Changing Work and Work-Family Conflict: Evidence from the Work, Family, and Health Network

This study assesses the effects of an innovative workplace intervention on work-family conflict and related work conditions. The analysis utilizes a group-randomized trial in which some units in an information technology workplace were randomly assigned to participate in an initiative, called STAR, that targeted work practices, interactions, and expectations. The study finds statistically significant, although modest, improvements in employees’ work-family conflict and family time adequacy, and larger changes in schedule control and supervisor support for family and personal life. The study uses a rigorous design to investigate deliberate organizational changes and their effects on work resources and the work-family interface, advancing our understanding of the impact of social structures on individual lives.

American Sociological Review
2014, Vol. 79(3) 485 –516
© American Sociological
Association 2014
DOI: 10.1177/0003122414531435
http://asr.sagepub.com
Work-family conflict is increasingly common
among U.S. workers (Jacobs and Gerson
2004; Nomaguchi 2009; Winslow 2005), with
about 70 percent of workers reporting some
interference between work and non-work
(Schieman, Milkie, and Glavin 2009). Work-
family conflict has grown due to increases in
women’s labor force participation (meaning
more households have all adults employed)
and rising expectations for fathers’ involve-
ment in children’s daily care (Nomaguchi
2009). Yet work organizations have not
changed much in response: the institutional-
ized expectation in U.S. workplaces is that
serious, committed, promotable employees
will work full-time (and longer), full-year, on
531435 ASRXXX10.1177/0003122414531435American Sociological ReviewKelly et al.
2014
Changing Work and Work-
Family Conflict: Evidence
from the Work, Family, and
Health Network
Erin L. Kelly,a Phyllis Moen,a J. Michael Oakes,a
Wen Fan,a Cassandra Okechukwu,b
Kelly D. Davis,c Leslie B. Hammer,d Ellen Ernst Kossek,e
Rosalind Berkowitz King,f Ginger C. Hanson,g
Frank Mierzwa,h and Lynne M. Casperi
Abstract
Schedule control and supervisor support for family and personal life may help employees
manage the work-family interface. Existing data and research designs, however, have made
it difficult to conclusively identify the effects of these work resources. This analysis utilizes
a group-randomized trial in which some units in an information technology workplace were
randomly assigned to participate in an initiative, called STAR, that targeted work practices,
interactions, and expectations by (1) training supervisors on the value of demonstrating
support for employees’ personal lives and (2) prompting employees to reconsider when
and where they work. We find statistically significant, although modest, improvements in
employees’ work-family conflict and family time adequacy, and larger changes in schedule
control and supervisor support for family and personal life. We find no evidence that this
intervention increased work hours or perceived job demands, as might have happened
with increased permeability of work across time and space. Subgroup analyses suggest the
intervention brought greater benefits to employees more vulnerable to work-family conflict.
This study uses a rigorous design to investigate deliberate organizational changes and their
effects on work resources and the work-family interface, advancing our understanding of the
impact of social structures on individual lives.
Keywords
work-family conflict, organizations, experiment, group-randomized trial, schedule control

486 American Sociological Review79(3)
a schedule determined by the employer, with
no significant breaks in employment (Blair-
Loy 2003, Moen and Roehling 2005, Wil-
liams 2000). Trying to live up to this ideal
creates work-family conflict for employees
who have significant caregiving responsibili-
ties, as well as the growing proportion of
single workers and dual-earning couples who
do not have a partner at home to take care of
all the “little things” that need to be done
(Schieman et al. 2009). The goal of this study
is to assess the effects of an innovative work-
place intervention on work-family conflict
and related work conditions.
Scholars and advocates concerned about
work-family conflict have advocated chang-
ing the social structure of workplaces, that is,
the largely taken-for-granted and mutually
reinforcing practices, interactions, expecta-
tions, policies, and reward systems that reflect
and reinforce the ideal-worker schema (Acker
1990; Albiston 2010; Williams 2000). These
calls recognize the constraining power of
social structures, understood as “mutually
sustaining cultural schemas and sets of
resources” (Sewell 1992:27), but also
acknowledge that agents can, in theory, recon-
figure those structures through changing their
everyday practices, interactions, and the
social meanings attached to them. These theo-
retical precepts inform our understanding of
the sources of work-family conflict, and point
to the possibility for meaningful change
through interventions that address the inter-
related practices, interactions, and meanings
at work in an organization.
This project is also informed by middle-
range theory regarding the work conditions
most relevant to work-family conflict. Guided
by the job demands-resources model (Bakker
and Demerouti 2007), scholars have viewed
flexibility (or schedule control) and support
as key work resources that can reduce work-
family conflict (Schieman et al. 2009; Voy-
danoff 2004). Work resources are the
“physical, psychological, social, or organiza-
tional aspects of the job” that help workers
accomplish their work tasks or reduce the
“physiological or psychological costs” of
work demands (Bakker and Demerouti
2007:312). Schedule control and support are
work resources that ameliorate work-family
conflict because they make it easier to get
work done and offset the stress of feeling
pulled in two directions.1
Many studies tie schedule control and
supervisor support to work-family conflict
and related outcomes (as we will review), but
the causal claims that can be made are lim-
ited. This study supports stronger causal
claims by conducting a group-randomized
trial in which some work units received an
intervention (i.e., a new workplace initiative
that represents the experimental treatment),
while other units continued with “business as
usual.” We evaluate the effects of this inter-
vention on employees’ schedule control,
supervisors’ support for family and personal
matters as reported by employees, and the
work-family interface. We utilize two waves
of data from employees in the information
technology (IT) division of a U.S. Fortune
aUniversity of Minnesota
bHarvard School of Public Health
cThe Pennsylvania State University
dPortland State University
ePurdue University
fEunice Kennedy Shriver National Institute of Child Health and Human Development
gKaiser Permanente Center for Health Research
hRTI International
iUniversity of Southern California
Corresponding Author:
Erin L. Kelly, Department of Sociology and Minnesota Population Center, University of Minnesota, 909
Social Sciences, 267 19th Avenue South, Minneapolis, MN 55455
E-mail: [e-mail]

Kelly et al. 487
500 organization; our pseudonym for the
company is TOMO. The intervention is called
STAR, short for “Support. Transform.
Achieve. Results.” STAR aimed to modify
the practices, interactions, and social mean-
ings within this workplace, specifically tar-
geting employees’ control over when and
where they worked and supervisors’ support
for family and personal life, in hopes of
reducing work-family conflict and promoting
employee well-being.
CHANGiNG WORK tO
REDuCE WORK-FAMiLy
CONFLiCt
Previous Research and Its Limitations
Before reviewing empirical studies on the
relationships between schedule control,
supervisor support for family and personal
life, and work-family conflict, we clarify our
understanding of these terms. Because “flex-
ibility” is sometimes used to refer to a man-
agement strategy of easily eliminating
workers or relying on contingent staff, we
prefer the more specific term, “schedule con-
trol,” to refer to employees’ control over the
timing of their work, the number of hours
they work, and the location of their work
(Berg et al. 2004; Kelly et al. 2011; Lyness
et al. 2012; Schieman et al. 2009). Supervisor
support for family and personal life involves
providing emotional support for employees’
work-life challenges, modeling how supervi-
sors themselves handle work-family issues,
looking for creative solutions that meet the
needs of both employees and organizations,
and facilitating employees’ flexible work
practices (Hammer et al. 2009). This form of
support is more closely associated with work-
family conflict than is general supervisor sup-
port when comparing effects in the same
sample (Hammer et al. 2009) or through
meta-analysis (Kossek et al. 2011). We use
the broad terms of work-family interface and
work-family conflict interchangeably to refer
to challenges managing paid work and non-
work and the sense that family time is
squeezed or inadequate. We use the
directional terms (work-to-family conflict
and family-to-work conflict) more specifi-
cally to describe the degree to which role
responsibilities from one domain are per-
ceived as interfering with the other domain
(Greenhaus and Beutell 1985; Netemeyer,
Boles, and McMurrian 1996). Changes in the
work environment may be more salient for
work-to-family conflict, but family-to-work
conflict may decrease as expectations shift
within the workplace (e.g., arriving at work
later due to a school appointment is no longer
experienced as a problem). Note that the mea-
sures of conflict refer to personal life and
family, so they are also salient for individuals
with few family responsibilities.
Many studies have considered the relation-
ship between these work resources and the
work-family interface. Employees who report
more control over their schedules have lower
work-family conflict (Byron 2005; Galinsky,
Bond, and Friedman 1996; Galinsky, Sakai,
and Wigton 2011; Hammer, Allen, and
Grigsby 1997; Kossek, Lautsch, and Eaton
2006; Moen, Kelly, and Huang 2008; Roeters,
Van der Lippe, and Kluwer 2010) and better
work-life balance (Hill et al. 2001; Tausig and
Fenwick 2001). Employees who report more
support from supervisors—particularly with
regard to work-family issues—also report
lower work-family conflict (Allen 2001; Batt
and Valcour 2003; Frone, Yardley, and Mar-
kel 1997; Frye and Breaugh 2004; Hammer
et al. 2009; Kossek et al. 2011; Lapierre and
Allen 2006; Thomas and Ganster 1995;
Thompson, Beauvais, and Lyness 1999) and
believe their organizations to be more helpful
with work-family balance (Berg, Kalleberg,
and Appelbaum 2003).
We identify two concerns regarding this
body of research. First, the vast majority of
these studies are cross-sectional and nonex-
perimental, and so do not fully support causal
claims. These design limitations are serious
because employees have differential access to
schedule control, supervisor support, and
organizational work-family policies, with
clear variation by education and occupational
status (Davis and Kalleberg 2006; Golden
2008; Lyness et al. 2012; Schieman et al.

488 American Sociological Review79(3)
2009; Swanberg et al. 2011). The apparent
inverse relationship between each resource
and work-family conflict may reflect, at least
in part, the selection of individuals with
higher human and social capital into “good
jobs” with “good employers” (Weeden 2005;
Wharton, Chivers, and Blair-Loy 2008).
Employees who enjoy these work resources
often have higher incomes, higher occupa-
tional status, and perhaps fewer family
demands, because they are more likely to
have spouses who are not employed, fewer
children, financially stable elders, and finan-
cial resources to outsource various “little
things” that need to get done. Some research,
however, finds that employees in these “good
jobs” often work longer hours, face higher job
demands, and invest more psychologically in
their paid work; the “stress of higher status”
helps explain the higher work-to-family con-
flict reported by these employees in some
studies (Schieman et al. 2009; Schieman,
Whitestone, and Van Gundy 2006). Previous
cross-sectional studies examining work-family
conflict generally control for work hours (and
sometimes control for job demands or psy-
chological involvement), but a stronger
design would attempt to manipulate these
work resources while holding work demands
constant, as we do here.
The second concern is that previous stud-
ies do not provide clear guidance on how to
foster these work resources. Research on
common work-life policies finds mixed evi-
dence regarding their effect on schedule con-
trol or supervisor support for family and
personal life (Kelly et al. 2008; Kossek and
Michel 2011). Flextime and telecommuting
policies may be formally available in a given
organization, but employees’ ability to use
these arrangements varies according to their
occupational status and their managers’ pref-
erences or whims (Blair-Loy and Wharton
2002; Eaton 2003). Furthermore, in most
organizations, these flexible work arrange-
ments are treated as individual accommoda-
tions for valued employees (Kelly and Kalev
2006) and often carry career penalties for use
(Glass 2004; Leslie et al. 2012; Wharton et al.
2008). When managers determine access to
flexible work options, employees may not
feel they have much schedule control and
may not experience lower work-family con-
flict (Batt and Valcour 2003; Tausig and Fen-
wick 2001). In light of the mixed evidence on
flextime and telecommuting policies, schol-
ars and practitioners have argued for broader
efforts to move beyond simply putting a new
policy “on the books” (Lewis 1997; Mennino,
Rubin, and Brayfield 2005; Thompson et al.
1999). This study involves a rigorous evalua-
tion of one such effort.
Few workplace interventions have been
made with regard to fostering supervisor sup-
port for family and personal life, and even
fewer have been studied. Management train-
ing is a viable option, but to design appropri-
ate training, scholars first need to identify
which behaviors constitute and convey super-
visor support for family and personal life
(Hammer et al. 2007). Scholars have long
recognized supervisors’ critical role in inter-
preting policies and acting as gatekeepers to
flexible work and family leave policies (Blair-
Loy and Wharton 2002; Hochschild 1997;
Kossek, Barber, and Winters 1999), but only
recently have researchers identified other
dimensions of supervisor support for family
and personal life, such as providing emotional
support, sharing how one handles work-family
challenges, and looking for creative solutions
that meet both employees’ and organizations’
needs (Hammer et al. 2009; Hammer et al.
2007).
Recent Studies of Workplace
Interventions
Building on cross-sectional research, two
recent studies provide the strongest evidence
to date on the possibility of manipulating
schedule control and supervisor support for
family and personal life and the effects of
those changes on the work-family interface.
In a study of the Results Only Work Environ-
ment (ROWE) initiative at the corporate
headquarters of Best Buy Co., Inc., Kelly and
colleagues (2011) found that employees in

Kelly et al. 489
departments participating in ROWE during
the study period saw significantly increased
schedule control and improvements in the
work-family interface, compared to changes
reported by employees in departments that
continued operating in traditional ways.
ROWE employees also had improved health
behaviors (e.g., sleep before work days and
going to the doctor when sick) compared to
employees in traditional departments (Moen
et al. 2011). These findings point to the pos-
sible benefits of broad initiatives targeting
schedule control—as opposed to individually
negotiated flexible work options—but the
study did not involve randomization to “treat-
ment” (instead studying a phased roll-out of
ROWE), and the intervention and control
groups were not fully equivalent at baseline
(Kelly et al. 2011).
Second, Hammer and colleagues (2011)
evaluated an intervention targeting supervi-
sors’ support for family and personal life in
12 grocery store sites. The training described
how supervisors could demonstrate support
for employees’ family and personal lives,
with a self-monitoring activity to help super-
visors practice supportive behaviors. Work-
family conflict was investigated as a
moderator of the intervention effects, rather
than as a primary outcome. Hammer and col-
leagues (2011) found that employees with
high family-to-work conflict at baseline who
worked in stores that received training
reported higher levels of job satisfaction and
physical health and lower turnover intentions
than did similar employees in control stores,
whereas employees who began with low lev-
els of family-to-work conflict reported lower
job satisfaction and physical health and higher
turnover intentions than did similar employ-
ees in control stores. The intervention may
have created a negative backlash among
workers who did not feel company resources
were used to benefit them, and supervisors’
attention to workers with high family-to-work
conflict may have frustrated other employees
(Hammer et al. 2011). This study demon-
strated the value of training supervisors to
express support for family and personal life,
while suggesting that work-family “interven-
tions may be most effective for those most in
need” (Hammer et al. 2011:147).
Contributions
We advance the work-family literature by
investigating an innovative workplace inter-
vention and utilizing a group-randomized
trial (GRT, also called a cluster-randomized
trial or place-based experiment). As we will
describe, we integrated the interventions
reviewed earlier to target both schedule con-
trol and supervisor support for family and
personal life. The intervention aimed to alter
the social environment itself, as experienced
through everyday work practices, interac-
tions, and the social meaning of work pat-
terns. Within the work-family field, almost no
GRTs or other experiments have attempted to
change the social environment. Two impor-
tant exceptions were conducted outside the
United States. First, a recent experiment in a
Chinese call center randomized individuals to
work at home or in the office; researchers
found improved work performance and job
satisfaction and reduced turnover for those
working at home (Bloom et al. 2013). Inter-
estingly, after the experimental period in
which those randomized to work at home
were obligated to do so, employees were able
to choose where they worked and outcomes
improved even more (Bloom et al. 2013),
suggesting the value of increased employee
control. Second, in a group-randomized trial
of self-scheduling among nurses in a Danish
hospital, nurses in the treatment teams
reported greater improvements in work-life
balance, job satisfaction, satisfaction with
hours, and social support than did nurses in
the control condition (Pryce, Albertsen, and
Nielsen 2006).2
This study also has implications well
beyond work-family scholarship and the
study of work organizations and employee
well-being. Sociologists and other social sci-
entists have turned their attention to rand-
omized experiments in conjunction with a
revived commitment to causal inference and

490 American Sociological Review79(3)
counterfactual thinking (Gangl 2010; Morgan
and Winship 2007; Winship and Morgan
1999). Yet, sociologists have rarely conducted
group-randomized trials—the very experi-
ments that would help identify effects of
social structures or social environments more
conclusively (Cook 2005; Oakes 2004). Some
recent educational research uses GRTs to
examine innovations in schools (e.g., Borman
et al. 2007; Cook, Murphy, and Hunt 2000;
Raudenbush, Martinez, and Spybrook 2007),
and occupational health studies increasingly
involve GRTs (Landsbergis et al. 2011; van
der Klink et al. 2001). Sociologists of work
and organizations have not yet pursued group-
randomized trials to investigate effects of
specific workplace policies or initiatives on
employees and organizations themselves.
In some cases, GRTs involve group rand-
omization simply to achieve “economies of
spatial concentration” (Bloom 2006:120–21).
In these studies, the intervention target is
individual behavior change and randomiza-
tion occurs at the group level primarily for
convenience and ease of intervention deliv-
ery. For example, when workplace-based
smoking cessation interventions randomize at
the workplace level, they do so for ease of
delivering smoking cessation messages to
individuals within a site and to avoid con-
tamination of intervention activities into con-
trol groups (e.g., Okechukwu et al. 2009;
Sorensen et al. 2002). Other GRTs aim to
induce organizational change, such as whole-
school reforms and employer-based initia-
tives that invite change in policies or practices
(Bloom 2006). We employ a GRT design
because randomizing individuals is not appro-
priate for a social intervention, such as STAR,
that targets individual and team practices,
interactions, expectations, and norms.
iNtERvENtiON OvERviEW
STAR included (1) supervisory training on
strategies to demonstrate support for employ-
ees’ personal and family lives while also sup-
porting employees’ job performance, and (2)
participatory training sessions to identify new
work practices and processes to increase
employees’ control over work time and focus
on key results, rather than face time. STAR as
implemented in TOMO included eight hours
of participatory sessions for employees (with
managers present) and an additional four
hours for managers. Managers were first ori-
ented to the STAR initiative in a facilitated
training session and then completed a self-
paced, computer-based training lasting about
an hour. The computer-based training
reviewed demographic changes, described
the impact of work-family conflict on busi-
ness outcomes (e.g., turnover and employee
engagement), and claimed that demonstrating
support for subordinates’ personal and family
life could benefit both employees and the
organization. The training reviewed ways
managers could demonstrate “personal sup-
port” and “performance support” and invited
managers to set goals for exhibiting support-
ive behaviors over the coming week. Manag-
ers carried an iPod Touch with an alarm
reminder to log these behaviors. They
received personalized feedback charts
describing which types of supportive behav-
iors they had concentrated on and whether
they had met their goals; the charts also
showed mean scores for other managers in
STAR. This self-monitoring task was intended
to help managers reflect on their own behav-
iors; feedback was delivered individually, and
information was not shared with executives.
A second self-monitoring task was completed
about one month after the first. Managers also
participated in a facilitated training session
specific to supervisors toward the end of the
STAR roll-out; this provided an opportunity
to share what was working well in their teams
and to ask questions of facilitators and peers.
Participatory training sessions attended by
employees and managers prompted discus-
sions of the organization’s expectations of
workers, everyday practices, and company
policies, and encouraged new ways of work-
ing to increase employees’ control over their
work time and demonstrate greater support
for others’ personal obligations. Sessions
were highly scripted and very interactive.

Kelly et al. 491
Structured messages were presented to all,
but participants responded differently to
activities and questions. Facilitators argued
that expectations that everyone work from
9 a .m. to 5 p.m. in the office do not reflect cur-
rent technologies, employees’ preferences
given their personal obligations, or some
teams’ need to interface with offshore staff.
Facilitators critiqued the assumption that
employees seeking more flexibility were less
committed or productive, instead claiming
that employees would be more engaged in
their work, more responsive to customers’
and co-workers’ needs, and happier if they
had more control over their schedules. Then,
using a variety of role plays and games, par-
ticipants discussed how, when, and where
they would like to work, how they could
coordinate and communicate if hours were
more varied and more employees worked
remotely, and what everyday practices and
interactions would need to change to support
new work patterns. Common changes dis-
cussed were setting up conference call lines
for meetings, clarifying tasks so “face time”
is not used to evaluate productivity or com-
mitment, contacting co-workers by instant
message rather than stopping by their cubi-
cles, and deciding whether a one- or two-hour
break (e.g., a walk or errand during the work
day) needed to be announced to one’s team.
Several work groups reporting to the same
executive participated in each session. This
allowed employees to hear their manager’s
perspective, and vice versa, and exposed them
to other teams’ approaches to these issues.
Although STAR primarily targeted prac-
tices and interactions at the team level, the
intervention aligned these practices with an
existing policy. Company policy required
employees who wanted to work at home rou-
tinely to file a telecommuting agreement that
had to be approved by their manager, director,
and vice president (VP). Employees in STAR
filled out the company’s regular telecommut-
ing agreement, but the whole group was
granted blanket approval by their VP, rather
than the case-by-case approval used before
STAR (and in the usual-practice groups). The
blanket approval was discussed in the first
session and signaled top management’s sup-
port for changes associated with STAR. Later
sessions helped employees and managers
jointly decide how much work at home was
appropriate for different jobs, and how teams
would communicate and coordinate with
more variable schedules and more remote
work.
Compared to most work-family initiatives,
STAR is different in its collective and multi-
level approach. Rather than provide select
employees access to a flexible schedule or
telecommuting based on a manager’s approval
of a request (Blair-Loy and Wharton 2002;
Briscoe and Kellogg 2011), groups of employ-
ees were randomized to STAR. STAR’s
attempt to shift schedule control from manag-
ers to employees facilitated work at home and
variability in work hours (i.e., changed indi-
vidual work practices), but it also changed
interactions at work because employees no
longer asked permission to adjust their sched-
ules or work location. STAR also altered the
social meaning of these work patterns from
being a special “accommodation” that may
signal lesser commitment to being routine
and accepted (Kelly et al. 2010; Kossek et al.
2011). Similarly, the broad effort to encour-
age managers to demonstrate support for
employees’ family and personal lives likely
increased conversations about what was hap-
pening outside of work (i.e., changed interac-
tions), while also encouraging changes in
work practices, such as a manager attending a
meeting for an employee who had an impor-
tant work deadline or family obligation.
These new interactions and practices have
broader social meaning, signaling leader-
ship’s recognition and legitimation of employ-
ees’ lives outside of work.
STAR’s approach is consistent with pio-
neering action research that uses collective
dialogues to reevaluate work processes and
practices in the hopes of advancing both an
organization’s goals and work-life fit (Bailyn
2011; Perlow 1997, 2012; Rapoport et al.
2002). Our experimental design, however,
allows for a more rigorous evaluation of the

492 American Sociological Review79(3)
initiative than has been possible in those stud-
ies. Moreover, STAR pairs bottom-up changes
identified by employees with structured train-
ing to promote managerial supportiveness.
REsEARCH QuEstiONs
We investigate four broad research questions.
(1) Does STAR increase employees’ schedule
control and their reports of supervisor support
for family and personal life? (2) Does STAR
improve employees’ experience of the work-
family interface? Specifically, does STAR
reduce work-to-family and family-to-work
conflict and increase perceived time adequacy
for family among TOMO employees at a six-
month follow up?
We hypothesize that STAR will increase
schedule control, employees’ perceptions of
their supervisors’ support for family and per-
sonal life, and family time adequacy, and that
STAR will reduce conflict between work and
family in both directions. This expectation is
based on cross-sectional research that finds a
relationship between the intended targets of
STAR—schedule control and family-supportive
supervision—and work-family conflict, as
well as recent studies of similar workplace
interventions. Yet there are several reasons
STAR might have no or very limited effects.
First, STAR critiques past management prac-
tices, such as managers setting schedules,
rewarding “face time” or visibility, and
expecting employees to drop personal con-
cerns while they are at work. Resistance to
these changes might arise from managers and
employees who have built careers under the
old expectations, as has been seen in other
participatory management initiatives (Smith
2001; Vallas 2003). Second, the study of
ROWE in Best Buy found positive effects
(Kelly et al. 2011; Moen et al. 2011), but a
randomized evaluation of a similar initiative
might not find changes in another organiza-
tion. ROWE was “homegrown” within the
company and therefore customized to that
organizational culture and workforce. STAR,
in contrast, was brought into the organization
and delivered by outside consultants. Addi-
tionally, STAR was implemented in TOMO
as a pilot program with the understanding that
top executives were not ready to adopt it
across-the-board. In this study, work units
were randomized to STAR or “usual practice”
conditions. Some employees and mid-level
managers may have believed that executives
above them were not supportive and were
therefore cautious about STAR themselves.
Third, STAR’s manager training component
has not been previously shown to reduce
work-family conflicts; the pilot study in gro-
cery stores evaluated work-family conflict as
a moderator of other work and health out-
comes (Hammer et al. 2011). Finally, during
the course of the study, it was announced that
TOMO would be acquired by another firm
(with the merger finalized after the follow-up
data analyzed here). This reflects the reality
of conducting field experiments, in that all
conditions could not be controlled. The
merger announcement raised questions about
whether the current organizational culture
would be sustained into the future and may
have decreased employees’ investment in
STAR. Employees facing organizational
restructuring often feel that implementing
workplace interventions is unwise (Egan et al.
2007; Olsen et al. 2008).
(3) Does the STAR initiative make condi-
tions worse for employees by increasing their
work hours or job demands? Such unintended
consequences might arise due to increased
permeability of work and non-work across
time and space and the resulting blurring of
work and family roles (Chesley 2005; Glavin
and Schieman 2010; Kelliher and Anderson
2010; Schieman and Glavin 2008). Employ-
ees may gain more control over when and
where they work but simultaneously find
themselves working more or feeling more
pressed at work. Although generally concep-
tualized as a work resource, schedule control
may operate more to intensify work demands
by increasing employees’ exposure to job
pressures (Schieman 2013). This dynamic
may be especially likely in a salaried, profes-
sional workforce like TOMO, where the
employer does not pay overtime (so the
employer has an interest in getting as many
hours of work as possible) and employees’

Kelly et al. 493
devotion to work is both expected and experi-
enced as intrinsically rewarding (Blair-Loy
2003, 2009; Perlow 2012).
(4) Do the effects of STAR differ depend-
ing on employees’ vulnerability to work-family
conflicts? In other words, are there heteroge-
neous treatment or intervention effects? Pre-
vious research finds that employees with
higher family demands, captured by having
children living at home and providing care for
elderly relatives or other dependent adults,
have greater work-family strain and thus a
greater need for a flexible, supportive work
environment (Michel et al. 2011; Moen et al.
2012). Family responsibilities are gendered,
with mothers and wives still doing signifi-
cantly more housework and childcare, on
average (Bianchi et al. 2012). Also, work-
family strains seem to weigh more heavily on
mothers’ well-being than on fathers (Noma-
guchi, Milkie, and Bianchi 2005), and there is
some evidence that mothers feel the burden of
normative judgments even when they are
viewed as high performers at work (Benard
and Correll 2010). This suggests that mothers
and fathers may benefit differently from
STAR, although it is unclear whether mothers
will benefit more because their family
demands are greater, or fathers will benefit
more because their work-family needs were
not previously recognized or they had not
pursued the marginalized flexible work
options.
Previous research also leads us to expect
that employees with fewer work resources at
baseline, that is, those who report less sched-
ule control and less supportive supervisors,
will benefit more from STAR because they
were more vulnerable to begin with. STAR
may level the playing field by raising these
employees’ sense of schedule control and
supervisor support to match that reported by
their peers whose supervisors had previously
been flexible and supportive. Employees with
high work demands are also expected to be
more vulnerable to work-family conflict and
thus should benefit more from the interven-
tion. However, it is unclear how increased
schedule control and supervisor support—the
work resources hypothesized to ameliorate
work-family conflict—stack up against high
work demands in the form of very long work
hours or perceived job pressures (Blair-Loy
2009; Kelly et al. 2011; Schieman et al.
2009).
We also investigate whether STAR bene-
fits employees with existing vulnerability as
indicated by higher work-to-family and family-
to-work conflict at baseline. Employees with
high conflict at baseline may receive more
benefit, in part because they have more room
for improvement. STAR may be more salient
and attractive to employees with high work-
family conflict (Hammer et al. 2011), even
though the initiative is not presented to
employees as a work-family initiative.
It is also plausible that STAR brings ben-
efits to parents and adult caregivers, but shifts
burdens to employees whose non-work obli-
gations are less extensive or obvious. If this is
the case, employees with no dependents may
experience more work-family conflicts under
STAR or begin working longer hours or more
intensely as their peers take advantage of the
initiative. The popular and business press are
attuned to the possibility of work-family
backlash prompted by singles and workers
without dependents taking on even more
work as parents and adult caregivers attend to
family needs (e.g., Shellenbarger 2012; Wells
2007), although there is little research evi-
dence to date (cf. Casper, Weltman, and
Kweisga 2007).
MEtHODs
Research Site and Interest in the
STAR Intervention
This field experiment was conducted in the
information technology (IT) division of a
U.S. Fortune 500 organization. Division
employees developed software, tested appli-
cations, responded to problems in applica-
tions and related networks, worked with
clients to plan how applications could meet
their needs, and worked as project managers
and administrative staff. Formative research
indicated that the organization had fairly tra-
ditional expectations for employees, who

494 American Sociological Review79(3)
attempted to prove themselves as serious,
dedicated, and committed by working long
hours, prioritizing work over family, pursuing
uninterrupted professional careers, and trav-
eling as requested. Historically, employees
received generous benefits and good wages in
return. Mean tenure was over 10 years.
As the organization grew over the past 10
years, it came to rely on technology to coor-
dinate projects. Many work groups were not
co-located in the same building, city, or state.
Additionally, since about 2005, some employ-
ees worked closely with offshore employees
and contractors (primarily in India). Many
U.S. employees were expected to be available
for questions from their offshore collabora-
tors at any hour and routinely participated in
early morning conference calls—usually
from home—to coordinate work. Approxi-
mately 20 percent of employees had a super-
visor in a different state at the time of the
baseline survey. Clearly, remote work and
coordination across time zones was happen-
ing even before STAR was introduced to
some groups.
Formative research revealed wide variation
in managers’ acceptance of variable schedules
and working at home, especially when employ-
ees did so to meet personal or family obliga-
tions rather than in response to work demands
(Leslie et al. 2012). Some employees expressed
frustration with the variation in managers’
approaches, expectations, and application of
the company’s telecommuting policy. Prior
managerial discretion meant some employees
experienced the STAR intervention as an
opportunity to implement new practices, and
others saw it as an endorsement of practices
that were already happening informally. Our
analysis examines whether effects were greater
or smaller depending on baseline schedule
control and supervisor support.
Human resources managers and IT execu-
tives were interested in STAR for several
reasons. They recognized that coordination
with offshore staff meant many U.S. employ-
ees were working longer or more variable
hours, with the possibility of burnout and
increased turnover. Executives also heard
employees’ frustrations after one vice
president clamped down on remote work.
Insiders recognized that the firm was not seen
as particularly innovative and hoped that
changes would attract applicants from newer,
smaller firms. Work-family conflict was not
presented as a central concern by leadership,
although there was recognition that employ-
ees with family responsibilities and high work
demands were especially vulnerable to burn-
out. Executives also expressed interest in the
possibility that improving work conditions
might improve employees’ health, and per-
haps help contain health care costs, but that
did not seem to be the initial motivation.
Researchers selected this organization
from possible industry partners because it
offered multiple work units sufficient to sup-
port random assignment, geographic proxim-
ity to minimize study personnel’s travel
between locations, site and workforce stabil-
ity to support the research for the study dura-
tion, and specific endorsement from the IT
executives to support all research activities.
Development and Delivery of STAR
Intervention
STAR was developed jointly by researchers
and outside consultants. Drawing on forma-
tive research in the company and the two
intervention studies reviewed earlier, the
researchers and consultants worked together
to customize the intervention materials for
this workforce. Computer-based training on
supervisor support for family and personal
life was customized to include appropriate
examples of managers’ support for profes-
sional development (e.g., asking employees
about adequacy of tools or resources and
providing help as needed) as well as a video
message from the top IT executive endorsing
STAR. Participatory training sessions were
customized by including IT-relevant discus-
sions of communicating by instant messen-
ger, coordinating with offshore staff, and
handling periods of high demands around
software releases. The intervention is
described further in Kossek and colleagues
(2014) and materials are available, at no cost,
at http://workfamilyhealthnetwork.org.

Kelly et al. 495
STAR was rolled out as a company-spon-
sored pilot program announced by IT execu-
tives. It was common within the IT division to
pilot new initiatives, including programs
developed in-house or brought in by consult-
ants. The company provided executive spon-
sorship, human resources staff time, and
space for training, and it allowed participants
to attend STAR sessions and complete related
activities during the workday. Four facilita-
tors delivered STAR training at TOMO; they
were supported by research grants but were
not identified with the researchers within the
company. Additional STAR coordinators
scheduled sessions, computer-based training,
and self-monitoring activities; they observed
training and later conducted interviews to
learn how STAR was implemented. This was
a hybrid role with some research elements,
but the outside facilitators served as the pri-
mary face of STAR. The separation of STAR
and the broader study was not complete,
because top IT executives, human resources
managers, and a small advisory board knew
of the link; we pursued this strategy to try to
avoid differential participation in the study by
the control group and to ensure the core data
for the evaluation were collected by research
staff who were “blind” to employees’ condi-
tion (i.e., STAR or control).
Randomization
The randomization process began by identify-
ing groups of employees and managers who
would be treated as “study groups.” Research-
ers identified 56 study groups in close coordi-
nation with company representatives. Some
study groups were large teams of workers
reporting to the same manager, while other
study groups included multiple teams who
either reported to the same senior leadership
or worked closely together on the same appli-
cation. We refer to these units as study groups
to denote they were aggregations of work
groups that operated in the organization.
Company representatives and our forma-
tive research suggested that study findings
would be discounted if all or most of the
groups receiving the intervention were in a
single job function, reported to any one VP, or
represented particularly small or particularly
large work groups. For example, if all groups
randomized to STAR happened to be software
development teams, managers and employees
in other job functions would likely view the
findings as irrelevant to them. We therefore
decided on a randomization design that would
ensure balance on job function, VP, and size of
the study group. We modified a biased-coin
randomization technique for use with group
randomization (Bray et al. 2013; Frane 1998).
The first four study groups were randomized
using simple randomization. Subsequent study
groups were hypothetically assigned to inter-
vention, and then the null hypothesis of bal-
ance across study conditions (i.e., intervention
or usual-practice control) was tested for each
randomization criterion (i.e., job function, size
of study group, and VP) separately; each
group was then hypothetically assigned to
usual practice. The lowest p-value derived
from the balance test across randomization
criteria was used in adaptive randomization
procedures, to minimize risk of imbalance.
Study Recruitment and Data
Collection
Workers were eligible to participate in the
study if they were employees (not contrac-
tors) located in the two cities where data col-
lection occurred. Additionally, one study
group whose employees were represented by
collective bargaining agreements was
excluded due to concerns the intervention
might conflict with contractual work rules.
Recruitment materials emphasized the value
of a study investigating connections between
employees’ work, family, and health for
employees (who received some health infor-
mation), the employing organization, and
scientific knowledge more broadly; there was
no reference to STAR. Recruitment materials
emphasized the independence of the research
team from TOMO and the confidentiality of
individual data. Computer-assisted personal
interviews, lasting approximately 60 minutes,
were conducted at the workplace on company
time, at baseline and six months later.

496 American Sociological Review79(3)
At baseline, 70 percent of eligible employ-
ees participated (N = 823), and 87 percent of
baseline participants completed the six-month
follow-up (N = 717). Figure 1 confirms that
response rates were similar for employees in
intervention and control conditions and that
all study groups identified as eligible for the
study were randomized and had some employ-
ees who participated. Our analyses focus on
the respondents who completed both baseline
and six-month surveys, with the following
exclusions. We excluded 15 employees who
were randomized to the intervention condi-
tion, but were never invited to participate in
STAR sessions due to a staff error. Addition-
ally, we excluded eight employees because
they shifted reporting structures and began
reporting to a manager already going through
STAR. The resulting analytic sample consists
of 694 employees nested in 56 study groups.
See Table S1 in the online supplement (http://
asr.sagepub.com/supplemental) for analyses
of response bias and baseline means by con-
dition to confirm balance (i.e., randomization
created comparable groups, and therefore we
can analyze effects of STAR without adjust-
ing for individual characteristics).
MEAsuREs
Outcomes
For all scales analyzed as outcomes, Table 1
provides wording of items, source, reliability
scores, and response values.
Schedule control measures the degree to
which employees report control over their
work time and work location.3 Family sup-
portive supervisor behaviors (FSSB) is
designed to measure employee perceptions of
supervisors’ behavioral support for family
and personal life. It is a separate construct
from general supervisor support, as some
supervisors are supportive of employees’ job
concerns but not their family concerns. We
use a four-item version, with questions meas-
uring emotional support, instrumental sup-
port, role modeling, and creative management,
validated by Hammer and colleagues (2013).
Work-to-family conflict and family-to-work
conflict reflect the degree to which role
responsibilities from one domain are incom-
patible with the other. Time adequacy with
family asks employees whether they had
enough time during the past year to spend
with their family. Weekly hours worked is
measured with a single question: “About how
many hours do you work in a typical week in
this job?” The mean at baseline was 45 hours,
with 29 percent reporting working more than
50 hours per week. Psychological job
demands is a subscale of the Karasek and
Theorell (1990) demands-control model
measuring perceived pressure and overload.
TOMO (IT) EMPLOYEES ELIGIBLE FOR STUDY:
M = 56 Study Groups, N = 1,171 EMPLOYEES
(609 Intervenon, 562 Control)
BASELINE CAPI
INTERVENTION SAMPLE
M = 27 study groups
N = 423 employees
Response rate = 69%
BASELINE CAPI
CONTROL SAMPLE
M = 29 study groups
N = 400 employees
Response rate = 71%
USUAL PRACTICE
Session 1
Session 2
Closing Session
Behavior Tracking
Acvies
STAR INTERVENTION
SIX-MONTH CAPI
INTERVENTION SAMPLE
M = 27 study groups
N = 371 employees
Retenon rate = 88%
SIX-MONTH CAPI
CONTROL SAMPLE
M = 29 study groups
N = 346 employees
Retenon rate = 87%
Exclude those not invited
to STAR, N = 15
Exclude those not
randomized because of
reorganizaon, N = 8
Final analyc sample
M = 27 study groups
N = 348 employees
Kick-off Session
Final analyc sample
M = 29 study groups
N = 346 employees
Figure 1. Study Design and Response Rates

497
table 1. Description of Scales
Scale Source Variable Description
Cronbach’s
Alpha
(Wave 1)
Cronbach’s
Alpha
(Wave 2) Values
Schedule
Control
Thomas and
Ganster 1995
How much choice do you have over when you take vacations or days off?
How much choice do you have over when you can take off a few hours?
How much choice do you have over when you begin and end each work
day?
How much choice do you have over the total number of hours you work
each week?
.802 .825 1 = Very little
2 = Little
3 = A moderate amount
4 = Much
5 = Very much
How much choice do you have over doing some of your work at home or at
another location, instead of [insert company name/location]?
How much choice do you have over the number of personal phone calls
you make or receive while you work?
How much choice do you have over the amount or times you take work
home with you?
How much choice do you have over shifting to a part-time schedule (or
full-time if currently part-time) while remaining in your current position
if you wanted to do so?
Family-Support-
ive Supervisor
Behaviors
Hammer et al.
2013
Your supervisor makes you feel comfortable talking to him/her about your
conflicts between work and non-work.
Your supervisor works effectively with employees to creatively solve
conflicts between work and non-work.
Your supervisor demonstrates effective behaviors in how to juggle work
and non-work issues.
Your supervisor organizes the work in your department or unit to jointly
benefit employees and the company.
.874 .876 1 = Strongly Disagree
2 = Disagree
3 = Neither
4 = Agree
5 = Strongly Agree
Work-to-Family
Conflict
Netemeyer et al.
1996
The demands of your work interfere with your family or personal time.
The amount of time your job takes up makes it difficult to fulfill your
family or personal responsibilities.
Things you want to do at home do not get done because of the demands
your job puts on you.
Your job produces strain that makes it difficult to fulfill your family or
personal duties.
.915 .914 1 = Strongly Disagree
2 = Disagree
3 = Neither
4 = Agree
5 = Strongly Agree
Due to your work-related duties, you have to make changes to your plans
for family or personal activities.
(continued)

498
Scale Source Variable Description
Cronbach’s
Alpha
(Wave 1)
Cronbach’s
Alpha
(Wave 2) Values
Family-to-Work
Conflict
Netemeyer et al.
1996
The demands of your family or personal relationships interfere with work-
related activities.
You have to put off doing things at work because of demands on your time
at home.
Things you want to do at work don’t get done because of the demands of
your family or personal life.
Your home life interferes with your responsibilities at work, such as getting
to work on time, accomplishing daily tasks, and working overtime.
.835 .863 1 = Strongly Disagree
2 = Disagree
3 = Neither
4 = Agree
5 = Strongly Agree
Family-related strain interferes with your ability to perform job-related
duties.
Psychological
Job Demands
Karasek et al.
1998
You do not have enough time to get your job done.
Your job requires very fast work.
Your job requires very hard work.
.575 .581 1 = Strongly Disagree
2 = Disagree
3 = Neither
4 = Agree
5 = Strongly Agree
Note: Employees are restricted to those (1) who completed both Wave 1 and Wave 2 CAPI, (2) excluding 15 employees in work groups 56b, 56b.1, 56b.2, and 56b
and (3) excluding eight employees in work group 14a.3 for whom we do not have randomization-related variables.
table 1. (continued)

Kelly et al. 499
:
If a respondent skipped a specific item but
completed at least 75 percent of the scale
(e.g., four out of five items in the work-to-
family conflict scale), we assigned the mean
from other responses by the same respondent
to other questions in that scale. If the respond-
ent did not complete at least 75 percent of the
scale (or did not complete the time adequacy
item or the job demands scale), they were
omitted from that model.
Variables for Subgroup Analyses
The subgroup analyses investigate whether
employees with greater vulnerability to work-
family conflict, as measured by high family
demands, low work resources, and more con-
flict at baseline, benefited more from STAR.
Child at home is an indicator of parents (or
step-parents) with children under age 18 liv-
ing in their home at least four days per week.
Childless employees and parents with grown
children or children who do not live in their
homes were coded as child not at home. We
investigated different effects for mothers and
fathers by interacting gender and child at
home (n = 121 mothers, 205 fathers). We also
created four categories of family demands:
child at home only (n = 261) for employees
with children at home but no adult care
reported; care for adults (n = 95) for respon-
dents reporting adult caregiving responsibili-
ties at least three hours per week but no
children under age 18 at home; child at home
and care for adults (“sandwich generation,” n =
65); and employees with no dependents (n =
273) reported. Employees with no dependents
may have a spouse or an adult child (not
receiving care) living in the home. Low
schedule control (n = 122) was indicated by a
mean response of “very little” or “little”
choice over one’s schedule (i.e., mean < 3 at
baseline). Lower supervisor support (n = 308)
reflects strong disagreement, disagreement,
or neutral responses to affirmative statements
about supervisors’ support for family and
personal life (i.e., mean < 4 at baseline); we
included neutral responses here because rela-
tively few employees (n = 84) disagreed out-
right with the affirmative statements regarding
supervisor support. High work-to-family con-
flict (n = 147) captured employees who
agreed or strongly agreed that work interfered
with family and personal life at baseline (i.e.,
mean ≥4). Family-to-work conflict was much
less common; only 86 employees strongly
agreed, agreed, or said they neither agreed
nor disagreed that family interfered with
work (coded higher family-to-work conflict if
mean > 3). The skewed distribution of family-
to-work conflict is not surprising, given pre-
vious research and the cultural expectation
that professional workers will organize their
lives around work (Blair-Loy 2003; Jacobs
and Gerson 2004). To assess whether STAR
brought benefits to employees with especially
high work demands, we compared employees
working 50 or more hours (n = 200) with
those working fewer than 50 hours (n = 494)
(Cha 2010; Schieman et al. 2009) and dichot-
omized high psychological job demands (n =
253) at the mean ≥4, indicating responses of
agreement or strong agreement that the job
required very hard work, it required very fast
work, and there was not enough time to do the
job.
ANALysis
We estimated generalized linear mixed mod-
els (using PROC MIXED in SAS) on repeated
measures with random effects for the level-2
unit nested in experimental condition, that is,
for study groups in STAR or usual practice.
This is a member cohort analysis, using pre-
and post- data on individuals nested in study
groups (Murray 1998). Specifically, we used
mixed models of the following form to assess
the effect of the intervention on outcomes:
(1)
Here Yij:k:l is the outcome for person i observed
at time j, nested within group k, which is in
condition l; f(• ) is a link function; and ε ij:k:l is
an iid (independent and identically distrib-
uted) error or residual; here we estimate lin-
ear models. The βs are fixed-effect parameters
to be estimated, and the γs are random-effect
I E E E ELM N O O M M O LM N O N N O< & 7 7 & ȕ ; ȕ 5$1' Ȗ * Ȗ
I E E E E HLM N O O M M O LM N O N N O L N O MN O LM N O< & 7 7 & ȕ ; ȕ 5$1' Ȗ * Ȗ 0 Ȗ 7*
: :
: :: :
: :
::
0 1 2 3 4
5 0 1 2
f

500 American Sociological Review79(3)
parameters (i.e., variance components) to be
estimated. Cl is a dichotomous variable indi-
cating membership in the STAR intervention
condition, Tj is a dichotomous variable indi-
cating the jth time point, and TjCl is the inter-
action between condition and time (here
STAR x Wave 2). X ij:k:l is a vector of demo-
graphic and other potential confounds; none
are included here because randomization cre-
ated balance on potential confounds. RAND k
is a vector of randomization factors used in
the biased coin algorithm, that is, job function
and study group size. These are included as
control variables. Gk:l is a vector indicating
group membership, M i:k:l is a vector of indi-
vidual indicators, and TGjk:l is a vector of
interactions between time points and group
membership. Given the specification of the
fixed effects, β3 captures the effect of the
intervention at that follow-up time point
(Murray 1998) and can be thought of as the
difference-in-difference estimate.
Our central analyses employ an intent-to-
treat framework that provides a conservative
estimate of the intervention effect. This means
all employees eligible for receiving the treatment
were coded as being in the STAR condition,
even though individuals in STAR-randomized
study groups decided how much to participate
in the training. Sessions were held during
work hours, and the mean attendance rate for
the analytic sample was 74 percent of ses-
sions; 10.4 percent of employees (n = 37)
attended fewer than half of the STAR ses-
sions, and 3.9 percent (n = 14) of those rand-
omized to STAR attended no sessions.
Supplemental analyses move beyond the
intent-to-treat framework by comparing
effects of STAR for employees with higher
and lower participation.
REsuLts
Findings on Central Research
Questions
We first investigate whether STAR improved
work resources for managing work-family
challenges. STAR increased employees’
schedule control and supervisor support for
family and personal life significantly, com-
pared to changes in the control groups, pro-
viding an affirmative answer to our first
research question. In Table 2, the STAR x
Wave 2 coefficient (bolded) is the interven-
tion (or treatment) effect of interest with a
difference-in-difference interpretation. Other
covariates include condition, time point,
study group size, and core job function.
Employees in STAR perceived more control
over where and when they worked and
described their supervisors as more support-
ive of their personal lives, as seen in panels A
and B of Table 2. Table 3 presents standard-
ized effect sizes calculated by dividing the
STAR x Wave 2 coefficient (from Table 2) by
the standard deviation of the outcome at
baseline.
In addition to evaluating whether employ-
ees’ sense of the control and support available
to them changed, we can also assess whether
their work practices changed. We find that
STAR encouraged employees to adjust their
schedules based on personal needs and to
work at home more. Employees in STAR
were twice as likely to describe their sched-
ules as “variable” at the six-month survey,
going from 17 to 35 percent. Among employ-
ees in the usual-practice group, 21 percent
reported a variable schedule at both waves. In
questions asked only on the six-month survey,
STAR respondents were significantly more
likely than usual-practice employees to agree
that they “fit in personal errands and appoint-
ments during work hours” and “change
schedule as needed for personal/family life.”
Finally, mean hours of work at home almost
doubled for STAR employees between the
surveys (from 10.2 to 19.6 hours per week),
while increasing significantly less for employ-
ees in usual-practice groups (from 10.8 to
12.3 hours per week). These findings provide
evidence that schedule control was both per-
ceived and enacted by employees in STAR.
Second, we find that all three measures of
the work-family interface improved more for
employees in STAR than for those in usual-
practice control groups. The intervention
effect for work-to-family conflict is margin-
ally significant ( p = .059) in panel C of Table

501
table 2. Multilevel Intervention Effects on Work-Family Outcomes
A. Schedule Control
(M = 56 study groups, N = 1,388 person-waves)
B. Supervisor Support for Family and Personal Life
(M = 56 study groups, N = 1,379 person-waves)
Estimate SE DF t Value Pr > |t| Estimate SE DF t Value Pr > |t|
STAR –.092 .079 52 –1.17 .247 STAR –.010 .087 52 –.12 .908
Wave 2 .035 .029 54 1.20 .235 Wave 2 –.063 .037 54 –1.71 .094
stAR x Wave 2 .231 .041 54 5.63 <.0001 stAR x Wave 2 .131 .052 54 2.51 .015
# Employees for
Randomization
.005 .003 52 1.45 .152 # Employees for
Randomization
–.001 .003 52 –.21 .834
Core Functiona .006 .076 52 .08 .939 Core Function –.094 .083 52 –1.13 .264
Intercept 3.505 .096 52 36.60 <.0001 Intercept 3.923 .105 52 37.51 <.0001
Subject Ratio Estimate Subject Ratio Estimate
Intercept studygroup(star) .316 .046 Intercept studygroup(star) .205 .047
CS ID(studygroup x
star)
1.782 .261 CS ID(studygroup x
star)
1.555 .361
Residual 1 .146 Residual 1 .232
C. Work-to-Family Conflict
(M = 56 study groups, N = 1,388 person-waves)
D. Family-to-Work Conflict
(M = 56 study groups, N = 1,388 person-waves)
Estimate SE DF t Value Pr > |t| Estimate SE DF t Value Pr > |t|
STAR .106 .117 52 .91 .366 STAR .088 .060 52 1.47 .147
Wave 2 –.103 .043 54 –2.42 .019 Wave 2 .030 .030 54 1.00 .320
stAR x Wave 2 –.116 .060 54 –1.93 .059 stAR x Wave 2 –.088 .043 54 –2.05 .045
# Employees for
Randomization
–.006 .005 52 –1.22 .226 # Employees for
Randomization
.001 .002 52 .31 .756
Core Function –.024 .114 52 –.21 .834 Core Function .117 .055 52 2.11 .040
Intercept 3.181 .142 52 22.44 <.0001 Intercept 1.999 .071 52 28.20 <.0001
Subject Ratio Estimate Subject Ratio Estimate
Intercept studygroup(star) .360 .113 Intercept studygroup(star) .094 .015
CS ID(studygroup x
star)
1.423 .447 CS ID(studygroup x
star)
1.338 .214
Residual 1 .314 Residual 1 .160
(continued)

502
E. Enough Time for Family
(M = 56 study groups, N = 1,360 person-waves)
Estimate SE DF t Value Pr > |t|
STAR –.092 .069 52 –1.34 .187
Wave 2 –.039 .042 54 –.92 .360
stAR x Wave 2 .137 .059 54 2.32 .024
# Employees for
Randomization
–.001 .002 52 –.22 .826
Core Function –.008 .062 52 –.12 .902
Intercept 3.515 .080 52 43.95 <.0001
Subject Ratio Estimate
Intercept studygroup(star) .056 .016
CS ID(studygroup x
star)
.825 .241
Residual 1 .292
Note: Employees are restricted to those (1) who completed both Wave 1 and Wave 2 CAPI, (2) excluding 15 employees in work groups 56b, 56b.1, 56b.2, and
56b.3, and (3) excluding eight employees in work group 14a.3 for whom we do not have randomization-related variables. For scales, we imputed the mean from a
respondent’s responses to other questions in the scale; it is only imputed if a respondent answered 75 percent or more of the questions in the scale.
aThe core function identifies groups where most individuals were involved in software development; groups dominated by other IT jobs are the reference group.
table 2. (continued)

Kelly et al. 503
2, and we see a statistically significant inter-
vention effect for family-to-work conflict (in
panel D of Table 2). STAR also significantly
increased reported time adequacy with family
members (in panel E of Table 2). Again,
STAR produced changes in work-family con-
flict and time adequacy while employees con-
tinued in the same jobs—many with the same
long hours and work pressures—and in the
same family situations. These findings pro-
vide clear evidence that a workplace’s expec-
tations, interactions, and practices affect
employees’ well-being, in the form of work-
family conflict, directly and in ways not dic-
tated by the work itself.
For our third research question, we turn to
models investigating whether STAR had neg-
ative consequences by increasing work hours
or psychological job demands. Employees
might experience work intensification as an
unintended byproduct of STAR increasing the
permeability of work and non-work across
time and space; this would be the double-
edged sword of workplace flexibility (Blair-
Loy 2009; Perlow 2012; Schieman et al.
2009). Table 3 shows no evidence that this
occurred. In fact, STAR respondents were less
likely to agree with the item (from the job
demands scale) stating “you do not have
enough time to get your job done,” at a mar-
ginally significant ( p = .06) level. This finding
suggests the intervention relieved time pres-
sures for employees (at work as well as with
family time, seen previously) and contradicts
the work intensification hypothesis.4 More
generally, Table 3 provides evidence that
STAR’s effects were larger for the outcomes
directly targeted by the intervention. Schedule
control moved the most, with modest but sig-
nificant increases in family-supportive super-
vision and adequate time for family.
Our fourth research question considers
whether employees with greater expected
vulnerability to work-family conflicts experi-
enced more benefits from the intervention.
We operationalize vulnerability as higher
family demands, lower work resources at
baseline, work-family interference reported at
baseline, and high work demands that might
overwhelm any positive changes that could
come with STAR. Figures 2a through 2e
show the intervention effect (STAR x Wave 2
coefficient) for the unstratified models with
the full sample (see Table 2), and then inter-
vention effects for stratified models estimated
separately for each subgroup. The overall
intervention effect for the full study popula-
tion is marked with a dotted horizontal line,
with confidence intervals from subgroup
models shown with bars. Table S2 in the
online supplement provides more detail.
We find that effects of STAR on schedule
control are of similar magnitude almost across
the board (see Figure 2a). Consistent increases
in schedule control suggest that the interven-
tion benefited employees with all types of
family situations and those with lower and
higher schedule control at baseline. Effects of
STAR on schedule control were somewhat
larger for employees who did not describe
their supervisors as supportive at baseline
(intervention effect of .32, p < .0001,
table 3. Intervention Effect Sizes
Outcomes Estimate Effect Size Pr > |t|
Schedule Control .231 .342 <.0001
Supervisor Support for Family and Personal Life .131 .160 .015
Work-to-Family Conflict –.116 –.122 .059
Family-to-Work Conflict –.088 –.138 .045
Enough Time for Family .137 .178 .024
Work Hours –.263 –.047 .482
Psychological Job Demands –.075 –.107 .106
Note: Effect size is STAR x Wave 2 coefficient from Table 2 (and parallel models for hours and job
demands) divided by the standard deviation of the outcome at baseline.

504 American Sociological Review79(3)
compared to .23 in the unstratified model).
STAR effects were similar across work hours
and job demands categories; this suggests that
even employees who faced high work
demands at baseline felt STAR gave them
more choice over when, where, and how
much they worked.
The effects of STAR on supervisor support
for family and personal life differed by family
status and work resources at baseline (see Fig-
ure 2b and supplement Table S2). Effects of
STAR on supervisor support for family and
personal life were notably larger for fathers
(.26) and sandwich-generation employees who
had at least one child at home plus adult-care
responsibilities (.30, compared to .13 in the
unstratified model). Perhaps mothers, whose
family responsibilities are often more visible
and more normative, received support from
managers regardless of condition, whereas
STAR encouraged managers to demonstrate
support for others’ family responsibilities.
Additionally, employees who reported lower
supervisor support, higher work-to-family
conflict, or higher family-to-work conflict at
baseline saw especially large effects of STAR
on supervisor support for family and personal
life. STAR also had greater effects on supervi-
sors’ family support among employees who
reported their jobs were quite demanding.
With regard to work-to-family conflict, we
find further evidence that employees with
more vulnerability benefited more from
STAR (see Figure 2c and supplement Table
S2). In particular, sandwich-generation
employees saw the largest benefits of STAR
with regard to work-to-family conflict; among
these employees, the STAR intervention
effect is –.48 ( p = .01), compared to –.12 for
the unstratified model. STAR effects on work-
to-family conflict are also larger for employ-
ees who did not rate their supervisors as
supportive of family at baseline (–.25, com-
pared to –.12 in the unstratified model). In
models of family-to-work conflict, women
who did not have children at home (most of
whom did have a spouse or partner) saw the
greatest effects of STAR (see Figure 2d),
although STAR mothers’ mean family-to-
work conflict also declined.
As seen in Figure 2e, STAR brought larger
benefits to mothers with regard to reporting
enough family time (intervention effect of
.30, p = .03, compared to the unstratified
effect of .14). Employees putting in more than
50 hours per week at baseline saw somewhat
greater increases in time adequacy under
STAR (intervention effects of .25, p = .02,
compared to .14 for the unstratified model),
although effects of STAR on work-to-family
conflict and family-to-work conflict were
nonsignificant among employees working
long hours (see Table S2 in the online
supplement).
In summary, employees with greater fam-
ily demands and fewer work resources (par-
ticularly moderate or low supervisor support
for personal life) at baseline experienced
greater effects of STAR. A partial exception is
the effect of STAR on schedule control, which
is similar across most subgroups. Addition-
ally, the findings suggest that STAR brought
some benefits to employees working longer
hours—by providing greater schedule con-
trol, supervisor support for family and per-
sonal life, and increasing time adequacy with
family—but did not override the effect of
long work hours interfering with family and
personal life.
The question remains, though, whether
employees with fewer family responsibilities
also benefited, or instead took on a greater
burden at work. Employees with no depend-
ents experienced the benefits of STAR with
regard to schedule control but saw smaller,
nonsignificant effects for the other outcomes.
STAR had no effects on work hours or psy-
chological job demands in the full sample, but
perhaps STAR had these pernicious effects
among employees who did not have depend-
ents making claims on their time or energy.
The effect of STAR on work hours was nega-
tive for parents of children under age 18
(–1.14 indicating a decline of about one hour
per week, p = .03), but it was not statistically
significant for employees with no dependents
(see Table S2 in the online supplement). This
suggests that STAR helped parents trim their
work time somewhat, but we find no evidence
that other employees took on that work in this

505
Figure 2a. Intervention Effects for Schedule Control, by Subgroups
Figure 2b. Intervention Effects for Supervisor Support for Family, by Subgroups

506
Figure 2c. Intervention Effects for Work-to-Family Conflict, by Subgroups
Figure 2d. Intervention Effects for Family-to-Work Conflict, by Subgroups

Kelly et al. 507
salaried professional setting. There is also no
evidence that psychological job demands
increased for any subgroup; in fact, STAR
may have reduced job demands for men with
no children at home (–.16, p = .06), as shown
in Table S2. We also investigated effects of
STAR specifically for singles with no chil-
dren or adult-care responsibilities (N = 77,
M = 35 study groups), the group that might
absorb additional work if work-family back-
lash claims are correct. We find no significant
effects of STAR on these employees’ work
hours or psychological job demands. STAR—
which was deliberately available to all (rand-
omized) employees, regardless of family
situations—did not shift burdens from parents
or caregivers to others, but it brought only
limited benefits to employees with few family
responsibilities.
Additional Analyses
Intent-to-treat analyses are expected in experi-
mental designs, because randomization creates
equivalent groups on observable and unob-
servable factors (Murray 1998; Schulz, Alt-
man, and Moher 2010). Analyses that compare
employees who participated more in STAR
with those who did not participate reintroduce
selection issues, because employees who chose
to attend more training and invest in the pro-
gram may differ from those who gave less time
and commitment, in both measured and
unmeasured ways (e.g., openness to change,
salience of work stressors, emotional intelli-
gence, or investment in an ideal-worker career
strategy) (Ludwig et al. 2008; Sampson 2008).
On the other hand, including employees with
low or no participation in STAR sessions may
dampen the apparent effects and perhaps hide
negative effects for employees who were ran-
domized to treatment but did not actively par-
ticipate. We expect employees who participated
in more sessions saw greater changes with
STAR than did employees who were random-
ized to treatment but “exposed” to fewer of
these messages when attending sessions.
Moving beyond the intent-to-treat frame-
work, we examine whether employees who
Figure 2e. Intervention Effects for Enough Time for Family, by Subgroups

508 American Sociological Review79(3)
attended more sessions benefited more from
STAR. We constructed two subsamples based
on attendance rate: (1) employees who were
randomized to STAR and participated in 75
percent or more sessions (n = 257, 72 percent
of those randomized to STAR) compared to
all employees in the control group, and (2)
employees who were randomized to STAR
and participated in less than 75 percent of ses-
sions (n = 98) compared to all employees in
the control group. Results from repeated
measures analysis for each group show that
employees who attended more sessions
indeed benefited more from STAR. The
STAR intervention effect for schedule control
was .28 ( p < .0001) for employees who
attended at least three-fourths of the sessions;
it was smaller and not significant for employ-
ees who participated in fewer sessions. For
supervisor support for family and personal
life, the intervention effect was .17 ( p = .01)
among employees with high attendance but
small and not significant for employees who
attended fewer sessions. Similarly, employees
who participated in more sessions saw sig-
nificant and larger decreases in work-to-fam-
ily (–.17, p = .01) and family-to-work (–.11,
p = .03) conflict; intervention effects were
much smaller and not significant for employ-
ees who participated in fewer sessions. Time
adequacy with family exhibited the same pat-
tern. STAR did not appear to affect work
hours regardless of attendance rates. Employ-
ees who attended more sessions, however,
saw a marginally significant reduction in per-
ceived job demands (–.10, p = .06); no sig-
nificant intervention effect was detected for
the other group. In summary, the benefits of
the initiative were concentrated among
employees who attended most or all of the
STAR sessions.
As noted earlier, randomization occurred at
the level of study groups, an aggregation of
work groups or teams by researchers. Analysis
at the work group (M = 120) level that better
reflects the day-to-day organization at TOMO
found nearly identical intervention effects.
Intervention effects are also robust to inclusion
of a variable indicating whether a respondent’s
baseline survey was completed before or after
announcement of the upcoming merger.
CONCLusiONs
This study makes theoretical and empirical
contributions to work-family research, to
broader sociological questions, and to policy
development. First, we provided a multilevel
conceptualization of how workplaces foster
work-family conflict. We argued that an ideal-
worker schema underlies workplace policies,
practices, and interactions that implicitly give
paid work primacy over personal or family
commitments and assumes managerial con-
trol over workers’ time. Mutually reinforcing
policies, practices, interactions, and expecta-
tions for oneself and others constitute a struc-
ture of work that exacerbates work-family
conflict. This structure operates at multiple
levels: the organization, work group or team,
dyads (supervisor-subordinate and peer-to-
peer), and individuals’ own work practices
and expectations. Second, we developed and
analytically described a social intervention
that operated at multiple levels: it attempted
to disrupt the structure of work by changing
established practices and interactions, provid-
ing new interpretations of formerly marginal-
ized ways of working, and aligning policies to
support new practices. Third, we utilized a
group-randomized trial to rigorously evaluate
the effects of this social intervention on
employees’ experience of the workplace (par-
ticularly employees’ control over work time
and supervisors’ support for personal life) and
the work-family interface. By doing so, we
demonstrated more conclusively that work-
family conflicts are not simply private trou-
bles of individual workers, but can be changed
within workplaces. More broadly, this study
contributes to the sociological literature on
social structures and individual well-being by
using an experimental design that supports
causal conclusions about the effect of changes
in a given social environment for individuals’
subjective experience of their lives.
We provide the first experimental evidence
that workplace interventions can reduce

Kelly et al. 509
work-family conflict among employees and
change work resources, specifically increas-
ing employees’ control over the time and tim-
ing of their work and the support they receive
from supervisors for their family and personal
lives. We find clear evidence of benefits for
employees, with regard to improvements in
schedule control, supervisor support for fam-
ily and personal life, work-family conflict,
and family time adequacy over six months,
although the magnitude of change is modest.
Additionally, STAR changed work practices,
increasing work at home and employee-
driven variability in schedules. We also find
that employees who are likely more vulnera-
ble to work-family conflicts, as measured by
greater family demands and lower supervisor
support at baseline, benefited more from the
intervention. Recent research suggests that
schedule control further blurs the boundaries
between work and non-work in ways that
may encourage longer and more intensive
work (especially among salaried, professional
workers) (Blair-Loy 2009; Schieman 2013),
but we find no evidence that the intervention
increased work hours or psychological job
demands; instead, work hours declined
slightly for parents randomized to STAR.
The reduction of work-family conflict has
implications for health, family well-being,
and gender inequality. Work-family conflict
has been linked to mental and physical health
and to health behaviors, in studies with cross-
sectional and longitudinal designs and with
both objective measures and self-reports
(Allen and Armstrong 2006). Health out-
comes associated with work-family conflict
include hypertension (Frone, Russell, and
Cooper 1997), sleep (Lallukka et al. 2010),
and use of alcohol and cigarettes (Frone,
Barnes, and Farrell 1994; Grzywacz and
Marks 2000; Roos, Lahelma, and Rahkonen
2006). Work-family conflict is associated
with strain in marriages (Matthews, Conger,
and Wickrama 1996) and parent-child rela-
tionships (Cinamon, Weisel, and Tzuk 2008),
including higher conflict and lower intimacy
in these relationships. Employees’ experience
of work-family conflict also has the potential
to cross over to affect the health and well-
being of spouses and children (Hammer et al.
1997; McLoyd, Toyokawa, and Kaplan 2008).
Work-family conflict can reinforce gender
inequality because women who experience
intense conflict and cultural pressure to
devote themselves fully to families are more
likely to “scale back” their hours or leave the
labor force (Moen and Roehling 2005; Stone
2007). These decisions affect the careers of
these individual women, but they also rein-
force gender beliefs and discrimination
against mothers and the marginalization of
caregivers (Correll, Benard, and Paik 2007;
Williams 2000).
Findings from this study can inform the
development of future corporate and public
policy, as well as research on organizational
change. The work redesign approach studied
here, in which employees and managers were
invited to reconsider when, where, and how
work was done and how they could offer pro-
fessional and personal support to colleagues,
differs from common flexible work policies
that allow some employees to alter their work
slightly, with managerial permission, without
a broad examination of the way work is done.
Our findings suggest the STAR approach
leads to more meaningful changes grounded
in and customized to specific organizational
contexts and job requirements—compared to
instituting a more standardized flexible work
policy that, in practice, is available only to
employees with supportive managers (Perlow
and Kelly 2014). One limitation of this
approach is that it requires support from man-
agers and executives and employees must be
confident they can take control of when,
where, and how they work and still be judged
as good workers. These initiatives can be can-
celled with changes in leadership or in leader-
ship’s evaluation of their relative costs and
benefits. However, this is true for any work-
place policy not required by law. In the spring
of 2013, a new executive at Best Buy ended
ROWE (an initiative quite similar to STAR),
just after a new executive at Yahoo cancelled
a more standard telecommuting policy (Cohan
2013; Maynard 2013; Raftery 2013).

510 American Sociological Review79(3)
Research comparing the effectiveness of
more common flexible work policies and
work redesign approaches, in terms of work-
family conflict and gender inequality, would
be valuable. Furthermore, it would be fruitful
to consider how public policies, with their
ability to reach more workers and their nor-
mative power for describing what good
employers should do, might incorporate ele-
ments of the approach studied here (Bielby
2013). Currently, the most innovative public
policies give individual employees a right to
request flexible work arrangements without
retaliation (Ben-Ishai 2013), but advocates
could couple that legal push with tools to look
more collectively and carefully at work pro-
cesses, practices, and interactions in pursuit
of a dual agenda of better work and better
lives (Rapoport et al. 2002). Engaging in this
process with the backing of an employment
law would likely increase employees’ confi-
dence in the legitimacy and sustainability of
these changes.
Additional research is also needed to
understand how organizational changes
unfold in a variety of settings and the longer-
term implications of this type of workplace
intervention. Group-randomized studies are
currently underway to examine innovative
work-family interventions in health care
organizations and among construction work-
ers (Hammer et al. 2013; Kossek et al. 2014).
Future analyses should investigate the persis-
tence of these effects for employees over a
longer period and in the face of organizational
restructuring, such as the merger in TOMO,
or leadership changes such as those seen at
Best Buy and Yahoo. Researchers should also
examine whether changes in work resources
and work-family conflict contribute to
improvements in employees’ health and to the
well-being of their partners and children.
Finally, it is important to investigate whether
employees exposed to work redesign inter-
ventions, and especially employees who
make more changes in their work practices,
suffer negative career consequences or
whether the broad attempt to change expecta-
tions and norms (as well as practices) avoids
“flexibility stigma” (Glass 2004; Leslie et al.
2012). Doing so will help establish whether
new ways of working become fully institu-
tionalized and legitimated in organizations
that pursue broad organizational interven-
tions, or whether the ideal-worker norm holds
even in the face of a direct challenge.
Acknowledgments
Special acknowledgement goes to Extramural Staff Sci-
ence Collaborator, Rosalind Berkowitz King, PhD and
Lynne Casper, PhD for design of the original Work, Fam-
ily, Health and Well-Being Initiative. Our thanks to the
TOMO managers and employees who participated in the
study and facilitated our research; to Rachel Magennis,
Kimberly Fox, Holly Whitesides, and Laurie Pasricha for
facilitating data collection and conducting field research;
to Sarah Kalsbeek and Leslie Erickson of RTI for coordi-
nating survey data collection; to other members of the
Work, Family and Health Network for research design
decisions and helpful comments; to CultureRx for collabo-
ration on the intervention; and to audiences at the Univer-
sity of Minnesota, Indiana University, and the Work and
Family Researchers Network conference for their ques-
tions and comments. This manuscript does not represent
an official position of the NICHD (NIH, USDHHS), nor
does it represent a promise that any investigator-initiated
grant applications on this topic will automatically be con-
sidered higher priority or given preference over others. It
does not present a prescriptive policy statement from the
NICHD on the conduct of research.
Funding
This research was conducted as part of the Work, Family
and Health Network (http://www.WorkFamilyHealthNet
work.org), which is funded by a cooperative agreement
through the National Institutes of Health and the Centers
for Disease Control and Prevention: Eunice Kennedy
Shriver National Institute of Child Health and Human
Development (Grant # U01HD051217, U01HD051218,
U01HD051256, U01HD051276), National Institute on
Aging (Grant # U01AG027669), Office of Behavioral
and Social Sciences Research, and National Institute for
Occupational Safety and Health (Grant # U01OH008788,
U01HD059773). Grants from the William T. Grant Foun-
dation, Alfred P. Sloan Foundation, and the Administra-
tion for Children and Families provided additional
funding. The contents of this publication are solely the
responsibility of the authors and do not necessarily repre-
sent the official views of these institutes and offices.
Notes
1. Another middle-range theory, job demands-control-
support theory, also claims that employees’ control

Kelly et al. 511
and the social support available in the workplace
help workers manage high-demand jobs (Karasek
and Theorell 1990). Applying this to work-family
concerns, schedule control has been conceptualized
as employees’ control over when, where, and how
much work is done (Kelly, Moen, and Tranby 2011;
Lyness et al. 2012), and supervisor support for fam-
ily as a specific form of social support (Kossek et al.
2011). These two perspectives offer similar predic-
tions, although the job demands-resources model is
broader than the demands-control-support theory
(Bakker and Demerouti 2007; Schieman 2013).
2. A few randomized studies of work-family interven-
tions have targeted employees’ coping and parent-
ing skills, with interested individuals randomized to
the intervention or a wait-list control (Hartung and
Hahlweg 2010; Martin and Sanders 2003); these
interventions offered training to individuals seeking
help rather than attempting to change the workplace.
3. Following recent research on schedule control,
our scale includes the items on employees’ choice
over working at home or another location and tak-
ing work home. In white-collar settings, control
over scheduling of work hours, total number of
work hours, and work location are closely related,
whereas that may not be the case in jobs involving
direct customer service (e.g., retail, hospitality, and
health care).
4. We thank a reviewer for suggesting we examine
each item in this scale independently. We found
no significant effects of STAR on the other job
demands items.
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Erin L. Kelly is Professor of Sociology at the University
of Minnesota and a faculty affiliate of the Minnesota
Population Center. She is an organizational sociologist
who has studied the adoption, implementation, and con-
sequences of anti-discrimination, diversity, and work-life
policies (including flexibility initiatives, family leaves,
and child care benefits) in U.S. workplaces.
Phyllis Moen holds a McKnight Presidential Chair and
is Professor of Sociology at the University of Minnesota.
She has published numerous books and articles on
careers, work organizations, health, well-being, gender,
policies, and families, as they intersect and change over
the life course. Prior to coming to Minnesota in 2003,
Dr. Moen spent 25 years at Cornell University. She cur-
rently co-directs the Flexible Work and Well-Being Cen-
ter with Erin Kelly, part of the Work, Family & Health
Network, and is completing a book, Boomers on the Edge.
J. Michael Oakes is an Associate Professor in the Divi-
sion of Epidemiology & Community Health and Co-
director of the University of Minnesota’s U.S. Census
Research Data Center. He is an active researcher and
occasional principal investigator on a wide variety of
NIH, NSF, and RWJF funded studies addressing a vast
array of methodological, health, social, and ethical top-
ics. He has authored over 100 papers exploring problems
at the intersection of social and health sciences. His first
book, Methods in Social Epidemiology, was released in
2006; the second edition is now being completed.

516 American Sociological Review79(3)
Wen Fan is a PhD candidate in sociology at the Univer-
sity of Minnesota. Her research interests focus on social
determinants of health-related outcomes over the life
course, particularly the roles of large-scale historical
events, education, gender, and work characteristics. Her
dissertation examines the life chances of a unique cohort
who came of age during China’s Cultural Revolution
(1966 to 1976), investigating how school closings and
the “sent-down” policy shaped their educational attain-
ment and midlife health, as well as how they reversed
early-life adversities by returning to school at later ages.
Cassandra Okechukwu is an Assistant Professor of
Social and Behavioral Sciences at Harvard School of
Public Health. Her research interests include occupa-
tional determinants of health, health impacts of the work-
family interface, and community-based intervention
research methods. She is currently investigating how
work, home, and neighborhood environments interact to
shape the health and cancer prevention behaviors of vul-
nerable populations with a focus on the working class,
immigrant communities, and women who earn low
wages. Before she joined the Harvard University faculty,
she was a Robert Wood Johnson Health and Society
Scholar at UCSF-UC Berkeley.
Kelly D. Davis is Research Assistant Professor of
Human Development Family Studies and Research and
Evaluation Scientist at the Clearinghouse for Military
Family Readiness at The Pennsylvania State University.
Broadly, Dr. Davis’s research focuses on family pro-
cesses in civilian and military family populations. Most
of her work focuses on the interconnection between work
experiences and family relationship quality. She also
investigates how adults’ work experiences can cross over
to impact spouses’ and children’s psychological well-
being and health. The overarching goal of Dr. Davis’s
work is to identify or adapt policies, practices, and
resources to improve individual and family well-being.
Leslie B. Hammer is Professor of Psychology in the
Department of Psychology at Portland State University.
Her research focuses on ways organizations can reduce
work and family stress and improve positive spillover by
facilitating both formal and informal workplace supports.
Dr. Hammer is Director of the Center for Work-Family
Stress, Safety, and Health, funded by the National Insti-
tute for Child Health and Human Development and the
National Institute for Occupational Safety and Health.
She is also Director of the Occupational Health Psychol-
ogy graduate training program at PSU, funded by the
National Institute for Occupational Safety and Health.
Ellen Ernst Kossek is Basil S. Turner Professor of Man-
agement at Purdue University’s Krannert School of
Management & Research Director of the Susan Bulkeley
Butler Center for Leadership Excellence. She is President
of the Work and Family Researchers Network and Asso-
ciate Director of the Center for Work-Family Stress,
Safety & Health. Elected a Fellow in APA & SIOP, to the
Academy of Management’s Board of Governors, and as
Chair of the Gender and Diversity Division, Dr. Kossek’s
research has won awards for advancing gender, work-
life, and diversity in organizations.
Rosalind Berkowitz King received her PhD in Sociol-
ogy and Demography from the University of Pennsylva-
nia in 2000. She is currently a Health Scientist
Administrator in the Population Dynamics Branch at the
Eunice Kennedy Shriver National Institute of Child
Health and Human Development (NICHD). She serves
as the Project Scientist to the Work, Family, Health, and
Well-Being Initiative. She also has primary responsibility
for research portfolios on fertility, infertility, kinship, and
adoption and biopsychosocial approaches to population
health. Her recent research interests and publications
focus on the areas of infertility and the connections
between poverty and child development.
Ginger C. Hanson is a Research Associate III at Kaiser
Permanente Northwest, Center for Health Research. Her
research interests include quantitative methods, occupa-
tional health psychology, work-life integration, and
workplace violence. She received her PhD in systems
science: psychology from Portland State University and
her master’s degree in applied industrial/organizational
psychology from Portland State University. Her research
has focused on the benefits of participating in multiple
roles, factors leading to increased family-supportive
supervisor support, and the prevalence and health conse-
quences of workplace violence and sexual harassment.
Frank Mierzwa is a Senior Research Survey Scientist at
RTI International. He has more than 25 years of experi-
ence in survey research, with expertise in survey research
methods and design, and directing large-scale and com-
plex field surveys.
Lynne M. Casper is Professor of Sociology at the Uni-
versity of Southern California. She is co-editor of the
books Work, Family, Health, and Well-being (2005 Erl-
baum) and The Handbook of Measurement Issues in
Family Research (2006 Erlbaum). Dr. Casper is recipient
of the American Sociological Association’s Otis Dudley
Duncan Award for outstanding scholarship in social
demography for her book Continuity and Change in the
American Family. She has published extensively in the
areas of families and households, work and family,
cohabitation, fatherhood, child care, voting, and demo-
graphic methods.