Copyright 1992 by the American Psychological Association, Inc. 0021-9010/92/S3.00
Journal of Applied Psychology 1992, Vol. 77, No. 1,65-78
Antecedents and Outcomes of Work-Family Conflict: Testing a Model of the Work-Family Interface Michael R. Frone and Marcia Russell Research Institute on Alcoholism Buffalo, New York
M. Lynne Cooper Center for the Study of Behavioral and Social Aspects of Health State University of New York at Buffalo A comprehensive model of the work-family interface was developed and tested. The proposed model extended prior research by explicitly distinguishing between work interfering with family and family interfering with work. This distinction allowed testing of hypotheses concerning the unique antecedents and outcomes of both forms of work-family conflict and a reciprocal relationship between them. The influence of gender, race, and job type on the generalizability of the model was also examined. Data were obtained through household interviews with a random sample of 631 individuals. The model was tested with structural equation modeling techniques. Results were strongly supportive. In addition, although the model was invariant across gender and race, there were differences across blue- and white-collar workers. Implications for future research on the work-family interface are discussed.
begun to examine work-family conflict (WFC) as another source of stress that may influence well-being (e.g., Greenhaus & Parasuraman, 1986; Voydanoff, 1987). We believe that this last line of research is especially promising in terms of integrating research on work and family stress. More specifically, because WFC reflects the goodness of fit between work and family life, it may function as a critical intervening pathway through which conditions at work affect the quality of family life and vice versa. To examine fully the relationship of WFC to well-being and its potential to integrate the study of work and family stress, WFC must be examined within the context of a multivariate model that meets at least four major criteria. First, key workand family-related antecedents of both WFC and well-being need to be assessed (e.g., Lambert, 1990). Second, both domainspecific and general measures of well-being should be examined (Kline & Cowan, 1988; Lambert, 1990). Third, the bidirectional nature of WFC (i.e., work interfering with family versus family interfering with work) must be addressed (Greenhaus, 1988; Greenhaus & Beutell, 1985; Gutek, Searle, & Klepa, 1991). Finally, the sample should be large, heterogeneous, and representative of employed adults. To date, however, research testing multivariate models of WFC that meet even the first two criteria is rare (Burke, 1988). Thus, the goal of this study is to develop and test a comprehensive model of the work-family interface that meets each of these criteria. Although such a model is by necessity complex, it is consistent with Lambert's (1990) recent admonition that "only by looking at the work/ family nexus in its entirety can we uncover the intricacies of the relationship between work and home" (p. 250).
Work and family represent two of the most central realms of adult life. Therefore, each offers a unique vantage point from which to investigate important aspects of human behavior. Although the study of work and family has spawned rich conceptual and empirical literatures, these two domains of life traditionally have been studied independently. More recently, however, the interface between work and family roles has captured the interest of a growing number of work and family researchers. This new focus has been fueled by several demographic trends that are reshaping American society. For example, there has been a dramatic increase in the number of married women who have young children and who are joining the work force and an increase in the prevalence of employed adults who are part of dual-earner families, single-parent families, and families facing the demands of elder care (e.g., Burke & Greenglass, 1987; Hall & Richter, 1988; Matthews & Rodin, 1989; Zedeck & Mosier, 1990). Interest in the work-family interface has produced a strong emphasis on integrating work and family research. Within the area of stress research, new questions are being asked regarding such issues as the relative impact of work and family stressors on overall well-being, the impact of job stressors on family life, and the impact of family stressors on work life (cf. Burke & Greenglass, 1987; Voydanoff, 1987). In addition, research has This research was supported by the National Institute on Alcohol Abuse and Alcoholism, Grant No. AA05702, awarded to Marcia Russell. We wish to thank Ann Gerber, Michael Windle, and the two anonymous reviewers for their thoughtful comments on an earlier version of this article. Correspondence concerning this article should be addressed to Michael R. Frone, Research Institute on Alcoholism, 1021 Main Street, Buffalo, New York 14203.
A Model of the Work-Family Interface The conceptual model that guided the present research is illustrated in Figure 1. We would like to acknowledge that this 65
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M. FRONE, M. RUSSELL, AND M. COOPER
study does not represent the first attempt to formulate and test a model of the work-family interface. Rather, our model should be viewed as a significant extension of several previous models (e.g., Bacharach, Bamberger, & Conley, 1991; Bedeian, Burke, & Moffett, 1988; Burke, 1988; Greenhaus & Beutell, 1985; Greenhaus & Parasuraman, 1986; Kopelman, Greenhaus, & Connolly, 1983; Rice, Frone, & McFarlin, in press). In outlining our model, we begin by discussing the bidirectional nature of WFC. Next we summarize hypotheses concerning the predictors of WFC. We then summarize hypotheses regarding the predictors of domain-specific distress (i.e., job distress and family distress) and overall psychological distress (i.e., depression). Finally, we discuss the importance of examining the influence of key sociodemographic characteristics on the generalizability of our model. Bidirectional Nature of Work-Family Conflict Definitions of WFC explicitly portray a bidirectional conceptualization (e.g., Greenhaus & Beutell, 1985). That is, they distinguish between work interfering with family (W -+• F conflict) and family interfering with work (F -*• W conflict). Despite this conceptual distinction, previous research has relied almost exclusively on measures that assess only W -»• F conflict (Greenhaus & Beutell, 1985). Furthermore, there is evidence that mixed directional measures of WFC (e.g., "How much do your job and family life interfere with one another?"; Pleck, Staines, & Lang, 1980) primarily assess W -*• F conflict (Frone, Russell, & Cooper, 1991; Gutek et al., 1991). For example, Frone et al. (1991) found that the experience of W -*• F conflict was reported almost three times more frequently than the experience of F -»• W conflict among men and women.
We concur with Greenhaus and Beutell (1985) that failing to examine both types of WFC may limit our understanding of the work-family interface to the extent that each is associated with different antecedents and consequences (as discussed below). However, another limitation of failing to examine both types of WFC simultaneously is that the potential reciprocal relationship between conflicting work and family roles has never been examined. As shown in Figure 1, we hypothesize a positive reciprocal relationship between the two types of WFC. The rationale for this relationship is based on the assumption that if one's work-related problems and responsibilities begin to interfere with the accomplishment of one's family-related obligations, these unfulfilled family obligations may begin to interfere with one's day-to-day functioning at work. Conversely, if one's family-related problems and responsibilities begin to interfere with the accomplishment of one's work-related obligations, these unfulfilled work obligations may begin to interfere with one's day-to-day functioning at home. This hypothesis is consistent with Schaubroeck's (1990) recent suggestion that the conflict relationship between work and family may be examined appropriately within the context of a reciprocal relationship. Predictors ofWork-Family Conflict The direct predictors of WFC portrayed in our model (Figure 1) are job stressors, family stressors, job involvement, and family involvement. We include these four predictors because each has been identified as an important antecedent of WFC (Greenhaus & Beutell, 1985). Moreover, each predictor was expected to be directly related to only one type of WFC. With regard to job and family stressors, Greenhaus and Beu-
W ->F Conflict
\
\
\\ +
•f
I
Conflict
Figure 1. Conceptual model of the work-family interface. (Pluses and minuses represent the direction of hypothesized relationships. Broken lines signify two nested models, one without [Model A] and one with [Model B ] paths from work-family conflict to depression. The letter d represents the disturbance term for each endogenous variable. To simplify presentation of the model, the correlations among the exogenous variables and the measurement model are not shown.)
WORK-FAMILY CONFLICT
tell (1985) hypothesized that exposure to stressors in a given domain (e.g., work) may lead to irritability, fatigue, or preoccupation with those problems, thereby limiting one's ability to meet the demands of other domains of life (e.g., family). Expanding this general argument to include the two types of WFC, we hypothesize that job stressors are directly and positively related to W -*• F conflict, whereas family stressors are directly and positively related to F -»• W conflict. Previous research strongly supports a positive relationship between perceived work stressors and W -*• F conflict (e.g., Bacharach et al, 1991; Bedeian et al., 1988; Burke, 1988; Greenhaus & Beutell, 1985; Kopelman et al., 1983; Parasuraman, Greenhaus, Granrose, Rabinowitz, & Beutell, 1989; VoydanofF, 1988). No research, however, has examined the relationship between family stressors and F -*• W conflict. The second major antecedent of WFC is the psychological importance of work and family roles. Greenhaus and Beutell (1985) argued that high levels of psychological involvement may lead to increased WFC in two ways. First, high levels of psychological involvement in one role may be associated with an increase in the amount of time and effort devoted to that role, thereby making it more difficult to comply with pressures associated with another role. Second, high levels of psychological involvement in a given role may cause one to be mentally preoccupied with that role even when physically attempting to fulfill the demands of a second role. Expanding these arguments to include the two types of WFC, we hypothesize that job involvement is directly and positively related to W -*• F conflict, whereas family involvement is directly and positively related to F -*• W conflict. Previous research supports a positive relationship between job involvement and W -*• F conflict (e.g., Beutell, 1983; Beutell & O'Hare, 1987; Frone & Rice, 1987; Greenhaus & Kopelman, 1981; Greenhaus, Parasuraman, Granrose, Rabinowitz, & Beutell, 1989; Wiley, 1987). Although the relationship between family involvement and F -»• W conflict has not been directly examined, there is indirect evidence to support such a relationship. Gutek et al. (1991) found that the number of hours devoted to family activities, which is presumably positively related to family involvement, was positively related to F -*• W conflict. Predictors of Domain-Specific Psychological Distress As indicated in Figure 1, the direct predictors of both job distress and family distress are WFC, domain-specific stressors, and domain-specific psychological involvement. With regard to WFC, we hypothesize that W -*• F conflict is directly and positively related to family distress, whereas F -> W conflict is directly and positively related to job distress. The rationale for this hypothesis is that high levels of psychological distress associated with a given role may be experienced if one is frequently struggling to meet the demands of that role because of interference from another role. For example, relative to individuals whose family life does not interfere with their jobs, individuals who experience high levels of F -*• W conflict may report elevated levels of job-related distress because they are more likely to feel overwhelmed by the ensuing struggle to meet their responsibilities at work and therefore experience a reduction in the quality of their work life. A parallel explanation may
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be offered for the hypothesized positive relationship between W -»• F conflict and family distress. Although previous research has found that W -»• F conflict is associated with low levels of family satisfaction (e.g., Bedeian et al., 1988; Beutell, 1983; Beutell & O'Hare, 1987; Coverman, 1989; Holahan & Gilbert, 1979; Parasuraman et al., 1989; Rice et al., in press), no research has examined the relationship between F -*• W conflict and job-related affect. With regard to job and family stressors, we hypothesize that they are directly and positively related to their respective within-domain measure of distress. Previous research lends strong support for an association between job and family stressors and decreased job and family satisfaction, respectively (e.g., Bedeian et al., 1988; Kandel, Davies, &Raveis, 1985; Kopelman et al., 1983; Parasuraman et al., 1989). Finally, with regard to job and family involvement, we hypothesize that they are directly and negatively related to their respective within-domain measure of distress. This hypothesis is based on the conceptual and empirical work of several researchers who have examined the relationship between job involvement and job satisfaction (e.g., Rabinowitz & Hall, 1977; Sekaran, 1989; Weiner, Muczyk, & Gable, 1987; Weiner & Vardi, 1980). For example, Sekaran (1989) postulated that highly job-involved workers invest greater amounts of time and effort in their work. As a result, they are more likely than low job-involved workers to develop a greater sense of work-related mastery, which in turn promotes higher levels of job satisfaction. Sekaran's (1989) study supports a positive relationship between job involvement and job satisfaction by way of increased perceptions of work-related mastery. Although a similar process may lead to a relationship between family involvement and family distress, no research has examined the relationship between these two variables. Predictors of Overall Psychological Distress As shown in our model, we hypothesize that both job- and family-related psychological distress are directly and positively related to depression (used here as an indicator of overall psychological distress). This hypothesis was derived from additive models of overall quality of life (e.g., Andrews & Withey, 1976; Campbell, Converse, & Rodgers, 1976; Michalos, 1985; Rice, McFarlin, Hunt & Near, 1985). Additive models propose that the perceived quality of life associated with each constituent domain of life combine additively to determine overall quality of life (Rice et al., in press). This proposition has been supported using both measures of satisfaction and psychological distress (e.g., Bedeian et al., 1988; Coverman, 1989; Kandel et al., 1985; Kopelman et al., 1983; Rice et al., in press). A strict interpretation of additive models implies that the effect of any other variable on overall quality of life must be indirect, that is, mediated by the quality of life in one or more specific domains of life (Rice et al., in press). Consistent with this proposition, our model indicates that the relationships of stressors, psychological involvement, and WFC to depression are mediated through their direct or indirect relationships to either job or family distress. With regard to work and family stressors, previous research lends strong support to this hypothesis (Bedeian et al., 1988; Coverman, 1989; Kandel et al.,
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M. FRONE, M. RUSSELL, AND M. COOPER
1985; Kopelman et al., 1983). In contrast, results concerning the relationship between W -*• F conflict and overall life satisfaction are inconsistent. Previous research has found indirect effects only, via job or family satisfaction (e.g., Coverman, 1989; Rice et al., in press); direct effects only (Kopelman et al., 1983); and both indirect (via job or family satisfaction) and direct effects (Bedeian et al., 1988). Because these mixed results do not lend strong support for a fully mediated relationship between WFC and overall psychological distress, we will examine (via nested models) whether a model containing the two direct paths between WFC and depression fits the data better than a model that does not contain these two paths (see broken lines in Figure 1).
Procedures Data for this study were collected by 20 professionally trained interviewers during the spring and summer of 1989 as part of a larger study of stress processes. Field work for this study (i.e., interviewer recruitment and training; supervision of interviewers; validation of interviewers' work; and data preparation, entry, and cleaning) was conducted by WESTAT, Inc., a nationally recognized survey research firm. Interviews were conducted in respondents' homes with a highly structured interview schedule that contained both interviewer- and self-administered sections. The complete interview required approximately 90 min to administer. Respondents were compensated $25 for their time. Measures
Generalizability of the Model Previous conceptual discussions of work-family stress processes propose that gender may represent an important moderator variable (e.g., Eckenrode & Gore, 1990; Kline & Cowan, 1988; Lambert, 1990). However, potential gender differences in work-family stress processes are not always examined. Furthermore, most research testing models of WFC has used relatively circumscribed samples of White individuals in white-collar jobs. Thus, we know little about the generalizability of previous research to minority and blue-collar workers (Burke & Greenglass, 1987). Given that the sociodemographic composition of the American work force is becoming more diverse (e.g., Jackson & Schuler, 1990; OfTerman & Gowing, 1990), the lack of knowledge concerning the generalizability of work-family stress processes represents an important limitation. To address this issue, we examine the fit of our model across gender, race, and job type.
Method Sample Respondents in this study were drawn from the longitudinal followup of a random sample survey of 1,933 adults residing in Erie County, New York. Designated respondents were identified in a three-stage probability sample, designed to yield approximately equal representation of two racial groups (Blacks and non-Blacks) and three education levels (less than high school graduate, high school graduate, and at least some college). The overall completion rate for Wave 1 was 78.3%. Of the 1,933 Wave 1 respondents, 1,616 were reinterviewed at Wave 2, representing 83.6% of the total original sample. Of the 1,616 respondents interviewed at Wave 2,631 met the following criteria for selection into this study: (a) employed at least 20 hr per week, (b) currently married or living as married, or had children living at home, or both, and (c) provided valid data on all measures described below. We used data only from the second wave of data collection because several of the major variables were not assessed in the first wave. On average, respondents were 40.7 years old (573 = 10.4) and had completed 13.3 years of formal education (SD = 2.3). Fifty-six percent were women and 42% were White. Seventy-three percent were married or living as married, and 78% had at least one child living at home. Respondents had worked in their current jobs for an average of 8.7 years (SD = 8.5); approximately equal numbers held blue-collar (49%) and white-collar (51%) jobs.
Each of the measures is described in detail below. All measures were administered by the interviewer, except those assessing psychological involvement and depression, which were self-administered. Descriptive statistics (i.e., means, standard deviations, and internal consistency reliability estimates) and zero-order correlations for the major study variables are presented in Table 1. Except where otherwise noted, all variables were created by averaging their respective items and were scored so that a high score represents higher levels of the construct. Sociodemographic Characteristics Gender. This variable was coded as 0 for men and 1 for women. Race. Respondents classified themselves into one of the following seven racial or ethnic groups: Aleut, Eskimo, or American Indian; Asian/Pacific Islander; Black Hispanic; Black, not Hispanic; White Hispanic; White, not Hispanic; and other. Respondents classifying themselves as White, not Hispanic were coded as White (0), whereas all others were coded non-White (1). Although it is not desirable to group all racial minorities together, we combined them into a single group because the non-White sample in this study was composed almost entirely of Blacks (96%). Age. This variable was based on self-reported date of birth and was coded in years. Education. This variable was based on self-reported years of formal education. Job type. This variable was based on responses to the following open-ended question: What is your occupation or job title and what sort of work do you do on your job? Professional coders classified responses into one of the 13 major occupational categories used by the U.S. Census Bureau (U.S. Department of Commerce, 1982). A respondent was coded as white-collar (1) if his or her job fell into categories 1 through 5, whereas a respondent was coded as blue-collar (0) if his or her job fell into categories 6 through 13. Although this categorization is somewhat crude, our measure of job type was, as expected, positively correlated with years of formal education (r = .43, p < .001). Job tenure. This variable was based on self-reported tenure in the person's current job and was coded in years. Marital status. Respondents were coded either as not married (i.e., never married, widowed, separated, or divorced [0]) or married or living as married (1). Number of children. Number of children currently living at home was coded into five categories ranging from 0 to 4 or more. Age of youngest child. Following a coding scheme outlined by Bedeian et al. (1988), we coded age of youngest child living at home into one of the following five categories: no children (1), youngest child over 18 years of age (2), youngest child 13-18 years of age (3), youngest child 6-12 years of age (4), and youngest child less than 6 years of age (5).
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WORK-FAMILY CONFLICT Table 1 Means, Standard Deviations, Reliability Estimates, and Intercorrelations for Major Study Variables
1
Variable
1. 2. 3. 4. 5. 6. 7. 8. 9.
Job involvement Family involvement Job stressor index Family stressor index W -* F Conflict F -* W Conflict Job distress Family distress Depression
(.88) .14* .02 -.02 .05 .09* -.21* -.03 -.05
(.88) -.06 -.22* -.06 .01 -.13* -.31* -.09*
(.72) .21* .31* .19* .53* .21* .28*
(.66) .23* .29* .25* .60* .38*
(.76) .33* .28* .21* .26*
(.56) .24* .25* .42*
(-84) .43* .37*
(.88) .48*
(.87)
M
SD
3.92 5.14 2.05 1.83 2.21 1.39 1.80 1.57 1.58
1.18 0.79 0.36 0.49 1.07 0.61 0.60 0.48 0.39
Note. Numbers on the diagonal are internal consistency reliability estimates (coefficient alpha). * p ^ .05.
Psychological Involvement Job involvement. Job involvement represents the degree to which one's job is central to one's self-concept or sense of identity (Kanungo, 1982; Lodahl & Kejner, 1965). This construct was assessed with five items adapted from a measure developed by Kanungo (1982). A study by Blau (1985) suggested that Kanungo's scale is unidimensional and a purer measure of psychological identification than Lodahl and Kejner's (1965) short-form measure. Each job involvement item used a 6-point agree/disagree response scale. Family involvement. Family involvement was assessed by modifying the job involvement items so that they referred to either the respondent's spouse or children). This procedure has been used successfully in previous WFC research (e.g., Frone & Rice, 1987). Specifically, five items assessed spouse involvement and five items assessed parental involvement. Each family involvement item used a 6-point agree/disagree response scale. An overall family involvement score was calculated as the average of the five spouse involvement items among respondents who were married only, or as the average of the five parental involvement items among respondents who were single parents, or as an average of the 10 spouse and parental involvement items among respondents who were both married and parents.
Work-Family Stressors Job stressors. The job stressor scale was based on 20 items taken from several previously published measures (Beehr, 1976; House, McMichael, Wells, Kaplan, & Landerman, 1979; Insel & Moos, 1974; Pearlin & Schooler, 1978; Rizzo, House, & Lirtzman, 1970; Sims, Szilagyi, & Keller, 1976). The 20 items assessed three dimensions of work stressors: work pressure, lack of autonomy, and role ambiguity. Work pressure (8 items) assessed the frequency with which individuals perceive high job-related demands resulting from heavy workloads and responsibilities. Lack of autonomy (6 items) assessed the frequency with which individuals perceive constraints on their ability to function autonomously and influence important job parameters. Role ambiguity (6 items) assessed the frequency of being confused or unclear about day-to-day tasks and expectations and job-related goals. Each job stressor item used a 4-point frequency-based response scale. Each of these three job stressor dimensions has been linked to W -»• F conflict and a variety of stress-related outcomes (see Voydanoff, 1987, for a review). Family stressors. The family stressor scale was composed of four parental stressor items and four marital stressor items. Of the parental stressor items, two were developed by Kessler (1985) and two were developed specifically for this study. Parental stressor items assessed the following two dimensions: parental workload and the extent of
child(ren)'s misbehavior. The marital stressor items were developed by Kessler (1985) and assessed the following two dimensions: lack of spouse support and the degree of tension or conflict in the relationship. Each family stressor item used either a 4-point or a 5-point frequency-based response scale. These eight items were combined to create an overall family stressor score (see the description of family involvement for the method of combining these items).
Work-Family Conflict Four items were developed to assess WFC: two items each assessed the degree to which a respondent's job interferes with his or her homelife (W -»• F conflict) and the degree to which a respondent's homelife interferes with his or her job (F -»• W conflict). The items assessing W -»• F conflict were: "How often does your job or career interfere with your responsibilities at home, such as yard work, cooking, cleaning, repairs, shopping, paying the bills, or child care?" and "How often does your job or career keep you from spending the amount of time you would like to spend with your family?" The items assessing F -»• W conflict were: "How often does your homelife interfere with your responsibilities at work, such as getting to work on time, accomplishing daily tasks, or working overtime?" and "How often does your homelife keep you from spending the amount of time you would like to spend on job or career-related activities?" Each item used a 5-point frequencybased response scale.
Psychological Distress Job distress. Job distress was assessed with six items developed by Kandel et al. (1985). This scale assessed the strength of negative emotional reactions to daily work experiences. Using a 4-point scale, respondents were asked to indicate the extent to which they feel each of six emotional reactions (e.g., bothered or upset, frustrated) when they think of their day-to-day experiences on the job. Family distress. Family distress was assessed with 12 items developed by Kandel et al. (1985). This scale assessed the strength of negative emotional reactions to daily experiences as a spouse (six items) or parent (six items). Using a 4-point scale, respondents were asked to indicate the extent to which they feel each of six emotional reactions (e.g., bothered or upset, frustrated) when they think of their day-to-day experiences as a marital partner or parent. These 12 items were combined to create an overall family distress score (see the description of family involvement for the method of combining these items). Depression. Depression was assessed with the 20-item Center for Epidemiologic Studies Depression Scale (CES-D; Radloff, 1977). A 4-point scale was used to determine how frequently each of 20 symptoms were experienced during the past month. The items in this scale
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assess several dimensions of depressive symptomatology, such as depressed mood, feelings of guilt and worthlessness, feelings of helplessness and hopelessness, psychomotor retardation, loss of appetite, and sleep disturbance.
Dimensionality of the Measures To assess the appropriateness of computing unidimensional scores for each of the major constructs described above, we submitted each scale to a principal components analysis (results can be obtained from Michael R. Frone). Specifically, principal components analyses for the job involvement, job stressor, WFC, job distress, and depression items were conducted using the full sample. In contrast, principal components analyses for the family involvement, family stressor, and family distress items were limited to married parents because they were the only respondents to have scores on each of the marital and parental items constituting these scales. With the exception of the WFC and job stressor items, an examination of the number of eigenvalues greater than or equal to one and scree plots for each of the remaining principal components analyses supported our decision to create unidimensional scales. With regard to the four WFC conflict items, the principal components analysis revealed two factors with eigenvalues greater than or equal to one, as expected. The two W -*• F conflict items loaded highly on the first factor (oblique rotated loadings = .87 and .92), whereas the two F -»• W conflict items loaded highly on the second factor (oblique rotated loadings = .82 and .85). In addition, the cross-factor loadings were small (ranging from -.05 to .06). These results support the conceptual distinction between W -»• F and F -» W conflict. The principal components analysis of the 20 job stressor items revealed four factors with eigenvalues greater than or equal to one. However, the scree plot suggested retaining two factors. A two-factor solution revealed that the ambiguity and lack of autonomy items loaded highly on the first factor (oblique rotated loadings = .33 to .72), whereas the work pressure items loaded highly on the second factor (oblique rotated loadings = .52 to .70). With two exceptions, the remaining 18 cross-factor loadings were all below .30. Because these results indicated that it may not be appropriate to treat the 20 job stressor items as unidimensional, we conducted two preliminary tests of our model, once using two separate job stressor scales and once using an overall job stressor scale. Consistent with our model, the analysis using two job stressor scales revealed that each was positively related to both W -*• F conflict and job distress. Moreover, the parameter estimates for the remaining paths were virtually identical to those from the analysis using the overall job stressor scale. Therefore, to simplify presentation of our results and to maintain consistency with previous WFC research (e.g., Bedeian et al., 1988; Burke, 1988; Parasuraman et al., 1989), we present our findings using the overall job stressor score.
Path Analyses We tested our model using Bentler's (1989) EQS structural equations program. Input for the EQS program consisted of a 9 X 9 covariance matrix and estimated reliabilities for each variable. We discuss below several issues concerning the estimation and evaluation of structural equation models. Identification. A structural equation model (SEM) must be identified before it can be estimated. Identification addresses the issue of whether enough information exists to yield unique parameter estimates (e.g., see Berry, 1984, Bollen, 1989, andSchaubroeck, 1990, fora detailed discussion of this complex issue). Although identification is relevant for both the measurement model and the SEM, we address the identification of the SEM only because each multi-item scale was
treated as a single indicator of each construct (see discussion of measurement error below). To determine whether a SEM is identified, one must first distinguish between nonrecursive and recursive models. A SEM is nonrecursive if any of the following conditions are met: (a) the disturbances for any two endogenous variables are allowed to correlate, (b) a reciprocal relationship exists between any two endogenous variables, or (c) a feedback loop connects any two endogenous variables (e.g., Berry, 1984; Bollen, 1989). Conversely, a model that meets none of these conditions is said to be recursive (e.g., Berry, 1984; Bollen, 1989). As can be seen in Figure 1, our model is nonrecursive because it meets conditions (a) and (b). Specifically, we hypothesize that W -»• F conflict and F -» W conflict are reciprocally related. Furthermore, as noted by Schaubroeck (1990), the disturbances for variables involved in a reciprocal relationship can be expected to covary because they may share common causes that are not explicitly modeled. Failure to estimate the covariation among the disturbances may lead to biased parameter estimates for the reciprocal relationship (Schaubroeck, 1990). Finally, because previous research has found a strong positive correlation between job and family satisfaction (e.g., Bedeian et al., 1988; Coverman, 1989; Kopelman et al., 1983; Rice et al., in press), we estimated a parameter corresponding to the noncausal relationship between job and family distress by permitting their disturbances to covary. Assuming one is estimating a SEM using either single indicators of each construct or latent variables in which the measurement model is identified, all recursive SEMs are either just-identified or overidentified, thereby yielding unique parameter estimates. In contrast, nonrecursive SEMs may be underidentified (Berry, 1984; Bollen, 1989). Underidentified SEMs contain at least one parameter for which unique estimates cannot be obtained. Thus, testing such a model limits inferences concerning hypothesized structural relationships among variables. As noted earlier, our model is nonrecursive because it contains a reciprocal relationship and two correlated disturbances; therefore, it may be underidentified. The identification of reciprocal relationships requires that instrument variables be incorporated into one's model. To function as an instrument, an antecedent variable must be theoretically related to only one of the variables that make up a reciprocal relationship (e.g., Berry, 1984; Schaubroeck, 1990). A simple rule of thumb is that a reciprocal relationship is overidentified if each reciprocally related variable has at least two associated instrument variables (e.g., Schaubroeck, 1990). Thus, it is clear from Figure 1 that the reciprocal relationship between W -»• F conflict and F -»• W conflict is overidentified. The instrument variable rule, however, does not address the identification of the remaining parameters. Therefore, an overall test of model identification is necessary. One approach to determining whether a nonrecursive SEM is identified is the use of necessary and sufficient rules of thumb or tests that exist for specific types of models (Berry, 1984; Bollen, 1989). A necessary and sufficient test for the identification of nonrecursive models is the rank test (see Berry, 1984, and Bollen, 1989, for a detailed discussion of this and other available tests for identification, including the solution of reduced-form equations). However, as a general check on model identification, this test is limited because it assumes that all disturbances are allowed to covary. Thus, if a SEM imposes some constraints on the covariation among disturbances (i.e., constraining some to equal zero) and fails the rank test, it is still possible that the model is identified. In contrast, if such a model passes the rank test, it is certainly identified, indicating that all structural relationships and relevant correlated disturbances can be freely estimated. Submitting our model to the rank test revealed that it is overidentified; therefore, not only can all parameters be uniquely estimated, but goodness-of-fit tests also can be performed. Measurement error. A major advantage of structural equation programs is their ability to estimate the parameters in a path model while
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WORK-FAMILY CONFLICT correcting for the biasing effects of random measurement error. The usual approach is to estimate structural relationships among latent variables that are free of random measurement error. In the present analyses, however, the multi-item scales were treated as single indicators of each construct. We decided to use a single-indicator approach rather than a latent variable approach because the latter would have required that each respondent answer each of the marital and parental items that went into the construction of the overall family involvement, family stressor, and family distress measures. Such an approach would have eliminated from the analyses 311 individuals (49.3% of the sample) who were either married without children living at home (n = 138) or single parents (n = 173). Given that there is no theoretical reason for restricting our analyses to married parents, we felt that the arbitrary elimination of single parents and married respondents without children would have compromised the generalizability of our results. Given our decision to treat each multi-item scale as a single indicator of its corresponding construct, we corrected for random measurement error by setting the random error variance associated with each construct equal to the product of its variance and the quantity one minus its estimated reliability (Bollen, 1989). This approach has been used in several recent studies (e.g., Bacharach et al., 1991; Farkas & Tetrick, 1989; Schaubroeck, Cotton, & Jennings, 1989; Wayne & Ferris, 1990; Williams & Hazer, 1986). Moreover, the utility of this approach was supported in a recent study (Netemeyer, Johnston, & Burton, 1990) that compared the parameter estimates for a SEM using a latent variable analysis, a single-indicator analysis corrected for measurement error (as discussed earlier), and a single-indicator analysis that did not correct for measurement error. Netemeyer et al.'s (1990) study revealed that the latent variable analysis and the corrected single-indicator analysis yielded virtually identical parameter estimates in terms of direction, magnitude, and significance. However, results from both of these procedures diverged substantially from the uncorrected single-indicator analysis. Model evaluation. To evaluate the overall fit of a model, EQS provides a chi-square goodness-of-fit statistic. The chi-square statistic is based on a comparison of the predicted and observed covariance matrices. A nonsignificant chi-square value indicates good fit. However, because trivial differences between the predicted and observed covariance matrices may lead to a significant chi-square when large samples are used, we also used the following goodness-of-fit indices that are less dependent on sample size: normed fit index (NFI), nonnormed fit index (NNFI), and comparative fit index (CFI; e.g., see Bentler, 1989, and Bollen, 1989, for computational details). Briefly, the NFI, CFI, and NNFI compare the fit of a substantive model to the fit of some predetermined baseline model, usually a null model in which covariation among variables is constrained to equal zero. The value for each of these indices varies between zero and one, with values greater than or equal to .90 used to indicate a good fit. To test whether adding the two paths connecting the WFC variables to depression improved the overall fit of the model, we used a chi-square difference test (Bentler, 1989) and compared the NFI, CFI, and NNFI for the two nested models. Finally, we examined the univariate modification indices (i.e., La Grange Multiplier tests) to see if overall model fit could be improved by freeing any of the 14 substantive paths that were constrained to equal zero (e.g., Bentler, 1989; Bollen, 1989). Multiple group comparisons. To examine whether the results obtained from the full sample were invariant across gender, race, and job type, we conducted both within- and between-group analyses. For example, to examine invariance across gender, we first computed the four fit indices described above separately for men and women to see if there was a good fit in each group. Second, to examine whether the magnitude or direction of each hypothesized relationship was invariant across gender, we specified two simultaneous between-group mod-
els. The first between-group model did not contain any cross-group invariance constraints. In other words, all of the parameter estimates were freely estimated within gender groups. The second betweengroup model, however, constrained each of the 16 hypothesized relationships to be invariant across gender. If the chi-square for the constrained model is significantly larger than the chi-square for the unconstrained model, the assumption of invariance is not tenable. Finally, if the overall chi-square difference test revealed a lack of invariance, we examined the univariate modification indices to locate specific parameters that significantly differed across gender (Bentler, 1989; Bollen, 1989).
Results Preliminary Analyses We examined the potential confounding influence of sociodemographic characteristics by estimating the two nested SEMs (see description below), both controlling and not controlling for the following variables: gender, race, age, education, job type, job tenure, marital status, number of children, and age of youngest child. The analyses revealed that the sociodemographic covariates had virtually no impact on the magnitude or significance of the parameter estimates. Therefore, to facilitate model estimation, especially the estimation of simultaneous between-group models, the covariates were dropped from the analyses. Although gender, race, and job type did not act as confounding variables, they may still moderate the relationships outlined in Figure 1. This issue is examined in more detail below.
Model Evaluation for the Full Sample Overall goodness-of-fit. Table 2 summarizes the goodnessof-fit indices for three models (one baseline model and two substantive models). The baseline model we estimated was the standard null model in which covariation among the variables was constrained to equal zero. As can be seen in Table 2, the large and highly significant chi-square value for the null model reveals a poor fit to the data, indicating that there is significant covariation among the model variables. On the basis of our earlier discussion, we estimated two nested substantive models—one that did not contain the two direct paths between the WFC conflict measures and depression (Model A) and one that did (Model B). Table 2 shows that Model A had a substantially and significantly smaller chisquare than the null model. Thus, Model A fit the data better than the null model. The values of the various goodness-of-fit indices, however, yielded inconsistent conclusions regarding the absolute fit of Model A. The relatively large and highly significant chi-square value, coupled with a NNFI below .90, indicated that substantial covariation may be unexplained, whereas the NFI and CFI suggested that Model A fit the data reasonably well. Turning to Model B, it can be seen that freeing the two paths between WFC and depression led to a substantial and statistically significant reduction in chi-square compared with Model A. The relatively small and nonsignificant chi-square associated with Model B indicated that it fit the data well. More-
72
M. FRONE, M. RUSSELL, AND M. COOPER
Table 2 Goodness-of-Fit Summary Model
df
X2
P
Mf
Ax2
P
NFI
NNFI
CFI
Null A B
36
1,255.04 80.40 15.13
Journal of Applied Psychology 1992, Vol. 77, No. 1,65-78
Antecedents and Outcomes of Work-Family Conflict: Testing a Model of the Work-Family Interface Michael R. Frone and Marcia Russell Research Institute on Alcoholism Buffalo, New York
M. Lynne Cooper Center for the Study of Behavioral and Social Aspects of Health State University of New York at Buffalo A comprehensive model of the work-family interface was developed and tested. The proposed model extended prior research by explicitly distinguishing between work interfering with family and family interfering with work. This distinction allowed testing of hypotheses concerning the unique antecedents and outcomes of both forms of work-family conflict and a reciprocal relationship between them. The influence of gender, race, and job type on the generalizability of the model was also examined. Data were obtained through household interviews with a random sample of 631 individuals. The model was tested with structural equation modeling techniques. Results were strongly supportive. In addition, although the model was invariant across gender and race, there were differences across blue- and white-collar workers. Implications for future research on the work-family interface are discussed.
begun to examine work-family conflict (WFC) as another source of stress that may influence well-being (e.g., Greenhaus & Parasuraman, 1986; Voydanoff, 1987). We believe that this last line of research is especially promising in terms of integrating research on work and family stress. More specifically, because WFC reflects the goodness of fit between work and family life, it may function as a critical intervening pathway through which conditions at work affect the quality of family life and vice versa. To examine fully the relationship of WFC to well-being and its potential to integrate the study of work and family stress, WFC must be examined within the context of a multivariate model that meets at least four major criteria. First, key workand family-related antecedents of both WFC and well-being need to be assessed (e.g., Lambert, 1990). Second, both domainspecific and general measures of well-being should be examined (Kline & Cowan, 1988; Lambert, 1990). Third, the bidirectional nature of WFC (i.e., work interfering with family versus family interfering with work) must be addressed (Greenhaus, 1988; Greenhaus & Beutell, 1985; Gutek, Searle, & Klepa, 1991). Finally, the sample should be large, heterogeneous, and representative of employed adults. To date, however, research testing multivariate models of WFC that meet even the first two criteria is rare (Burke, 1988). Thus, the goal of this study is to develop and test a comprehensive model of the work-family interface that meets each of these criteria. Although such a model is by necessity complex, it is consistent with Lambert's (1990) recent admonition that "only by looking at the work/ family nexus in its entirety can we uncover the intricacies of the relationship between work and home" (p. 250).
Work and family represent two of the most central realms of adult life. Therefore, each offers a unique vantage point from which to investigate important aspects of human behavior. Although the study of work and family has spawned rich conceptual and empirical literatures, these two domains of life traditionally have been studied independently. More recently, however, the interface between work and family roles has captured the interest of a growing number of work and family researchers. This new focus has been fueled by several demographic trends that are reshaping American society. For example, there has been a dramatic increase in the number of married women who have young children and who are joining the work force and an increase in the prevalence of employed adults who are part of dual-earner families, single-parent families, and families facing the demands of elder care (e.g., Burke & Greenglass, 1987; Hall & Richter, 1988; Matthews & Rodin, 1989; Zedeck & Mosier, 1990). Interest in the work-family interface has produced a strong emphasis on integrating work and family research. Within the area of stress research, new questions are being asked regarding such issues as the relative impact of work and family stressors on overall well-being, the impact of job stressors on family life, and the impact of family stressors on work life (cf. Burke & Greenglass, 1987; Voydanoff, 1987). In addition, research has This research was supported by the National Institute on Alcohol Abuse and Alcoholism, Grant No. AA05702, awarded to Marcia Russell. We wish to thank Ann Gerber, Michael Windle, and the two anonymous reviewers for their thoughtful comments on an earlier version of this article. Correspondence concerning this article should be addressed to Michael R. Frone, Research Institute on Alcoholism, 1021 Main Street, Buffalo, New York 14203.
A Model of the Work-Family Interface The conceptual model that guided the present research is illustrated in Figure 1. We would like to acknowledge that this 65
66
M. FRONE, M. RUSSELL, AND M. COOPER
study does not represent the first attempt to formulate and test a model of the work-family interface. Rather, our model should be viewed as a significant extension of several previous models (e.g., Bacharach, Bamberger, & Conley, 1991; Bedeian, Burke, & Moffett, 1988; Burke, 1988; Greenhaus & Beutell, 1985; Greenhaus & Parasuraman, 1986; Kopelman, Greenhaus, & Connolly, 1983; Rice, Frone, & McFarlin, in press). In outlining our model, we begin by discussing the bidirectional nature of WFC. Next we summarize hypotheses concerning the predictors of WFC. We then summarize hypotheses regarding the predictors of domain-specific distress (i.e., job distress and family distress) and overall psychological distress (i.e., depression). Finally, we discuss the importance of examining the influence of key sociodemographic characteristics on the generalizability of our model. Bidirectional Nature of Work-Family Conflict Definitions of WFC explicitly portray a bidirectional conceptualization (e.g., Greenhaus & Beutell, 1985). That is, they distinguish between work interfering with family (W -+• F conflict) and family interfering with work (F -*• W conflict). Despite this conceptual distinction, previous research has relied almost exclusively on measures that assess only W -»• F conflict (Greenhaus & Beutell, 1985). Furthermore, there is evidence that mixed directional measures of WFC (e.g., "How much do your job and family life interfere with one another?"; Pleck, Staines, & Lang, 1980) primarily assess W -*• F conflict (Frone, Russell, & Cooper, 1991; Gutek et al., 1991). For example, Frone et al. (1991) found that the experience of W -*• F conflict was reported almost three times more frequently than the experience of F -»• W conflict among men and women.
We concur with Greenhaus and Beutell (1985) that failing to examine both types of WFC may limit our understanding of the work-family interface to the extent that each is associated with different antecedents and consequences (as discussed below). However, another limitation of failing to examine both types of WFC simultaneously is that the potential reciprocal relationship between conflicting work and family roles has never been examined. As shown in Figure 1, we hypothesize a positive reciprocal relationship between the two types of WFC. The rationale for this relationship is based on the assumption that if one's work-related problems and responsibilities begin to interfere with the accomplishment of one's family-related obligations, these unfulfilled family obligations may begin to interfere with one's day-to-day functioning at work. Conversely, if one's family-related problems and responsibilities begin to interfere with the accomplishment of one's work-related obligations, these unfulfilled work obligations may begin to interfere with one's day-to-day functioning at home. This hypothesis is consistent with Schaubroeck's (1990) recent suggestion that the conflict relationship between work and family may be examined appropriately within the context of a reciprocal relationship. Predictors ofWork-Family Conflict The direct predictors of WFC portrayed in our model (Figure 1) are job stressors, family stressors, job involvement, and family involvement. We include these four predictors because each has been identified as an important antecedent of WFC (Greenhaus & Beutell, 1985). Moreover, each predictor was expected to be directly related to only one type of WFC. With regard to job and family stressors, Greenhaus and Beu-
W ->F Conflict
\
\
\\ +
•f
I
Conflict
Figure 1. Conceptual model of the work-family interface. (Pluses and minuses represent the direction of hypothesized relationships. Broken lines signify two nested models, one without [Model A] and one with [Model B ] paths from work-family conflict to depression. The letter d represents the disturbance term for each endogenous variable. To simplify presentation of the model, the correlations among the exogenous variables and the measurement model are not shown.)
WORK-FAMILY CONFLICT
tell (1985) hypothesized that exposure to stressors in a given domain (e.g., work) may lead to irritability, fatigue, or preoccupation with those problems, thereby limiting one's ability to meet the demands of other domains of life (e.g., family). Expanding this general argument to include the two types of WFC, we hypothesize that job stressors are directly and positively related to W -*• F conflict, whereas family stressors are directly and positively related to F -»• W conflict. Previous research strongly supports a positive relationship between perceived work stressors and W -*• F conflict (e.g., Bacharach et al, 1991; Bedeian et al., 1988; Burke, 1988; Greenhaus & Beutell, 1985; Kopelman et al., 1983; Parasuraman, Greenhaus, Granrose, Rabinowitz, & Beutell, 1989; VoydanofF, 1988). No research, however, has examined the relationship between family stressors and F -*• W conflict. The second major antecedent of WFC is the psychological importance of work and family roles. Greenhaus and Beutell (1985) argued that high levels of psychological involvement may lead to increased WFC in two ways. First, high levels of psychological involvement in one role may be associated with an increase in the amount of time and effort devoted to that role, thereby making it more difficult to comply with pressures associated with another role. Second, high levels of psychological involvement in a given role may cause one to be mentally preoccupied with that role even when physically attempting to fulfill the demands of a second role. Expanding these arguments to include the two types of WFC, we hypothesize that job involvement is directly and positively related to W -*• F conflict, whereas family involvement is directly and positively related to F -*• W conflict. Previous research supports a positive relationship between job involvement and W -*• F conflict (e.g., Beutell, 1983; Beutell & O'Hare, 1987; Frone & Rice, 1987; Greenhaus & Kopelman, 1981; Greenhaus, Parasuraman, Granrose, Rabinowitz, & Beutell, 1989; Wiley, 1987). Although the relationship between family involvement and F -»• W conflict has not been directly examined, there is indirect evidence to support such a relationship. Gutek et al. (1991) found that the number of hours devoted to family activities, which is presumably positively related to family involvement, was positively related to F -*• W conflict. Predictors of Domain-Specific Psychological Distress As indicated in Figure 1, the direct predictors of both job distress and family distress are WFC, domain-specific stressors, and domain-specific psychological involvement. With regard to WFC, we hypothesize that W -*• F conflict is directly and positively related to family distress, whereas F -> W conflict is directly and positively related to job distress. The rationale for this hypothesis is that high levels of psychological distress associated with a given role may be experienced if one is frequently struggling to meet the demands of that role because of interference from another role. For example, relative to individuals whose family life does not interfere with their jobs, individuals who experience high levels of F -*• W conflict may report elevated levels of job-related distress because they are more likely to feel overwhelmed by the ensuing struggle to meet their responsibilities at work and therefore experience a reduction in the quality of their work life. A parallel explanation may
67
be offered for the hypothesized positive relationship between W -»• F conflict and family distress. Although previous research has found that W -»• F conflict is associated with low levels of family satisfaction (e.g., Bedeian et al., 1988; Beutell, 1983; Beutell & O'Hare, 1987; Coverman, 1989; Holahan & Gilbert, 1979; Parasuraman et al., 1989; Rice et al., in press), no research has examined the relationship between F -*• W conflict and job-related affect. With regard to job and family stressors, we hypothesize that they are directly and positively related to their respective within-domain measure of distress. Previous research lends strong support for an association between job and family stressors and decreased job and family satisfaction, respectively (e.g., Bedeian et al., 1988; Kandel, Davies, &Raveis, 1985; Kopelman et al., 1983; Parasuraman et al., 1989). Finally, with regard to job and family involvement, we hypothesize that they are directly and negatively related to their respective within-domain measure of distress. This hypothesis is based on the conceptual and empirical work of several researchers who have examined the relationship between job involvement and job satisfaction (e.g., Rabinowitz & Hall, 1977; Sekaran, 1989; Weiner, Muczyk, & Gable, 1987; Weiner & Vardi, 1980). For example, Sekaran (1989) postulated that highly job-involved workers invest greater amounts of time and effort in their work. As a result, they are more likely than low job-involved workers to develop a greater sense of work-related mastery, which in turn promotes higher levels of job satisfaction. Sekaran's (1989) study supports a positive relationship between job involvement and job satisfaction by way of increased perceptions of work-related mastery. Although a similar process may lead to a relationship between family involvement and family distress, no research has examined the relationship between these two variables. Predictors of Overall Psychological Distress As shown in our model, we hypothesize that both job- and family-related psychological distress are directly and positively related to depression (used here as an indicator of overall psychological distress). This hypothesis was derived from additive models of overall quality of life (e.g., Andrews & Withey, 1976; Campbell, Converse, & Rodgers, 1976; Michalos, 1985; Rice, McFarlin, Hunt & Near, 1985). Additive models propose that the perceived quality of life associated with each constituent domain of life combine additively to determine overall quality of life (Rice et al., in press). This proposition has been supported using both measures of satisfaction and psychological distress (e.g., Bedeian et al., 1988; Coverman, 1989; Kandel et al., 1985; Kopelman et al., 1983; Rice et al., in press). A strict interpretation of additive models implies that the effect of any other variable on overall quality of life must be indirect, that is, mediated by the quality of life in one or more specific domains of life (Rice et al., in press). Consistent with this proposition, our model indicates that the relationships of stressors, psychological involvement, and WFC to depression are mediated through their direct or indirect relationships to either job or family distress. With regard to work and family stressors, previous research lends strong support to this hypothesis (Bedeian et al., 1988; Coverman, 1989; Kandel et al.,
68
M. FRONE, M. RUSSELL, AND M. COOPER
1985; Kopelman et al., 1983). In contrast, results concerning the relationship between W -*• F conflict and overall life satisfaction are inconsistent. Previous research has found indirect effects only, via job or family satisfaction (e.g., Coverman, 1989; Rice et al., in press); direct effects only (Kopelman et al., 1983); and both indirect (via job or family satisfaction) and direct effects (Bedeian et al., 1988). Because these mixed results do not lend strong support for a fully mediated relationship between WFC and overall psychological distress, we will examine (via nested models) whether a model containing the two direct paths between WFC and depression fits the data better than a model that does not contain these two paths (see broken lines in Figure 1).
Procedures Data for this study were collected by 20 professionally trained interviewers during the spring and summer of 1989 as part of a larger study of stress processes. Field work for this study (i.e., interviewer recruitment and training; supervision of interviewers; validation of interviewers' work; and data preparation, entry, and cleaning) was conducted by WESTAT, Inc., a nationally recognized survey research firm. Interviews were conducted in respondents' homes with a highly structured interview schedule that contained both interviewer- and self-administered sections. The complete interview required approximately 90 min to administer. Respondents were compensated $25 for their time. Measures
Generalizability of the Model Previous conceptual discussions of work-family stress processes propose that gender may represent an important moderator variable (e.g., Eckenrode & Gore, 1990; Kline & Cowan, 1988; Lambert, 1990). However, potential gender differences in work-family stress processes are not always examined. Furthermore, most research testing models of WFC has used relatively circumscribed samples of White individuals in white-collar jobs. Thus, we know little about the generalizability of previous research to minority and blue-collar workers (Burke & Greenglass, 1987). Given that the sociodemographic composition of the American work force is becoming more diverse (e.g., Jackson & Schuler, 1990; OfTerman & Gowing, 1990), the lack of knowledge concerning the generalizability of work-family stress processes represents an important limitation. To address this issue, we examine the fit of our model across gender, race, and job type.
Method Sample Respondents in this study were drawn from the longitudinal followup of a random sample survey of 1,933 adults residing in Erie County, New York. Designated respondents were identified in a three-stage probability sample, designed to yield approximately equal representation of two racial groups (Blacks and non-Blacks) and three education levels (less than high school graduate, high school graduate, and at least some college). The overall completion rate for Wave 1 was 78.3%. Of the 1,933 Wave 1 respondents, 1,616 were reinterviewed at Wave 2, representing 83.6% of the total original sample. Of the 1,616 respondents interviewed at Wave 2,631 met the following criteria for selection into this study: (a) employed at least 20 hr per week, (b) currently married or living as married, or had children living at home, or both, and (c) provided valid data on all measures described below. We used data only from the second wave of data collection because several of the major variables were not assessed in the first wave. On average, respondents were 40.7 years old (573 = 10.4) and had completed 13.3 years of formal education (SD = 2.3). Fifty-six percent were women and 42% were White. Seventy-three percent were married or living as married, and 78% had at least one child living at home. Respondents had worked in their current jobs for an average of 8.7 years (SD = 8.5); approximately equal numbers held blue-collar (49%) and white-collar (51%) jobs.
Each of the measures is described in detail below. All measures were administered by the interviewer, except those assessing psychological involvement and depression, which were self-administered. Descriptive statistics (i.e., means, standard deviations, and internal consistency reliability estimates) and zero-order correlations for the major study variables are presented in Table 1. Except where otherwise noted, all variables were created by averaging their respective items and were scored so that a high score represents higher levels of the construct. Sociodemographic Characteristics Gender. This variable was coded as 0 for men and 1 for women. Race. Respondents classified themselves into one of the following seven racial or ethnic groups: Aleut, Eskimo, or American Indian; Asian/Pacific Islander; Black Hispanic; Black, not Hispanic; White Hispanic; White, not Hispanic; and other. Respondents classifying themselves as White, not Hispanic were coded as White (0), whereas all others were coded non-White (1). Although it is not desirable to group all racial minorities together, we combined them into a single group because the non-White sample in this study was composed almost entirely of Blacks (96%). Age. This variable was based on self-reported date of birth and was coded in years. Education. This variable was based on self-reported years of formal education. Job type. This variable was based on responses to the following open-ended question: What is your occupation or job title and what sort of work do you do on your job? Professional coders classified responses into one of the 13 major occupational categories used by the U.S. Census Bureau (U.S. Department of Commerce, 1982). A respondent was coded as white-collar (1) if his or her job fell into categories 1 through 5, whereas a respondent was coded as blue-collar (0) if his or her job fell into categories 6 through 13. Although this categorization is somewhat crude, our measure of job type was, as expected, positively correlated with years of formal education (r = .43, p < .001). Job tenure. This variable was based on self-reported tenure in the person's current job and was coded in years. Marital status. Respondents were coded either as not married (i.e., never married, widowed, separated, or divorced [0]) or married or living as married (1). Number of children. Number of children currently living at home was coded into five categories ranging from 0 to 4 or more. Age of youngest child. Following a coding scheme outlined by Bedeian et al. (1988), we coded age of youngest child living at home into one of the following five categories: no children (1), youngest child over 18 years of age (2), youngest child 13-18 years of age (3), youngest child 6-12 years of age (4), and youngest child less than 6 years of age (5).
69
WORK-FAMILY CONFLICT Table 1 Means, Standard Deviations, Reliability Estimates, and Intercorrelations for Major Study Variables
1
Variable
1. 2. 3. 4. 5. 6. 7. 8. 9.
Job involvement Family involvement Job stressor index Family stressor index W -* F Conflict F -* W Conflict Job distress Family distress Depression
(.88) .14* .02 -.02 .05 .09* -.21* -.03 -.05
(.88) -.06 -.22* -.06 .01 -.13* -.31* -.09*
(.72) .21* .31* .19* .53* .21* .28*
(.66) .23* .29* .25* .60* .38*
(.76) .33* .28* .21* .26*
(.56) .24* .25* .42*
(-84) .43* .37*
(.88) .48*
(.87)
M
SD
3.92 5.14 2.05 1.83 2.21 1.39 1.80 1.57 1.58
1.18 0.79 0.36 0.49 1.07 0.61 0.60 0.48 0.39
Note. Numbers on the diagonal are internal consistency reliability estimates (coefficient alpha). * p ^ .05.
Psychological Involvement Job involvement. Job involvement represents the degree to which one's job is central to one's self-concept or sense of identity (Kanungo, 1982; Lodahl & Kejner, 1965). This construct was assessed with five items adapted from a measure developed by Kanungo (1982). A study by Blau (1985) suggested that Kanungo's scale is unidimensional and a purer measure of psychological identification than Lodahl and Kejner's (1965) short-form measure. Each job involvement item used a 6-point agree/disagree response scale. Family involvement. Family involvement was assessed by modifying the job involvement items so that they referred to either the respondent's spouse or children). This procedure has been used successfully in previous WFC research (e.g., Frone & Rice, 1987). Specifically, five items assessed spouse involvement and five items assessed parental involvement. Each family involvement item used a 6-point agree/disagree response scale. An overall family involvement score was calculated as the average of the five spouse involvement items among respondents who were married only, or as the average of the five parental involvement items among respondents who were single parents, or as an average of the 10 spouse and parental involvement items among respondents who were both married and parents.
Work-Family Stressors Job stressors. The job stressor scale was based on 20 items taken from several previously published measures (Beehr, 1976; House, McMichael, Wells, Kaplan, & Landerman, 1979; Insel & Moos, 1974; Pearlin & Schooler, 1978; Rizzo, House, & Lirtzman, 1970; Sims, Szilagyi, & Keller, 1976). The 20 items assessed three dimensions of work stressors: work pressure, lack of autonomy, and role ambiguity. Work pressure (8 items) assessed the frequency with which individuals perceive high job-related demands resulting from heavy workloads and responsibilities. Lack of autonomy (6 items) assessed the frequency with which individuals perceive constraints on their ability to function autonomously and influence important job parameters. Role ambiguity (6 items) assessed the frequency of being confused or unclear about day-to-day tasks and expectations and job-related goals. Each job stressor item used a 4-point frequency-based response scale. Each of these three job stressor dimensions has been linked to W -»• F conflict and a variety of stress-related outcomes (see Voydanoff, 1987, for a review). Family stressors. The family stressor scale was composed of four parental stressor items and four marital stressor items. Of the parental stressor items, two were developed by Kessler (1985) and two were developed specifically for this study. Parental stressor items assessed the following two dimensions: parental workload and the extent of
child(ren)'s misbehavior. The marital stressor items were developed by Kessler (1985) and assessed the following two dimensions: lack of spouse support and the degree of tension or conflict in the relationship. Each family stressor item used either a 4-point or a 5-point frequency-based response scale. These eight items were combined to create an overall family stressor score (see the description of family involvement for the method of combining these items).
Work-Family Conflict Four items were developed to assess WFC: two items each assessed the degree to which a respondent's job interferes with his or her homelife (W -»• F conflict) and the degree to which a respondent's homelife interferes with his or her job (F -»• W conflict). The items assessing W -»• F conflict were: "How often does your job or career interfere with your responsibilities at home, such as yard work, cooking, cleaning, repairs, shopping, paying the bills, or child care?" and "How often does your job or career keep you from spending the amount of time you would like to spend with your family?" The items assessing F -»• W conflict were: "How often does your homelife interfere with your responsibilities at work, such as getting to work on time, accomplishing daily tasks, or working overtime?" and "How often does your homelife keep you from spending the amount of time you would like to spend on job or career-related activities?" Each item used a 5-point frequencybased response scale.
Psychological Distress Job distress. Job distress was assessed with six items developed by Kandel et al. (1985). This scale assessed the strength of negative emotional reactions to daily work experiences. Using a 4-point scale, respondents were asked to indicate the extent to which they feel each of six emotional reactions (e.g., bothered or upset, frustrated) when they think of their day-to-day experiences on the job. Family distress. Family distress was assessed with 12 items developed by Kandel et al. (1985). This scale assessed the strength of negative emotional reactions to daily experiences as a spouse (six items) or parent (six items). Using a 4-point scale, respondents were asked to indicate the extent to which they feel each of six emotional reactions (e.g., bothered or upset, frustrated) when they think of their day-to-day experiences as a marital partner or parent. These 12 items were combined to create an overall family distress score (see the description of family involvement for the method of combining these items). Depression. Depression was assessed with the 20-item Center for Epidemiologic Studies Depression Scale (CES-D; Radloff, 1977). A 4-point scale was used to determine how frequently each of 20 symptoms were experienced during the past month. The items in this scale
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assess several dimensions of depressive symptomatology, such as depressed mood, feelings of guilt and worthlessness, feelings of helplessness and hopelessness, psychomotor retardation, loss of appetite, and sleep disturbance.
Dimensionality of the Measures To assess the appropriateness of computing unidimensional scores for each of the major constructs described above, we submitted each scale to a principal components analysis (results can be obtained from Michael R. Frone). Specifically, principal components analyses for the job involvement, job stressor, WFC, job distress, and depression items were conducted using the full sample. In contrast, principal components analyses for the family involvement, family stressor, and family distress items were limited to married parents because they were the only respondents to have scores on each of the marital and parental items constituting these scales. With the exception of the WFC and job stressor items, an examination of the number of eigenvalues greater than or equal to one and scree plots for each of the remaining principal components analyses supported our decision to create unidimensional scales. With regard to the four WFC conflict items, the principal components analysis revealed two factors with eigenvalues greater than or equal to one, as expected. The two W -*• F conflict items loaded highly on the first factor (oblique rotated loadings = .87 and .92), whereas the two F -»• W conflict items loaded highly on the second factor (oblique rotated loadings = .82 and .85). In addition, the cross-factor loadings were small (ranging from -.05 to .06). These results support the conceptual distinction between W -»• F and F -» W conflict. The principal components analysis of the 20 job stressor items revealed four factors with eigenvalues greater than or equal to one. However, the scree plot suggested retaining two factors. A two-factor solution revealed that the ambiguity and lack of autonomy items loaded highly on the first factor (oblique rotated loadings = .33 to .72), whereas the work pressure items loaded highly on the second factor (oblique rotated loadings = .52 to .70). With two exceptions, the remaining 18 cross-factor loadings were all below .30. Because these results indicated that it may not be appropriate to treat the 20 job stressor items as unidimensional, we conducted two preliminary tests of our model, once using two separate job stressor scales and once using an overall job stressor scale. Consistent with our model, the analysis using two job stressor scales revealed that each was positively related to both W -*• F conflict and job distress. Moreover, the parameter estimates for the remaining paths were virtually identical to those from the analysis using the overall job stressor scale. Therefore, to simplify presentation of our results and to maintain consistency with previous WFC research (e.g., Bedeian et al., 1988; Burke, 1988; Parasuraman et al., 1989), we present our findings using the overall job stressor score.
Path Analyses We tested our model using Bentler's (1989) EQS structural equations program. Input for the EQS program consisted of a 9 X 9 covariance matrix and estimated reliabilities for each variable. We discuss below several issues concerning the estimation and evaluation of structural equation models. Identification. A structural equation model (SEM) must be identified before it can be estimated. Identification addresses the issue of whether enough information exists to yield unique parameter estimates (e.g., see Berry, 1984, Bollen, 1989, andSchaubroeck, 1990, fora detailed discussion of this complex issue). Although identification is relevant for both the measurement model and the SEM, we address the identification of the SEM only because each multi-item scale was
treated as a single indicator of each construct (see discussion of measurement error below). To determine whether a SEM is identified, one must first distinguish between nonrecursive and recursive models. A SEM is nonrecursive if any of the following conditions are met: (a) the disturbances for any two endogenous variables are allowed to correlate, (b) a reciprocal relationship exists between any two endogenous variables, or (c) a feedback loop connects any two endogenous variables (e.g., Berry, 1984; Bollen, 1989). Conversely, a model that meets none of these conditions is said to be recursive (e.g., Berry, 1984; Bollen, 1989). As can be seen in Figure 1, our model is nonrecursive because it meets conditions (a) and (b). Specifically, we hypothesize that W -»• F conflict and F -» W conflict are reciprocally related. Furthermore, as noted by Schaubroeck (1990), the disturbances for variables involved in a reciprocal relationship can be expected to covary because they may share common causes that are not explicitly modeled. Failure to estimate the covariation among the disturbances may lead to biased parameter estimates for the reciprocal relationship (Schaubroeck, 1990). Finally, because previous research has found a strong positive correlation between job and family satisfaction (e.g., Bedeian et al., 1988; Coverman, 1989; Kopelman et al., 1983; Rice et al., in press), we estimated a parameter corresponding to the noncausal relationship between job and family distress by permitting their disturbances to covary. Assuming one is estimating a SEM using either single indicators of each construct or latent variables in which the measurement model is identified, all recursive SEMs are either just-identified or overidentified, thereby yielding unique parameter estimates. In contrast, nonrecursive SEMs may be underidentified (Berry, 1984; Bollen, 1989). Underidentified SEMs contain at least one parameter for which unique estimates cannot be obtained. Thus, testing such a model limits inferences concerning hypothesized structural relationships among variables. As noted earlier, our model is nonrecursive because it contains a reciprocal relationship and two correlated disturbances; therefore, it may be underidentified. The identification of reciprocal relationships requires that instrument variables be incorporated into one's model. To function as an instrument, an antecedent variable must be theoretically related to only one of the variables that make up a reciprocal relationship (e.g., Berry, 1984; Schaubroeck, 1990). A simple rule of thumb is that a reciprocal relationship is overidentified if each reciprocally related variable has at least two associated instrument variables (e.g., Schaubroeck, 1990). Thus, it is clear from Figure 1 that the reciprocal relationship between W -»• F conflict and F -»• W conflict is overidentified. The instrument variable rule, however, does not address the identification of the remaining parameters. Therefore, an overall test of model identification is necessary. One approach to determining whether a nonrecursive SEM is identified is the use of necessary and sufficient rules of thumb or tests that exist for specific types of models (Berry, 1984; Bollen, 1989). A necessary and sufficient test for the identification of nonrecursive models is the rank test (see Berry, 1984, and Bollen, 1989, for a detailed discussion of this and other available tests for identification, including the solution of reduced-form equations). However, as a general check on model identification, this test is limited because it assumes that all disturbances are allowed to covary. Thus, if a SEM imposes some constraints on the covariation among disturbances (i.e., constraining some to equal zero) and fails the rank test, it is still possible that the model is identified. In contrast, if such a model passes the rank test, it is certainly identified, indicating that all structural relationships and relevant correlated disturbances can be freely estimated. Submitting our model to the rank test revealed that it is overidentified; therefore, not only can all parameters be uniquely estimated, but goodness-of-fit tests also can be performed. Measurement error. A major advantage of structural equation programs is their ability to estimate the parameters in a path model while
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WORK-FAMILY CONFLICT correcting for the biasing effects of random measurement error. The usual approach is to estimate structural relationships among latent variables that are free of random measurement error. In the present analyses, however, the multi-item scales were treated as single indicators of each construct. We decided to use a single-indicator approach rather than a latent variable approach because the latter would have required that each respondent answer each of the marital and parental items that went into the construction of the overall family involvement, family stressor, and family distress measures. Such an approach would have eliminated from the analyses 311 individuals (49.3% of the sample) who were either married without children living at home (n = 138) or single parents (n = 173). Given that there is no theoretical reason for restricting our analyses to married parents, we felt that the arbitrary elimination of single parents and married respondents without children would have compromised the generalizability of our results. Given our decision to treat each multi-item scale as a single indicator of its corresponding construct, we corrected for random measurement error by setting the random error variance associated with each construct equal to the product of its variance and the quantity one minus its estimated reliability (Bollen, 1989). This approach has been used in several recent studies (e.g., Bacharach et al., 1991; Farkas & Tetrick, 1989; Schaubroeck, Cotton, & Jennings, 1989; Wayne & Ferris, 1990; Williams & Hazer, 1986). Moreover, the utility of this approach was supported in a recent study (Netemeyer, Johnston, & Burton, 1990) that compared the parameter estimates for a SEM using a latent variable analysis, a single-indicator analysis corrected for measurement error (as discussed earlier), and a single-indicator analysis that did not correct for measurement error. Netemeyer et al.'s (1990) study revealed that the latent variable analysis and the corrected single-indicator analysis yielded virtually identical parameter estimates in terms of direction, magnitude, and significance. However, results from both of these procedures diverged substantially from the uncorrected single-indicator analysis. Model evaluation. To evaluate the overall fit of a model, EQS provides a chi-square goodness-of-fit statistic. The chi-square statistic is based on a comparison of the predicted and observed covariance matrices. A nonsignificant chi-square value indicates good fit. However, because trivial differences between the predicted and observed covariance matrices may lead to a significant chi-square when large samples are used, we also used the following goodness-of-fit indices that are less dependent on sample size: normed fit index (NFI), nonnormed fit index (NNFI), and comparative fit index (CFI; e.g., see Bentler, 1989, and Bollen, 1989, for computational details). Briefly, the NFI, CFI, and NNFI compare the fit of a substantive model to the fit of some predetermined baseline model, usually a null model in which covariation among variables is constrained to equal zero. The value for each of these indices varies between zero and one, with values greater than or equal to .90 used to indicate a good fit. To test whether adding the two paths connecting the WFC variables to depression improved the overall fit of the model, we used a chi-square difference test (Bentler, 1989) and compared the NFI, CFI, and NNFI for the two nested models. Finally, we examined the univariate modification indices (i.e., La Grange Multiplier tests) to see if overall model fit could be improved by freeing any of the 14 substantive paths that were constrained to equal zero (e.g., Bentler, 1989; Bollen, 1989). Multiple group comparisons. To examine whether the results obtained from the full sample were invariant across gender, race, and job type, we conducted both within- and between-group analyses. For example, to examine invariance across gender, we first computed the four fit indices described above separately for men and women to see if there was a good fit in each group. Second, to examine whether the magnitude or direction of each hypothesized relationship was invariant across gender, we specified two simultaneous between-group mod-
els. The first between-group model did not contain any cross-group invariance constraints. In other words, all of the parameter estimates were freely estimated within gender groups. The second betweengroup model, however, constrained each of the 16 hypothesized relationships to be invariant across gender. If the chi-square for the constrained model is significantly larger than the chi-square for the unconstrained model, the assumption of invariance is not tenable. Finally, if the overall chi-square difference test revealed a lack of invariance, we examined the univariate modification indices to locate specific parameters that significantly differed across gender (Bentler, 1989; Bollen, 1989).
Results Preliminary Analyses We examined the potential confounding influence of sociodemographic characteristics by estimating the two nested SEMs (see description below), both controlling and not controlling for the following variables: gender, race, age, education, job type, job tenure, marital status, number of children, and age of youngest child. The analyses revealed that the sociodemographic covariates had virtually no impact on the magnitude or significance of the parameter estimates. Therefore, to facilitate model estimation, especially the estimation of simultaneous between-group models, the covariates were dropped from the analyses. Although gender, race, and job type did not act as confounding variables, they may still moderate the relationships outlined in Figure 1. This issue is examined in more detail below.
Model Evaluation for the Full Sample Overall goodness-of-fit. Table 2 summarizes the goodnessof-fit indices for three models (one baseline model and two substantive models). The baseline model we estimated was the standard null model in which covariation among the variables was constrained to equal zero. As can be seen in Table 2, the large and highly significant chi-square value for the null model reveals a poor fit to the data, indicating that there is significant covariation among the model variables. On the basis of our earlier discussion, we estimated two nested substantive models—one that did not contain the two direct paths between the WFC conflict measures and depression (Model A) and one that did (Model B). Table 2 shows that Model A had a substantially and significantly smaller chisquare than the null model. Thus, Model A fit the data better than the null model. The values of the various goodness-of-fit indices, however, yielded inconsistent conclusions regarding the absolute fit of Model A. The relatively large and highly significant chi-square value, coupled with a NNFI below .90, indicated that substantial covariation may be unexplained, whereas the NFI and CFI suggested that Model A fit the data reasonably well. Turning to Model B, it can be seen that freeing the two paths between WFC and depression led to a substantial and statistically significant reduction in chi-square compared with Model A. The relatively small and nonsignificant chi-square associated with Model B indicated that it fit the data well. More-
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Table 2 Goodness-of-Fit Summary Model
df
X2
P
Mf
Ax2
P
NFI
NNFI
CFI
Null A B
36
1,255.04 80.40 15.13
