A meta-analytic review of work–family conXict and its...

Journal of Vocational Behavior 67 (2005) 169–198

www.elsevier.com/locate/jvb

A meta-analytic review of work–family conXict and its antecedents �

Kristin Byron ¤

College of Business, Rochester Institute of Technology, 103 Lomb Memorial Drive,Rochester, NY 14623, USA

Received 6 November 2003Available online 2 March 2005

Abstract

This meta-analytic review combines the results of more than 60 studies to help determinethe relative eVects of work, nonwork, and demographic and individual factors on work inter-ference with family (WIF) and family interference with work (FIW). As expected, work factorsrelated more strongly to WIF, and some nonwork factors were more strongly related to FIW.Demographic factors, such as an employee’s sex and marital status, tended to relate weakly toWIF and FIW. Overall the analysis supports the notion that WIF and FIW have uniqueantecedents, and therefore, may require diVerent interventions or solutions to prevent orreduce their occurrence. Lastly, the analysis suggests that demographic variables, such as sexand marital status, are alone poor predictors of work–family conXict. Researchers are advisedto attend to more Wnely grained variables that may more fully capture employees’ likelihood ofexperiencing work–family conXict. 2005 Elsevier Inc. All rights reserved.

� I thank Margaret LaSalle for help with coding the studies and Frank Schmidt and Allen HuVcutt fortheir advice on calculations used in the analysis. I also thank Ross Rubenstein, Bill H. Bommer, EdwardW. Miles, Corinne Post, Tammy Allen, and two anonymous reviewers for their helpful comments and sug-gestions. Some of the results from this analysis were presented at the 2002 Southern Management Associa-tion Annual Meeting in Atlanta, Georgia and at the 2004 Society of Industrial and OrganizationalPsychologists Annual Meeting in Chicago, Illinois.

¤ Fax: +1 585 475 4423.E-mail address: [email protected].

0001-8791/$ - see front matter 2005 Elsevier Inc. All rights reserved.doi:10.1016/j.jvb.2004.08.009

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170 K. Byron / Journal of Vocational Behavior 67 (2005) 169–198

Keywords: Work; Family; ConXict; Interference; Meta-analysis; Review

1. Introduction

The increase in dual-career couples and single-parent households and the concom-itant decrease in traditional, single-earner families mean that responsibilities forwork, housework, and childcare are no longer conWned to traditional gender roles.Increasingly, employees Wnd themselves struggling to juggle the competing demandsof work and family. The problems and issues encountered by employees taking partin this balancing act has prompted a burgeoning body of research and theory on theintersections of individuals’ work and family lives (e.g., Kossek, Noe, & DeMarr,1999; Perrewe & Hochwarter, 2001). One of the most studied concepts in the work–family literature is work–family conXict. Work–family conXict, also called work–family interference, is a type of interrole conXict (Kahn, Wolfe, Quinn, Snoek, &Rosenthal, 1964) that occurs when the demands of work and family roles conXict.

Since the construct of work–family conXict was introduced, a large body of litera-ture has examined its causes and consequences. Recent meta-analyses have examinedthe relation between work–family conXict and its consequences, such as job and lifesatisfaction, burnout, and absenteeism (Allen, Herst, Bruck, & Sutton, 2000; Kossek& Ozeki, 1998, 1999). These meta-analyses underscore the potentially negative eVectsof work–family conXict for individuals and their employing organizations. However,among the published meta-analyses on work–family conXict, only one has examineda potential antecedent, job/work involvement (Kossek & Ozeki, 1999). No meta-anal-ysis to date has comprehensively considered the myriad causes of work–family con-Xict that have been examined in the literature.

In addition, the concept of work–family conXict has changed over time. Increas-ingly, researchers have acknowledged the direction of interference (O’Driscoll, Ilgen,& Hildreth, 1992). That is, work–family conXict is increasingly recognized as consist-ing of two distinct, though related, concepts, work interference with family (WIF)and family interference with work (FIW). WIF (also termed work-to-family conXict)occurs when work interferes with family life, and FIW (known also as family-to-work conXict) occurs when family life interferes with work (Frone, Yardley, & Mar-kel, 1997). Support for distinguishing these two concepts comes from several sources.First, in their meta-analysis, Kossek and Ozeki (1998) reported consistent support fordistinguishing between the direction of work–family conXict. Second, recent theoryand research on WIF and FIW suggests that these two concepts may have diVerentcauses and eVects (e.g., Frone, Russell, & Cooper, 1992a, 1992b; Kelloway, Gottlieb,& Barham, 1999).

In summary, while the potentially harmful eVects of work–family conXict are rec-ognized, we know less about the causes of work–family conXict and their relativeeVects on WIF and FIW. Consequently, a systematic review of the literature onwork–family conXict antecedents is needed to explain the experience of work–familyconXict in employees’ lives. The present study oVers such an analysis by providing a

K. Byron / Journal of Vocational Behavior 67 (2005) 169–198 171

quantitative review of potential antecedents and their relation to two types of work–family conXict, work interference with family (WIF) and family interference withwork (FIW).

2. Work–family conXict and its antecedents

Researchers have considered a number of diVerent variables as possible anteced-ents of WIF and FIW. Consistent with Eby, Casper, Lockwood, Bordeaux, and Brin-ley (in press) classiWcation scheme for antecedents of work–family conXict, thepreviously researched antecedents can be classiWed into three categories: workdomain variables, nonwork domain variables, and individual and demographic vari-ables. Work domain variables consider the eVect of job and workplace factors, suchas schedule Xexibility and job stress. Nonwork domain variables consider the familydemands and other nonwork factors, such as marital conXict, number of hours spenton housework or childcare, and age of youngest child. Demographic or individualvariables include personality, behaviors, and other individual diVerences, such as sex,income, and coping style.

While other theories about the intersection of work and family exist, the con-structs of WIF and FIW have their roots in conXict theory. ConXict theory proposesthat work and family domains are incompatible due to their diVerent norms andresponsibilities (Greenhaus & Beutell, 1985). The diVering norms and responsibilitiesof work and family cause intrusion and negative spillover of one domain on theother. Consistent with research and theory on WIF and FIW, antecedents related todiVerent domains may have diVerential eVects on WIF and FIW. Factors related toan individual’s job (work domain variables) are expected to be more related to WIFthan to FIW. For example, the more hours individuals spend at work, the more likelyit is that their work will interfere with their family life. Similarly, factors related toindividuals’ family and nonwork life (nonwork domain variables) are expected torelate more to FIW than to WIF. For example, individuals with more supportivefamilies may experience less FIW, yet may not have less WIF. In contrast, individualand demographic variables, such as sex or income, are expected to have equivalenteVects on WIF and FIW because they may simultaneously inXuence both work andnonwork. I propose the following hypotheses:

Hypothesis 1. Work-domain variables relate more to WIF than to FIW.

Hypothesis 2. Nonwork-domain variables relate more to FIW than to WIF.

Hypothesis 3. Demographic and individual variables have equivalent eVects on WIFand FIW.

Research examining proposed antecedents of WIF and FIW has produced mixedresults. Therefore, when warranted, I consider several potential moderators of theserelationships. DiVerences in sampling strategies may explain some of the variation inresults between studies. For example, some studies enlisted only parents, whereasothers made no such restrictions. In addition, some studies sampled only women or


only men, whereas others had a mixed sex sample. I explore whether diVerences insample composition, such as the percentage of parents or females in them sample,may moderate the relationship between antecedents and WIF and FIW. In thestudies used in the analysis, the percentage of parents ranged from 16 to 100 per-cent; and the percentage of females in the sample ranged from 0 to 100 percent.Previous research and theory suggests that being female or having children mayexplain diVerences in results across studies (e.g., Eagle, Icenogle, Maes, & Miles,1998; VoydanoV, 2002). Therefore, the present analysis considers whether the per-centage of females or the percentage of study participants with children explainsbetween-study variance.

In addition, other meta-analyses have reported that variation in how variablesare measured account for signiWcant diVerences in results across studies (e.g.,Verquer, Beehr, & Wagner, 2003). Therefore, in the present analysis, I considerwhether variation in how antecedents were measured accounts for diVerences inresults between studies. For example, many studies examined whether having morechildren related to more WIF or FIW. Among the studies considering thisrelationship, study participants’ number of children was measured in diVerentways. Some asked participants how many children they had living at home, othersasked participants how many children they had (with no restrictions), and othersasked participants whether they had children or not. The diVerent methods ofmeasuring this and other proposed antecedents of WIF and FIW may account forobserved diVerences in Wndings between studies. Therefore, I explored whetherdiVerences in measurement moderate the relationships between proposed anteced-ents and WIF and FIW.

Fig. 1 demonstrates the relationships examined in the present meta-analysis. Thesolid lines represent relationships that are proposed to be stronger (i.e., of highermagnitude) than those represented by dashed lines. Dotted lines represent relation-ships of undetermined magnitude, and curved lines represent the relationshipbetween WIF and FIW. It should be noted that the relationships represented in theWgure do not imply that alternate relationships are implausible. Rather, some of thesealternate relationships are mathematically equivalent and their plausibility is notbeing rejected, e.g., family conXict may also be a consequence, rather than a cause, ofWIF and FIW. It should also be noted that more complex relationships than thoserepresented in Fig. 1 are similarly plausible. For example, family support may moder-ate the relationship between spousal employment and WIF or FIW. Unfortunately,the data available to meta-analytic researchers can preclude the investigation of morecomplex relationships. Therefore, this meta-analysis focuses on the factors that havebeen identiWed in the literature as potential antecedents and their relationship toWIF and FIW.

In the next sections, I describe the method for Wnding, selecting, and coding stud-ies for the meta-analysis. Then, I specify the method for quantitatively cumulatingthe results in the studies. In the following section, I present the results of the reviewfor each of the three categories of antecedents of WIF and FIW, and the relationshipof WIF and FIW. Lastly, I discuss the implications of the results of the meta-analysisand provide suggestions for future research.


3. Method

3.1. Search strategy

I searched the computer database Psych-Info of the American PsychologicalAssociation using the keywords “work, family, conXict,” and “work, family, interfer-ence” for articles published in academic journals, resulting in more than 500 studies.After eliminating duplicates and studies that were not related to work–family conXict(e.g., those that were related to family conXict), the 243 remaining studies werereviewed for possible inclusion. In addition, I searched the reference lists of threerecently published meta-analyses on work–family conXict (Allen et al., 2000; Kossek& Ozeki, 1998, 1999) and one review article (Swanson, 1992) to locate articles that

Fig. 1. Proposed relationships between variables in meta-analysis. Note. Solid lines represent direct rela-tionships hypothesized to be stronger in magnitude than those represented by dashed lines. The dottedlines represent relationships of undetermined magnitude, and curved lines represent correlation ratherthan causation.


had not turned up in my computer database search. Lastly, I posted a message on thegeneral on-line forum on the Sloan Work and Family Research Network website,and on the Workfam Newsgroup of the Work/Family Initiative at PennsylvaniaState University soliciting research on work–family conXict. Because so few studiesthat were not subsequently published were located, the present analysis is restrictedto published studies. There are several factors that should mitigate concern aboutpublication bias. First, many of the relationships included in the present analysiswere from studies that were not explicitly considering the relationships. For example,most of the studies included in the meta-analysis of sex and work–family conXictwere not explicitly considering this relationship. Second, I included two estimates ofthe stability of each eVect size, (1) the number of studies needed to meaningfullychange the estimated eVect size and (2) 95% conWdence intervals of each eVect size.

3.2. Criteria for inclusion

For a study to be considered for inclusion, the study had to meet the following cri-teria:

1. WIF and FIW had to be quantitatively measured; qualitative studies of work–family conXict were eliminated.

2. The study had to report the relationship between a previously proposed anteced-ent and WIF and FIW or between WIF and FIW in a form that could be con-verted to a correlation. Studies that merely stated Wndings without providingdetails of those results, or did not provide data in a usable form were eliminated.

3. Only studies published by 2002, and written in the English language wereincluded.

4. Only studies that examined both WIF and FIW were included to increase the con-Wdence that observed diVerences were due to diVerences in the relationship ratherthan due to diVerences in samples.

5. When fewer than Wve studies could be located for a particular antecedent, theantecedent was not examined. For example, antecedents such as supervisoryresponsibilities, job type or level, child care satisfaction, and self-eYcacy wereexcluded.

Because the formula used in meta-analysis assumes that the studies used are statis-tically independent, I avoided violating the assumption of independence of studies byeliminating duplicate results from the same dataset. When more than one study usedthe same sample, only one was included, when a sample was a subset of a larger sam-ple, only the study that used the larger sample was included. However, when studiesusing the same sample considered diVerent antecedents, each was included in itsrespective analysis, but only the one with the larger sample was included when theyconsidered the same variable. In all, 61 studies met these criteria and were included inthe analysis. Some of these studies had multiple independent samples, which wereincluded as independent outcomes. Table 1 lists the studies by sample included in themeta-analysis, their sample characteristics, and the measure of WIF and FIW used.


3.3. Coding of variables

When a study gave a range for the sample size, I recorded the lowest number.Apparent typographical errors were clariWed with the study’s lead author. To observethe need for independence among studies, I averaged multiple time periods, andmultiple measures of FIW or WIF or the antecedent, which is consistent with othermeta-analyses of work–family conXict (e.g., Allen et al., 2000). When provided, the reli-ability (internal consistency) for each measure was recorded. When it was not pro-vided, the average reliability for that variable was inputted (except for measures thatwere assumed to be perfectly reliable, e.g., sex and number of children). Table 2 detailshow each of the antecedents were coded or measured in the studies used in the presentanalysis. In addition, it includes the range of reliability (internal consistency) of themeasure used for each antecedent and the average reliability weighted by sample size.

To test for accuracy in coding, a quarter of the studies were randomly selected tobe coded twice. The interrater agreement between the two coders was calculated bydividing the number of data points in agreement by the total number of data pointscoded. The interrater agreement between the two coders was .99. The high level ofagreement between the two coders was likely due to the fact that most of the codingin the present meta-analysis involved merely recording data and possibly applyingsimple decision rules, and that both coders had prior experience coding studies formeta-analysis.

3.4. Moderators

Several potential moderators were investigated to determine if they explained var-iation in the eVect sizes between studies. Two continuous moderators were consid-ered, the percentage of females in the sample and the percentage of parents in thesample. In addition, one categorical moderator was considered, the use of diVerentcoding schemes or measurement of antecedent variables. For example, most studiescoded spousal employment as a dichotomous variable (i.e., a spouse was eitheremployed or not); whereas three studies each used a diVerent coding scheme (e.g.,trichotomized or number of hours spouse works). When diVerent coding schemeswere used, studies that used similar coding schemes were grouped and analyzed. Ianalyzed sub-group analyses with Wve or more studies only. In addition, some ante-cedents were operationalized diVerently across studies (see Table 2). For example,some studies used measures of overall job stress, which often combined role ambigu-ity and role overload, whereas other studies measured only role overload. When aparticular antecedent was measured using diVerent constructs, I conducted sub-group analyses for each diVerent construct when there were more than Wve studies fora given construct. All moderators were coded by recording the sample characteristicsof each study (i.e., percentage of females in sample and percentage of parents in sam-ple) and information about the types of measures used in each study (e.g., whetherthe measure was continuous or categorical). Nearly all studies were conducted in theUS or Canada (as shown in Table 1), therefore, the country in which the study wasconducted could not be considered as a potential moderator.

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. Byron / Journal of V

ocational Behavior 67 (2005) 169–198

Table 1

asure N Country

t al. (1992a,1992b; 2) 490 US

1989; 5) 146 US

t al. (1991; 4) 320 Hong Kong

yer et al. (1996; 5) 243 Hong Kong

an et al. (1983; 4) 141 Canada

an et al. (1983; 4) 177 US

yer et al. (1996; 5) 144 US

yer et al. (1996; 5) 691 New Zealand

et al. (2000; 9) 160 US

1989; 4) 527 Canada

t al. (1991); Frone et al. 992b; 5)

314 US

et al. (2000; 9) 225 US

1989; 4) 143 US

Summary of studies and their characteristics

Author(s) and Publication year/sample characteristics WIF measure FIW

Adams and Jex (1999)Part-time university students Frone et al. (1992a,1992b; 2) Fron

Adams, King, and King (1996)Varied professions, full-time and livingwith a family member

Kopelman, Greenhaus, and Connolly (1983; 4)

Burle

Aryee, Fields, et al. (1999)Varied professions Gutek, Searle, and Klepa (1991; 4) Gute

Aryee, Luk, Leung, and Lo (1999)Public sector and university employees, parents in dual-earner families

Netemeyer, Boles, and McMurrian (1996; 5)

Netem

Barling, MacEwen, Kelloway, and Higginbottom (1994)University employees *Kopelman et al. (1983; 4) *Kop

Beutell and Witting-Berman (1999)Varied professions, married, and employed Kopelman et al. (1983; 6) Kope

Boles, Howard, and Donofrio (2001)Probation and parole oYcers Netemeyer et al. (1996; 5) Netem

Brough and Kelling (2002)Varied professions Netemeyer et al. (1996; 5) Netem

Bruck, Allen, and Spector (2002)Hospital employees, married or livingwith partner or child

Carlson, Kacmar, and Williams (2000; 9)

Carls

Burke and Greenglass (2001)Registered nurses Kopelman et al. (1983; 4) Burle

Carlson and Kacmar (2000)State government employees, married or with children

Gutek et al. (1991); Frone et al. (1992a,1992b; 5)

Gute(1992

Carlson et al. (2000)Varied; employed full-time Carlson et al. (2000; 9) Carls

Casper, Martin, BuVardi, and Erdwins (2002)Female employees with preschool-aged child Kopelman et al. (1983; 4) Burle

me

e e

y (

k e

e

elm

lm

e

e

on

y (

k ea,1

on

y (

K. B

yron / Journal of Vocational B

ehavior 67 (2005) 169–198177

Cinamon and Rich (2002)Lawyers and employees in computer or software Gutek et al. (1991); Self-developed; Gutek et al. (1991); Self-developed; 213 Israel

meyer (1993; 8) 227 Canada

and Viveros-Long (1981; 5) 1989 Canada

et al. (1992a, 1992b); Gutek 1991); Wiley (1987); 13

318 US

et al. (1992a, 1992b); Gutek 1991); Wiley (1987); 13

493 US

eveloped; 2 113 US

et al. (1992a, 1992b); Gutek 1991); 6

252 Canada

eveloped; 2 2700 US

et al. (1992a,1992b; 2) 496 US et al. (1992a,1992b; 2) 605 US

et al. (1992a,1992b; 2) 631 US

et al. (1992a, 1992b); Gutek 1991); 6

372 Canada

n et al. (2000; 9) 267 Hong Kong

(continued on next page)

Weld, married 7 7Cohen and Kirchmeyer (1995)

Nurses Shamir (1983; 6) KirchDuxbury et al. (1994)

Varied professions, parents of child age 6–12 Bohen and Viveros-Long (1981; 4) BohenEagle et al. (1998)

Varied professions, employed full-time, married or living with child

Frone et al. (1992a, 1992b); Gutek et al. (1991); Wiley (1987); 11

Froneet al. (

Eagle, Miles, and Icenogle (1997)University employees (married or with children at home)

Frone et al. (1992a, 1992b); Gutek et al. (1991); Wiley (1987); 11

Froneet al. (

Fox and Dwyer (1999)Registered nurses Self-developed; 2 Self-d

Frone and Yardley (1996)Financial services company employees Frone et al. (1992a, 1992b); Gutek

et al. (1991); 6Froneet al. (

Frone (2000)National Commorbidity Survey, employed and married or parent of child under 18

Self-developed; 2 Self-d

Frone, Russell, and Barnes (1996)Longitudinal follow-up (Erie County, NY), Wave 2 Frone et al. (1992a,1992b; 2) FroneLongitudinal follow-up (BuValo, NY), Wave 3 Frone et al. (1992a,1992b; 2) Frone

Frone et al. (1992a)Longitudinal follow-up (Erie County, NY), employed and married or with child at home

Frone et al. (1992a,1992b; 2) Frone

Frone et al. (1997)Varied professions, married or with children living at home

Frone et al. (1992a, 1992b); Gutek et al. (1991); 6

Froneet al. (

Fu and ShaVer (2001)University employees Carlson et al. (2000; 9) Carlso

178K



measure N Country

k et al. (1994; 4) 659 Canada

eveloped; 5 132 US

; 4 199 US

eveloped; 4 1986 US

y (1989; 4) 423 USy (1989; 4) 176 US

elman et al. (1983; 4) 17 Canada

n and Viveros-Long (1981; 3616 Canada

eveloped; 6 429 US

eveloped; 6 522 US

e et al. (1992a,1992b; 2) 515 US

k et al. (1991; 4) 1062 US

eveloped; 11 236 Canada

e et al. (1992a, 1992b); and Yogev (1985); 3

501 Finland

Table 1 (continued)


Gignac, Kelloway, and Gottlieb (1996)Work and family survey, employees caring for elderly relative

Gutek et al. (1994; 4) Gute

Grandey and Cropanzano (1999)University faculty Kopelman et al. (1983; 6) Self-d

Greenhaus, Parasuraman, and Collins (2001)Public accountants, married and with one or more children

Greenhaus and Beutell (1985; 9) DNR

Grzywacz and Marks (2000)National Survey of Midlife Development Self-developed; 4 Self-d

Gutek et al. (1991)Psychologists Kopelman et al. (1983; 4) BurleSenior managers in executive education Kopelman et al. (1983; 4) Burle

Hepburn and Barling (1996)University employees *Kopelman et al. (1983; 4) *Kop

Higgins, Duxbury, and Lee (1994)Federal and private-sector employees, married with children

Bohen and Viveros-Long (1981; 4) Bohe8)

Hughes and Galinsky (1994)Married employees of large corporation Self-developed; 8 Self-d

Hughes, Galinsky, and Morris (1992)Pharmaceutical company employees Self-developed; 8 Self-d

Jex and Elacqua (1999)Varied professions, employed students Frone et al. (1992a,1992b; 2) Fron

Judge, Boudreau, and Bretz (1994)Male executives from search Wrm database Gutek et al. (1991; 4) Gute

Kelloway et al. (1999)Healthcare and grocery organizations employees Self-developed; 11 Self-d

Kinnunen and Mauno (1998)Public sector, manufacturing, and home market employees

Frone et al. (1992a,1992b; 2); Brett and Yogev (1985); 3

FronBrett

K. B


ehavior 67 (2005) 169–198179

Kirchmeyer and Cohen (1999)yer (1993; 8) 200 Canada

loped; 3 573 US

al. (1991; 4) 490 US

989; 4) 151 Canada

loped; 12 156 US

an et al. (1983; 8) 40 Canada

loped; 15 143 US

loped; 4 5782 US

loped; 5 131 Japan

et al. (1998; 11) 178 Canada

al. (1991; 4) 72 Canada

loped; 21 182 USloped; 21 162 USloped; 21 186 US

(continued on next page)

School teachers, administrators, and staV Shamir (1983; 6) Kirchm

Klitzman, House, Israel, and Mero (1990)Manufacturing company employees Self-developed; 3 Self-de

Kossek, Colquitt, and Noe (2001)University employees Gutek et al. (1991; 4) Gutek

Leiter and Durup (1996)Hospital employees with families Kopelman et al. (1983; 4) Burley

Loerch, Russell, and Rush (1989)University support staV and administrators Thompson (1985); Wiley (1987);

Self-developed; 18Self-de

MacEwen and Barling (1994)Police, employed full-time, married, and with child at home

Kopelman et al. (1983; 8) Kopel

Mallard and Lance (1998)Federal employees with children living at home Self-developed; 17 Self-de

Marks (1998)Wisconsin Longitudinal Study, varied; full- or part-time employees

Self-developed; 3 Self-de

Matsui, Ohsawa, and Onglatco (1995)Employees at reWnery, advertising and HR consulting companies

Self-developed; 5 Self-de

McManus, Korabik, Rosin, and Kelloway (2002)Healthcare and grocery employees, married or single mothers

Gottlieb, Kelloway, and Barham (1998; 11)

Gottli

Accountants, engineers, and banking and telecommunications employees, married or single mothers

Gutek et al. (1991; 4) Gutek

Netemeyer et al. (1996)Teachers Self-developed; 22 Self-deSmall business owners Self-developed; 22 Self-deReal estate agents Self-developed; 22 Self-de

e

ve

et

(1

ve

m

ve

ve

ve

eb

et

veveve

180K



d in study was an adaptation. DNR, did not report.

measure N Country

k et al. (1991); Frone et al. 2a, 1992b); 5

272 US

developed; 7 121 US

elman et al. (1983; 4) 111 US

k et al. (1991; 4) 263 US

k et al. (1991; 3) 324 Varied

; 4 327 Canada

ey (1991; 4) 147 Hong Kong

rke et al. (1979; DNR) 234 US

rts (2000; 6) 751 Netherlands

e et al. (1992a,1992b; 2) 525 US

y (1987; DNR) 191 US

k et al. (1991; 3) 41 US

Table 1 (continued)

Note. Measures of WIF and FIW indicate source of scale and number of items; *indicates that scale use


Nielson, Carlson, and Lankau (2001)Undergraduate business graduates, married or living with child

Gutek et al. (1991); Frone et al. (1992a, 1992b); 5

Gute(199

O’Driscoll et al. (1992)Varied professions, working at least 20 h/week Self-developed; 7 Self-

Parasuraman, Purohit, Godshalk, and Beutell (1996)Business owners Kopelman, et al. (1983; 6) Kop

Perrewe, Hochwarter, and Kiewtiz (1999)Hotel managers Gutek et al. (1991; 4) Gute

ShaVer, Harrison, Gilley, and Luk (2001)Married international assignees, varied professions Gutek et al. (1991; 3) Gute

Shannon et al. (2001)Hospital employees DNR; 3 DNR

Stoeva, Chiu, and Greenhaus (2002)Senior civil servants Kopelman et al. (1983; 4) Burl

Thompson and Blau (1993)Varied professions *Burke, Weir, and DuWor (1979;

DNR)*Bu

Van der Hulst and Guerts (2001)Postal service employees Geurts (2000; 9) Geu

Vinokur, Pierce, and Buck (1999)Females who served in air force Frone et al. (1992a,1992b; 2) Fron

Wiley (1987)MBA students and graduate students enrolled in evening classes

Wiley (1987; DNR) Wile

Williams and Alliger (1994)Full-time employees Gutek et al. (1991; 3) Gute

K. B


ehavior 67 (2005) 169–198181

Table 2

s Reliability (weighted reliability) Categories

0 .63–.90 (.81) —N/A 2–5; #

5 .73–.94 (.83) —

.52–.68 (.58) —2 .67–.89 (.79) —

1 .60–.92 (.82) —

N/A 4, #

4 .75–.95 (.87) —

.66–.83 (.76) —9 .77–.87 (.80) —

N/A 2–4; #

N/A 3–5; #N/A 2N/A 2–3; #

N/A 2N/A 3–22; #

3 .73–.83 (.79) —

Summary of measures used for antecedent variables

Antecedent Measures Item

Work variablesJob involvement Work involvement; Job involvement 4–1Hours spent at work Number of hours worked; hours worked outside

the normal work week (e.g., working on weekends or at night or traveling for business)

1

Work support Supervisor support; organizational support; co-worker support; mentor support

3–3

Schedule Xexibility Schedule (in)Xexibility 1–7Job stress Job stress; role stress; role conXict; role

ambiguity; role overload; Psychological demands3–5

Nonwork variablesFamily/nonwork involvement Family involvement; child care involvement;

household involvement2–1

Hours of nonwork Hours spent on family; hours spent on housework; percentage of time spent parenting

1

Family support Family support; spouse support (instrumental and emotional support)

2–4

Family stress Role stress; role conXict; role ambiguity; role overload 4–9Family conXict Marital conXict; parental conXict; marital tension;

relationship agreement; marital anger5–1

Number of children Number of children; number of family dependents; number of children living at home; number of children under a particular age; have children or not

1

Age of youngest child Age of youngest child 1Marital status Marital status (married coded higher) 1Spousal employment Spousal employment; number of hours spouse works 1

Demographic/individual variablesSex Sex (female coded higher) 1Income Personal income; family income 1Coping style and skills Active coping style; time management

behaviors; personal coping style5–3


3.5. Analysis strategy

For the meta-analysis, I calculated eVect sizes with the Hunter and Schmidt (1995)meta-analytic method, which provides the most accurate method of estimating popu-lation eVect sizes from heterogeneous eVect sizes (Field, 2001). To estimate the popu-lation eVect size, I calculated a frequency weighted mean eVect size (corrected formeasurement error, when applicable). Before proceeding, I determined whether therewere outliers in each analysis by calculating the sample-adjusted meta-analytic devi-ancy statistic (SAMD) and inspecting the plot of SAMDs for each relationship ana-lyzed (HuVcutt & Arthur, 1995). A study was removed from the analysis for aparticular antecedent when a study was identiWed as an outlier for both WIF andFIW. One antecedent, job insecurity, was not further analyzed because there were toofew studies (k < 5) remaining after removing outlier studies. For the remainingrelationships considered, I continued the analysis after removing any outliers byassessing the stability of the estimated population eVect size by inspecting its modi-Wed fail-safe N (MFN) (HuVcutt, Roberts, & Steel, 2004) and by inspecting its 95%conWdence interval. The MFN for each eVect size indicates the number of additionalstudies (not additional subjects) with results diVering by two standard deviations thatwould need to be discovered to eVect a meaningful change (deWned as plus or minus.10 based on Cohen’s (1998) framework for evaluating eVect sizes) in the estimatedeVect size.

In addition, I subjected each analysis to two tests of homogeneity to determinehow diVerent the results for each analysis are. First, I examined the percentage of var-iance explained by artifacts. Possible moderators were investigated if the percentageof variance explained was less than 60%. The use of a lower threshold when eVectsizes are only corrected for reliability is consistent with Meyer, Stanley, Herscovitch,and Topolnytsky (2002). Second, I inspected the range of the 90% credibility intervalto determine if zero was included, which may indicate that moderators are present(Whitener, 1990).

To test the moderating eVects of continuous variables, I Wt a series of weightedleast-square regression models for each eVect size and moderator, weighted by theinverse variance of each eVect size (Callendar & Osburn, 1988; Hedges & Olkin, 1985;Sanchez-Meca & Marin-Martinez, 1998), which provides the most accurate resultsfor testing continuous moderators in meta-analysis (Steel & Kammeyer-Mueller,2002). The correlations were transformed to Fisher’s Z, and the inverse samplingerror variance was estimated by the sample size for each study minus three (Callen-dar & Osburn, 1988; Steel & Kammeyer-Mueller, 2002). To test the signiWcance ofmoderators, I calculated ZHO, the unstandardized regression coeYcient divided bythe corrected standard error (Hedges, 1994; Sanchez-Meca & Marin-Martinez, 1998),and determined whether it exceeded the critical value. I included studies that werepreviously identiWed as potential outliers because it was possible that the aberrantresults were due to diVerences in sample composition.

To test the moderating eVects of categorical variables, I split the group intosubgroups based on the categorical moderator and conducted a separate meta-analy-sis on each subgroup. However, in some of the analyses, there was no comparison


sub-group because the coding schemes in the remaining studies were all diVerent orbecause the sub-group contained too few studies. The percent of variance explainedby artifacts for the subgroup(s) was compared to the percent of varianceexplained by artifacts for the overall group analysis to determine if the moderatorexplained some between-study variation.

4. Results

Meta-analytic results of correlations between work interference with family andfamily interference with work and the proposed antecedent variables are presented inTable 3. As anticipated, all work variables had a greater impact on WIF than on FIWin the expected direction. For all of the six work variables, the relationship betweenWIF and the work-related antecedent was of greater magnitude than the relationshipbetween the antecedent and FIW, and the 95% conWdence (not credibility) intervalsdid not signiWcantly overlap. Employees who have higher job involvement or jobstress, or spend more time at work have more WIF than FIW, and employees whohave less supportive co-workers or supervisors or less Xexible schedules have moreWIF than FIW. Among work variables, job stress (�D .48) and schedule Xexibility(�D¡.30) were most strongly correlated with WIF. Employees who have more jobstress have more WIF, and those with more Xexible job schedules have less WIF.

Contrary to expectations, the correlations between nonwork variables and FIW (ascompared to WIF) did not have consistently stronger relationships in the expecteddirection. Several of the nonwork variables showed similar relationships to WIF andFIW. The 95% conWdence intervals for the estimated population eVect sizes for family/nonwork involvement, family support, family conXict, age of youngest child, andspousal employment overlapped in their relationships to WIF and FIW. The remain-ing four nonwork variables, hours of nonwork, family stress, number of children, andmarital status, demonstrated a pattern that was consistent with expectations. Themore hours spent on family, housework, childcare or other nonwork-related activities,the more FIW experienced (�D .21), but not signiWcantly more WIF, as indicated by95% conWdence interval that included zero (�D¡.02, 95% CI: ¡.06/+.02). In addition,employees who experienced more family-related stress experienced more FIW (�D .47,95% CI: +.44/+.50) than WIF (�D .30, 95% CI: +.27/+.33). Employees with more chil-dren or who were single had more FIW (�D .16, 95% CI: +.14/+.17, and �D¡.05, 95%CI: ¡.07/¡.03; respectively) than WIF (�D .09, 95% CI: +.07/+.11, and �D .03, 95%CI: +.01/+.05; respectively). Among all nonwork variables, family stress (�D .47) andfamily conXict (�D .32) were most strongly related to FIW. Employees who had morestress and more conXict at home had more family interference with work.

Demographic and individual antecedent variables were expected to have equiva-lent eVects on WIF and FIW. To determine the extent to which the variables havesimilar relationships to WIF and FIW, I inspected the 95% conWdence intervals ofthe estimated population eVect sizes for each demographic variable and itsrelationship to WIF and FIW. Of the demographic and individual variables, onlyone, coping style and skills, tended to have a similar relationship to both WIF and

184K



amily interference with work (FIW)

SDO SD� 95% CI MFN

.07 .11 .09 +.03/+.10 9

.01 .07 .05 ¡.01/+.03 75

.01 .07 .05 ¡.02/+.03 60

.12b .11 .09 ¡.15/¡.09 15

.17 .14 .13 ¡.21/¡.13 4

.29b .12 .11 +.27/+.31 15

.29b .10 .09 +.26/+.32 8

.40b .18 .18 +.38/+.42 4

.02 .15 .14 ¡.05/+.02 5

.21b .14 .12 +.17/+.24 6

.21b .14 .13 +.17/+.24 5

.17b .13 .11 ¡.21/¡.14 10

.47b .18 .18 +.44/+.50 3

.49 .20 .19 +.46/+.52 2

Table 3Work, nonwork, and demographic antecedents of WIF and FIW

k N Work interference with family (WIF)

� SDO SD� 95% CI MFN

Work variablesJob involvement 10 2766 .14 .15 .14 +.11/+.18 5Hours spent at work 22 9527 .26b .12 .11 +.24/+.27 17

Continuous only 18 8092 .27b .10 .09 +.25/+.29 18Work support 17 4165 ¡.19b .09 .07 ¡.22/¡.16 21Schedule Xexibility 8 2620 ¡.30 .26 .26 ¡.34/¡.27 2Job stress 19 7034 .48b .13 .12 +.46/+.49 12

Overall stress only 7 3183 .48b .07 .07 +.46/+.51 15Role overload only 10 4402 .65b .16 .16 +.63/+.67 5

Nonwork variablesFamily/nonwork involvement 9 2741 ¡.02 .08 .05 ¡.07/+.00 18Hours of nonwork 10 2875 ¡.02 .09 .07 ¡.06/+.02 13

Continuous only 9 2764 ¡.01a .07 .04 ¡.04/+.03 26Family support 14 2886 ¡.11b .10 .07 ¡.14/¡.07 14Family stress 8 2937 .30b .13 .12 +.27/+.33 5

Overall stress only 5 2008 .32 .12 .11 +.28/+.36 4

F

�

¡¡

¡

¡

K. B


ehavior 67 (2005) 169–198185

ent samples included in each analysis; N, combinedd standard deviation of corrected correlation; SD�,

FN, modiWed fail-safe N (indicates the number of

ere living at home, how many children were living atticipants to report how many children they had, the

change in the estimated population correlation.

Family conXict 8 1674 .35b .10 .07 +.31/+.40 9 .32b .16 .15 +.28/+.36 4.16b .12 .11 +.14/+.17 19.17b .12 .11 +.13/+.21 6.15b .12 .11 +.13/+.18 11

¡.22b .08 .07 ¡.24/¡.19 16¡.05 .08 .07 ¡.07/¡.03 25

.03 .10 .09 +.00/+.06 10

.02 .09 .08 ¡.02/+.05 7

.06 .12 .11 +.04/+.07 21

.00 .08 .07 ¡.03/+.02 21¡.15b .11 .10 ¡.19/¡.10 5

Note. WIF, Work interference with family; FIW, Family interference with work; k, number of independsample sizes of studies included in each analysis; �, weighted average corrected correlation; SDO, observeestimated true/population standard deviation of corrected correlation; 95% CI, 95% conWdence interval, Madditional studies needed to cause a meaningful change in the estimated population eVect size).

a More than 60% of the observed variance is accounted for by sampling error.b Zero is not included in the 90% credibility interval.c Number children living at home includes studies that asked individuals to report how many children w

home under a particular age, or how many family dependents they had. In contrast, when studies asked parnumber could include older children, adult children, or other nondependents.

d The ModiWed Fail-Safe N was negative, indicating that no number of studies could exact a meaningful

Number of children 27 10,467 .09 .12 .11 +.07/+.11 21Number of children 8 2557 . 05 .12 .11 +.01/+.09 6Number living at homec 15 6700 .08b .08 .06 +.06/+.11 28

Age of youngest child 9 7303 ¡.17a,b .04 .02 ¡.20/¡.15 N/Ad

Marital status 14 9378 .03 .10 .09 +.01/+.05 14Spousal employment 9 4358 .01a,b .05 .00 ¡.02/+.04 189

Dichotomous coding 6 3413 ¡.01a .05 .03 ¡.04/+.03 299

Demographic variablesSex 27 18,125 ¡.03 .11 .10 ¡.04/¡.01 24Income 13 7046 .10b .08 .07 +.08/+.12 22Coping style and skills 6 2002 ¡.12a,b .03 .00 ¡.16/¡.08 14


FIW. Having a positive coping style or having better coping skills seems to providesome protection from WIF and FIW (� D ¡.12 and �D ¡.15, respectively). The othertwo demographic and individual variables, sex and income, tended to vary in theirrelationship to WIF and FIW, as indicated by nonoverlapping 95% conWdence inter-vals. Male employees tended to have slightly more WIF (� D ¡.03, 95% CI: ¡.04/¡.01) and female employees tended to have more FIW (�D .06, 95% CI: +.04/+.07),although the diVerences between sexes and the diVerence between sex’s relationshipto WIF and FIW were small. There were also signiWcant diVerences between the rela-tionship of income to WIF and FIW. Employees with higher incomes had more WIF(� D .10, 95% CI: +.08/+.12), whereas income was not signiWcantly related to FIW(� D .00, 95% CI: ¡.03/+.02).

Next, I considered whether a search for potential moderators was warranted. Fornearly all antecedents, there seemed to be signiWcant variation between studies usedin the meta-analysis. In fact, the two tests of homogeneity used in this analysis indi-cated homogeneity in their relationship to WIF or FIW for only two antecedents, ageof youngest child and coping style/skills. However, the 90% credibility intervals sug-gested homogeneity for most of the relationships. Because the results for the homo-geneity tests failed to be consistent for the majority of analyses, I proceeded toconduct the proposed moderator analyses.

First, I considered the categorical moderator, diVerences in coding scheme or mea-surement (as shown in Table 3). Six variables that did not meet the two tests ofhomogeneity had diVerences in coding schemes between studies, hours spent at work,job stress, hours spent on nonwork, family stress, number of children, and spousalemployment. The estimated population eVect sizes for the overall group analysis andthe sub-group analysis of those that measured the variable continuously do not diVergreatly, however, for nearly all analyses, the sub-group analyses explain more of thevariance by artifacts or have more stable eVect size estimates. When considering onlystudies that measured time at work continuously, employees who spend more time atwork experience slightly more WIF but the same amount of FIW (�D .27 and �D .01,respectively). On the other hand, employees who spend more time in family or house-hold duties and activities experience less WIF although the same amount of FIW(� D ¡.02 and �D .21, respectively).

Studies that examined the relationship between job and family stress and WIF andFIW diVered in their measurement of job and family stress. Some studies used overallmeasures of job or family stress, some used more speciWc measures of job stress, suchas role overload or role ambiguity. For job stress, the sub-group analysis of thosestudies that used overall measures of job stress explained more variation than did theoverall analysis, suggesting the sub-group analysis may reXect more accurate esti-mates of the relationship between job stress and WIF and FIW (�D .48 and �D .29,respectively). When considering only studies that examined role overload, the esti-mated eVect sizes in regard to WIF and FIW tended to be greater but less stable andhomogenous (�D .65 and �D .40, respectively). For family stress, the only sub-groupanalysis that could be conducted was on those studies that used overall measures offamily stress. The estimated eVect sizes for this sub-group analysis did not signiW-cantly diVer from or improve upon those of the overall analysis.


Lastly, I considered whether diVerences in coding might account for between-study variation for two other family domain variables, number of children andspousal employment. Some studies asked employees to indicate how many children(with no restrictions) they had; whereas other asked employees to indicate howmany children they had living at home or under a particular age. The codingscheme for number of children explained signiWcant between-study variation,although the 95% conWdence intervals for the estimated eVect sizes tended to over-lap across each coding scheme for WIF (� D .05, 95% CI: +.01/+.09, and � D .08,95% CI: +.06/+.11; respectively) or FIW (� D .17, 95% CI: +.13/+.21, and � D .15,95% CI: +.13/+.18). Most studies dichotomized spousal employment (i.e., either thespouse works or not); the remaining three studies each used a diVerent codingscheme, and therefore were not considered in a sub-group analysis. Compared tothe overall analysis, the sub-group analysis for studies that dichotomized spousalemployment accounted for less between-study variation and had larger 90% credi-bility intervals. This suggests that the overall analysis provides a more accurateestimate of the relationship between spousal employment and WIF and FIWdespite the fact that there were diVerences in coding schemes. In summary, for mostof the relationships considered, diVerences in coding schemes tended to explainsome of the variance between studies or provide more stable estimated eVect sizesover the overall analyses.

Next, each of the two proposed continuous moderators were Wtted into a separateweighted least square equation for each relationship considered. The antecedent, cop-ing style and skills, was excluded for the proposed moderator, percent of sample withchildren, due to too few studies (k < 4) that provided data. I excluded other anteced-ents because their interpretation lacked conceptual meaning (i.e., for percent of par-ents in sample, number of children and age of youngest child, and, for percent femalein sample, sex was excluded). The results of the regression models for percent of sam-ple with children are included in Table 4, and the results of the regression models forpercent female in sample are included in Table 5.

The percent of parents in the sample related signiWcantly to the study eVect size forover 32% of the relationships considered, suggesting that diVerences in the composi-tion of the sample explains between-study variation for some relationships. In partic-ular, the percent of parents in the sample seems to aVect the relationship between jobstress and work–family interference. The more parents in the sample, the stronger thepositive relationship between job stress and WIF and FIW. While sex had a verysmall direct eVect on WIF or FIW, the percent of parents in the sample does moder-ate this relationship. Namely, when there are more parents in the sample, there is agreater sex diVerence in the experience of WIF and FIW, such that mothers experi-ence more WIF and FIW than fathers. When there are fewer parents in the sample,men tend to experience more WIF and FIW. Lastly, while there tended to be nodiVerence between married and single employees in their experience of WIF and FIWoverall, marital status is negatively related to WIF and FIW as the number of parentsin the sample increases. This suggests that single parents have more WIF and FIWthan parents who are married; whereas married and single employees without chil-dren tend to have similar levels of WIF and FIW.


The percent of female employees in the sample related signiWcantly to the studyeVect size for more than half of the relationships considered, suggesting that diVer-ences in the sex composition of the sample explains between-study variation for somerelationships. For example, having a higher percentage of women in a sample associ-ated with a weaker positive relationship between job involvement and WIF and FIW.Job involvement seems to relate more positively to WIF and FIW for men than forwomen. Conversely, family involvement related more positively to WIF and FIW forwomen than for men (although only approaching statistical signiWcance at the � levelof .05 for WIF). For men (as compared to women), being more highly involved intheir jobs is linked to more interference whereas, for women (as compared to men),being more highly involved in their family lives is linked to more interference. Inaddition, a higher percentage of females in a sample is negatively related to the studyeVect size for schedule Xexibility and WIF and FIW. Flexible schedules appear toprovide more of a protective beneWt for women than for men. However, family stressand family conXict are more positively related to WIF and FIW for men than forwomen. Stress and conXict in the family domain is linked to more interference formen as compared to women. When more women are represented in the sample, the

Table 4Percent of study sample with children as a moderator of eVect size

Note. r, Pearson correlation; �, standardized regression coeYcient WLS regression; ZHO, test of nullhypothesis that � D 0; * indicates 95% conWdence that � does not equal zero.

a Both antecedents concerned with children, number of children and age of youngest child, wereexcluded from analysis because considering the percentage of parents as a moderator of their relationshipsto WIF and FIW lacked conceptual meaning. Only three studies that considered coping style and skillsprovided data on the percent with children in the study; therefore, these analyses were excluded.

k WIF FIW

r � ZHO r � ZHO

Work variablesJob involvement 10 ¡.21 ¡.20 1.20 .00 .23 0.95Hours spent at work 21 ¡.12 ¡.08 1.29 ¡.05 ¡.10* 2.13Work support 14 ¡.35 ¡.31 1.14 ¡.39 ¡.30 1.79Schedule Xexibility 5 ¡.72 ¡.72* 4.73 ¡.18 ¡.55 1.30Job stress 13 ¡.01 .29* 3.21 .13 .43* 2.88

Nonwork variablesFamily/nonwork involvement 9 ¡.19 ¡.10 0.00 .35 .24 0.95Hours of nonwork 4 .02 ¡.34 0.41 ¡.12 .22 0.98Family support 11 ¡.26 ¡.26 1.16 .23 .33 1.70Family stress 7 ¡.31 ¡.07 0.00 ¡.24 ¡.01 0.06Family conXict 7 .08 ¡.01 0.04 ¡.48 ¡.72* 3.89Number of childrena — — — — — — —Age of youngest child a — — — — — — —Marital status 13 .17 ¡.45* 2.54 ¡.35 ¡.78* 3.50Spousal employment 10 ¡.59 ¡.52 0.88 ¡.02 ¡.36 1.58

Demographic variablesSex 23 .06 .65* 4.85 .24 .46* 19.52Income 14 ¡.05 ¡.09 0.00 .02 ¡.03 0.00Coping style and skillsa — — — — — — —


employee’s number of children is less positively related to WIF and FIW. For exam-ple, the weighted mean average correlation between number of children and WIF is.15 for all male samples and .02 for all female samples; and, for FIW, is .21 and .08,respectively. The percentage of female employees in the sample also moderates therelationship between marital status and WIF and FIW. For men, more so than forwomen, being married is associated with more WIF and FIW. For women, maritalstatus had a near-zero relationship to WIF and FIW, suggesting that being marriedor single has little eVect on female employees’ experience of WIF and FIW. In partic-ular, the percentage of females in the sample aVected the relationship between theantecedents and FIW (as compared to WIF), as the percentage of females in the sam-ple was a signiWcant moderator in 11 of the 16 (69%) relationships considered.

Lastly, I considered the relation between WIF and FIW. The weighted averagecorrected correlation is .48 (SDO D .11; SD� D . 10; 95% CI: +.46/+.49), which is theresult of cumulating the results of 47 studies with a total sample size of 13,384 (aftereliminating 9 studies identiWed as outliers). In all studies, WIF and FIW related posi-tively. More interference in one domain tends to associate with more interference inthe other. In fact, only one antecedent, job stress, related nearly as strongly to WIF

Table 5Percent female in study sample as a moderator of eVect size

Note. r, Pearson correlation; �, standardized regression coeYcient WLS regression; ZHO, test of nullhypothesis that � D 0; * indicates 95% conWdence that � does not equal zero.

a Sex was excluded from analysis because considering the percentage of females as a moderator of itsrelationships to WIF and FIW lacked conceptual meaning.

k WIF FIW

r � ZHO r � ZHO

Work variablesJob involvement 13 ¡.66 ¡.71* 6.47 ¡.33 ¡.35* 3.24Hours spent at work 27 ¡.10 ¡.03 0.28 ¡.14 ¡.12 0.00Work support 18 ¡.20 ¡.10 0.00 .06 .24 1.39Schedule Xexibility 8 .10 ¡.39* 2.83 ¡.63 ¡.78* 5.19Job stress 20 ¡.04 ¡.08 0.00 ¡.32 ¡.22* 2.80

Nonwork variablesFamily/nonwork involvement 11 .33 .53 1.91 .35 .23* 2.02Hours of nonwork 10 ¡.12 ¡.39 1.48 ¡.02 .21* 2.42Family support 16 ¡.15 ¡.30 1.35 ¡.56 ¡.40* 3.03Family stress 9 ¡.45 ¡.56* 3.67 ¡.62 ¡.78* 11.44Family conXict 8 ¡.22 ¡.30 1.28 .15 ¡.13 1.12Number of children 31 ¡.22 ¡.34* 2.09 ¡.24 ¡.27* 2.37Age of youngest child 12 ¡.17 ¡.14 0.00 .03 ¡.22* 2.19Marital status 15 ¡.02 ¡.32* 2.49 ¡.18 ¡.57* 3.38Spousal employment 11 .16 .20 0.00 ¡.26 ¡.15 0.00

Demographic variablesSexa — — — — — — —Income 14 .17 .16 0.00 .30 .43 1.57Coping style and skills 6 .11 ¡.12 0.00 ¡.66 ¡.60* 2.36


(� D .48), and only one antecedent, family stress, is nearly as strongly related to FIW(� D .47) as WIF and FIW related to each other.

5. Discussion

The results of this meta-analytic review support the diVerentiation between workinterference with family and family interference with work. Employees seem to diVer-entiate between the source, or direction, of interference, and the two types of interfer-ence appear to have diVerent antecedents. The results of the analysis partiallysupport the pattern of relationships expected: work-related antecedents tend to asso-ciate with more work-related interference than nonwork interference. Nonwork-related antecedents tend to relate to more family interference with work than workinterference with family, although the diVerences were not always statistically signiW-cant. However, of all of the antecedents, job stress, family stress, and family conXicthave among the strongest associations with both WIF and FIW, suggesting thatwhile there is diVerentiation, some work and family factors can have simultaneouslydisruptive eVects on employees’ work and family lives.

Surprisingly, the two demographic variables, sex and income, which have oftenbeen proposed in the literature as antecedents of WIF and FIW, had relatively lowrelationships to WIF and FIW. For example, sex, which has been proposed as anantecedent in dozens of studies, had a near zero relationship to WIF and a weak, pos-itive relationship to FIW. Contrary to hypotheses in many studies, the present analy-sis suggests that overall men and women have similar levels of WIF and FIW. ThisWnding coincides with other research that has reported no sex diVerence in the experi-ence or perception of occupational stress (Martocchio & O’Leary, 1989). The onlyindividual variable considered in the analysis, coping style and skills, seemed to oVersome beneWt to employees. Those with better time management skills or a better cop-ing style tended to have less WIF and FIW.

While demographic variables tended to be weak predictors of WIF and FIW, theydid tend to have indirect eVects on WIF and FIW. The percentage of women or par-ents in the sample explained between-study variance in more relationships thanwould be expected by chance. This coincides with recent theory that supports the useof social categories as moderators in the work–family literature (VoydanoV, 2002). Ingeneral, being male appears to exacerbate any negative eVects of family domain ante-cedents, such as family stress, family conXict, number of children, and marital status,related to work–family conXict. Paradoxically, females tend to enjoy greater protec-tive beneWts from those antecedents, such as Xexible work schedules, and, to someextent, supportive families, that lessen the experience of interference. While not asconsistently as the percentage of females in the sample, the percentage of parents inthe sample also explained some diVerences in results across studies. For employeeswith children as compared to those without children, having more job stress, beingsingle, and being is related to more work–family conXict.

Overall, the results provide partial support for the hypotheses of the study. In viewof that, some exceptions and other surprising results deserve note. First, for WIF and


FIW, only one antecedent each was as correlated with WIF and FIW as they werewith each other. While the diVerential eVects of antecedents provides support for dis-criminating between the two constructs, the strong positive relationship betweenthem deserves further study. Perhaps the perception of interference between domainscan be explained by a common third variable, such as being high in negative aVect orhaving expectations of separate domains. Second, contrary to expectations, nonworkdomain variables did not have a consistently stronger relationship to FIW than toWIF. Nonwork domain variables that have been referred to as family demands (i.e.,number of children, age of youngest child, marital status, and spousal employment)were nearly as related to FIW as to WIF. Perhaps this speaks to the asymmetric per-meability of domains, such that family demands cause family life to interfere withwork and for work to interfere with the relatively greater family demands. Lastly,family involvement had a near-zero correlation with FIW (and WIF), rather thanbeing positively related to FIW as expected. Employees who had higher familyinvolvement experience the same amount of FIW (and WIF) as those who were lessinvolved with their families.

5.1. Theoretical implications

Several theoretical models can glean support from these Wndings. Overall, theresults provide support for conXict theory (Greenhaus & Beutell, 1985). They high-light the potential incompatibility of work and family roles and ensuing conXict fromhaving multiple roles, at least for some people. For example, the present analysisfound that employees who experience more stress on the job are more likely to expe-rience interference from their work into their family lives. Likewise, employees whoexperience stress at home are more likely to experience interference from their familylives into their work day.

Furthermore, the results suggest that both spillover and congruence are apparentlinking mechanisms between the work and family domains (Edwards & Rothbard,2000). Spillover as a linking mechanism occurs when stress or strain from onedomain surface in another domain. Congruence as a linking mechanism betweenwork and family domains occurs when a third variable links the domains of workand family domains by having a congruent eVect on both (Edwards & Rothbard,2000). Support for spillover as a linking mechanism can be seen in the positive rela-tionship between job stress and WIF and between family stress and conXict and FIW.Stress from one domain is interfering with the other domain. While the results sug-gest that negative spillover can occur from one domain to another, the results alsosupport the notion that positive spillover can occur. For example, employees who areemployed in more supportive workplaces or who have more supportive families tendto experience less work–family conXict. Support for congruence as a linking mecha-nism is found in the similar relationship employees’ coping style and skills to bothWIF and FIW. Employees who have better time management skills and copingbehaviors experience less WIF and FIW.

The results of the meta-analysis provide some support for the rational view, whichpredicts that the more time one spends in a role, or the more one specializes or is


involved in a role, the more he or she will perceive interference in the secondary rolefrom the participation in the primary role (Pleck, 1977). Consistent with this view, thenumber of hours spent on work was more positively related to WIF than to FIW,and the number of hours spent on nonwork was more positively related to FIW thanto WIF. Similarly, employees with higher job involvement had more WIF than FIW.However, inconsistent with the predictions of this view, employees with more familyor nonwork involvement did not tend to have more WIF or FIW.

Alternatively, the results do oVer some support for the sex-role hypothesis, whichproposes that sex or sex roles moderate the relationship between role involvementand psychological distress (VoydanoV, 2002). As mentioned above, the present analy-sis found an inconsistent relationship between role involvement in a given role andWIF and FIW, which is surprising given the frequency with which these relationshipshave been explored in the literature. Employee’s sex does seem to moderate the rela-tionship between job and family involvement and WIF and FIW. For three out of thefour eVect sizes, having more females in the sample related to the strength of the rela-tionship between role involvement and work–family interference. Namely, jobinvolvement seems to relate more positively to WIF and FIW for men than forwomen. In addition, when more of a study’s participants were parents, there was agreater sex diVerence in the experience of WIF and FIW, such that mothers experi-ence more WIF and FIW than fathers. When there were fewer parents in the sample,men tended to experience more WIF and FIW. Perhaps because women tended totake on greater responsibilities for childcare, mothers experience more distress fromthe greater workload but only when they are also highly involved in their work.

In summary, the results provide support for multiple theoretical models. This sug-gests that no single model can fully explain how employees experience the intersec-tion between their work and nonwork domains. Future theorizing should worktoward creating an integrative model that more fully explains the complexity sug-gested by the results presented here.

5.2. Future research

The present study also oVers some suggestions for future research. First, thecontinued use of bidirectional measures is supported. The present results providesupport for the discriminant validity of these constructs. Second, the relativeimportance of these antecedents may guide future research aimed at better under-standing the causes and prevention of work–family conXict. Factors such as jobstress and family conXict, which were strong predictors of both WIF and FIW, areimportant topics for future research. Lastly, diVerences in the composition of thestudy sample (i.e., percentage of females and percentage of parents) and the lackof homogeneity in many of the analyses suggest that researchers should bethoughtful about choosing their sample. In cumulating these studies, diVerencesbetween sampling strategies became apparent. For example, some studies onlyconsidered parents (e.g., Duxbury, Higgins, & Lee, 1994), some only included mar-ried participants (e.g., Beutell & Witting-Berman, 1999), and some did not employrestrictions (e.g., Aryee, Fields, & Luk, 1999). Future research should further


investigate whether diVerences in sampling strategies explain diVerences in resultsacross studies.

In addition, in reviewing the primary research that considers antecedents of work–family interference, several gaps in the literature became apparent. Few studiesexplicitly considered the diVerence between caring for adults and children. This gapin the literature has been noted by others: “[H]uman resource and organizationalbehavior scholars often overlook how caring for a parent diVers from caring for achild” (Kossek et al., 1999, p. 114). In addition, most studies began with the assump-tion that having multiple roles would necessarily lead to distress. However, researchin other areas supports the idea that individuals who hold multiple roles reap beneWtsfrom doing so. In the management literature, there should be more recognition of thebeneWts (rather than detriments) that arise from the participation in multiple roles(e.g., Thoits, 1992). Research should discard the overly simplistic notion that distressmust be found at the intersection of work and family, and instead focus on determin-ing the conditions that distinguish when multiple roles leads to distress and whenmultiple roles leads to increased fulWllment (Barnett & Hyde, 2001; Greenhaus &Parasuraman, 1999). Lastly, few studies considered individual variables, such as per-sonality or skill level. “It seems as though work–family conXict research, for the mostpart, has not focused on individual diVerences in success at handling work–familyconXict” (Baltes & Dickson, 2001, p. 53). Future research should consider whetherother individual variables are useful at explaining diVerences in outcomes in thework–family literature.

5.3. Practical implications

In addition to providing guidance to work–family researchers, the results of theanalysis oVer some practical implications. Namely, employers who seek to reducetheir employees’ perceived stress from competing demands should focus on reducingjob-related stress. Job-related stress, such as role conXict, ambiguity, and overload,seems to be spilling over into employees’ lives away from work. Furthermore, whileoutside of the work domain, employers may consider oVering guidance to theiremployees on improving their relationships with their spouses and children to reducefamily conXict. Employees who reported more marital strife or more conXicts withtheir children had more interference between their work and family lives. Perhapsmore employers should oVer training to their employees on managing family conXict,although the beneWts of this type of training to employers are not well established.Clearly, employees are not checking their family concerns at the workplace door, sug-gesting that employers may have an interest in helping employees with these concerns.

The analysis also suggests that employers can eVectively reduce the experience ofwork–family interference among their employees. Namely, employees who had moreXexible schedules or who had more supportive coworkers or supervisors reported lessWIF and FIW. This suggests that some employer interventions may be beneWcial atreducing distress for employees.

While the present analysis does provide guidance for future research and has somepractical implications, the analysis is not without its limitations. One of the primary


limitations is that meta-analysis cannot partial out the eVects of other variables onthe relationships considered. For example, spousal employment relate more stronglyto WIF and FIW when the presence of young children in the home is simultaneouslyconsidered. In addition, some of the eVect sizes were cumulated from a small numberof studies. Field (2001) warned that “estimates and signiWcance tests from meta-ana-lytic studies containing less than 30 samples should be interpreted very cautiously”(p. 179). However, the two estimates of eVect size stability generally provide supportfor the relative stability of the eVect sizes. As with all meta-analyses, more stable andaccurate estimates may be obtained with the addition of more primary research.Lastly, as mentioned previously, the eVect size estimates have signiWcant between-study variation. The present analyses suggest that sample composition is one sourceof between-study variation, and future research should seek to identify other sourcesof variation between studies of work–family interference.

This study provides support for the bidirectional nature of work–family conXict,and it suggests that researchers should employ measures that distinguish betweenWIF and FIW. Furthermore, it supports the notion that WIF and FIW haveunique antecedents, and therefore, may require diVerent interventions or solutionsto prevent or reduce their experience. Lastly, the analysis suggests that demo-graphic variables, such as sex and marital status, are alone poor predictors ofwork–family conXict. Researchers are advised to examine more Wnely-grainedvariables that may more fully capture employees’ likelihood of experiencing work–family conXict.

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