The Theoretical and Empirical Utility of Dimension-Based Work–Family Conflict: A Meta-Analysis

Abstract

Most work–family conflict (WFC) research does not theorize, hypothesize, or empirically test phenomena at the dimension level. Instead, researchers have predominantly used composite-level approaches based on the directions of WFC (work-to-family and family-to-work conflict). However, conceptualizing and operationalizing WFC at the composite level instead of at the dimension level has not been confirmed as a well-founded strategy. The goal of the current research is to explore whether there is theoretical and empirical evidence in the WFC literature to support the importance of dimension-level theorizing and operationalization when compared to composite-level approaches. To advance theory related to the dimensions of WFC, we begin by reviewing WFC theories and then demonstrate the relevance of resource allocation theory to the time-based dimension, spillover theory to the strain-based dimension, and boundary theory to the behavior-based dimension. From this theorizing, we highlight and meta-analytically test the relative importance of specific variables from the WFC nomological network that are theoretically connected to each dimension: time and family demands for the time-based dimension, work role ambiguity for the strain-based dimension, and family-supportive supervisor behaviors and nonwork support for the behavior-based dimension. Reviewing and drawing from bandwidth-fidelity theory, we also question whether composite-based WFC approaches are more appropriate for broad constructs (i.e., job satisfaction and life satisfaction). The results of our meta-analytic relative importance analyses generally support a dimension-based approach and overall follow the pattern of results expected from our dimension-level theorizing, even when broad constructs are considered. Theoretical, future research, and practical implications are discussed.

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WORK–FAMILY CONFLICT DIMENSIONS META
Abstract
Most work-family conflict (WFC) research does not theorize, hypothesize, or empirically test
phenomena at the dimension-level. Instead, researchers have predominantly used composite-
level approaches based on the directions of WFC (work-to-family and family-to-work conflict).
However, conceptualizing and operationalizing WFC at the composite-level instead of at the
dimension-level has not been confirmed as a well-founded strategy. The goal of the current
research is to explore whether there is theoretical and empirical evidence in the WFC literature to
support the importance of dimension-level theorizing and operationalization when compared to
composite-level approaches. To advance theory related to the dimensions of WFC, we begin by
reviewing WFC theories and then demonstrate the relevance of resource allocation theory to the
time-based dimension, spillover theory to the strain-based dimension, and boundary theory to the
behavior-based dimension. From this theorizing, we highlight and meta-analytically test the
relative importance of specific variables from the WFC nomological network that are
theoretically connected to each dimension: time and family demands for the time-based
dimension, work role ambiguity for the strain-based dimension, and family-supportive supervisor
behaviors and nonwork support for the behavior-based dimension. Reviewing and drawing from
bandwidth fidelity theory, we also question whether composite-based WFC approaches are more
appropriate for broad constructs (i.e., job satisfaction and life satisfaction). The results of our
meta-analytic relative importance analyses generally support a dimension-based approach and
overall follow the pattern of results expected from our dimension-level theorizing, even when
broad constructs are considered. Theoretical, future research, and practical implications are
discussed.
Keywords: work-family conflict, meta-analysis, time, strain, behavior

WORK–FAMILY CONFLICT DIMENSIONS META 1
The Theoretical and Empirical Utility of Dimension-Based Work-Family Conflict:
A Meta-Analysis
“Life is in the details. If you generalize, it doesn't resonate. The specificity of it is what
resonates.” -Jacqueline Woodson
The idea that work and family roles can conflict with one another is termed work–family
conflict (WFC), defined as “a form of interrole conflict in which the role pressures from the
work and family domains are mutually incompatible in some respect” (Greenhaus & Beutell,
1985, p. 77). In their seminal article, Greenhaus and Beutell (1985) argued that specific WFC
dimensions (i.e., time-based, strain-based, and behavior-based) have unique antecedents and
consequences. However, to date, most empirical examinations of WFC—even when they cite
Greenhaus and Beutell (1985) and use dimension-based measures of WFC (e.g., Carlson et al.,
2000)—do not theorize, hypothesize, or empirically test phenomena at the dimension-level.
The literature has established the importance of separately examining the different
directions of WFC (work-to-family conflict or work interfering with family [WIF] and family-to-
work conflict or family interfering with work [FIW]; e.g., Michel et al., 2011; Shockley &
Singla, 2011). Cumulative evidence supports that the WIF and FIW directions of conflict have
unique antecedents and outcomes and demonstrate adequate discriminant validity from one
another (Byron, 2005; Mesmer-Magnus & Viswesvaran, 2005). Researchers have largely
responded to this direction-based approach by forming WFC composites in which the
dimensions (i.e., time-based, strain-based, and behavior-based) are combined and averaged by
direction to create WIF and FIW composite constructs of WFC. However, this approach to
conceptualizing WFC at the composite-level instead of at the dimension-level has not been
theoretically or empirically confirmed as a well-founded strategy. In fact, item-level, statistical
comparisons of dimension-based and composite-based WFC operationalizations favor the
dimension-based operationalization (Annor & Amponsah-Tawiah, 2017; Carlson et al., 2000;
Pujol-Cols, 2021; Vieira et al., 2014).

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To further complicate things, the WFC literature has been disjointed in its
conceptualization and operationalization of WFC as it pertains to which specific dimensions are
considered. As stated previously, a majority of researchers have used WIF and FIW composites
to study WFC, however, in many cases the behavior-based dimension has commonly been
omitted from the composite—and often without theoretical or empirical justification. A minority
of other researchers have taken a different approach, examining the separate dimensions of WFC
(e.g., Brown, 2013; Elliyoon, 2010), but, even in these instances, oftentimes the behavior-based
dimension is excluded. More specifically, nearly half of the articles in this current meta-analysis
(42 out of 92 effect sizes) measured the time and strain dimensions without the behavior
dimension.
Compounding the problem, prior meta-analyses of WFC have mostly treated various
WFC composites as interchangeable. In other words, as long as a primary study measured one or
more dimensions of conflict, a composite for WIF and/or FIW was calculated (e.g., Allen et al.,
2020; Liao et al., 2019). As a result, effect sizes could be included into a given analysis of
overall WIF or FIW conflict if the primary study included any combination of one or more
dimensions of conflict. Additionally, effect sizes often include a combination of different work-
family measures, even though some measures were designed to assess all three dimensions (e.g.,
Carlson et al., 2000) and other measures were not (e.g., Netemeyer et al., 1996 captures only
time and strain dimensions). As a result, existing meta-analyses cannot speak to whether the
time, strain, and behavior dimensions have different antecedents and outcomes.
Clearly, there is not a consensus regarding if, how, and when composites should be
developed, if at all, for the WFC construct. As a whole, WFC researchers have yet to stop and
thoughtfully ask, are composite-level conceptualizations a better approach, theoretically and
empirically, than dimension-level conceptualizations of WFC? Our paper contributes to the WFC
literature by exploring this question, which can set the groundwork for future theory-driven,
dimension-based WFC research.

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It is imperative that the WFC literature address this question, and the present effort
intends to spearhead this inquiry. In particular, this investigation is important because evidence
may suggest that dimension-level consideration of WFC does not provide meaningful
information beyond what is already covered by unitary WIF and FIW operationalizations. If this
is the case, the widespread use of composite-based strategies in the WFC literature should
continue uninterrupted. On the other hand, if dimension-level WFC approaches do provide
important information that could be missed from unitary conceptualizations, the ubiquitous
practice of averaging across dimensions to create composites may lead researchers to draw
inaccurate inferences about the relationship between WFC and important correlates, especially if
the behavior-based dimension is inconsistently included into these composites.
Indeed, seminal works in WFC have expressed this concern and endorsed a more
nuanced approach (e.g., Carlson et al., 2000; Greenhaus & Beutell, 1985). For example,
Greenhaus and Beutell (1985) urged researchers to consider the possibility that “if different
forms of incompatibility [i.e., time-based, strain-based, behavior-based] … have unique
antecedents and consequences, global assessments of conflict may not reveal these relationships”
(p. 86). Concern with the global approach to the study of WFC has most recently been echoed by
Min et al. (2021), who used confirmatory factor analysis in two primary studies to demonstrate
that different WFC measures with different multidimensional components are not
interchangeable despite being highly correlated and relating to the same higher-order construct.
One reason for the inconsistent conceptualization of WFC may be a lack of theoretical
guidance for if, how, and when specific WFC dimensions would differentially relate to other
constructs in its nomological network. While the seminal paper by Greenhaus and Beutell (1985)
lays out a helpful general dimension-based WFC framework, their specificity for dimension-
level hypothesis development is (understandably) narrow. A key goal of the current research,
therefore, is to not only empirically reconsider the importance of the composite-level approach
versus the dimension-level approach to WFC research, but to also begin theory-building in this

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domain. To do so, we begin by reviewing current WFC theories and then establish the usefulness
of resource allocation theory to the time-based dimension, spillover theory to the strain-based
dimension, and boundary theory to the behavior-based dimension. With this theorizing as a
guide, we narrow our focus to the exploration of key constructs from the WFC nomological
network that are theoretically associated with each dimension: time and family demands for the
time-based dimension, work role ambiguity for the strain-based dimension, and familysupportive
supervisor behavior (FSSB) and nonwork support for the behavior-based dimension. Further, we
review and test bandwidth-fidelity theory, which suggests that composite-level strategies are
more appropriate when examining broad constructs (i.e., job satisfaction and life satisfaction).
We develop three research questions related to these goals that contribute in unique ways
to theoretically and empirically inform future WFC research. First, we ask whether a dimension-
based conceptualization and operationalization of work-family conflict provides utility over the
typical composite-level approach. Second, we ask, if there is evidence for differential
relationships among WFC dimensions, does the pattern of relationships align with our
dimension-level theorizing? Finally, even if we do show utility of a dimension-based approach,
we consider whether there are circumstances where a composite-level approach is still more
advisable. Specifically, drawing from the bandwidth-fidelity approach, we examine whether the
composite-level approach is more appropriate when examining relationships with broader
constructs—specifically, job and life satisfaction. We test these research questions with both
primary and secondary uses of meta-analysis (Oh, 2020).
Literature Review of Dimension-Based WFC Meta-Analyses
Despite the fact that numerous meta-analyses of WFC exist, these manuscripts have
rarely focused on the time-based, strain-based, and behavior-based dimensions of WFC. There
are a couple of notable exceptions, however. Allen et al.’s (2012) meta-analysis found
differential dimension-level relationships with neuroticism, agreeableness, and conscientiousness
across the time-based, strain-based, and behavior-based dimensions of WFC. In another meta-

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analysis, Shockley et al. (2017) explored whether men and women differed regarding time-
based, strain-based, and behavior-based WFC. Importantly, both of these meta-analyses found
evidence for significant moderation by WFC dimension, suggesting dimension-level
examinations are an important avenue for future research. Because these prior meta-analyses
were limited to the examination of personality and gender, we expand upon these meta-analytic
efforts by exploring the dimension- and composite-level relationships of other important,
theoretically-relevant constructs in the WFC literature. We also build upon prior work by
proposing new theoretical considerations for dimension- and composite-based WFC research.
Overall, our aim is to establish whether future research would benefit from a stronger emphasis
on a dimension-based approach in addition to the typical composite-based approach.
Dimension-Based WFC Theoretical Examination
Greenhaus and Beutell’s (1985) seminal work laid the building blocks of dimension-
based WFC research by establishing and defining the dimensions of WFC, proposing important
correlates and their relationships to the dimensions, and reviewing empirical WFC studies.
However, theorizing at the dimension level is largely absent within their paper, with their
propositions for future research involving WFC at the global—rather than dimensional—level.
Thus, while most studies examining dimension-level WFC cite Greenhaus and Beutell (1985),
this citation alone does not provide thorough theoretical grounding for dimension-level
hypothesizing. For example, Greenhaus and Beutell’s model would not provide researchers the
specificity required for hypotheses about when, why, or how behavior-based WIF would relate
more (or less) strongly to work demands than time- or strain-based WIF.
As such, some researchers have drawn on other theories to supplement Greenhaus and
Beutell’s (1985) model of WFC. However, there does not appear to be a clear consensus
regarding dimension-level theorizing and hypothesizing within the WFC literature. Resource and
role theories (including scarcity theories) are most commonly included in WFC theory building
(e.g., Cullen & Hammer, 2007; Golden, 2012). Beyond these, dimension-level WFC studies

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reference a multitude of other theoretical frameworks including social cognitive theory (e.g.,
Clayton et al., 2015), spillover theory (e.g., Cunningham & De La Rosa, 2008) the social
information processing model (e.g., Gaitley, 1996), role conflict theory (e.g., Hargis et al., 2011),
boundary theory (e.g., Halbesleben et al., 2012), ecological systems theory (e.g., Kalliath &
Kalliath, 2013), and the effort recovery model (e.g., Van Hooff et al., 2005). Importantly, even
when these theories are used to supplement Greenhaus and Beutell, hypotheses are almost never
at the dimension level. Moreover, these theories do not necessarily apply equally well across all
dimensions. This shows that, overall, WFC theorizing and hypothesizing at the dimension-level
involves using a piecemeal approach that mentions theories that are not specifically, consistently,
or robustly applied, demonstrating a need for more consistent theorizing and hypothesizing.
After careful review of theoretical frameworks from the work-family literature and
related literatures (e.g., stressor-strain), we present rationale for three theories at the dimension-
level: resource allocation theory (for time-based conflict), spillover theory (for strain-based
conflict), and boundary theory (for behavior-based conflict).1 We argue that using these theories
enables a precise and necessary approach to dimension-level theorizing that has been largely
absent in the literature to date. Overall, each dimension focuses on different aspects of the
broader WFC construct (e.g., time demands, stress/strain, conflicting role identities and/or cross-
domain behaviors for the time, strain, and behavior-based dimensions of WFC, respectively). We
also identify theoretically-relevant correlates based on the primary features emphasized by each
dimension of conflict (e.g., time demands for time-based WFC).
Time-based WFC: Resource allocation theory. The vast majority of work-family
conflict studies rely on scarcity theories, including resource allocation theory2 (Becker, 1965;
Hockey, 1997); indeed, the concept of scarcity is particularly relevant for the time-based
1We thank an anonymous reviewer for specifically suggesting the use of spillover theory and boundary theory.
2The effort recovery model was initially considered for time-based WFC due to its application in the organizational
literature surrounding taking breaks at work and after work. However, after a thorough and critical examination, the
nature of these breaks and whether or not they replenished energy seemed to be more central to the effort recovery
model as opposed to time-based replenishment as would be associated with time-based WFC. Resource allocation
theory, therefore, was deemed more appropriate to time-based WFC than the effort recovery model.

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dimension of work-family conflict. Time-based conflict occurs when time allocated to one
domain reduces the time available for another domain, thus hindering role performance in that
other domain (Greenhaus & Beutell, 1985; Kahn et al., 1964; Pleck et al., 1980). As described by
resource allocation theory, time is a finite and fixed resource, which means that the amount of
time individuals can dedicate to the various roles they hold is limited (Cho & Allen, 2012). Time
can be viewed as coming from a common pool of units that must be distributed or allocated
across an individual’s different role domains (e.g., work, family). This shared nature forces
individuals to make choices as to how much time they dedicate to different roles in that spending
time on one activity means spending less time on another (Bergeron, 2007). The interdependence
of time for work and family makes it important to consider how time is allocated across both
domains because, if one domain requires too much time, conflict will exist with the other domain
due to time’s becoming scarce in the other domain.
Time-based work-family conflict is experienced when an individual feels discord or
incompatibility when allocating time between the domains of work and family (Bergeron, 2007).
Specifically, this discord occurs when an individual who spends time in their role at work
sacrifices time in their role within their family, or when an individual who spends time in their
role within their family sacrifices time from their role at work. Drawing from this theoretical
grounding of time-based work-family conflict, we identified several constructs reflecting time
demands and requirements from a resource allocation theory perspective. Overtime, work hours,
and work time demands would be relevant to time-based WIF whereas family time demands and
number of children would be relevant for time-based FIW. To be precise in our pairing between
WIF and FIW, we chose to include work time demands and family time demands.
Strain-based WFC: Spillover theory. Spillover theory has been used as an overarching
theory throughout the WFC literature, and we argue that negative spillover is particularly
relevant for theorizing relationships with strain-based WFC. Strain-based conflict occurs when
increased stress or tension generated in one domain produces strain symptoms that hinder role

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performance in another role (Greenhaus & Beutell, 1985). For example, if workplace
expectations are unclear and confusing, leading to a worker’s frustration and anxiety, and these
concerns preoccupy that worker and cause strife between them and their spouse, they are
experiencing strain-based conflict. In support of this notion, the measures of negative spillover
(Belsky, 1985) and measures of strain-based WFC (Carlson et al., 2000) include the common
elements of exhaustion, tension, frustration, strain, and stress. Spillover occurs when there is
similarity, extension, familiarity, continuation, or generalization between one’s state between
work and family, including when one’s mood carries over between the domains (Belsky et al.,
1986; Edwards & Rothbard, 2000; Staines, 1980; Westaby et al., 2016). Negative emotional
spillover occurs when negative emotions and tension, including strain, from work impact family
life, and vice-versa. This is the case when work tension overrides one’s satisfaction and feelings
of anxiety, depression, guilt, disappointment, irritability, stress, fatigue, worry, frustration, or
boredom (from repetition, understimulation, or isolation; Bromet et al., 1990) spill negatively
and detract from one’s private life (Eckenrode & Gore, 1990; Evans & Bartolomé, 1986; Frone,
2003; Lambert, 1990). This phenomenon has also been labeled as a transmission process or
strain contagion process (Eckenrode & Gore, 1990).
Strain that originates from work is often discussed as being born from pressure to
increase production, poor management (including lack of clarity), and job-related stressors,
whereas stress originating from family is often cited as related to conflict with family members
and spouses or overwhelming amounts of housework or childcare demands (Bromet et al., 1990).
Individuals experiencing negative spillover may become disconnected, distracted, disassociated,
and unable to focus or sleep, and spillover can be a result of ambiguity, doubt, and anxiety on the
job regarding their ability to perform well (Evans & Bartolomé, 1986; Lambert, 1990; Weiss,
1990). This is because demands that are too taxing and overwhelming in one domain can lead to
a negative spillover effect (Emmons et al., 1990; Staines, 1980), leaving employees in a
depleted, physically and mentally exhausted state with inadequate energy—unable to complete

WORK–FAMILY CONFLICT DIMENSIONS META 9
their duties in the other domain (Eckenrode & Gore, 1990; Piotrowski & Keller, 1978). Spillover
and strain-based WFC are of importance, as researchers have demonstrated that stress in one
domain can more than double the probability of stress in the subsequent domain (Bolger et al.,
1990). In particular, we include work role ambiguity in our meta-analysis as a stressful demand-
related variable3, as it is a taxing psychological experience for workers, depleting their energy
and ability and increasing the extent to which they feel their work life interferes with their home
life.
Behavior-based WFC: Boundary theory. Boundary theory considers the dynamics by
which individuals maintain, negotiate, and transition across boundaries, including boundaries
between work and family (Ashforth et al., 2000). Drawn from role theory and concepts of role
conflict (Biddle, 1986; Kahn et al., 1964; Katz & Kahn, 1978), boundary theory describes how
individuals’ roles in their working and family environments define the behaviors they are
expected to exhibit in each role (Allen et al., 2014). There can be different expectancies
regarding language, word use, actions that are appropriate, and the behaviors required to
accomplish tasks in work and family domains; the work environment and the home environment
may be very similar or radically different, shaping the extent to which an individual needs to
modify their behaviors when transitioning from family to work and vice-versa (Clark, 2000). For
example, a daycare worker may find it easy to transition between their family and work lives if
they have kids at home, whereas a construction worker may find the transition more difficult.
Behavior-based conflict occurs when behaviors required and expected in one role are
incompatible with and hinder role performance in another role (Greenhaus & Beutell, 1985).
Because different behaviors are often expected of individuals in the work versus the home
domain, individuals may experience behavior-based conflict when they fail to adjust their
behaviors from one role to the other (Carlson et al., 2000). Due to its focus on expected
behaviors and the compatibility (or incompatibility) of these behaviors across domains, boundary
3 The studies available to meta-analyze did not produce a theoretically-fitting family-related strain correlate.

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theory is a fitting theory for hypothesizing relationships with the behavior-based conflict
dimension of work-family conflict. Specifically, boundary theory can guide which antecedents
are relevant to the behavior-based work family conflict dimension. In particular, family-
supportive supervisor behaviors (FSSB) are characterized by high levels of support and resources
to assist employees in managing the role and behavioral expectations that are necessary for their
family roles while at work (Crain & Stevens, 2018; Hammer et al., 2009). Thus, the extent to
which an individual perceives that they work for a supportive supervisor may be particularly
relevant for an individual’s experience of behavior-based WIF. This is because supervisors who
are supportive convey a sense of understanding of the behaviors that are necessary for employees
to engage in for their family life, which can help mitigate the extent to which one’s work life
interferes with their family life. Turning to the family domain, nonwork support provides
employees the comfort and understanding they need in the shift from the behaviors they are
required to display at work to the behaviors they need to display at home.
Utility of Dimension- Vs. Composite-Based WFC
The first question we address is if there is utility in using a dimension-based
conceptualization and operationalization of WFC rather than the typical composite-level
operationalization. Does a dimension-level conceptualization provide advantages over and above
WFC composites? Examining the relationships of these theoretically chosen constructs—work
time demands, family time demands, work role ambiguity, FSSB, and nonwork support—with
the dimensions compared to their relationships with WFC composites can shed light on whether
dimension-level WFC conceptualization is beneficial. If the WFC dimension relationships—
time-based, strain-based, and behavior-based—demonstrate distinct relationships with these
constructs and account for more variance in these constructs when compared to the WFC
composites, then it would suggest researchers should be more thoughtful in their decision to
examine WFC at the dimension- or composite-level. Therefore, we ask:
Research Question 1: Is there evidence when examining relationships of WFC and

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theoretically-relevant constructs to support a dimension-based WFC conceptualization?
Dimension-Based WFC Theorizing
Next, if we find evidence for differential relationships among WFC dimensions, we will
want to examine if the pattern of relationships aligns with the expectations from our dimension-
level theory building. Specifically and as described above, based on resource allocation theory,
we would expect that time-based WIF and FIW would relate more strongly to work and family
time demands, respectively, compared to strain- and behavior-based WFC dimensions. Based on
spillover theory, we would expect strain-based WIF to relate more strongly to work role
ambiguity than time- and behavior-based WFC dimensions. Finally, based on boundary theory,
we would expect that behavior-based WIF and FIW would be more strongly related to FSSB and
nonwork support, respectively, than time- and strain-based WFC dimensions. With these
theoretical arguments in mind, we ask:
Research Question 2: Is there dimension-level WFC evidence that aligns with the
expectations from our dimension-level theory building?
Dimension-Based WFC and Satisfaction: Bandwidth Fidelity Theory
Although these theoretically-chosen WFC constructs may result in meta-analytic
evidence supporting dimension-based conceptualization, it is important to also consider if WFC
composites may be more appropriate or advisable in certain circumstances. Drawing from the
bandwidth-fidelity approach (Cronbach & Gleser, 1957), while a dimension-level approach may
be advisable when one is interested in specific phenomena, overall or composite measures may
be best suited to examine broad phenomenon. To examine this idea, we next turn to an
examination of the relationship between WFC and job and life satisfaction. Both job and life
satisfaction can be considered broad phenomena, capturing satisfaction with one’s job or life in
general, or as a whole, as opposed to asking about specific facets of the job or their life. Indeed,
scholars have argued that when predicting job and life satisfaction, broad phenomena/constructs
are best suited (Erdogan et al., 2012; Judge & Kammeyer-Mueller, 2012). Therefore, applying

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this logic to the present study, one would expect a stronger relationship between the composite
WFC and FWC conceptualizations and job/life satisfaction than between the specific time, strain,
and behavior dimensions and job/life satisfaction.
On the other hand, there continues to be ongoing debate regarding the degree to which
broad criteria are best examined with broad predictors (Ashton et al., 2014; Steel et al., 2019).
Scholars have argued that a composite approach may only be ideal when it should be assumed
that every facet contributes equally to the prediction (O'Neill & Steel, 2018; Schimmack et al.,
2004). Work-family scholars frequently drop the behavior-based dimension from assessments of
work-family conflict, which implies that not all dimensions of work-family conflict contribute
equally to the prediction of correlates and suggests an advantage to a facet-level approach even
when examining broader criterion. Prior studies have also refuted the bandwidth-fidelity
approach. For example, Quevedo and Abella (2011) found facet-level assessments of the Big
Five personality traits consistently explained greater variance in subjective well-being (a broad
construct) compared to trait-level assessments of the Big Five. Finally, in their meta-analysis of
the relationship of personality with job and life satisfaction, Steel et al. (2019) found a facet-level
approach explained less variance when predicting a narrower criterion compared to a broad
criterion. These researchers subsequently concluded, “the traditional heuristic of matching
specificity of a criterion with predictors cannot possibly be right” (p. 237).
Due to the general nature of job and life satisfaction, we do not expect them to be more
strongly related to any one conflict dimension. Unlike the other constructs in WFC’s
nomological network that have theoretical rational pointing to the potential for differential
strength in their relationships, the broadness of these types of satisfaction does not necessarily
lend them to specific theories that would suggest differentiation among correlate relationships.
However, we would anticipate the WIF direction dimensions to be more strongly related to job
satisfaction than the FIW direction dimensions and the FIW direction dimensions to be more
strongly related to life satisfaction than the WIF direction dimensions, as has been established by

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previous research (e.g., Shockley & Singla, 2011). We build upon this prior work, however, in
our curiosity regarding whether there is a benefit to dimension-level examination over and above
composite-level examination, even considering the general nature of job and life satisfaction.
Given the ongoing debate and mixed findings pertaining to the bandwidth-fidelity assumption,
we pose the following as a research question:
Research Question 3: Do composite conceptualizations of WFC account for greater
variance in job satisfaction and life satisfaction than dimension-based WFC
conceptualizations?
Method
Literature Search
We used four approaches to locate articles for the present meta-analysis. First, an in line
with prior WFC meta-analyses, literature searches were conducted using several online databases
including PSYCinfo, Business Source Complete, and ProQuest Dissertations and Theses. We
searched combinations of the term “work–family conflict” with “time,” “strain,” and “behavior.”
Second, Google Scholar citation searches were conducted online to identify all studies citing the
original articles of the most commonly used measures of the WFC directions and dimensions
(WIF, FIW; time, strain, and behavior). Citation searches were completed for Carlson et al.
(2000) and Frone et al. (1997). Third, reference lists of past meta-analyses were used to locate
studies, including Allen et al. (2012), Amstad et al. (2011), Byron (2005), Ford et al. (2007),
Michel et al. (2011), and Shockley et al. (2011). Fourth, we performed online searches through
the database hosted by the Work and Family Researchers Network Conference website
(https://workfamily.sas.upenn.edu/content/wfc). Data collection included studies through the end
of May 2021, and these searches yielded 2,898 manuscripts. Because human subjects data were
not directly collected, we did not receive institutional review board (IRB) approval for this study.
Inclusion Criteria Prior to Coding
We used three decision rules concerning which studies were included in our analyses,

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which were assessed by referencing the full text of the manuscripts. First, working adult samples
were required. Second, it was necessary for each study to report sample sizes along with at least
one self-reported between-subjects correlation between one other construct and WFC;
information about the work-family dimension (i.e., time-based, strain-based, or behavior-based)
and direction (WIF or FIW) also needed to be present. Third, only studies written in English
were included in analyses. Last, articles were only included if they contained one of the seven
aforementioned theoretically-relevant constructs. After these decision rules were applied, 66
articles remained and were coded, one of which was a conference paper (Armstrong-Stassen et
al., 2007, June; Administrative Sciences Association of Canada); none of the remaining articles
contained research designs that were experimental (i.e., contained manipulations) in nature.
Coding Procedures
Consistent with prior meta-analyses (e.g., Ford et al., 2007), each study was coded by at
least two coders. Our meta-analytic coding team included five pairs of trained research
assistants. Each research assistant had taken at least two research-related courses (including
research methods and research design) and had received four hours of meta-analytic coding
training regarding the procedures for the current meta-analysis. During this training, agreement
between all coders was calculated after an initial three articles were coded, and the team met to
review the articles. After this meeting, the assistants were given another three articles to code.
Agreement of 95% was reached during this second set of articles, and thus training was deemed
complete. Following the training, each of the remaining articles was independently coded by two
coders, and any disagreements in coding were resolved by the authors.
The ratings of one research assistant per pair were randomly selected for our analyses,
and the ratings from the other assistant in the pair were used to identify discrepancies and
conduct interrater reliability. To test for overall accuracy in coding, as in Byron (2005), we
randomly selected a quarter of the studies to calculate interrater agreement across the seven data
features used in analyses: the correlation, sample size, WFC direction, WFC dimension, WFC

WORK–FAMILY CONFLICT DIMENSIONS META 15
construct reliability, other construct reliability, and other construct label. Interrater agreement
was calculated by dividing the number of data points in agreement by the total number of data
points coded. The interrater agreement across all examined study features was 94%. Coders also
collected additional information, including: the type of measure used for the WFC dimension,
whether time separation or separate samples were reported, sample statistics (i.e., gender, age,
marital status), occupation, industry, number of children, fulltime status, education, hours
worked per week, income, tenure, and country.
Inclusion Criteria After Coding
A number of these articles were removed after coding due to the following criteria. We
used the procedures outlined by Wood (2008) to detect duplicate study effects. In the event that
multiple studies reported the same relationships from the same sample, which was the case for
two samples across two studies, we coded the study with the largest sample size and only
included unique effect sizes from the smaller sample studies. Thus, one duplicate sample was
removed. Therefore, 65 records remained (see Figure S1 for a flow chart of search and inclusion
protocols).
This procedure resulted in the meta-analysis of 65 articles with 77 independent studies.
Of these articles, 25 were dissertations and one was a conference paper; thus, 39 published
articles were included. The samples within these articles were from a variety of countries
(including China, the United States, and Australia) industries (including education, finance,
healthcare, and government), and occupations (including teachers, nurses, and police officers).
They were diverse in regard to gender, race, education level, age, parental status, and marital
status.
Composite Creation
To maintain the assumption of independence of effect sizes, when a single sample
provided more than one effect size for any given construct relationship, composite formulas were
used (Ghiselli et al., 1981; Schmidt & Hunter, 2015). If composite formulas could not be used

WORK–FAMILY CONFLICT DIMENSIONS META 16
due to missing information, we used the mean sample-size-weighted correlation. If a study
assessed the same relationships with the same sample at different time points (Halbesleben et al.,
2009), correlations were averaged together. It is typical in WFC meta-analyses to create
composites of WIF and FIW based on one or more WFC dimensions (e.g., Allen et al., 2020;
Liao et al., 2019). Given our focus on all three WFC dimensions, we formed WIF and FIW
composites only if a sample included all three dimensions of WIF or FIW (50 effect sizes). We
created composites that collapsed across WIF for WIFComp (WIF composite) and across FIW for
FIWComp (FIW composite).
Meta-Analytic Calculations
Psychometric meta-analytic estimates were calculated using the psychmeta package
(Dahlke & Wiernik, 2019, version 2.4.2) in R (R Core Team, 2013, 2020, version 4.0.3) within
the RStudio environment (RStudio Team, 2020, version 1.3.1093). We estimated bivariate
random effects models for each pair of constructs. Artifact distribution corrections for
measurement error in both constructs were applied using the Taylor series approximation method
and Cronbach’s alpha internal consistency estimates provided in the primary studies. This
method shows comparable accuracy to Schmidt and Hunter’s (2015) interactive method and is
the default correction method in psychmeta because of its flexibility and computational
efficiency (see Dahlke & Wiernik, 2020).
For each meta-analytic relationship, in the supplemental materials (Table S1), we report
the: total number of effect sizes (k), total sample size (N), sample-weighted mean correlation (r),
standard deviation of sample-weighted mean correlations (SDr), standard deviation of sample-
weighted mean correlations after removing predicted sampling-error and artifact variance (SDres),
mean artifact-corrected correlation (ρ), standard deviation of artifact-corrected correlations after
removing predicted sampling-error and artifact variance (SDρ), 95% confidence intervals (CIs),
and 80% credibility intervals (CVs). We calculated 95% CIs and 80% CVs around ρ using the t-
distribution rather than the normal distribution as this results in more accurate estimates when k

WORK–FAMILY CONFLICT DIMENSIONS META 17
is less than 30 (Schmidt & Hunter, 2015).
Analytic Strategy
We followed best practices for leveraging secondary uses of meta-analytic data (Oh,
2020) to examine the relative contribution of dimensions to the variance shared with the variable
of interest (e.g., work time demands).4 Variable importance can take on different meanings
depending on the methods used (Johnson & LeBreton, 2004; Lebreton et al., 2004). Following
best practices, we calculated multiple metrics of importance to triangulate inferences (LeBreton
et al., 2007; Nathans et al., 2012; Nimon & Oswald, 2013; Oh, 2020). In addition to the mean
uncorrected and corrected correlation, we used the meta-analytic correlation and inter-correlation
matrices (Table S1 and Table 1, respectively) to calculate usefulness metrics (R-squared;
Darlington, 1968) and raw and rescaled relative weights (Johnson, 2000).
We decided to use relative weights analyses (RWA) because strong intercorrelations
were demonstrated among the WFC dimensions (see Table 1), and RWA is better suited to
manage these high intercorrelations (LeBreton et al., 2007; Tonidandel & LeBreton, 2015).5 We
used RWA Web (Tonidandel & LeBreton, 2015) and adapted the yhat R package (2.0-3; Nimon
et al., 2021) to use the meta-analytic correlation and intercorrelation matrices as input data. We
followed the approaches by Lyubykh et al. (2022) in interpreting and reporting our RWA results.
For Research Question 1, we compare the R-squared values of WIF/FIW dimensions to
WIFComp/FIWComp across all variables. Differences between the dimension and composite models
indicate the amount of shared variance lost or gained when using a dimension- vs. composite-
based approach. When a model including dimensions shows more shared variance with a
4 We use this language rather than typical language around variable importance intentionally. Variable importance
(e.g., relative weights analysis) tends to use terminology implying causality (predictor, outcome, variance
explained). However, we are examining relationships with theoretical antecedents as well as outcomes. Importantly,
the analyses used are mathematically and statistically agnostic towards causality. It is theory and research design
that inform causality (Cohen, 1968; Pedhazur, 1997; Stone-Romero & Rosopa, 2008)—the formulas simply
partition shared variance.
5 We calculated several other variable importance metrics recommended by Nimon & Oswald (2013). Due to the
near-perfect agreement among the metrics for ranked ordering of importance, we focus on and report the raw and
rescaled values from RWA.

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variable than the composite model, this indicates that the act of averaging across the dimensions
to form a composite results in a loss of shared variance that would be accounted for if
dimensions were used instead.
To evaluate Research Question 2, we examine the variable importance metrics described
above. Evidence supporting our dimension-level theory-driven expectations would include raw
and relative weight values for the expected dimension–variable pairings (e.g., time-based WIF
with work time demands) that are larger than other dimension pairings with that same variable
(e.g., behavior-based WIF with work time demands; Lyubykh et al., 2022).
Finally, Research Question 3 is answered using a combination of the two above
approaches. A greater gain in shared variance with job and life satisfaction for WIFComp and
FIWComp compared to WIF and FIW dimensions would provide support for the bandwidth
fidelity perspective. Our variable importance analyses include both directions (i.e., WIF and
FIW) within the same model as opposed to the direction-separated analyses used for Research
Question 1. R-squared values between dimension and composite models are compared for job
and life satisfaction in these models as well. Comparing differences for direction-specific
approaches (Table 2) as well as approaches collapsing across dimension (Table 3) provides a
robustness check across the WFC construct as a whole, as it allows for consistency in R-squared
values to be examined.
Transparency and Openness
We adhered to the Journal of Applied Psychology methodological checklist for meta-
analytic research. All data, analysis code, and publication bias results will be made available
upon request from the first author. Above, we indicate the software used in analysis, and the
computer code and syntax are included within the reference manuals for these software
programs. This study was not preregistered due to its exploratory nature (i.e., research questions
and not hypotheses).
Results

WORK–FAMILY CONFLICT DIMENSIONS META 19
Research Question 1
Research Question 1 asked whether there was evidence when examining relationships of
WFC and theoretically-relevant constructs to support dimension-based WFC conceptualization
compared to the unitary composite conceptualization. Evidence of the utility of WFC dimensions
above and beyond WFC composites was generally found across the constructs in both direction-
separate models and models collapsing across direction.
First, we turn to the results from Table 2 to compare the total R-squared values (total
shared variance) of each dimension model directly with its relevant composite counterpart for
each variable (Dimension - Composite). For work time demands, the WIFComp model had an R-
squared value 0.04 lower than the WIF dimensions model, indicating that averaging across
dimensions to form a composite resulted in an R-squared value 25% lower than operationalizing
work-family conflict with separate WIF dimensions. FIWComp similarly showed an R-squared
value 20% lower than the FIW dimensions model (∆R2 = -0.01). Family time demands, on the
other hand, had equal shared variance between FIW dimensions and FIWComp models as well as
WIF dimensions and WIFComp models.
For work role ambiguity, the WIFComp model demonstrated a 25% loss in total shared
variance compared to the WIF dimensions model (∆R2 = -0.03) whereas the FIWComp model
showed no difference in total shared variance compared to the FIW dimensions model. The
WIFComp model showed a substantial (i.e., 80%) loss in total shared variance with FSSB
compared to the WIF dimensions model (∆R2 = -0.04) while the FIWComp model again showed
no differences in total R2 compared to the FIW dimensions model. Finally, for nonwork support,
the FIWComp model had an R2 value 29% lower than the FIW dimensions model (∆R2 = -0.02)
and the WIFComp model shared 50% less variance with nonwork support compared to the WIF
dimensions model (∆R2 = -0.01).
For models containing both directions of WFC simultaneously (see Table 3), the results
were largely consistent with the results above. The composite model had an R2 25% lower than

WORK–FAMILY CONFLICT DIMENSIONS META 20
the dimensions model (∆R2 = -0.04) for work time demands, 25% lower for family time demands
(∆R2 = -0.01), 6% lower for work role ambiguity (∆R2 = -0.01), 80% lower for FSSB (∆R2 = -
0.04), and 38% lower for nonwork support (∆R2 = -0.03).
Research Question 2
Research Question 2 asked whether there was dimension-level evidence that aligns with
the expectations from our dimension-level theory building. Overall, the evidence generally
suggests support for our theoretical framework. Turning to Table 3, for work time demands,
time-based WIF had the largest raw and rescaled RWA values (.08, 46.64%), and for family time
demands, time-based FIW had the largest raw and rescaled values (.02, 35.60%), which provides
evidence for our theoretical assertions. For work role ambiguity, the largest raw and rescaled
values were accounted for by strain-based WIF (.04, 26.15%), which provides evidence for our
theoretical arguments. For FSSB, the largest raw and rescaled values were accounted for by
time-based WIF (.02, 37.05%), although behavior-based WIF showed the second largest values
(.01, 23.25%), providing minimal evidence for our theoretical arguments. For nonwork support,
the largest raw and rescaled values were accounted for by behavior-based FIW (.03, 41.88%),
which provides evidence for our theoretical expectations.
Research Question 3
Research Question 3 asked if composite conceptualizations of WFC account for greater
variance in job satisfaction and life satisfaction than dimension-based WFC conceptualizations.
Mixed evidence of the utility of WFC composites above and beyond WFC dimensions was found
across job satisfaction and life satisfaction when both the R-squared and relative weights values
were considered. Notably, more support was found for composites with domain-matching
relationships (e.g., WIFComp and job satisfaction) than for cross-domain relationships (e.g.,
FIWComp with job satisfaction).
First, we again turn to the results from Table 2 to compare the total R-squared values
(total shared variance) of each dimension model directly with its relevant composite counterpart

WORK–FAMILY CONFLICT DIMENSIONS META 21
for each variable. For job satisfaction, there was a 17% gain in the shared variance for the
WIFComp model (∆R2 = +0.02) but a 14% loss in shared variance for the FIWComp model (∆R2 = -
0.01) compared to WIF and FIW dimension models, respectively. For life satisfaction, there was
an 8% gain for the FIWComp model (∆R2 = +0.01) and a 6% loss for the WIFComp model (∆R2 = -
0.01). Table 3 also compares the R-squared values for the dimension and composite models
where the directions were included in the same models simultaneously. For job satisfaction, the
WIFComp/FIWComp models demonstrated a 15% gain in shared variance (∆R2 = +0.02) compared
to the dimension-based model whereas there was no difference between dimension and
composite models for life satisfaction.
Supplemental Analyses: Comparison of Composites
As mentioned in the introduction, there is a great deal of inconsistency in the literature on
the operationalization of WFC, even at the composite level. For example, it is fairly common for
researchers to averaging across time- and strain-based WIF or FIW dimensions to assess overall
WIF and overall FIW, respectively—dropping the behavior-based dimension from these
calculations. When different operationalizations of overall WFC are entered into a meta-analysis,
this compounds the issue. In particular, WFC meta-analyses tend to treat various WFC
composites as interchangeable (e.g., an effect size for WIF including only time and strain
dimensions is not distinguished from an effect size for WIF that includes all three dimensions).
Similar issues can occur if a meta-analysis combines different WFC measures into the same
analysis, when some measures are designed to assess all three dimensions (e.g., Carlson et al.,
2000) and other measures are designed to assess only one or two dimensions (e.g., Netemeyer et
al., 1996 captures only time and strain dimensions). Considering evidence from Min and
colleagues (2021) showing that common measures of WFC are not interchangeable, combining
differing numbers of dimensions and measures into a composite may create an uninterpretable
blend of WFC dimensions.
As a supplemental set of analyses, we wanted to examine the degree to which the

WORK–FAMILY CONFLICT DIMENSIONS META 22
construction of WIF and FIW composites influences the total variance accounted for in our
constructs. To this aim, we compared three composites in Table 4. The first composite is what
we have up to now labeled WIFComp and FIWComp and, again, is comprised of the time-, strain-,
and behavior-based dimensions. Second, based on the prevalence of combining only the time and
strain dimensions into a composite while not measuring the behavior dimension, we recalculated
composites using only the time and strain dimensions to create alternative versions of the
composites, represented by WIFTS and FIWTS. Finally, we also examine a third approach, which
we label WIFMix and FIWMix. Because work-family meta-analyses tend to include primary
studies that use various combinations of one or more time, strain, and behavior dimensions into
their composite as well as a variety of WFC measures which are designed to assess different
combinations of dimensions, we would expect that WIF and FIW composites in existing meta-
analyses will include a blend of one or more dimensions of time, strain, and behavior. Thus, we
characterize this composite as a “mix.” We took the input values (rho, ρ) for the job and life
satisfaction variables from Amstad et al.’s (2011) meta-analysis, and the input values for the
remaining variables from Michel et al.’s (2011) meta-analysis.
Examining Table 4, we see large differences in the variance accounted for across the
composite forms for most variables, as evidenced by the range of those values. Across all 7
variables and directions, there is an average R2 range of 0.04 across the three composite types,
with some differences as large as 0.09 (FIW and work role ambiguity). Importantly, in most
cases, the composites with all three dimensions (i.e., WIFComp/FIWComp) have the highest total
shared variance with the variables. This means that the results and conclusions drawn based on
our main analyses can be viewed as conservative estimates of differences between dimension-
based and composite-based operationalizations of WFC. For example, if we use the WIF
composite averaging across time-, strain-, and behavior-dimensions (WIFComp) in a model, then
we see a 17% gain in shared variance with job satisfaction compared to a model with WIF
dimensions entered into the model separately (∆R2 = +0.02). Notably, however, if we use the

WORK–FAMILY CONFLICT DIMENSIONS META 23
composite based on previous meta-analyses (WIFMix) for the relationship between WIF and job
satisfaction, we see a 50% loss in shared variance compared to a model with WIF dimensions
(∆R2 = -0.06).
Overview of Results
Overall, our findings generally support a dimension-based approach to WFC research.
Models with all three WIF/FIW dimensions included generally showed the same amount or—in
most cases—more shared variance with variables than WIF/FIW composites (Research Question
1). Variable importance metrics (i.e., R2, raw and relative weights) consistently demonstrated
distinct relationships between WFC dimensions and correlates and typically followed our
theoretical expectations—expectations based on resource allocation theory for time-based WFC,
spillover theory for strain-based WFC, and boundary theory for behavior-based WFC (Research
Question 2). Minimal support was found for bandwidth fidelity arguments, as support for
dimension-based approaches appeared for both narrow and broad (i.e., job and life satisfaction)
constructs (Research Question 3). Supplemental analyses highlighted the wide variation in R-
squared values depending on how the WIF/FIW composites are created. Thus, how one creates
WIF and FIW composites appears to have a large influence on relationships. Importantly, across
all models and composite forms (Tables 2, 3, and 4), composites demonstrated a gain in shared
variance when compared to dimensions in only 3 instances—including job and life satisfaction—
which accounted for a small minority of the differences examined (3 out of 49 comparisons,
6%).
Discussion
It has been over three decades since the seminal WFC theory paper by Greenhaus and
Beutell (1985) conceptually developing the case for WFC dimensions and over two decades
since the effort by Carlson and colleagues (2000) to match this conceptualization in the
measurement of WFC. In the paper by Carlson and colleagues, they put forth this call:
“The multidimensional measure of the concept of work–family conflict developed in the

WORK–FAMILY CONFLICT DIMENSIONS META 24
present study is a more accurate depiction of the construct as it allows each of the six
dimensions to be examined. Future use of this scale should provide a greater
understanding regarding how the separate work–family conflict dimensions relate to
attitudes and behaviors of interest.” (p. 269, emphasis added).
When considering the vast WFC literature as a whole, this call has not been well-heeded,
as aggregation of the dimensions, especially the time-based and strain-based dimensions, has
been a common practice within the WFC literature (Allen et al., 2015). However, in this paper,
we demonstrate evidence for the importance of WFC research to better align with this call.
Overall, the main takeaway from this work is that there is theoretical and empirical utility in a
dimension-level conceptualization of WFC, and thus, future WFC should reconsider composite-
based approaches. Overall, we establish the usefulness of dimension-level WFC
conceptualization and operationalization through the scant empirical evidence (relative to the
large WFC literature) that has individually examined the time-, strain-, and behavior based WIF
and FIW dimensions—evidence that has accumulated over the past few decades. Further, our
results also highlight the importance of the behavior-based dimension, demonstrating that
behavior-based conflict can no longer be dismissed as an unimportant aspect of WFC theorizing
and measurement. Instead, our results suggest that the behavior-based dimension needs to
become part of the conversation in future WFC research.
Theoretical and Research Implications
The present study offers several theoretical and research implications. First, our work is
novel in that it offers theoretical grounding for dimension-based WFC hypothesizing. We detail
how, due to the fixed and finite nature of time, resource allocation theory can best inform
hypothesizing related to time-based WFC. Further, in future WFC research, resource allocation
theory, we argue, should also be drawn from when examining variables that capture elements of
time, including but not limited to overtime, work hours, number of children, and having a
working spouse. Given the nature of strain-based conflict to have elements of exhaustion,

WORK–FAMILY CONFLICT DIMENSIONS META 25
tension, frustration, and stress, we argue for future WFC research to use negative spillover theory
as a framework in the examination of strain-based WFC. Many other important correlates within
the WFC literature would also benefit from theorizing and hypothesizing related to spillover
theory, including emotional exhaustion, well-being, work role conflict, enrichment, affect,
autonomy, and psychological strain. Boundary theory, in its focus on how people manage and
transition across the boundaries of the differing roles they hold, we establish, is a natural fit for
exploring behavior-based conflict. Some important theoretically-relevant constructs to explore in
regard to boundary theory include family-friendly organization, social support, coworker
support, perceived organizational support, family support, and spousal support. Overall, our
empirical results echo and support the theoretical differences we propose distinguishing the WFC
time-, strain-, and behavior-based dimensions as dissimilar constructs.
Bandwidth fidelity theory was proposed as an important theoretical consideration when
examining broad constructs to potentially provide theoretical justification for the use of the WIF
and FIW composites. However, our empirical results do not support the bandwidth fidelity
approach and are instead in line with studies that have refuted the theory (Quevedo & Abella,
2011; Steel et al., 2019); demonstrating even stronger support to consider the WFC dimensions
separately. By taking a more robust and tailored approach to theoretical grounding and
hypothesis development and using our proposed theories as a guide, future dimension-based
WFC research can investigate the dynamics of the dimensions of WFC in a more streamlined
and appropriate manner.
Second, the present effort demonstrates the usefulness and importance of using RWA to
explore constructs that are distinct but demonstrate high intercorrelations, as is the case within
dimension-based WFC research. Perhaps prior research employing more traditional regression-
based approaches has decided to collapse the dimensions due to issues of multicollinearity within
the analyses, leading to an overrepresentation of composite-based WFC research when
dimension-based examination may be more appropriate. Thus, by highlighting the

WORK–FAMILY CONFLICT DIMENSIONS META 26
methodological strengths of an RWA-based approach to overcome analytic issues when
variables are highly correlated, we hope researchers become inspired to use this approach in
future WFC studies.
Overall, our results suggest that is crucial for WFC research to conduct more dimension-
based primary studies examining important correlates to the WFC nomological network. In
doing so, a more accurate and holistic understanding of the WFC construct can emerge. This is
particularly important in regard to increasing the representation in the literature of the behavior-
based dimension, which has been substantially understudied in comparison to the time- and
strain-based dimensions. We were unable to pair work role ambiguity with a theoretically-related
variable in the family domain for strain-based WFC due to a lack of studies examining these
variables at the dimension-level. Given the underrepresentation of family-related variables,
particularly at the dimension-level, it is also important for future research to study family-related
constructs—research needs to distinguish more general constructs in regard to how employees
feel about the construct at work versus at home. For example, emotional exhaustion is a very
commonly studied outcome variable in the WFC literature, but distinguishing emotional
exhaustion from one’s job from emotional exhaustion from one’s family life could be an
important distinction in future research.
In developing the theoretical grounding for the dimensions, it was noted that the
behavior-based items may demonstrate more of a focus on performance, effectiveness,
productivity, and usefulness as opposed to conflict, whereas the time- and strain-based items
focus more on conflict and interference between the domains. This may explain why some or
findings for behavior-based conflict were counter to our theoretical expectations. Thus, future
research may consider updating the items of the behavior-based Carlson et al. (2000) measure to
better align these items with a conflict—as opposed to performance-based—perspective. For
example, one item for behavior-based WIF might read “Behavior that is necessary for me at
work would be incompatible at home” instead of “Behavior that is effective and necessary for

WORK–FAMILY CONFLICT DIMENSIONS META 27
me at work would be counterproductive at home.”
Practical Recommendations
Based on these findings, we can make several recommendations for practice. First, our
results suggest that managers may want to pay close attention when employees talk about
experiencing time- and strain-based WIF, as it emerged as a strong correlate for a number of
negative outcomes. A sign that might be indicative of time-based WIF could include employees
feeling as though their job time requirements keep them away from family and household
responsibilities and activities. For strain-based WIF, this may manifest in employees
experiencing feelings of being frazzled, emotionally exhausted, and stressed when they are away
from work. In particular, this is important for managers to pay attention to because it could be
indicative of the time demands at work being too extreme for employees to handle. Thus, it
would be suggested that if employees are experiencing this time- and strain-based WIF, that their
time demands at work be lessened so that they can experience less distress regarding the
allocation of their time and also less spillover of their tension from work to home.
Next, work role ambiguity is suggested from our findings as particularly problematic due
to its strong relationships to both strain-based WIF and FIW. It may be a sign that employees’
work is too ambiguous if they are experiencing preoccupation with, a decrease in their job/home
abilities because of, and stress and depletion as a result of issues stemming from their home life
when at work and from their work life when at home. If employees’ stress and strain are
heightened, managers may want to investigate to see if more clarity and precision regarding the
tasks they are required to complete could be beneficial.
FSSB emerged as a particularly important construct for all forms of WIF. Thus, it further
emphasizes the significance of supervisors in providing emotional and instrumental support
regarding balancing workplace responsibilities in a manner that reduces the extent to which these
responsibilities impact one’s home life. For example, supervisors can foster emotional FSSB if
they are willing to listen regarding, take the time to learn about, and foster comfortable and

WORK–FAMILY CONFLICT DIMENSIONS META 28
effective conversations surrounding juggling work and nonwork life. For instrumental support,
they can help employees navigate and better troubleshoot scheduling conflicts and unanticipated
nonwork demands and conflicts. Further, managers can foster FSSB by acting as a role model for
what healthy work-life balance behaviors look like, as well as coming up with creative solutions
for how the work in one’s workgroup gets allocated and managed to better foster work-life
balance. However, employees may want to bolster their nonwork support if they are
experiencing heightened FIW, as FSSB seems to be much more effective for WIF, whereas
nonwork support is a more effective remedy for FIW.
If bolstering employee satisfaction is a managerial priority, strain-based conflict needs to
be addressed and allayed. In particular, strain-based WIF was the strongest and strain-based FIW
was the second-strongest driver of life satisfaction, whereas strain-based WIF was the most
dominant dimension in the prediction of job satisfaction. This signals the potential importance
for organizations to invest in stress-management programs for employees. For example,
companies may train employees in mindfulness techniques, construct on-site physical activity
facilities, provide mental health resources, and/or encourage them to take breaks throughout their
workday.
Finally, there is continual pressure in practice to shorten surveys. Using an 18-item
measure such as Carlson et al.’s (2000) is simply infeasible in the large majority of survey
research in practice. Our theoretical guidance also offers a practical solution to this problem.
Practitioners can use this theoretical framework to choose which specific dimensions are most
relevant to their research questions, hypotheses, and desired outcomes. They can then include
only those relevant dimensions in their survey, saving a great deal of survey real estate.
Limitations and Recommendations for Future Research
Our research is not without limitations. Because the behavior-based dimension has been
understudied, there were fewer studies for some of the meta-analyzed constructs than there were
for the time-based and strain-based dimensions. Further, work role ambiguity, in particular, had

WORK–FAMILY CONFLICT DIMENSIONS META 29
an adequate yet small number of studies. Having a small number of studies can lead to issues
when conducting analyses and raise generalizability concerns. For example, small sample sizes
can lead to second-order sampling error and can make issues due to effect size variability more
of a concern. Future research can work to develop and establish validity evidence for a shorter
measure of the dimension-level WFC measure by (Carlson et al., 2000). It is possible that survey
length limitations led to the prevalence of the decision to drop the behavior-based WFC measure
in many studies. This is also perhaps why shorter general work-family measures have been
commonly used in WFC research (Fisher et al., 2016; Grzywacz et al., 2006; Matthews et al.,
2010). However, because these measures do not distinguish between the dimensions, their utility
may be limited, as our results suggest. We hope that our work will inspire future studies to
include the behavior-based dimension and to also take a dimension-based approach to examining
work stressors such as work role ambiguity.
Another limitation of the current meta-analytic effort is that we limited our examination to
the time-, strain-, and behavior-based dimensions of WFC. Future research that examines other
important dimensions of WFC is also needed. For example, we encourage researchers to
consider assessing additional dimensions of WFC, such as cognitive-based (see Ezzedeen &
Swiercz, 2007) and energy-based WFC (see Greenhaus et al., 2006). These additional
dimensions of work–family conflict are rarely mentioned or measured, and when they are, they
tend to be forced into the existing time, strain, and behavior categories. Preoccupation was
originally included within Greenhaus and Beutell’s (1985) conceptualization of time-based
WFC, but, despite the development of a measure assessing cognitive-based conflict separately
from time-based conflict (Ezzedeen & Swiercz, 2007), this specific dimension of conflict is
rarely assessed. Additionally, despite theoretical developments that propose energy-based
conflict as an additional important dimension of conflict (Greenhaus et al., 2006), energy-based
conflict is almost never assessed. Our results highlight the importance of embracing a fine-
grained approach to understanding the work–family domain, and this should not be limited to

WORK–FAMILY CONFLICT DIMENSIONS META 30
just the three dimensions of conflict that were meta-analyzed here.
Finally, results from this study highlight the pitfalls associated with inconsistent
composite-based approaches, where overall WFC composites are sometimes formed with the
behavior-based dimension and sometimes without. We hope the evidence provided here will
enhance the construct validity and methodological soundness of future WFC research. Future
research that is conducted in this domain will increase the number of studies in the literature at
the dimension level and help give us a better understanding of these relationships without the
limitations that come with a lack of available studies in the present effort. Similarly, due to a lack
of research examining family-related stressors, a family-oriented variable was not able to be
paired with work role ambiguity for strain-based WFC. We hope future research will examine
additional family-related demand variables at the dimension-level of WFC.
Conclusion
Dimension-level WFC has historically been understudied compared to composite-level
WFC, especially when it comes to behavior-based conflict. The current study complements and
extends prior work-family meta-analyses by separately examining the relationships between
constructs in the WFC literature and the time-, strain-, and behavior-based WIF and FIW
dimensions. We contribute new insights to the WFC literature regarding the importance of
theorizing and conceptualizing WFC at the dimension level. We provide evidence that by
focusing on the individual WFC dimensions, we gain a more nuanced understanding of many
commonly-studied and theoretically-relevant constructs in work-family conflict’s nomological
network than with a composite-based approach. All in all, we hope that our research inspires
more dimension-level WFC research, especially for the behavior-based dimension.

WORK–FAMILY CONFLICT DIMENSIONS META 31
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Table 1. Meta Analytic Inter-correlations of Work–family Conflict Dimensions: Time, Strain, and Behavior
Construct
1
2
3
4
5
1.Time-based WIF
2.Strain-based WIF
r / ρ
SD r / SD ρ
k / N
0.52 / 0.61
0.10 / 0.11
62 / 26,825
3.Behavior-based WIF
r / ρ
SD r / SD ρ
k / N
0.29 / 0.35
0.11 / 0.12
37 / 15,399
0.37 / 0.45
0.12 / 0.13
34 / 14,082
4. Time-based FIW
r / ρ
SD r / SD ρ
k / N
0.29 / 0.36
0.12 / 0.13
47 / 14,861
0.30 / 0.36
0.12 / 0.12
43 / 13,173
0.30 / 0.38
0.11 / 0.13
26 / 8,383
5. Strain-based FIW
r / ρ
SD r / SD ρ
k / N
0.21 / 0.25
0.11 / 0.11
45 / 14,233
0.34 / 0.39
0.11 / 0.11
45 / 14,182
0.36 / 0.43
0.11 / 0.12
26 / 8,383
0.51 / 0.61
0.11 / 0.12
47 / 14,808
6. Behavior-based FIW
r / ρ
SD r / SD ρ
k / N
0.26 / 0.30
0.11 / 0.12
28 / 9,443
0.36 / 0.43
0.11 / 0.11
28 / 9,443
0.67 / 0.81
0.09 / 0.10
29 / 9,700
0.32 / 0.38
0.14 / 0.15
29 / 9,700
0.37 / 0.43
0.15 / 0.16
26 / 8,383
Note. k = number of studies. N = total sample size. r = averaged correlation. ρ = rho, corrected correlation. SD = standard deviation. WIF = work
interference with family. FIW= family interference with work. WIFComp / FIWComp = composites with time, strain, and behavior dimensions.

WORK–FAMILY CONFLICT DIMENSIONS META 50
Table 2. RWA R-squared Values for Dimension-Based Models Compared with Composite Models
Note. FSSB = family-supportive supervisor behavior. WIF = work interference with family; FIW = family interference with work. The WIF time-,
strain-, and behavior-based dimensions were added in the model labeled WIF Dimensions. The FIW time-, strain-, and behavior-based dimensions
were added in the model labeled FIW Dimensions. WIFComp is a composite of the time-, strain-, and behavior-based WIF dimensions, and FIWComp
is a composite of the time-, strain-, and behavior-based FIW dimensions. ΔR2 represents the difference in the composite column from the dimension
column and % ΔR2 represents the percentage change in R-squared in the composite column from the dimension column.
Variable
WIF
Dimensions
WIFComp ΔR2 % ΔR2
FIW
Dimensions
FIWComp ΔR2 % ΔR2
Work time demands
.16 .12 -.04 -25% .05 .04 -.01 -20%
Family time demands
.01 .01 .00 0% .03 .03 .00 0%
Work role ambiguity
.12 .09 -.03 -25% .12 .12 .00 0%
FSSB
.05 .01 -.04 -80% .01 .01 .00 0%
Nonwork support
.02 .01 -.01 -50% .07 .05 -.02 -29%
Job satisfaction
.12 .14 +.02 +17% .07 .06 -.01 -14%
Life satisfaction
.17 .16 -.01 -6% .12 .13 +.01 +8%

WORK–FAMILY CONFLICT DIMENSIONS META 51
Table 3. RWA Results (1/3)
Model/Variable Work Time Demands Family Time Demands Work Role Ambiguity
r ρ Raw Rescaled r ρ Raw Rescaled r ρ Raw Rescaled
Time-based WIF .31 .37 .08 46.64 .05 .06 .00 6.09 .22 0.27 0.03 16.74
Strain-based WIF .29 .34 .05 33.09 .01 .01 .00 7.98 .27 0.32 0.04 26.15
Behavior-based WIF .15 .18 .01 4.35 .05 .06 .00 6.27 .18 0.23 0.01 6.90
Time-based FIW .15 .19 .01 7.58 .13 .16 .02 35.60 .25 0.31 0.04 25.98
Strain-based FIW .09 .11 .00 1.86 .12 .14 .01 24.69 .22 0.26 0.02 12.89
Behavior-based FIW .17 .19 .01 6.48 .09 .11 .01 19.37 .21 0.25 0.02 11.35
Dimension Totals .16 100.00 .04 100.00 0.16 100.00
WIFComp .30 .34 .10 79.40 .01 18.50 .26 0.30 0.06 41.27
FIWComp .19 .21 .03 2.60 .09 .10 .02 81.50 .29 0.34 0.09 58.73
Composite Totals .12 100.00 .15 .17 .03 100 0.15 100.00
ΔR2 from Dimensions (%)
-
.04 (25%) -.01 (-25%) -.01 (-6%)
(continues)

WORK–FAMILY CONFLICT DIMENSIONS META 52
Table 3. RWA Results (2/3)
Model/Variable FSSB Nonwork Support Job Satisfaction
r ρ Raw Rescaled r ρ Raw Rescaled r ρ Raw Rescaled
Time-based WIF -.17 -.19 .02 37.05 -0.02 -0.02 0.00 1.70 -.19 -.23 .02 15.09
Strain-based WIF -.15 -.17 .01 21.28 -0.07 -0.08 0.00 3.05 -.27 -.32 .05 38.91
Behavior-based WIF -.13 -.15 .01 23.25 -0.12 -0.14 0.01 11.48 -.22 -.27 .03 20.00
Time-based FIW -.06 -.08 .00 2.69 -0.10 -0.12 0.01 6.63 -.12 -.14 .00 2.76
Strain-based FIW -.10 -.11 .00 8.75 -0.19 -0.21 0.03 35.26 -.17 -.20 .01 10.27
Behavior-based FIW -.07 -.08 .00 6.98 -0.19 -0.22 0.03 41.88 -.21 -.24 .02 12.96
Dimension Totals .05 100.00 0.08 100.00 .13 100.00
WIFComp -.10 -.11 .01 71.01 -0.11 -0.12 0.01 14.15 -.33 -.38 .12 78.38
FIWComp -.12 -.13 .00 28.99 -0.21 -0.23 0.05 85.85 -.22 -.24 .03 21.62
Composite Totals .01 100.00 0.05 100.00 .15 100.00
ΔR2 from Dimensions (%) -.04 (-80%) -.03 (-38%) +.02 (+15%)

WORK–FAMILY CONFLICT DIMENSIONS META 53
Table 3. RWA Results (3/3)
Model/Variable Life Satisfaction
r ρ Raw Rescaled
Time-based WIF -.22 -.26 .02 11.13
Strain-based WIF -.34 -.40 .08 41.61
Behavior-based WIF -.23 -.28 .02 11.58
Time-based FIW -.20 -.24 .01 7.30
Strain-based FIW -.27 -.32 .04 22.02
Behavior-based FIW -.21 -.24 .01 6.35
Dimension Totals .20 100.00
WIFComp -.35 -.40 .12 59.30
FIWComp -.30 -.35 .08 40.70
Composite Totals .20 100.00
ΔR2 from Dimensions (%) .00 (0%)
Note. Raw = raw relative weights R-squared estimate, rescaled = % represented of all variables in the model out of 100%. Totals = the total R-
squared value for the model and the total rescaled relative weights value (which is always 100%). WIF = work interference with family; FIW =
family interference with work. Comp. = composite. FSSB = family-supportive supervisor behavior. The dimensions were included all in one model
to demonstrate their unique variance. WIFComp includes the time-, strain-, and behavior-based WIF dimensions, and FIWComp includes the time-,
strain-, and behavior-based FIW dimensions, and these composites were both included in one model. ΔR2 from dimensions = the total R-squared
values from the composite model subtracted from the dimension model total R-squared values, which represents whether the composite model lost or
gained variance.

WORK–FAMILY CONFLICT DIMENSIONS META 54
Table 4. R-Squared Comparison of Different Forms of Composites
Note. WIFComp = composite with time-, strain-, and behavior-based WIF; FIWComp = composite with time-, strain-, and behavior-based FIW.
WIFTS = composite with time- and strain-based WIF; FIWTS = composite with time- and strain-based FIW. WIFMix = WIF composite from
previous meta-analyses including one or more WIF dimensions; FIWMix = FIW composite from previous meta-analyses including one or more
FIW dimensions. The Amstad et al. (2011) meta-analysis provided values for the job and life satisfaction and the Michel et al. (2011) meta-
analysis provided the values for the remaining variables.
Variable WIFComp WIFTS WIFMix Range FIWComp FIWTS FIWMix Range
Work time demands .12 .15 .09 .06 .04 .02 .00 .04
Family time demands .01 .00 .00 .01 .03 .02 .02 .01
Work role ambiguity .09 .11 .04 .07 .12 .11 .03 .09
FSSB .01 .04 .05 .04 .01 .01 .01 .00
Nonwork support .01 .00 .01 .01 .05 .04 .03 .02
Job satisfaction .14 .10 .06 .08 .06 .04 .02 .04
Life satisfaction .16 .15 .10 .06 .13 .10 .05 .08