (1975) reported that holders of blue-ver-sus white-collar positions had different exposure and responseto work stressors. This finding may be due to the different so-cioeconomic status levels that tend to be associated with suchpositions. Social class has been shown to influencevulnerabilityto stress and also may determine whether social support mod-erates the stress-strain relation (Turner &Noh, 1983). Further-more, othercharacteristicssuchas sex and education might alterthe buffering effect. Women tend to have more and differenttypes of supportive ties than men (Leavy, 1983). Etzion (1984)reported that the work stress-burnout relation was moderatedby work sources ofsupport for men and by nonwork sources forwomen. Finally, because a purpose of education is to improveindividuals' abilities to cope, it may influence stress reactions.Furthermore, it is a component of social class. Its role with regardto stress, social support, and strain is therefore explored.

The present study, then, examines(a) the main effects of socialsupport on strain outcomes, (b) the interactive, or moderating,effect of social support incombination with work stressors, and(c) higher order interactions involving social support, stressors,and personal and job variables. The personal and occupationalfactors examined are sex, educational level, and job type, as in-dexed by blue- versus white-collar position. Moreover, the studyexamines multiple sources of social support and stressand bothwork and non-work related strains.

MethodSubjects

Employees of a large contracting firmwere recruited to participate inthe study. The sample wascontacted at two of the firm's job sites and atits corporate headquarters. In all, 326employees provided complete data.Eighty-four percent of them were male, they had an average educationof 14.4 years, and averaged 32.5 years inage. Approximately 60%occupiedconstruction trade jobs such as electrician, welder, and plumber, whilethe rest were employed as accountants, engineers, and clerical supportstaff, and middle to upper level managers. Subjects were recruited asvolunteers fora"work stressstudy." Participants receivedapersonal reportof their responses and also received time offfrom their job to providedata. This sample represented about 80% of the employee populationsat these sites. This relatively high responserate was probably due in partto the employersallowing datacollection sessionsduring regular workinghours.Measures

Social support from three sources (supervisor, co-workers, and familyand friends) was measured with the three subscales used in the Caplanet al. (1975) survey. Each subscale consists of four 5-point Likert-scaleditems. Unlike some procedures, which measure social support indirectly(for example, as number of social contacts), these scales were chosenbecause they directly assess the subject's perception regarding the levelof social support received. The types of support assessed by these scalesare illustrated by the following items: "How much does each of thesepeople go out of their way to do things to make your life easier for you?Howeasy is it to talk with each of the followingpeople? How much caneach of these people be relied on when things get tough at work? How


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104 D. GANSTER, M. FUSILIER, AND B. MAYESmuch is each of the following people willing to listen to your personalproblems?" Subjects responded tothese itemsintermsofsupport receivedfrom "your immediate supervisor," "other people at work," and "yourspouse, friends, and relatives."

Six job stressors were assessed. They were chosen because they areoften found to be the strongest correlates of individual strain (see Caplanet al., 1975). Role conflict and ambiguity were measured with the scalesof Rizzo, House, and Lirtzman (1970). The items use 7-point responseformats. The other stressors were measured with scales from the Caplanet al. (1975) survey. Quantitative work underload refers to how muchwork one has to do and wasmeasured with an 11-item scale. Highscoreson this variable indicate the subject perceives he or she has too little todo. LackofVariability refers to the extent that the level ofone'sworkloadremains constant rather than changes from low to high levels and wasmeasured with 3 items. Skill underutilization refers to the degree towhichone'sjob does not require the worker to use his or her valued skills, andwas indexedwith 3 items. Responsibility forothers refers to the level ofresponsibility that a worker has for the welfare and future of others, andwasassessedwith 4 items. Thepreceding four scalesemployed the sameitemsas reportedbyCaplanet al. (1975). Theyinvolved 5-point responseformats. Forease of interpretation, someof the scales were simply reversescored and renamed so that "stressor" variableswere all operationalizedsothat high scores were associated with high levelsofstress.Forexample,what we refer to as "skill underutilization" is simply the original "skillutilization" scale with reversescoring.

The outcome variables consisted ofdepression, job dissatisfaction, lifedissatisfaction, and somatic complaints. Depression was assessed withthe Caplan et al. (1975) scale. Job dissatisfaction was measured with asexless form of the "faces" scale (Kunin, 1955), and general life dissat-isfaction with the Quinn and Shepard (1974) scale. The somatic com-plaints scale asked the respondent to list the frequency of 17 symptomssuch as headaches, nausea,and sweaty palms. An overall value for somaticcomplaints was obtained by averaging the scores on the 17 individualsymptoms. All of the measures used in this study are fairly standardscales that have demonstrated acceptable reliability in past studies. Re-liabilityestimates for the present sample are reported in Table 1 and areconsistent with those reported previously. The job dissatisfaction scale,although consisting of a single item, has shown high convergence withother multi-item satisfaction scales.Oanster(1978) reported correlationsranging from .80 to .90 with a multi-item Likert-scaled measure and asemantic differential measure.

ProcedureIn most studies of job stress, measures of stressors and strains are

obtained from the self-reportsof respondents in one questionnaire ad-ministration. This practice can lead to inflated correlations betweenstressors and strains because of the influence ofcommon method varianceand response consistencyeffects. In an attempt to minimize such artifacts,the task and role stressor scalesand the social support scales were ad-ministered in one questionnaire, and the outcome measures were ad-ministered in another questionnaire several days later. Althoughweknowof nodata that empirically support the effects of temporal separation onthese measurement artifacts, that such separation might lessen themseemed a plausible assumption. Such temporal separation should alsoserve to reduce the fatigue associated with long questionnaire adminis-tration sessions. All data wereobtained in group administrations super-vised by membersof the research team during working hoursin facilitiesprovided by theorganization.

ResultsMain Effects of Stressors on Strains

The means, standard deviations, reliabilities, and intercorre-lations of all study variables are listed in Table I. As can be seen

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SOCIAL SUPPORT 105from the zero-order correlations, the stressor variables generallyshowsmall,but statisticallysignificant, relations with the outcomevariables.

To get a more accurate picture of the combined main effectsof the task and role variables on the outcome variables, a ca-nonical analysiswascomputed. As is the case inmost work stressstudies, the various stressor variables are intercorrelated. Thesame is true for the strain variables.Canonical analysis providesa multivariateassessment of the overall relation between stressorsand strains while considering the correlations within the two setsof variables (see Cohen & Cohen, 1983 for an explanation ofcanonical analysis). Results are displayed in Table 2.

Two of the four canonical correlations were statistically sig-nificant. As indicated by a redundancy analysis (see Cohen &Cohen, 1983), a total of 13% ofthe variance of the strain measureswas explained by the stressors through these twosets of canonicalvariates, with the first root explaining almost all of this (12%).An examination of the structural coefficients, which are simplythe correlations of the variables with the canonical variates, pro-vides a method of interpreting the variates themselves. The twostrain variables with the highest structural coefficients for Variate1 are job dissatisfaction (.89) and depression (.63), whereas thecoefficients for life dissatisfaction (.32)and somatic complaints(.24) are of considerably less magnitude. On the stressor side,skill underutilizationhas the highest structural coefficient (.77),but role conflict (.48), role ambiguity (.53), and underload (.54)are also fairly strongly related to the variate. Thus, high scoresonthis variate appear toreflect high levelsofskill underutilizationand work underload, as well as high levels of role conflict andambiguity. Because the two variates are correlated positively (ca-nonical r = .59),jobs with high levels of role conflict, ambiguity,skill underutilization, and underload are associated with bothjob dissatisfaction and depression.

The second strain variate essentially reflects somatic com-plaints, whereas its associated stressor variate primarily reflectswork underload, which is negatively loaded (.82). Because thesecond set of variates is orthogonal to the first set, it appears thatunderload is associated with lower levels of somatic complaints,and this effect is independent of its association with job dissat-isfaction and depression.

Main Effects of Social Support on StrainsThe same analytical strategywasfollowed inassessing the main

effects ofsocial support on strain outcomes. Acanonical analysisused the three social support variables and the four strain vari-ables. Results are displayed in Table 3.

Two significant canonical correlationswere yielded with a totalof 6% of the strain variance being explained through the twosets of variates. The first variate set accounted for 5% of thestrain variance, whereas the second accounted for 1%. The struc-tural coefficients suggest that the first strain variate primarilyreflects high job dissatisfaction (.87). However,the other strainsare also moderately represented in the variate, with life dissat-isfaction (.56), somatic complaints (.44), and depression (.65)positively related. On the social support side, support from thesupervisor (.91) is clearly the dominant factor, with 80% of itsvariance accounted for by the variate. Co-worker support is alsosignificantly related (.61), though at a lower level; and support

Table 2Canonical Analysis of Stressors and Outcomes

Structural Coefficients

VariableLife dissatisfactionJob dissatisfactionSomatic complaintsDepression

RedundancyTotal redundancyRole conflict

Role ambiguityUnderloadLack of variabilityUnderutilizationResponsibility

CanonicalVariate 1

.32

.89

.24

.63

.12.1 3

.48

.53.54

.38

.77-.21

CanonicalVariate 2

.02

.16.82-.12

.01

.49.20-.82-.42.32

.26Note. For Root No. 1,canonical correlation = .59, F(24, 1107) = 7.38,p < .01. For Root 2, canonical correlation = .22, fl(15, 878)= 1.73.Structural coefficients are the correlations between the individual variablesand their associatedcanonical variates.

from family and friends makes a smaller contribution (.39).Thus it appears that a lack of social support from individuals atwork, and in particular, from the supervisor, is most stronglyrelated to workplace strain (job dissatisfaction), but is also relatedto strains not specific to work. The second variate set indicatesthat a lack of support from family and friends isassociated withhigher levels of somatic health complaints.

Moderating Effects of Social SupportTo say that social support moderates the effects of stressors

on strains is to assert that there is a significant interaction betweensocial support and stressors such that the effects of stressors areless pronounced when accompanied by high levels of social sup-port. In this study there are multiple measures of all three factors,thus somewhat complicating the analysis of interactions. Al-though hierarchical multiple regression using product terms tocarry interactions is the appropriate analytical model (Cohen&Cohen, 1983), one has some choice in deciding how manystress-orsand social supports willbe examinedineach regression anal-ysis. This decision involves making some trade-off between sac-rificing power by putting all variables into one analysis and tol-erating some amount of Type I error inflation by examiningseparate models. The lower power option is to regress each strainvariable on all 6 stressors, all 3 social support measures, and all18 product terms (Each Social Support X Each Stressor). Thistype of omnibusbuffering test consumes 27degrees of freedom.An analysisthat considers each stressor variable separately alongwith the 3 social support measures will yield higher power testsbut also more Type 1 error inflation. Each of these regressionsconsumes only 7 degrees of freedom, but with 6 stressors and 4dependent variables, 24 different regressions need to be com-puted.

Beginning with the more conservative approach, 4 omnibusregressions were computed, each of which involved a differentstrain outcome as the dependent variable and all the stressors


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106 D. GANSTER, M FUSILIER, AND B. MAYESTable 3Canonical Analysis of Social Support and Outcomes

Structural Coefficients

VariableLife dissatisfactionJob dissatisfactionSomatic complaintsDepression

RedundancyTotal redundancySupervisor support

Coworker supportFriends support

CanonicalVariate 1

.56.87

.44

.65

.05

.06-.91-.61-.39

CanonicalVariate 2

.46-.45

.65.40

.01

.32-.20-.87

Note. For Root No. 1, canonical correlation = .34,f\l2, 844) =5.12,p < .01.For Root No. 2, canonical correlation = .23,F{6, 640) =3.47,p < .01. Structural coefficients are the correlations between the individualvariables and their associated canonical variates.

and social supports. These 4 regressions yielded no significantinteractions between social support and stressors. Thus,althoughsocial support is associated with lower strain, as indicated by themain effects analyses, support does not appear to have a signif-icant buffering effect.

Following the analytic logic discussed above, however, it ispossible that no interactions were discovered because the testswere of low power. To give every benefit of the doubt to thebuffering hypothesis, the alternate strategy, involving thecom-putation of 24 regressions, was also implemented. In each ofthese regressions, a strain variable was regressed on a stressorand the 3 social support variables, then the set of 3 productterms was entered into the regression. If the interaction set madea significant contribution to R, then the significance of each ofthe 3 product variables within the set was considered. Of these24 regression analyses, only 3 yielded evidence of interactionsbetween social support and a stressor. Table 4 provides summaryresults of the 3 regression analyses that yielded significant inter-actions.

In Case 1, lack of variability and support from coworkers sig-nificantly interact to affect life dissatisfaction. A graph of thisinteractionappears in Figure 1. For illustrative purposes, inFig-ure 1 and in the following figures, a value of 4 (on the 5-pointscale) was chosen to represent high support, and a value of 2was chosen to represent low support. Thus, in the first case, lackof variability in workload leads to greater dissatisfaction for thosewith high levels of co-worker support, but not for those with lowsupport. This case is clearly not consistent with the bufferinghypothesis. In fact, it might even be termed opposite buffering.In Case 2, skill Underutilization interacts with support from thesupervisor in affecting life dissatisfaction (seeFigure 2). Again,opposite buffering seems evident in that skill Underutilizationleads to more dissatisfaction for workers with high support fromthe supervisor than for workers with less support. Figure 3 il-lustrates one of the two significant interactions in Case 3yetanother case ofapparent opposite buffering. A lack of variabilityleads to greater job dissatisfaction, and this effect is morepro-nounced for workers with high support from the supervisor thanfor those with lowsupport. Finally, Figure 4 demonstrates the

interaction between social support from family and friends andlack of variability. Lack of variability has a slight positive effecton job dissatisfaction for those with lowsupport. For those withhigh support, lack of variability has a relatively stronger, andnegative, impact on job dissatisfaction. This result is not consis-tent with our definition of the buffering effect, but it does suggestthat thosewith high support suffer less dissatisfaction when ex-posed to a lack of variability than those with lowsupport.

In light of the total lack of interactions in the omnibus tests,

Table 4Social Support InteractionsTotal Sample

Dependent variable:Stepl

Lack of variability (LV)Supervisor support(SS)Co-worker support(CS)Friends support(FS)AS2 = .05

Step 2L V X SSLVXCSLVXFSInterceptAJ?! = .03Total R3 =.08Adj. R* = .06

B1Life dissatisfaction

-.85-.31-.80.05

.06.27-.087.16

F

2.482.027.16*.034.51"

.626.89".8620.17

3.00'3.91"

Dependent variable: Life dissatisfactionStep 1

Underutilization (SU)Supervisor support(SS)Co-worker support(CS)Friends support(FS)AJ?2 = .06

Step 2SUXSSSUXCSSUXFSInterceptAJ?2 = .03Total R2 =.09Adj. R2 =.07

Dependent variable:Step 1

Lack of variability (LV)Supervisor support (SS)Co-worker support (CS)Friends support(FS)AJ?2 = .14

Step 2LVXSSLVXCSLVX FSIntercept&R2 = .03Total R2 = .17Adj. 2 = .15

-.10-.44-.21

.15

.1 1

.07-.124.83

Job dissatisfaction.77

-1.15-.23

.98

.24

.01-.312.75

.077.09".86.424.97"

4.49*.82

3.2819.113.18*4.27**

.9612.99"

.276.45*

13.04"

4.70*.005.80*1.413.26*9.22**

*p


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SOCIAL SUPPORT 107I' High Coworker SupportY=.23(LV)+4.19Low Coworkar SupportY=-.31(LV)t5.56

1 2 3 4 5Lack of Variability

Figure 1. Interaction between co-worker support and lack of variability.

we are very reluctant to attribute much importance to the fewinteractions that were indicated by these fairly liberal tests.Moreover, of the interactions uncovered, none supports the hy-pothesized buffering effect for social support.Sttbsample Analyses

In the present study a sufficiently large and heterogeneoussample exists to allow for the examination of higher order in-teractions involving social support. In particular, the followinganalyses examine interactionsbetween stressors and social sup-port within subsamples across which the buffering effect mightdiffer. These variables consist of sex,education, and blue- versuswhite-collar position. For each variable two subgroups were

Low Supervisor SupportY=.12(SU)+3.95

High Supervisor SupportY=.34(SU)+3.07

1 2 3 4 SSkill Underutilization

Figure 2. Interactionbetween skill Underutilizationandsupervisor support.

O

QO

Low Supervisor SupportY=1.23(LV)t.45

High Supervisor SupportY=1.73(LV)-1.85


Figure 3. Interactionbetween lack ofvariability and supervisor support.

formed, and the interactions between stressors and social supportwere examined within each subgroup. The interaction tests con-sisted of the same type of regression analyses reported previously.However, because subgrouping produces smaller samples, thelower power omnibus tests were not performed, but only themore liberal tests which examined onestressor variable at atime.Thus 24 regressions were performed on each of 6 subgroupsformed by the 3 classification variables.

Tables 5, 6, and 7 display some of the results of the subgroupregressions. All significant interaction effects found in anysubgroup are listed in these tables along with the regression coef-ficient for that interaction in the corresponding subgroup, evenif it was not significant. The lower and upper bounds of the 95%

c.21 5o >

-i ^

Low SupportY=.15(LV)+4.71

High SupportY=-.47(LV)+6.67


Figure 4. Interaction between lack of variability and supportfrom friends and family.


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108 D. GANSTER, M. FUSILIER, AND B. MAYESTable 5Subsample Interaction Tests: White Collar versus Blue Collar

DependentvariableJob dissatisfactionJob dissatisfactionJob dissatisfactionLife dissatisfactionDepression

B"Interaction term

Underload X SSLack of Variety XSSUnderutilization X SSLack of Variety X CSResponsibility X CS

White collar.03-.19-.12.36*-.16*

Blue collar.65*.45*.37*.16.05

95 % confidence interval of BWhite collar-.37 to .43-.59 to .21-.40 to .16.04 to .68-.28 to -.04

Blue collar.22 to 1.08.13 to .77.15 to .59-.12 to .44-.09 to .19

Note. SS = support from supervisor; CS = support from co-workers. White collar n = 124; Blue collar n = 202." B values are unstandardized regression coefficients.

confidence interval for each coefficient are also displayed in thetables.Several interactions a re evident in the groups fo rmed on thebasis of a bl ue- versus white-collar distinction (see Tab le 5). Whengraph ed, some of these interactions are consistent with the buff-ering hypothesis and some are not. Note tha t no ne of the inter-actions found in one subgroup are replicated in the othersubgroup. This pattern is also true with the subgroups formedon the basis of education (Table 6) and sex (Table 7). That is,interactions found in one group are not evident in the othergroup. At first glance one might be tempted to conclude thatthese results suggest that models positing three-way (or higher)interactions are needed to expla in the apparently complex effectsof social sup port. Perform ing similar analyses on groups formedon the ba sis of personality variab les, Lefcourt et al. (1984) reachedthis same conclusion. However, at least in the present case thisconclusion is wrong.If one notes the confidence interva ls listed in Tables 5 through7, it can be seen that they overlap across subgroups. Thus, a l-though a coefficient might be significant at p < .05 in onesubgroup and not significant in the other subgroup, they are notsignificantly different from each other. This conclusion wa s con-firmed, of course, when we computed hierarchical regressionsthat tested three-way interactions between stressors, social sup-port, and each of the three classification variables (following theproceduresofCohen &Cohen, 1983). Theconclusion that higherorder interaction models are necessary to explain the effects ofsocial support receives no validation here.

DiscussionThis investigation attempted to make a comprehensive ex-amination of the main and interactive effects of social support

in the experience of stress a t work. Major work and demographicsubgroups were examined to assess the generality of any mod-erating effects that social support might have. A number ofmethodological considerations were addressed in this study toprovide more rigorous tests than have been previously reportedconcerning the effects of social support at work. A su mmary anddiscussion of the findings follows.Main E f f e c t s

The stressors expla in 13 % of the total va riance of the strainmeasures, as indicated by the canonical analyses. Moreover, wheneach strain is regressed on the six stressor variables, it is evidentthat they are not all equally affected by the stressors. Only 4%of the variance in life dissatisfaction is accounted for, whereasthe stressors explain 29% of the va riance in job dissatisfaction.Similarly, the stressors exp lain 16% of the variance in depressionand only 6% of the variance in somatic health complaints. If thetemporal separation of stressor and strain measures had its in-tended effect of reducing common method and consistency effects(which is impossible to assess empirically), the m ain effects re-ported here might be viewed as being more conservative thanmost pub lished results.Social sup port a lso shows a significant multivariate associationwith strain. Although job dissatisfaction has the strongest relationwith social support, significant amounts of the variance in lifedissatisfaction, depression, and somatic complaintsare also ex-plained by support. Of the different sources of support, thosesources from the workplace, especially the supervisor, are themost important in affecting strains. Furthermore, support fromfamily an d friends is significantly associated with lower levels ofsomatic health symptom s. Overall, however, ess of the total va ri-ance in strain (6%), as indicated by the canonical analysis, isaccounted for by social support than by the stressors.

Table 6Subsample Interaction Tests: High School Diploma or Less versus More than High School95 % confidence interval of BDependentvariable Interaction term < high school > high school < high school > high school

Job dissatisfaction Lack of Variety XSS .23 .31 -.15 to.61 .03 to .59Note, n = 156 for high school diploma or less; n = 170 for more than high school. SS = support from supervisor.* B values are unstandardized regression coefficients.*p < .05.


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SOCIAL SUPPORT 109Table 7Subsample Interaction Tests: Males versus Females

95 % confidence interval of BDependent va riable Interaction term Males Females Males FemalesSomatic complaintsDepressionDepression

Underutilization X FS .02Responsibility X CS .00Ambiguity X SS .03-.33**-.31".27*

-.09 to. 13-.11 t o . 1 1-.05 to. 11-.59 to -.07-.53 to -.09.01 to .53

Note, n - 28 1 for males and 4 5 for females. SS = support from supervisor, CS = support from co-workers, FS = support from family and friends.' B values areunstandardized regression coefficients.*p < . 0 1 .Moderating Effects

The general hypothesis tha t social support moderates the im-pact that job stresses have onstrain responses wasexamined ina seriesof regression analyses. The first approachwas to simul-taneously examine all the interactions between each of thestressors and each source of social support. Such an "omnibus"test wa s replicated for each of the strain measures. No evidenceof a buffering effect emerged. This analytical strategy wa s fol-lowed by one tha t tested the in teraction of each of the stressorswith the three sources of support separately. This approachshould yield tests of higher power, but because many noninde-pendent tests were run the overall level of Type I error might beconsiderably infla ted. Despite the lib erality of these latter tests,only four significant interaction effects were found, and none isconsistent with the buffering hypothesis. W e would argue thatthe evidence is thus v ery weak regarding the buffering hypothesisof social support.Higher Order Interactions

One of the methodological advantages of the present study isthat the sample is large enough and diverse enough to a ccommo-date the examination of higher order interactions involving socialsupport. If such higher order interactions do exist they mightobscure two-way interactions involving social support. Three-way interactions involving support, stressors, and sex, education,and b lue- versus white-collarposition w ere tested, b ut none wasfound to be significan t, despite the tests b eing liberal with regardto overall Type I error level. Thus we find little reason to advocatethe complex models regarding social support that have recentlybeen suggested.

To fit the present negative findings on the buffering hypothesiswith previous conflicting evidence, three questions might b econsidered. First, is the buffering effect specific with regard toparticular stressors, strains, and sources of social support? Second,is the effect general acrossdifferent stressors, sourcesof support,and strains for some population groups but not others? An d third,is the effect specific to particular stressors, sources of support,and strainsincertainpopulation groupsand specific to differentstressors, sources of support, and strains in other groups? Withregard to the first question, there is no obvious theoretical basisfor expecting social support to act as amoderator ofsome stress-strain relations and not others. Although statistically significantmoderator effects have been found for certain variable combi-nations, this does not mean that these effects necessarily differfrom the nonsignificant effects found for other combinations ofvariables.With regard to the second and third questions, if the existenceof a moderating effect varies across popula tion groups, such dif-

ferences ca n only be ascertained b y testing the three-way inter-actions among stress, social support, and group characteristics.Unfortunately, reporting of such tests, as well as interaction effectsizes, is rare (fo r exceptions, see Etzion, 1984; and Ga nellen &Blaney, 1984). Fo r example, both Turner and Noh (1983) andLefcourt et al. (1984) concluded tha t group characteristics dic-tated the existence of the moderating effect of social support.Lefcourt et al. investigated locus of control, and Turner and Nohdealt with social class. In both studies, the Social Support XStress intera ction was significantly different from zero in somegroups but not in others. These intera ction terms, however, werenot compared across groups, therefore the size and statisticalsignificance of the three-way interaction of Social Support XStress X Group was not assessed.Because so few investigators have explicitly tested higher orderinteractions involving social supp ort, the only way to determinewhether the apparent va riability of social support buffering effectsreally does reflect the presence of compl ex processes, as opposedto sa mpling error, is to pe rform a meta-analysis (Hunter et al.,1982). However, we are not hopeful that such an analysis will becompleted soon, given the great variety of statistical tests thathave been used to assess the buffering effect and the propensityof authors to omit the reporting of effect sizes of interactions.Until a meta-analysiscan be performed, attempts to reconcileconflicting findings can only proceed by first identifying majordifferences between supportive and nonsupp ortive studies, thenseeking explanations for why these differences should influencethe buffering effect. With regard to the first point, support forthe buffering hypothesis appears to be more prevalent in studiesconcerning life events as sources of stress than in studies con-cerning work stressors. A possible explana tion for this differencehinges on the fact that many life events directly reduce socialsupport, whereas work stressors do not. Examples of such lifeevents are divorce, death of a spouse, and change of residence.Stressors that are causally related to aparticular type ofsupportma y have a different impact on strain depending on one'soveralllevel of social support. The work stressor-strainrelation, however,ma y not vary with regard to one'soverall support level, asworkstressors typically are not causally related to support. Hence thebuffering effect might be found for certain types of life eventstressors but not for work stressors. This suggests tha t w hen con-structing models of the joint effects of stress and social support,the causal nature of the stress-support relation should be con-sidered.Conclusions

From the foregoing discussion several conclusions seem war-ranted. First, social support, especially from one's supervisor,


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110 D. GANSTER, M. FUSILIER, AND B. MAYESshows aconsistent relation witha varietyofaffective and somaticoutcomes. It appears that interventions designed to provide thistype of support might have a beneficial impact on the mentaland physical welfare of workers, and should be encouraged. Sec-ond, work stressors such as role conflict, role ambiguity, and skillunderutilization are associated with indices of mental and phys-ical poor health.

Third, the effects ofjob stressors appear to exist independentlyof the level of social support. That is, although social supportmay have main effects on strains, itdoes not moderate the effectsof stressful conditions in the workplace. The distinction betweenmain effects and buffering effects has important implications forpractice. Social support appears to have beneficial (albeit small)effects, but it does not seem to reduce the impact that workstressors have on strains. Moreover, there is little convincing ev-idence that certain groups of individuals will derive benefit fromthe receipt of social support, whereas others will not. Thus, cer-tain groups of workers should not be denied increases in socialsupport on the assumption that it will not help them. Finally, amain effects model of social support implies the implementationof a stress management strategy that focuses on both the reduc-tion of harmful job and role characteristics, such asconflict andambiguity, and the augmentation of such positive factors as useof skills and social support.

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Received December 14, 1984Revision received April 26,1985

role of social support in the experience of stress at work

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