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Are Genetic and Environmental Influences on Job Satisfaction Stable over Time? A Three-Wave Longitudinal Twin Study

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Job satisfaction research has unfolded as an exemplary manifestation of the "person versus environment" debate in applied psychology. With the increasing recognition of the importance of time, it is informative to examine a question critical to the dispositional view of job satisfaction: Are genetic influences on job satisfaction stable across different time points? Drawing upon dispositional and situational perspectives on job satisfaction and recent research in developmental behavioral genetics, we examined whether the relative potency of genetic (i.e., the person) and environmental influences on job satisfaction changed over time in a three-wave longitudinal twin study. Biometric behavioral genetics analyses showed that genetic influences accounted for 31.2% of the variance in job satisfaction measured at approximately age 21, which was markedly greater than the 18.7% and 19.8% of variance explained by genetic factors at age 25 and age 30. Such genetic influences were mediated via positive affectivity and negative affectivity, but not via general mental ability. After partialling out genetic influences, environmental influences on job satisfaction were related to interpersonal conflict at work and occupational status, and these influences were relatively stable across the three time points. These results offer important implications for organizations and employees to better understand and implement practices to enhance job satisfaction.
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Journal of Applied Psychology
Are Genetic and Environmental Influences on Job
Satisfaction Stable Over Time? A Three-Wave Longitudinal
Twin Study
Wen-Dong Li, Kevin C. Stanek, Zhen Zhang, Deniz S. Ones, and Matt McGue
Online First Publication, August 8, 2016. http://dx.doi.org/10.1037/apl0000057
CITATION
Li, W.-D., Stanek, K. C., Zhang, Z., Ones, D. S., & McGue, M. (2016, August 8). Are Genetic and
Environmental Influences on Job Satisfaction Stable Over Time? A Three-Wave Longitudinal Twin
Study. Journal of Applied Psychology. Advance online publication. http://dx.doi.org/10.1037/
apl0000057
Are Genetic and Environmental Influences on Job Satisfaction Stable Over
Time? A Three-Wave Longitudinal Twin Study
Wen-Dong Li
Chinese University of Hong Kong and Kansas State University
Kevin C. Stanek
University of Minnesota, Twin Cities
Zhen Zhang
Arizona State University and Sun Yat-sen University
Deniz S. Ones and Matt McGue
University of Minnesota, Twin Cities
Job satisfaction research has unfolded as an exemplary manifestation of the “person versus environment”
debate in applied psychology. With the increasing recognition of the importance of time, it is informative
to examine a question critical to the dispositional view of job satisfaction: Are genetic influences on job
satisfaction stable across different time points? Drawing upon dispositional and situational perspectives
on job satisfaction and recent research in developmental behavioral genetics, we examined whether the
relative potency of genetic (i.e., the person) and environmental influences on job satisfaction changed
over time in a 3-wave longitudinal twin study. Biometric behavioral genetics analyses showed that
genetic influences accounted for 31.2% of the variance in job satisfaction measured at approximately Age
21, which was markedly greater than the 18.7% and 19.8% of variance explained by genetic factors at
Age 25 and Age 30. Such genetic influences were mediated via positive affectivity and negative
affectivity, but not via general mental ability. After partialing out genetic influences, environmental
influences on job satisfaction were related to interpersonal conflict at work and occupational status,
and these influences were relatively stable across the 3 time points. These results offer important
implications for organizations and employees to better understand and implement practices to
enhance job satisfaction.
Keywords: job satisfaction, heritability, interpersonal conflict, negative affectivity and positive affectiv-
ity, occupational status
Research on the antecedents of job satisfaction has unfolded as
an exemplary manifestation of the “person versus environment”
debate, which has long received intense interest in applied psy-
chology (e.g., Arvey, Bouchard, Segal, & Abraham, 1989; House,
Shane, & Herold, 1996; Judge, Ilies, & Zhang, 2012; Salancik &
Pfeffer, 1978; Staw & Ross, 1985). The debate perhaps dates back
to Lewin’s (1935) famous formula that behavior is a joint product
of the person and the environment (i.e., B f[P, E]). Importantly,
Lewin’s theme has been split into two contrasting views on the
antecedents of job satisfaction. The situational perspective, as
embraced by work design research, has stressed the importance of
work environments (e.g., Hackman & Oldham, 1975; Morgeson,
Garza, & Campion, 2012; Parker, 2014). In contrast, the disposi-
tional perspective has focused on the role of the person in shaping
job satisfaction (e.g., Ilies, Arvey, & Bouchard, 2006; Judge &
Hulin, 1993; Staw & Cohen-Charash, 2005). The latter line of
research has focused on the effects of general mental ability (e.g.,
Judge, Higgins, Thoresen, & Barrick, 1999), personality (e.g.,
Staw, Bell, & Clausen, 1986), and genetic factors (e.g., Arvey et
al., 1989). Because of their ability to disentangle influences related
Wen-Dong Li, Department of Management, Chinese University of Hong
Kong, and Department of Psychological Sciences, Kansas State University;
Kevin C. Stanek, Department of Psychology, University of Minnesota,
Twin Cities; Zhen Zhang, W. P. Carey School of Business, Arizona State
University, and Lingnan (University) College, Sun Yat-sen University;
Deniz S. Ones and Matt McGue, Department of Psychology, University of
Minnesota, Twin Cities.
This project was partially funded by the National Natural Science
Foundation of China (Grant 71072024), the Singapore Ministry of
Education Research Grants (R-317-000-085-112, R-317-000-95-112,
R-317-000-099-112, and R-317-000-102-112), and by grants from the
U.S. National Institute on Alcohol Abuse and Alcoholism (AA09367,
AA11886), National Institute of Mental Health (MH066140), and Na-
tional Institute on Drug Abuse (DA05147, DA013240, DA036216).
Zhen Zhang’s work on this research was partially supported by the
Lingnan (University) College, Sun Yat-sen University. We thank Rich-
ard Arvey, Michael Frese, Peter Harms, Zhaoli Song, and Mike Zyphur
for their helpful comments on an earlier version of this article. We are
also grateful to Thomas Bouchard, Mike Miller, Michael Neale, Kris
Preacher, and Eric Turkheimer for helpful comments on testing medi-
ation, and to Paul Spector and Jason Shaw for insightful discussions.
We are also indebted to Wendy Johnson for her generous help in the
early review process.
Correspondence concerning this article should be addressed to Wen-
Dong Li, Department of Management, Chinese University of Hong Kong,
Cheng Yu Tung Building, No.12, Chak Cheung Street, Shatin, Hong Kong,
China. E-mail: oceanbluepsy@gmail.com
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Journal of Applied Psychology © 2016 American Psychological Association
2016, Vol. 101, No. 6, 000 0021-9010/16/$12.00 http://dx.doi.org/10.1037/apl0000057
1
to the nature from influences related to the nurture (Plomin, Owen,
& McGuffin, 1994), twin studies have been increasingly embraced
by researchers to inform the “person versus environment” debate
on job satisfaction (e.g., Arvey et al., 1989; Ilies & Judge, 2003;
Judge, Ilies, et al., 2012).
A critical underlying assumption of the dispositional perspective
is that influences from individual characteristics on job satisfaction
are stable over time (e.g., George, 1992; Staw et al., 1986). The
assumption has been particularly evident in research inferring
dispositional influences through the demonstration of the stability
of job satisfaction (Staw & Ross, 1985) and genetic influences on
job satisfaction (Arvey et al., 1989). In fact, this assumption is not
unique to job satisfaction research. In a review of the job perfor-
mance literature, Sturman (2007) found that most research on job
performance had examined “the correlates of various sorts of job
performance ratings, with the often implicit assumption that the
results would generalize to the same population of subjects at any
other point in time” (p. 50). Nevertheless, this crucial assumption,
to our knowledge, has yet to be rigorously tested for job satisfac-
tion.
Investigating whether influences from the person (e.g., through
genetic influences) on job satisfaction are constant over time has
important theoretical and practical implications. Theoretically, ex-
plicitly incorporating a temporal perspective into the dispositional
approach to job satisfaction “can change the ontological descrip-
tion and meaning” of dispositional influences (George & Jones,
2000, p. 675) and therefore contributes to better theory building in
job satisfaction research (Mitchell & James, 2001). Practically,
findings of significant dispositional influences on job satisfaction
have often been incorrectly interpreted as indicating, as Staw and
Cohen-Charash (2005) lamented, that “there was little organiza-
tions could do to improve the lot of workers” (p. 66). In this regard,
examining whether and how genetic influences on job satisfaction
may change over time can, at least partly, mitigate such misinter-
pretations, and thus contribute to forming a healthy public image
of applied psychology.
The purpose of the current study is threefold. First, using a
three-wave longitudinal twin design, we investigate whether ge-
netic influences—reflecting influences from the person— on job
satisfaction are constant over time in early adulthood. In doing so,
this study contributes to the job satisfaction literature by enriching
the dispositional approach to job satisfaction from a temporal
perspective. We note that there is an important advantage of
utilizing genetic influences to reflect effects of the “whole” person
(vs. the environment). A large number of individual difference
variables may affect job satisfaction (Arvey & Bouchard, 1994;
Ilies & Judge, 2003). It is thus impractical to examine their effects
all at once in a single study to capture person-related effects
collectively. The longitudinal twin design, however, enables us to
examine the aggregate contribution of these individual difference
variables as reflected in the (potentially time-varying) estimates of
overall genetic influences (Johnson, Turkheimer, Gottesman, &
Bouchard, 2009), because virtually all individual characteristics
are genetically influenced (Bouchard, 2004; Turkheimer, 2000).
Second, we discern the roles of general mental ability, positive
affectivity (PA), and negative affectivity (NA) in mediating ge-
netic influences on job satisfaction over time. These three individ-
ual characteristics have been most widely suggested or examined
in previous job satisfaction research (e.g., Cropanzano, James, &
Konovsky, 1993; Ilies & Judge, 2003; Staw et al., 1986; Watson &
Slack, 1993) as well as in research on how genetic factors affect
work-related outcomes through individual characteristics (Arvey
& Bouchard, 1994; Ilies et al., 2006). The current study thus sheds
light on the pathways through which genetic influences affect job
satisfaction over time and the relative explanatory power of the
three individual characteristics in the mediating processes. As
such, this study serves as a critical first step to advance our
understanding of the biological foundations of job satisfaction
(Becker, Cropanzano, & Sanfey, 2011; Ilies et al., 2006; Judge,
Piccolo, & Kosalka, 2009; Senior, Lee, & Butler, 2011).
Third, drawing upon the literature on the situational approach to
job satisfaction, we investigate whether influences from two crit-
ical work environmental factors— occupational status and inter-
personal conflict at work— contribute to job satisfaction over time
while partialing out the aggregated, genetically involved influ-
ences that are attributable to the person (Johnson et al., 2009).
Such an investigation not only complements the inquiries into
genetic influences on job satisfaction over time but also addresses
one important limitation of classical behavioral genetics research:
a failure to examine specific genetic and/or environmental factors.
The two work environment variables have been suggested and
extensively examined in the literatures on omnibus and discrete
work contexts (Johns, 2006) as well as on environmental influ-
ences on job satisfaction (e.g., Ilies, Johnson, Judge, & Keeney,
2011; Judge & Kammeyer-Mueller, 2012; Locke, 1976; Spector &
Jex, 1998). It is vitally important to control for effects of the
person (i.e., through genetic influences) when examining environ-
mental influences on job satisfaction, because many presumed
“environmental variables” are confounded by influences from the
person through self and/or organizational selection (Arvey, Zhang,
Avolio, & Krueger, 2007; Judge, Ilies, et al., 2012; Kendler &
Baker, 2007). In fact, most research on situational influences of job
satisfaction has not been able to partial out effects from the person,
resulting in potential overestimates of situational effects (Judge,
Ilies, et al., 2012). Therefore, by controlling for genetic influences,
this study represents a more stringent test of the situational per-
spective on job satisfaction over time. Figure 1 depicts the con-
ceptual model of this study adapted from Ilies and Judge (2003).
Theoretical Development and Hypotheses
Potential Change of Genetic Influences on Job
Satisfaction Over Time
Genetic influences are a consequence of inherited variations in
the DNA sequence, which are “responsible for all inherited phys-
ical, physiological, and psychological differences between individ-
uals” (Plomin & Simpson, 2013, p. 1263). Statistically, the mag-
nitude of genetic influences is estimated as heritability,
representing “the percentage of the total phenotypic variance ac-
counted for by genetic variance” (Arvey & Bouchard, 1994, p. 60).
Environmental factors denote “all non-heritable factors,” and can
be further decomposed into shared environments and unique en-
vironments (Plomin et al., 1994, p. 1735). Classical twin studies
have been conducted to examine the relative potency of overall
genetic and environmental influences of “any sort” (Plomin, De-
Fries, Knopic, & Neiderhiser, 2013, p. 73). This “holistic” ap-
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2LI, STANEK, ZHANG, ONES, AND MCGUE
proach represents a major strength, but it is also limited for not
being able to pinpoint specific genetic or environmental factors. To
partially address this limitation, we incorporate specific genetic
factors related to general mental ability, PA, and NA, as well as
work environments related to occupational status and interpersonal
conflict at work. These investigations provide a more nuanced
picture of job satisfaction’s antecedents.
Previous dispositional research has implicitly or explicitly por-
trayed genetic influences on job satisfaction as relatively stable.
Researchers have used two reasons to explain rank-order stability
in job satisfaction: (a) the genetic variants influencing individual
differences seldom change over time, and (b) the magnitude of
genetic influences on job satisfaction are relatively stable (Arvey
& Bouchard, 1994; Bouchard, Arvey, Keller, & Segal, 1992;
Dormann & Zapf, 2001; George, 1992; Staw & Ross, 1985). Put
differently, if influences from genetic factors or genetically af-
fected dispositional variables change substantially over time, then
we would not have observed between-person rank-order stability
in job satisfaction longitudinally. Multiple single-time-point,
cross-sectional twin studies, which suggest that the magnitude of
genetic influence does not vary with age, seem to indirectly sup-
port stable genetic influences on job satisfaction. In their seminal
article, Arvey et al. (1989) reported that approximately 30% of the
variance in general job satisfaction was attributable to genetic
differences. A follow-up study using a larger sample confirmed
this finding (Arvey, McCall, Bouchard, Taubman, & Cavanaugh,
1994). This notion and related research findings have also become
the foundation for subsequent meta-analyses examining mediating
mechanisms in the relationship between genetic factors and job
satisfaction (Ilies & Judge, 2003). Bouchard et al. (1992) even
predicted that “twin studies of job satisfaction using adult samples
in the age range 25 to 50 years will yield a heritability of about
0.30” (p. 92). However, to date, there have been no longitudinal
examinations of whether genetic influences on job satisfaction are
constant over time, especially during early adulthood when
changes in one’s work environment are prevalent (Rindfuss,
1991).
With regard to the direction of the potential change (i.e., in-
crease or decrease) of genetic influence on job satisfaction over
time, the person– environment fit literature and the situational
perspective on job satisfaction offer two contrasting predictions.
On the one hand, person– environment fit research posits that
individual characteristics (e.g., abilities and personality traits) be-
come increasingly important in shaping job satisfaction, because as
individuals age, they select themselves into life experiences that
are more compatible with their characteristics (Edwards, 2008;
Kristof-Brown & Guay, 2010). As such, job satisfaction over time
would be more reflective of the person, indicating increasing
genetic influences on job satisfaction over time.
Multiple research streams support this argument. First, the grav-
itational hypothesis and related research suggest that through mul-
tiple processes (e.g., occupational and organization selection, and
job crafting), individuals gradually gravitate into work environ-
ments compatible with their abilities (McCormick, Jeanneret, &
Mecham, 1972; Wilk, Desmarais, & Sackett, 1995) and personal-
ity traits (Judge et al., 1999). By extension, via such selection
processes, characteristics of the person become increasingly influ-
ential over the life span. Second, the ecology model of individu-
ality (e.g., Mumford & Stokes, 1992; Mumford, Stokes, Owens, &
Stokes, 1990; Stokes, Mumford, & Owens, 1989) indicates that the
importance of individual characteristics in shaping work experi-
ences may increase with age. Third, personality psychologists have
also theorized and found that personality traits that lead people to
compatible experiences are also likely to be further strengthened
by such experiences (i.e., the corresponsive principle; Caspi, Rob-
erts, & Shiner, 2005; Roberts, Caspi, & Moffitt, 2003). Therefore,
the enhanced correspondence over time between individual
characteristics and life experiences would lead to an increased
degree of influence from the person. Similarly, behavioral
genetic research suggests that as people age, they have increas-
ing control over selecting or creating experiences that foster the
development of their genetically influenced dispositions. Be-
cause such self-selected or self-created experiences partially
reflect genetic dispositions, the enhanced control over environ-
ments can amplify genetic influences over time (Bouchard,
1997; McGue, Bouchard, Iacono, & Lykken, 1993; Plomin &
Spinath, 2004; Scarr & McCartney, 1983).
Environmental Influences
Genetic Influences
General Mental Ability
Positive Affectivity
Negative Affectivity
Occupational Status
Interpersonal Conflict
Job Satisfaction
Other Environmental Influences
Figure 1. A conceptual model of genetic and environmental influences on job satisfaction through mediators.
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3
JOB SATISFACTION, GENETICS, ENVIRONMENTS, & CHANGE
On the other hand, the situational perspective on job satisfaction
suggests decreasing genetic influences on job satisfaction over
time. As they age, individuals are gradually exposed to more and
more work experiences that are exogenously introduced by orga-
nizations (Rindfuss, 1991). Given that individuals’ work experi-
ences are not solely influenced by their individual characteristics,
over time, the work experiences may be increasingly reflective of
environmental influences. Indeed, considering the importance of
organizational contexts, Staw (2004) stated, “It is possible that
genetic effects become so diluted by strong work situations that
they ultimately have little influence on job satisfaction” (p. 169).
We therefore expect that such a diluting effect may be more
pronounced in early adulthood because of the dramatic changes in
one’s work-related experiences (e.g., from being a newcomer to an
experienced employee, switching employers, getting promoted and
taking more responsibilities at work, starting to balance work and
family demands; Rindfuss, 1991). Prior research suggests at least
two broad forms of environmental influences on job satisfaction
that can dilute the person’s influences. The first form is related to
the nature of work, including task, social, and physical aspects of
work (e.g., Hackman & Oldham, 1975; Morgeson et al., 2012;
Parker, 2014). Second, the Cornell model of job satisfaction sug-
gests that economic situations and labor markets also affect job
satisfaction by changing individuals’ frames of reference (Judge,
Hulin, & Dalal, 2012). Similar arguments have also been provided
in behavioral genetics research: As individuals accumulate more
experiences when they develop into adulthood, genetic influences
on individual characteristics can become less important throughout
the adult life span (Bleidorn, Kandler, & Caspi, 2014; Eaves,
Long, & Heath, 1986; Plomin & Spinath, 2004).
Despite the lack of direct evidence, indirect evidence supports
both predictions. Regarding the increasing importance of genetic
influences over time, research has shown that genetic influences on
general mental ability augment with age, from explaining approx-
imately 60% of the variance at Age 20 to 80% at Age 30 (Bergen,
Gardner, & Kendler, 2007; Bouchard, 2013; Johnson, 2010; Wil-
son, 1978). In contrast, genetic influences on personality traits
have been found to decrease over time (Kandler, 2012; McCart-
ney, Harris, & Bernieri, 1990). For example, the amount of vari-
ance in neuroticism explained by genetic factors diminished from
approximately 40% in early adulthood to 20% in late adulthood
(Kandler, 2012), though there is also evidence supporting the
stability of genetic influences on personality traits (Hopwood et
al., 2011). Given that the theoretical and empirical support for both
predictions seems equally strong, we offer the following compet-
ing hypotheses:
Hypothesis 1a: Genetic influences on job satisfaction increase
over time.
Hypothesis 1b: Genetic influences on job satisfaction decrease
over time.
Individual Difference Variables as Mediators of
Genetic Influences
In order to delineate the pathways through which genetics shape
job satisfaction, we focus on three individual difference variables
suggested by previous research: general mental ability, PA, and
NA. These variables were identified by two means. First, Ilies and
Judge (2003) specifically urged researchers to study the mediating
role of general mental ability in probing genetic influences on job
satisfaction. Second, PA and NA have long been examined as
central dispositional sources of job satisfaction in previous re-
search (e.g., Cropanzano et al., 1993; Staw et al., 1986; Watson &
Slack, 1993). Moreover, general mental ability, PA, and NA have
been found to be significantly influenced by genetic factors
(Bouchard, 2004; Turkheimer, 2000). As such, they may mediate
genetic influences on job satisfaction. Yet no previous research has
examined whether they influence job satisfaction across different
time points, let alone their mediating role in transmitting genetic
influences on job satisfaction over time.
General mental ability. General mental ability, or general
intelligence, refers to individuals’ general cognitive abilities to
learn, reason, and solve problems (Spearman, 1904). It has been
regarded as one of the most useful constructs in psychology in
terms of predictive validity for various achievement measures
(Gottfredson & Deary, 2004). Individuals with high levels of
general mental ability tend to learn quickly and easily obtain job
knowledge (Ones, Dilchert, & Viswesvaran, 2012; Schmidt &
Hunter, 2004). As a result, they are likely to outperform individ-
uals with low general mental ability (Li, Arvey, & Song, 2011).
Indeed, research has consistently shown that general mental ability
facilitates job training effectiveness, overall job performance, and
occupational achievement (Judge et al., 1999; Ones, Dilchert,
Viswesvaran, & Salgado, 2010; Salgado, Anderson, Moscoso,
Bertua, & De Fruyt, 2003; Schmidt & Hunter, 2004). Thus, as
reported in previous research (e.g., Judge et al., 1999), general
mental ability is likely to be positively correlated with job satis-
faction.
General mental ability has also been reported to be significantly
influenced by genetic factors (Plomin & Spinath, 2004). In fact,
given its importance in shaping job satisfaction, Ilies and Judge
(2003) have called for examinations of the role of “intelligence in
explaining the genetic source of job satisfaction” (p. 755). Taken
together, we hypothesize the following:
Hypothesis 2: General mental ability mediates genetic influ-
ences on job satisfaction at multiple time points.
PA and NA. PA and NA have received the most research
attention in previous research on dispositional sources of job
satisfaction (e.g., Cropanzano et al., 1993; Staw et al., 1986;
Watson & Slack, 1993). PA and NA, as independent constructs,
are defined as the general tendencies to experience positive and
negative affective states across time and situations, respectively
(Watson & Clark, 1984, 1992; Watson, Clark, & Tellegen, 1988).
Judge and Larsen (2001) asserted that “PA and NA dimensions
may be the most proximal dispositional influences on job satisfac-
tion” (p. 82). Accordingly, in this study, we build on previous
research and probe whether, and to what extent, PA and NA
mediate genetic influences on job satisfaction at different time
points.
Brief, Butcher, and Roberson (1995) argued that PA and NA are
likely to influence job satisfaction through both actions and per-
ceptions. With respect to actions, high-PA individuals are confi-
dent, active, and energetic, and are likely to experience positive
emotions. Through their actions, they may select or create positive
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4LI, STANEK, ZHANG, ONES, AND MCGUE
situations at work, which in turn boosts their job satisfaction.
High-NA individuals, in contrast, tend to dwell on their shortcom-
ings and personal failures, and thus experience negative emotions.
As such, they may foster negative circumstances at work, which in
turn decrease their job satisfaction. Regarding perceptions as the
second mechanism, high-PA individuals are sensitive to positive
environmental cues and thus likely to perceive themselves and the
world in a positive light, whereas high-NA individuals tend to
view themselves and their environment through a negative lens.
Two meta-analyses have supported the positive and negative re-
lations of PA and NA with job satisfaction, respectively (Connolly
& Viswesvaran, 2000; Thoresen, Kaplan, Barsky, Warren, & de
Chermont, 2003).
Behavioral genetics research has found sizable genetic influ-
ences on PA and NA (Tellegen et al., 1988). Furthermore, Ilies and
Judge (2003) reported that compared with the Big Five personality
traits, PA and NA were stronger mediators of genetic influences on
job satisfaction. Yet research has not examined whether the mag-
nitudes of such mediating effects through PA and NA change
across the life span. Taken in concert, we propose the following:
Hypotheses 3 and 4: PA (H3) and NA (H4) mediate genetic
influences on job satisfaction at multiple time points.
We do not propose any directional hypotheses regarding the
potential changes (e.g., increase, decrease, or lack thereof) of the
mediating effects of the three individual characteristics in trans-
mitting genetic influences on job satisfaction over time. One major
reason is that the existing literature does not provide sufficient
theoretical and empirical grounds for such hypotheses. For exam-
ple, in perhaps the most recent review of developmental genetics
research, Turkheimer, Pettersson, and Horn (2014) found that
researchers have just started to examine how genetic influences on
individual difference variables change over time; no study has
been found to examine possible magnitude of changes in the
mediating effects of variables in channeling genetic influences on
outcome variables. Because of its exploratory nature, we propose
the following research question:
Research Question 1: Do the magnitudes of the mediating
effects via general mental ability, PA, and NA in the relation-
ships between genetic factors and job satisfaction change over
time?
Influences of Work Environment Variables Over Time
As noted before, previous research investigating environmental
influences on job satisfaction has been limited in two regards (for
notable exceptions, see Arvey et al., 2007; Judge, Ilies, et al.,
2012). First, research in both behavioral genetics and applied
psychology has shown that many work variables presumed to be
environmental are affected by individual characteristics. This is
because people are not randomly assigned to work environments;
instead, they select themselves, and/or are selected into, compati-
ble work environments to garner some level of person–
environment fit (Holland, 1996; McCormick et al., 1972; Sch-
neider, 1987; Vinson, Connelly, & Ones, 2007). Kendler and
Baker (2007) have found that putative environmental variables
such as life stressors, family environment, social support, and peer
relationships are all subject to significant genetic influences. Ap-
plied psychology researchers have also found that leadership ex-
periences (Arvey et al., 2007) and work stress (Judge, Ilies, et al.,
2012) are influenced by genetic factors. Therefore, a more accurate
examination of environmental influences on job satisfaction ne-
cessitates partialing out influences from the person, specifically
those reflected in people’s genetic endowments (Johnson et al.,
2009; Judge, Ilies, et al., 2012).
Second, prior research on work characteristics has tended to take
a static approach and has mostly employed cross-sectional designs
(Morgeson et al., 2012; Parker, 2014), although a few studies have
shown that effects of certain work characteristics on job satisfac-
tion dissipated over time (Boswell, Boudreau, & Tichy, 2005;
Campion & McClelland, 1993; Champoux, 1978; Griffin, 1991).
Longitudinal research is critically needed in order to extend this
line of research and gain a dynamic understanding of these effects
over time (George & Jones, 2000; Mitchell & James, 2001).
Drawing from previous research on job satisfaction, subjective
well-being, and related literatures (e.g., Bowling & Beehr, 2006;
Judge & Klinger, 2008; Lyubomirsky & Layous, 2013; Sheldon &
Lyubomirsky, 2012), we examine whether the relationships be-
tween work characteristics and job satisfaction change across three
waves of measurement.
We focus on two work environment variables: objectively mea-
sured occupational status and perceived interpersonal conflict at
work. Selection of the two variables was based on Johns’ (2006)
research on context, “situational opportunities or constraints” that
affect organizational variables and their relationships (p. 386).
Johns put forth two critical levels of measuring context: omnibus
(i.e., broad/general) and specific. Occupational status reflects the
amount of authority and social recognition granted to individuals
by their occupations (Wegener, 1992), and represents a broad
contextual variable. It has been viewed to be a composite construct
related to work tasks (and was thus used as an omnibus contextual
variable) representing the complexity level, extrinsic rewards (e.g.,
income and reputation), education requirements, and decision-
making authority afforded by one’s occupation (Blaikie, 1977). All
these component characteristics have been theorized and studied as
important predictors of job satisfaction (Judge & Kammeyer-
Mueller, 2012; Locke, 1976; Staw & Ross, 1985). Interpersonal
conflict at work refers to various overt or covert negative encoun-
ters with people at work, typically coworkers and supervisors, and
represents a specific contextual variable (Keenan & Newton,
1985). As one critical source of stressors stemming from the social
aspect of one’s job, the effect of interpersonal conflict has been
increasingly highlighted in research on employee well-being (e.g.,
job satisfaction; Bowling & Beehr, 2006; Ilies et al., 2011; Spector
& Jex, 1998).
Occupational status. Occupational status tends to be posi-
tively related to job satisfaction cross-sectionally, although there
are scant theoretical grounds or empirical evidence indicating the
stability of its link with job satisfaction over time when both are
measured repeatedly. As mentioned, occupational status is an
omnibus work environment variable covering various characteris-
tics that may have significant effects on job satisfaction. For
example, decision-making authorities and explicit rewards inher-
ent to occupational status are important predictors of job satisfac-
tion (Locke, 1976). Thus, occupational status tends to be positively
related to job satisfaction. Indeed, Super (1939) reported such a
substantive relationship, which was replicated in later research
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5
JOB SATISFACTION, GENETICS, ENVIRONMENTS, & CHANGE
(e.g., Ducharme & Martin, 2000; Gerhart, 1987; Staw & Ross,
1985).
Although typically deemed as an environmental factor related to
ones’ occupation and work, occupational status may be partially
affected by influences from the person. For example, through
occupational (Holland, 1996) and organizational (Schneider, 1987)
selection, individuals may select themselves and/or be selected
into occupations and jobs compatible with their individual char-
acteristics. Thus, it seems necessary to partial out such person-
related effects (i.e., genetic influences) when examining the effects
of occupational status, as a purely environmental factor, on job
satisfaction. Nevertheless, as a distal work variable, occupational
status is less likely to be affected by genetic factors than person-
ality traits are (Arvey, Li, & Wang, 2016; Plomin et al., 2013). As
such, we expect that after controlling for genetic influences, envi-
ronmental influences can still explain why occupational status is
related to job satisfaction.
Interpersonal conflict at work. Interpersonal conflict at
work tends to be negatively related to job satisfaction. Interper-
sonal conflict derived from social relationships at work has re-
ceived continued research attention (Spector & Jex, 1998). It has
long been recognized as one of the most significant work stressors
(Keenan & Newton, 1985) and has been incorporated as part of job
demands in Karasek’s (1979) job demand-control model. It may be
manifested in a number of ways, including minor and major
disagreements, rumor spreading, and even physical assault. Such
negative encounters likely drain individuals’ resources and induce
emotional reactions of frustration, annoyance, and even anger
toward coworkers and supervisors, leading to low levels of job
satisfaction (Hobfoll, 1989; Keenan & Newton, 1985; Spector &
Jex, 1998).
Empirical evidence for a negative relationship between inter-
personal conflict at work and overall job satisfaction has been
provided by a meta-analysis (Bowling & Beehr, 2006). This quan-
titative review summarized cross-sectional relationships regarding
antecedents and outcomes of an overarching variable composed of
conceptually equivalent constructs of interpersonal conflict. These
authors reported a meta-analytic correlation of .39 between this
variable and job satisfaction, which is similar in magnitude to the
unreliability corrected correlations of job satisfaction with other
important job characteristics (Humphrey, Nahrgang, & Morgeson,
2007).
Admittedly, negative encounters at work may be partially due to
influence from the person, because such encounters may be trig-
gered by or perceived based on individual characteristics (Judge,
Ilies, et al., 2012; Kendler & Baker, 2007). In addition, similar to
other distal work outcome variables (Arvey et al., 2007; Judge,
Ilies, et al., 2012), interpersonal conflict at work may also to a
large extent be affected by environmental factors related to the
work context. We therefore expect that environmental influences
may still account for the relationship between interpersonal con-
flict and job satisfaction after partialing out genetic influences.
In sum, although previous research has suggested cross-
sectional relationships of occupational status and interpersonal
conflict at work with job satisfaction, there seems to be little
theoretical or empirical grounds for predicting whether the rela-
tionships between those variables may change across time, and if
so, what direction such changes would take. Taken in concert, we
propose the following two hypotheses and two exploratory re-
search questions:
Hypotheses 5 and 6: With possible genetic influences par-
tialed out, environmental influences from occupational status
(H5) and interpersonal conflict at work (H6) still affect job
satisfaction over time.
Research Questions 2 and 3: With possible genetic influences
partialed out, do environmental influences on job satisfaction
that stem from occupational status (RQ2) and interpersonal
conflict at work (RQ3) change over time?
Method
Participants and Procedures
The data used in this study were collected as part of the Min-
nesota Twin and Family Study (MTFS; Iacono & McGue, 2002),
a state population-based longitudinal investigation of reared-
together, same-sex twin pairs. This project examines genetic and
environmental influences on development from adolescence
through adulthood. The work-related variables contained in the
MTFS (i.e., job satisfaction, occupational status, and interpersonal
conflict at work) have not been published in previous research. In
the process of applying for access to the data, the MTFS project
directors thoroughly reviewed the purpose of the current study and
ensured that it did not overlap with other studies using the MTFS
data.
The MTFS consists of two nonoverlapping cohorts of twins
followed longitudinally. The younger cohort was initially assessed
at Age 11. The older cohort was initially assessed at Age 17. The
current study is based on both cohorts; participation rates at the
follow-ups ranged from 87% to 92% (McGue, Irons, & Iacono,
2014). Their educational attainment and career development were
assessed at approximately 21, 25, and 30 years of age. More
information on the MTFS can be found in previous publications
(e.g., Iacono, Carlson, Taylor, Elkins, & McGue, 1999; Iacono &
McGue, 2002). Only those participants who worked during the
three waves of data collection and provided full information on job
satisfaction were included in analyses to ensure accuracy in the
behavioral genetic modeling of job satisfaction at multiple points
in time. Our final sample included 712 same-sex twin pairs (i.e.,
1,424 individuals).
Of these participants, 61% were male (463 identical and 249
fraternal twin pairs) and over 98% were Caucasian. Their average
year of education was 16.28 (SD .81) at the first wave of job
information collection, which was above the national average of
educational attainment in the United States (Newburger & Curry,
2000). At the first wave, 12% held administrative or minor pro-
fessional jobs; 27% held clerical, sales, or technician jobs; 14%
had skilled manual jobs; 35% had semiskilled jobs; and 11% were
unskilled employees.
Measures
Job satisfaction. Using one item to assess overall job satis-
faction is a common practice in applied psychology. Such a mea-
sure has been shown to have appreciable reliability and validity
(Wanous, Reichers, & Hudy, 1997). Job satisfaction in this study
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6LI, STANEK, ZHANG, ONES, AND MCGUE
was measured at the three waves by one item—“I enjoy my
job”— on a 4-point scale (1 very true,4not true at all)to
reflect overall job satisfaction. Similar questions (e.g., “I find real
enjoyment in my work” and “My job is enjoyable”) have been
used in well-validated job satisfaction scales (e.g., Overall Job
Satisfaction Scale [Brayfield & Rothe, 1951]; Job Satisfaction
Survey [Spector, 1985]). A similar one-item measure has also been
used in previous research (e.g., Judge & Hurst, 2008). Participants’
responses were recoded, with higher scores indicating higher lev-
els of job satisfaction.
General mental ability. Participants’ general mental abilities
were evaluated through assessments administered in person, at
approximately Age 17 using the Wechsler Adult Intelligence Scale
Revised (Wechsler, 1974). The verbal and quantitative scores were
prorated and summed to give an overall indicator of general mental
ability on an IQ scale (i.e., mean of 100 and SD of 15 in the
normative sample). The overall IQ scores were used as the indi-
cator for general mental ability in all analyses.
PA and NA. PA (␣⫽.92) and NA (␣⫽.92) were assessed
using the 198-item form of the Multidimensional Personality
Questionnaire (MPQ; Tellegen, 1982; Tellegen & Waller, 2008), a
widely used, broad-band personality questionnaire. They (also
called positive and negative emotionality) are two higher order
factors of the MPQ. PA is primarily based on the social potency,
well-being, social closeness, and achievement facets of the MPQ.
NA is primarily based on the dimensions of aggression, alienation,
and stress reaction. Although participants’ personality data were
obtained at the age of approximately 25 years, previous research
has suggested that individuals’ between-person rankings of per-
sonality traits in a sample are relatively stable and rarely display
dramatic changes in a short period of time (Caspi et al., 2005;
McCrae et al., 2000; Roberts & DelVecchio, 2000). Thus, the PA
and NA scores are useful predictors of job satisfaction from Age
21 to Age 30.
Occupational status. Following previous research (Judge et
al., 1999; Lykken & Tellegen, 1996; Zhang, Ilies, & Arvey, 2009),
occupational status was assessed using the widely adopted and
well-validated Hollingshead Index of Social Position (Hollings-
head, 1975; also see D. C. Miller, 1991). During each of the three
waves of data collection, participants provided descriptions of their
job titles and major job responsibilities at their current jobs. This
information was coded by trained MTFS researchers according to
the Hollingshead index on a 7-point scale (1 business executives
and major professionals with doctoral degrees;2lesser profes-
sionals [e.g., upper-level managers, accountants, and editors];
3administrative, minor professionals;4clerical, sales, and
technicians;5skilled manual;6semiskilled;7unskilled
employees). Jobs were coded only if the person was working full
time, so that the occupational status variable was missing for
students, homemakers, and those working part time. We reversed
these scores so that higher scores reflect higher status. Previous
research shows that the Hollingshead index of occupational status
correlates at r .74 with the Duncan Socioeconomic Index,
another widely used system of evaluating occupational status
(Haug & Sussman, 1971).
Interpersonal conflict at work. Consistent with previous re-
search showing that coworkers and supervisors are two major
sources of work interpersonal conflict (e.g., Frone, 2000; Spector
& Jex, 1998), interpersonal conflict at work in this study was
measured three times. Two items were administered in each wave
to measure the degree of conflict with coworkers and supervisors,
respectively (“I have problems getting along with my co-workers”
and “I have problems getting along with my supervisors”). Re-
sponse options ranged from 1 (very true)to4(not true at all). We
reversed the scores so that higher scores represent higher conflict.
The correlation between the two items was .60, .63, and .49 at each
of the three waves of data collection, respectively.
Control variables. In all the analyses, we controlled for gen-
der because it may affect estimates of genetic and environmental
influences (Johnson & Krueger, 2006; McGue & Bouchard, 1984;
Zhang, Zyphur, et al., 2009; Zyphur, Li, Zhang, Arvey, & Barsky,
2015) as well as levels of job satisfaction (Judge & Kammeyer-
Mueller, 2012). Age was not used as a control variable because the
three waves of work information collection were conducted at
approximately the same ages for all the participants. It is not
appropriate to include years of education as a control variable
because educational attainment has been theorized as a mechanism
through which general mental ability exerts its influence on career
outcomes (Schmidt & Hunter, 2004); thus, controlling for educa-
tion would have partialed out the substantive effects of general
mental ability (Spector & Brannick, 2011).
Analytical Strategy
Univariate biometric analyses. We performed three sets of
biometric analyses: univariate, bivariate, and trivariate, all based
on multigroup (i.e., monozygotic [MZ] and dizygotic [DZ] twin
groups) structural equation modeling (Neale & Cardon, 1992;
Plomin et al., 2013). Univariate biometric analyses were used to
examine the stability and change of genetic influences on job
satisfaction across the three measurement times (Hypotheses 1a
and 1b). In univariate biometric analyses, an observed variable, P,
is modeled to be influenced by three latent factors—A (additive
genetic factors), C (shared environmental factors that cause simi-
larity among family members), and E (unique environmental in-
fluences that make individuals different such as unique family and
occupational experiences and potential measurement error):
PuaAcCeE (1)
where P represents an observed variable; A, C, and E are stan-
dardized latent genetic and environmental variables (with means
and variances specified at 0 and 1, respectively); a,c, and eare
their corresponding coefficients to be estimated; and udenotes the
intercept or mean phenotypic score.
Because the A, C, and E components are assumed to be uncor-
related, variance in P can be decomposed into three variance
components, a
2
,c
2
, and e
2
, which are associated with genetic,
shared environmental, and unique environmental factors, res-
pectively. Genetic influences on P can be estimated as a
2
/
(a
2
c
2
e
2
). Although it seems that such biometric analyses
are similar to simple regression analyses in terms of variance
decomposition, using multigroup structural equation modeling
has three advantages. First, it readily offers estimates of con-
fidence intervals (CIs), which can be used to assess the stability
and change of genetic influences on job satisfaction over time.
Second, it allows researchers to parametrize the corresponding
cross-twin correlations (e.g., Twin 1 and Twin 2 of the same
twin pair) of latent genetic and environmental factors to differ-
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7
JOB SATISFACTION, GENETICS, ENVIRONMENTS, & CHANGE
ent values for the MZ and DZ twin groups. As such, simulta-
neous estimation of all the parameters for the two groups is
feasible. As shown in Figure 2, for the MZ (DZ) twin group, the
correlation between two genetic factors, A
1
and A
2
, was set to
1.0 (0.5) because co-twins, on average, share 100% (50%) of
their genetic makeup. The correlation between two shared en-
vironmental factors, C
1
and C
2
, was set to 1.0, by definition, for
both twin groups. The correlation between two unshared envi-
ronmental factors, E
1
and E
2
, was set to zero for both groups.
Third, this approach allows “different types of models to be
explicitly tested and compared” (Plomin et al., 2013, p. 384) to
determine the most parsimonious model “with the smallest
number of parameters that generates expectations that match the
observed data as closely as possible” (p. 383). To determine the
best-fitting model, we compared the fit indices of alternative
models (ACE, AE, CE, and E models) and tested the signifi-
cance of the influences of A, C, and E (Kline, 2005). To assess
model fit, we used fit indices including the chi-square differ-
ence test, the Comparative Fit Index, the Tucker-Lewis Index,
the Root Mean Square Error of Approximation, and Akaike’s
information criterion, following previous research (e.g., John-
son & Krueger, 2006; Judge, Ilies, et al., 2012; Li, Arvey,
Zhang, & Song, 2012).
Bivariate biometric analyses. Bivariate biometric analyses
were carried out to test (a) whether general mental ability, PA,
and/or NA mediated genetic influences on job satisfaction at the
three points in time (Hypotheses 2, 3, and 4), as well as the
magnitudes of such mediating effects (Research Question 1); and
(b) the stability and change of environmental influences related to
occupational status and interpersonal conflict at work on job
satisfaction over time with genetic influences controlled for (Hy-
potheses 5 and 6 as well as Research Questions 2 and 3). The
standard approach in behavioral genetics research, the Cholesky
decomposition model (Neale & Cardon, 1992), was adopted. Spe-
cifically, bivariate biometric analyses decompose observed rela-
tionships into genetic and environmental components. Figure 3
illustrates an example with a predictor and job satisfaction (the
effects of shared environmental factors, C1, were not modeled
because their influences were negligible or null, a consistent find-
ing in behavioral genetics research; Bouchard, 2009; McCartney et
al., 1990; McGue et al., 1993).
It is notable that total variance in job satisfaction is the same in
univariate and bivariate biometric models. Furthermore, the pa-
rameters used in calculating the heritability of job satisfaction is
also the same in both types of models (Neale & Cardon, 1992;
Plomin et al., 2013). The variance component a
2
in a univariate
model (see Figure 2) is further decomposed into two variance
components a21
2and a22
2(see Figure 3), where a
21
is genetic
influence related to the predictor and a
22
represents the “residual
genetic influence” that is not related to the predictor. Thus, alge-
braically, a
2
a21
2a22
2.
We adopted two approaches to examine the mediation effects
via the individual difference variables. Our first approach is the
dominant method employed in behavioral genetics studies. Ac-
cording to Jockin, McGue, and Lykken (1996), in order to examine
“through which proximate causal variables do genes pass on their
influence” (p. 288) to an outcome variable, three criteria should be
met. First, a potential mediator, as well as the outcome variable,
“must to some extent be genetically influenced.” Second, the
potential mediator must be related to the outcome variable. Third,
the same genetic factors must influence both the mediator and the
outcome in the Cholesky decomposition. The three criteria have
been adopted in testing mediation in previous research using twin
designs (Arvey, Rotundo, Johnson, Zhang, & McGue, 2006;
Jockin et al., 1996; Judge, Ilies, et al., 2012; Saudino, Pedersen,
Lichtenstein, McClearn, & Plomin, 1997; Shane, Nicolaou, Cher-
kas, & Spector, 2010; Zhang, Zyphur, et al., 2009). The Cholesky
decomposition is particularly useful for meeting the third criterion,
because it can examine whether the same genetic factors affect
both a mediator and job satisfaction (i.e., through the CI associated
with a
21
in Figure 3).
This approach adopts the same theoretical rationale in inferring
mediation as the conventional approaches used in applied psychol-
ogy. Mathieu and Taylor (2006) suggested three important pre-
conditions to infer mediation: experimental designs, temporal pre-
cedence, and theoretical guidance. Behavioral genetics research
approximates the three preconditions because (a) twin study de-
signs are naturally occurring quasi-experiments (Plomin et al.,
1994); (b) genetic factors form before individual difference vari-
ables, and individual difference variables come into being before
job satisfaction (Arvey & Bouchard, 1994; Plomin et al., 2013);
and (c) previous research has suggested and found that one of the
pathways through which genetic factors affect outcome variables
is through individual difference variables such as general mental
ability and personality traits (e.g., Arvey & Bouchard, 1994; Ilies
& Judge, 2003; Plomin et al., 2013).
Under this first approach, we followed the variance decompo-
sition tradition in behavioral genetics to use explained variance
values to quantify mediation effects. For example, we estimated
the percentage of the total variance in job satisfaction that was
explained by PA’s genetic factors (a21
2/[a21
2a22
2e21
2e22
2]),
and this value represents the mediating effect of PA in the
genetics/job-satisfaction relationship. This explained variance in-
dex has been suggested as an alternative effect size measure for
testing of mediation effects (Preacher & Kelley, 2011). Similarly,
the percentage of the total variance in job satisfaction accounted
for by environmental factors related to an environmental predictor
is e21
2/(a21
2a22
2e21
2e22
2), which was used to test Research
Questions 2 and 3.
δ
c
ae
A1
E1
Job Satisfactiontwin1
C1
c
ae
A2
E2
Job Satisfactiontwin2
C2
λ
Figure 2. Univariate biometric analyses for job satisfaction. A additive
genetic factors; C shared environmental factors; E unique environ-
mental factors and/or measurement error; ␭⫽1 for monozygotic twins and
.5 for dizygotic twins; ␦⫽1 for both types of twins.
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8LI, STANEK, ZHANG, ONES, AND MCGUE
Admittedly, this first approach of mediation testing in the
behavioral genetics literature is different from the product-of-
coefficients approach or the difference-in-coefficients approach
used in the psychology literature (MacKinnon, 2008; MacKin-
non, Lockwood, Hoffman, West, & Sheets, 2002; Preacher &
Kelley, 2011). The product-of-coefficients approach uses a
m
b
m
to quantify the mediation effect, where a
m
is the path from
the predictor to the mediator, and b
m
is the path from the
mediator to the outcome while controlling for the predictor
(with path c
m
=). The difference-in-coefficients approach uses c
m
c
m
=, where c
m
is the total effect of the predictor on the outcome and c
m
=
is the predictor’s residual effect after controlling for the mediator.
Prior research has shown that a
m
b
m
and c
m
c
m
=are algebraically
equivalent (MacKinnon, Warsi, & Dwyer, 1995), and thus both have
been used for mediation testing (Muller, Yzerbyt, & Judd, 2008).
As such, our second approach for mediation testing used the
c
m
c
m
=method that is more accessible to applied psychology
researchers. Because the total amount of variance in job satisfac-
tion explained by genetic factors is the same in univariate as in
bivariate models, the total effect of genetic influences (i.e., “c
m
)
on job satisfaction is captured by the path ain a univariate model
(see Figure 2), which is equivalent to the square root of (a21
2a22
2),
where a
21
and a
22
are path coefficients in a bivariate model (see
Figure 3). After an individual difference variable is introduced to
the model, the a
22
path represents the residual genetic effect “c
m
=.”
Therefore, translating the c
m
c
m
=formula into a bivariate biomet-
ric model, our second approach used [sqrt(a21
2a22
2)–a
22
]to
quantify a mediation effect.
Trivariate biometric analyses. Lastly, trivariate biometric
analyses were conducted to corroborate the results of univariate
analyses, examining the change of genetic influences on job
satisfaction by simultaneously including all the three job satis-
faction measures in one model (see Figure 4). Such analyses
provided a more rigorous test of the possible change of genetic
influences on job satisfaction over time (Hypotheses 1a and 1b).
We used 1,000 resamples and reported the bias-corrected boot-
strapped 95% confidence intervals (CIs). Because many esti-
mated coefficients based on the ACE models have a lower
bound of zero, the boostrapped CIs may not center around the
point estimates.
Results
Table 1 displays the means, standard deviations, and correla-
tions among the study variables. It shows that the three individual
characteristics variables were substantively correlated with job
satisfaction at all the three measurement occasions, though job
satisfaction stability coefficient were low in magnitude. In addi-
tion, interpersonal conflict measured at each time was substan-
tively related to concurrent job satisfaction. Occupational status
appeared to show a similar pattern. These findings provide empir-
ical grounds for further biometric analyses. Table 2 presents the
within-twin pair correlations (e.g., the correlation between Twin
1’s job satisfaction at Time 1 and Twin 2’s job satisfaction at
Time 1) of study variables for identical and fraternal twins. An
inspection of Table 2 indicates greater similarities for identical
twins (values in the upper diagonal) than for fraternal twins
(values in the lower diagonal) on all study variables. These
results suggest likely genetic effects on these variables (Loeh-
lin, 1992; Plomin et al., 2013).
Tests of Hypotheses and Research Questions
Hypotheses 1a and 1b concerned possible changes of genetic
influences on job satisfaction over time. In univariate biometric
analyses, fitting the data with genetic factors (A), shared environ-
mental factors (C), and unique environmental factors (E) for job
satisfaction at Time 1 indicated that effects of the shared environ-
mental factors were negligible (Model 1, Table 3). Such a finding
is consistently reported in the behavioral genetic literature
(Bouchard, 2009; McCartney et al., 1990; McGue et al., 1993;
Plomin et al., 2013). Thus, following previous research (e.g.,
Arvey et al., 2007; Judge, Ilies, et al., 2012; Shane et al., 2010),
effects of C were fixed to zero in the subsequent analyses. Model
2, with only genetic factors and unique environmental factors, fit
the data best among all the alternative models. In this model,
genetic factors accounted for 31.2% of the variance in job satis-
faction at Time 1 (95% CI [28.8%, 37.1%]). Models with genetic
and unique environmental factors (AE models) were also adopted
as the best-fitting models for other study variables.
Similar results were observed for the job satisfaction variables
measured at Time 2 and Time 3. The best-fitting models were the
a22
e22
a11
e21
e11
a21
λλ
A1
E1
A2
E2
Predictortwin1 Job Satisfactiontwin1
a22
e22
a11
e21
e11
a21
A3
E3
A4
E4
Predictortwin2 Job Satisfactiontwin2
Figure 3. Bivariate biometric analyses for job satisfaction with a predictor using the Cholesky decomposition.
Aadditive genetic factors; E unique environmental factors and/or measurement error; effects of shared
environmental factors (C) were not modeled because the effects were not significant, which is also a consistent
finding in previous research; ␭⫽1 for monozygotic twins and .5 for dizygotic twins.
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9
JOB SATISFACTION, GENETICS, ENVIRONMENTS, & CHANGE
models with only genetic and unique environmental factors (AE
models: Model 6 and Model 10; Table 3). Genetic factors ac-
counted for 18.7% (95% CI [11.4%, 25.6%]) and 19.8% (95% CI
[10.3%, 28.1%]) of the variance in job satisfaction at Time 2 and
Time 3, respectively. Because the 95% CI for genetic influences at
Time 1 did not overlap with the two CIs at Time 2 and Time 3, we
concluded that genetic influences on job satisfaction significantly
decreased from Time 1 to Time 2 and Time 3. There seemed to be
no significant difference in genetic influences between Time 2 and
Time 3.
The findings of decreased genetic influences on job satisfaction
were also corroborated in trivariate analyses (see Table 4). Allow-
ing genetic influences to be freely estimated in Model 1 yielded
good model fit. However, fixing genetic influences at Time 1 equal
to those influences at Time 2 (Model 2) or at Time 3 (Model 3), or
specifying equal genetic influences across the three times (Model
5) produced poorer model fit. Fixing genetic influences at Time 2
equal to those at Time 3 did not substantially change model fit
(Model 4). This result suggested that genetic influences on job
satisfaction did not change substantially from Time 2 to Time 3.
Thus, Hypothesis 1b, that genetic influences become weaker at
later time points (i.e., at Ages 25 and 30), was supported.
Hypotheses 2, 3, and 4 focused on the role of general mental
ability (H2), PA (H3), and NA (H4) in mediating genetic influ-
ences on job satisfaction at different time points. With the behav-
ioral genetics approach for mediation testing, univariate analyses
e32
e11
e21
a32
a31
a21
a11
e31
A1
E1
Job Satisfactiontime1
a22
e22
A2
E2
Job Satisfactiontime2
a33
e33
A3
E3
Job Satisfactiontime3
Figure 4. Trivariate biometric analyses for job satisfaction across time using the Cholesky decomposition. This
is a partial diagram showing additive genetic factors (A
1
,A
2
, and A
3
) and unique environmental factors (E
1
,E
2
,
and E
3
) for one twin for the sake of clarity. A additive genetic factors; E unique environmental factors
and/or measurement error; effects of shared environmental factors (C) were not modeled because the effects were
not significant, which is also consistent with previous research.
Table 1
Descriptive Statistics and Zero-Order Correlations for Study Variables at the Individual Level
Variables MSD1234567891011
1. General mental ability 101.25 14.24
2. Positive affectivity 124.70 12.87 .05
3. Negative affectivity 81.77 13.42 .04 .13 —
4. Occupational status, Time 1 5.11 1.31 .07 .08 .08 —
5. Occupational status, Time 2 6.11 1.59 .18 .09 .12 .28 —
6. Occupational status, Time 3 6.56 1.56 .24 .11 .13 .23 .50 —
7. Interpersonal conflict, Time 1 2.54 .83 .05 .14 .20 .05 .13 .09 —
8. Interpersonal conflict, Time 2 2.53 .85 .10 .18 .25 .00 .05 .10 .29 —
9. Interpersonal conflict, Time 3 2.39 .76 .10 .10 .17 .02 .05 .05 .26 .30 —
10. Job satisfaction, Time 1 3.26 .70 .05 .25 .10 .12 .07 .04 .24 .14 .09 —
11. Job satisfaction, Time 2 3.30 .69 .10 .32 .15 .03 .07 .01 .17 .33 .11 .25 —
12. Job satisfaction, Time 3 3.40 .62 .07 .20 .17 .05 .03 .07 .07 .17 .26 .17 .31
Note.N743–1,424 individuals. Correlations with absolute values of .06 or higher are significant at p.05; correlations with absolute values of .09
or higher are significant at p.01. Time 1 Age 21; Time 2 Age 25; Time 3 Age 30.
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10 LI, STANEK, ZHANG, ONES, AND MCGUE
Table 2
Within-Twin-Pair Correlations for the Study Variables
Variables 1 2 3456789101112131415161718192021222324
1. General mental ability, Twin 1 .07 .09 .18 .11 .22 .05 .09 .10 .06 .08 .13 .80 .01 .07 .14 .21 .26 .04 .14 .09 .04 .05 .09
2. Positive affectivity, Twin 1 .05 .11 .09 .07 .12 .19 .14 .02 .30 .32 .20 .01 .47 .02 .03 .12 .04 .01 .00 .01 .15 .20 .24
3. Negative affectivity, Twin 1 .02 .18 — .14 .10 .08 .15 .19 .04 .15 .15 .19 .12 .12 .46 .12 .20 .12 .18 .07 .07 .10 .14 .14
4. Occupational status, Time 1, Twin 1 .12 .18 .07 .31 .20 .05 .01 .05 .16 .07 .10 .10 .01 .07 .36 .19 .13 .13 .05 .01 .00 .03 .03
5. Occupational status, Time 2, Twin 1 .33 .12 .05 .34 .46 .10 .03 .06 .05 .03 .05 .07 .05 .05 .26 .43 .32 .09 .07 .03 .00 .02 .15
6. Occupational status, Time 3, Twin 1 .28 .09 .25 .28 .43 — .07 .10 .02 .08 .03 .00 .17 .05 .12 .20 .43 .48 .02 .05 .06 .03 .04 .01
7. Interpersonal conflict, Time 1, Twin 1 .10 .13 .25 .05 .06 .07 .29 .20 .23 .17 .08 .02 .06 .17 .05 .09 .02 .20 .15 .23 .08 .17 .15
8. Interpersonal conflict, Time 2, Twin 1 .09 .24 .34 .01 .09 .04 .32 .18 .15 .33 .16 .08 .00 .05 .16 .00 .01 .21 .13 .20 .06 .10 .12
9. Interpersonal conflict, Time 3, Twin .12 .22 .37 .03 .03 .14 .26 .36 .00 .07 .23 .14 .02 .04 .05 .07 .04 .17 .13 .21 .14 .12 .07
10. Job satisfaction, Time 1, Twin 1 .09 .23 .18 .09 .07 .12 .29 .18 .10 .25 .18 .04 .15 .10 .04 .05 .01 .08 .03 .12 .28 .20 .19
11. Job satisfaction, Time 2, Twin 1 .19 .27 .10 .04 .09 .04 .23 .38 .19 .28 .35 .08 .15 .07 .03 .12 .06 .12 .04 .10 .24 .25 .17
12. Job satisfaction, Time 3, Twin 1 .01 .11 .14 .08 .04 .18 .13 .29 .38 .28 .25 — .10 .16 .08 .01 .09 .03 .12 .08 .07 .20 .30 .18
13. General mental ability, Twin 2 .41 .08 .14 .11 .05 .17 .19 .04 .15 .03 .06 .01 .01 .10 .07 .22 .28 .00 .15 .09 .04 .07 .07
14. Positive affectivity, Twin 2 .01 .21 .04 .16 .14 .12 .05 .22 .17 .07 .06 .09 .08 .11 .10 .10 .09 .06 .16 .12 .21 .35 .27
15. Negative affectivity, Twin 2 .09 .02 .21 .08 .03 .06 .08 .16 .11 .04 .02 .09 .14 .15 — .07 .22 .15 .25 .25 .16 .05 .19 .14
16. Occupational status, Time 1, Twin 2 .02 .02 .13 .25 .12 .03 .01 .09 .06 .01 .10 .07 .04 .09 .00 .25 .23 .05 .00 .04 .15 .02 .03
17. Occupational status, Time 2, Twin 2 .10 .03 .14 .02 .14 .00 .04 .07 .00 .13 .02 .07 .05 .13 .04 .21 .55 .22 .12 .12 .10 .14 .05
18. Occupational status, Time 3, Twin 2 .13 .05 .14 .06 .21 .08 .05 .08 .05 .10 .07 .05 .16 .16 .08 .26 .55 — .05 .08 .07 .01 .08 .05
19. Interpersonal conflict, Time 1, Twin 2 .12 .04 .12 .04 .03 .09 .15 .02 .20 .08 .03 .06 .09 .18 .14 .06 .10 .22 .28 .33 .24 .17 .05
20. Interpersonal conflict, Time 2, Twin 2 .13 .08 .05 .20 .06 .08 .08 .11 .12 .01 .00 .05 .06 .26 .28 .04 .10 .19 .30 .41 .11 .29 .12
21. Interpersonal conflict, Time 3, Twin 2 .13 .20 .12 .05 .16 .07 .06 .04 .07 .04 .10 .04 .09 .08 .25 .08 .01 .01 .27 .24 .15 .10 .26
22. Job satisfaction, Time 1, Twin 2 .01 .05 .10 .01 .03 .04 .03 .12 .18 .17 .03 .07 .13 .27 .00 .02 .07 .03 .21 .19 .10 — .23 .15
23. Job satisfaction, Time 2, Twin 2 .08 .08 .10 .07 .01 .01 .02 .17 .17 .01 .03 .05 .15 .35 .17 .08 .00 .08 .09 .33 .12 .21 — .32
24. Job satisfaction, Time 3, Twin 2 .02 .03 .06 .03 .05 .04 .01 .08 .19 .02 .07 .08 .03 .20 .21 .09 .01 .13 .01 .15 .16 .09 .30 —
Note.N463 monozygotic (MZ) and 249 dizygotic (DZ) twin pairs; Twin 1 and Twin 2 refer to two co-twins of the same twin pair. Values in the upper diagonal are within-pair correlations for
MZ twins, and values in the lower diagonal are within-pair correlations for DZ twins. Correlations with absolute values of .10 or higher are significant at p.05; correlations with absolute values
of .17 or higher are significant at p.01. Bolded values indicate within-twin pair correlations for the same study variables.
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11
JOB SATISFACTION, GENETICS, ENVIRONMENTS, & CHANGE
showed that all the three individual difference variables were
under significant genetic influences. In addition, all of them were
significantly related to job satisfaction (except for the correlation
between general mental ability and job satisfaction at Time 1; see
Table 1). Thus, to meet the last precondition for testing mediation
in behavioral genetics research (Jockin et al., 1996), we needed to
examine whether the same genetic factors affected one of the three
variables and job satisfaction simultaneously. Bivariate biometric
analyses (see Table 5) revealed that genetic factors associated with
general mental ability did not appreciably affect job satisfaction (in
Models 1, 2, and 3 of Table 5, the 95% CIs for a
21
,[.05, .05],
[.05, .06], and [.08, .03], all included zero). Such findings did
not support H2. However, in support of H3 and H4, genetic factors
related to PA (in Models 4, 5, and 6, the 95% CIs for a
21
were [.16,
.30], [.14, .28], and [.16, .31]) and NA (in Models 7, 8, and 9,
the 95% CIs for a
21
were [.17, .04], [.22, .09], and
[.24, .09]) had moderate influences on job satisfaction at the
three time points. The above findings were further confirmed by
Table 3
Results of Univariate Biometric Analyses for the Three Job Satisfaction Variables
Growth models for
Model fit indices
2
(df)⌬␹
2
(df)CFI TLI AIC RMSEA
Job satisfaction, Time 1
Model 1: A,C,E 2.26 (6) 1.00 1.01 6253.19 .001
Model 2: A,E
a
2.36 (7) .10 (1) 1.00 1.01 6251.29 .001
Model 3: C,E 7.01 (7) 4.75
(1) 1.00 1.00 6255.94 .002
Model 4: E 68.81
ⴱⴱⴱ
(8) 66.55
ⴱⴱ
(2) .06 .76 6315.73 .112
Job satisfaction, Time 2
Model 5: A,C,E 8.18 (6) .91 .97 6183.16 .024
Model 6: A,E
a
8.18 (7) .00 (1) .95 .99 6181.16 .017
Model 7: C,E 14.83
(7) 6.65
ⴱⴱ
(1) .69 .91 6187.82 .043
Model 8: E 32.14
ⴱⴱⴱ
(8) 23.96
ⴱⴱⴱ
(2) .04 .76 6203.13 .071
Job satisfaction, Time 3
Model 9: A,C,E 4.04 (6) 1.00 1.01 4415.58 .001
Model 10: A,E
a
4.04 (7) .00 (1) 1.00 1.01 4413.58 .001
Model 11: C,E 8.42 (7) 4.38
(1) .93 .98 4417.96 .022
Model 12: E 25.03
ⴱⴱⴱ
(8) 20.99
ⴱⴱⴱ
(2) .17 .79 4432.57 .071
Note. Sample sizes were 463 and 249 twin pairs for monozygotic and dizygotic twins, respectively. A, C, and E denote additive genetic factors, shared
environmental factors, and unique environmental factors, respectively. df degrees of freedom; CFI comparative fit index; TLI Tucker–Lewis index;
AIC Akaike’s information criterion; RMSEA root mean square error of approximation.
a
Indicates the best-fitting model.
p.05.
ⴱⴱ
p.01.
ⴱⴱⴱ
p.001.
Table 4
Results of Trivariate Biometric Analyses for Job Satisfaction Over Time
Trivariate biometric
models for job satisfaction
over time
Model fit indices Path coefficients estimates
2
(df)⌬␹
2
(df)CFI TLI AIC RMSEA a
11
a
21
a
31
a
22
a
32
a
33
e
11
e
21
e
31
e
22
e
32
e
33
No constraints of equal
influences, Model 1 33.45 (39) 1.00 1.00 16563.81 .001 .57 .33 .35 .30 .28 .00 .83 .07 .03 .90 .13 .88
Equal genetic influences at
Time 1 and Time 2,
Model 2 39.52 (40) 6.07
(1) 1.00 1.00 16567.88 .001 .51 .37 .38 .35 .23 .00 .86 .07 .03 .87 .12 .88
Equal genetic influences at
Time 1 and Time 3,
Model 3 38.68 (40) 5.32
(1) 1.00 1.00 16567.04 .001 .51 .34 .39 .28 .33 .00 .86 .08 .03 .90 .11 .86
Equal genetic influences at
Time 2 and Time 3,
Model 4
a
33.46 (40) .01 (1) 1.00 1.00 16561.82 .001 .57 .33 .35 .30 .28 .00 .83 .07 .03 .90 .13 .88
Equal genetic influences at
Time 1, Time 2, and
Time 3, Model 5 41.09 (41) 7.64
(2) 1.00 1.00 16567.45 .002 .49 .37 .41 .32 .26 .04 .87 .07 .03 .88 .10 .87
Note. Sample sizes were 463 and 249 twin pairs for monozygotic and dizygotic twins, respectively. AE models were used in Cholesky decomposition.
Parameters a
11
,a
21
,a
31
,e
11
,e
21
, and e
31
denote paths presented in Figure 3. Path coefficient estimates below .06 are not significant at the .05 level; estimates
within the range between .07 and .10 are significant at the .05 level; and estimates larger than .10 are significant at the .001 level. Time 1 Age 21; Time
2Age 25; Time 3 Age 30. df degrees of freedom; CFI comparative fit index; TLI Tucker–Lewis index; AIC Akaike’s information criterion;
RMSEA root mean square error of approximation.
a
Indicates the best-fitting model.
p.05.
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12 LI, STANEK, ZHANG, ONES, AND MCGUE
the proportion of the total variance in job satisfaction accounted
for by those genetic factors related to the three individual charac-
teristics (see the right portion of Table 5). The amount of variance
accounted for by general mental ability seemed to be negligible,
but the variance accounted for by PA and NA was sizable.
Using the difference-in-coefficients approach for mediation test-
ing, we found similar results. Specifically, the difference-in-
coefficients estimates of indirect effect were .001 (95% CI [0,
.002]), .001 (95% CI [0, .002]), and .001 (95% CI [0, .007]) for
general mental ability at the three time points, respectively. Given
that all the three CIs included zero, it appears that general mental
ability mediated little, if any, of the genetic influences on job
satisfaction. PA and NA seemed to play a more important role in
mediating genetic influences. For PA, the difference-in-
coefficients estimates were .036 (95% CI [.019, .058]), .050 (95%
CI [.025, .078]), and .057 (95% CI [.026, .100]), respectively, at
the three time points. The estimates were .008 (95% CI [.001,
.020]), .026 (95% CI [.009, .052]), and .029 (95% CI [.009, .058])
for NA.
To address Research Question 1, we compared the magnitudes
of the mediating effects of the three individual difference variables
in the relationship between genetic factors and job satisfaction
over time. This was done with both the behavioral genetics
approach and the difference-in-coefficients approach. With the
behavioral genetics approach, as shown in the right portion of
Table 5, general mental ability did not mediate much of the
genetic influence on job satisfaction. Genetic factors associated
with PA explained appreciable amount of total variance in job
satisfaction at the three time points. The magnitudes of the
mediating effects of PA did not seem to change substantially
across time. NA’s mediating role in transmitting genetic influence
on job satisfaction did not change substantially either. Using the
difference-in-coefficients approach generated similar results. The
mediating role of the three individual difference variables did not
change substantially over time.
Overall, trait affectivity, especially PA, played a more important
role in mediating genetic influences on job satisfaction than gen-
eral mental ability. These results, based on bivariate analyses, were
confirmed in additional analyses incorporating four variables si-
multaneously in one model (three job satisfaction variables plus
each of the three individual characteristic variables).
We were also interested in examining environmental influences
related to occupational status (H5 and Research Question 2) and
interpersonal conflict (H6 and Research Question 3) on job satis-
faction over time, after controlling for genetic influences on all
variables in the model. Univariate biometric analyses revealed that
genetic factors accounted for 27.7% (95% CI [20.3%, 34.0%]),
40.8% (95% CI [35.2%, 46.8%]), and 46.5% (95% CI [38.8%,
53.3%]) of the variance in occupational status at the three mea-
surement times, respectively. Genetic factors also explained 22.9%
(95% CI [15.4%, 30.2%]), 14.7% (95% CI [7.6%, 22.5%]), and
18.1% (95% CI [10.0%, 27.1%]) of the variance in interpersonal
conflict at work for the three measurement occasions. Such find-
ings provided empirical evidence that measured, putative environ-
mental variables are subject to genetic influences. As a result, such
genetic influences needed to be partialed out when we examine
environmental (vs. person-related) influences on job satisfaction.
Bivariate biometric analyses (see Table 6) showed that with
genetic influences controlled for, environmental influences related
to occupational status influenced job satisfaction over time: Envi-
ronmental factors stemming from occupational status at the three
measurement times also significantly affected job satisfaction at
the corresponding time points (95% CIs for e
21
in Models 1, 2, and
3 were [.06, .17], [.01, .12], and [.05, .19]). Those environmental
factors accounted for approximately 1% of the total variance in job
satisfaction across the three time points (the right portion of Table
6). Such findings provided support to H5 and H6. Occupational-
status-related environmental influences on job satisfaction did not
appear to have changed. Similarly, with genetic influences con-
trolled for, environmental factors related to interpersonal conflict
Table 5
Results of Bivariate Biometric Analyses for Individual Characteristics and Job Satisfaction Over Time
Bivariate biometric models
Model fit indices Path coefficients estimates
Variance explained
by genetic factors
associated with
individual
characteristics (%)
2
(df)CFI TLI AIC RMSEA a
11
a
21
a
22
e
11
e
21
e
22
Estimate 95% CI
GMA with
Job satisfaction T1, Model 1 12.48 (20) 1.00 1.01 12229.84 .001 .80 .01 .56 .61 .02 .83 .1 [0, .2]
Job satisfaction T2, Model 2 18.87 (20) 1.00 1.00 12157.64 .001 .80 .01 .43 .61 .06 .90 .1 [0, .2]
Job satisfaction T3, Model 3 15.14 (20) 1.00 1.01 10392.14 .001 .80 .02 .44 .61 .01 .90 .1 [0, .4]
Positive affectivity with
Job satisfaction T1, Model 4 12.20 (20) 1.00 1.01 12372.46 .001 .68 .23 .51 .74 .11 .83 5.2 [2.6, 8.5]
Job satisfaction T2, Model 5 22.47 (20) .99 1.01 12193.60 .014 .68 .21 .37 .74 .23 .88 4.5 [2.1, 7.9]
Job satisfaction T3, Model 6 19.09 (20) 1.00 1.00 10564.71 .001 .68 .24 .37 .74 .07 .89 5.5 [2.4, 9.7]
Negative affectivity with
Job satisfaction T1, Model 7 19.38 (20) 1.00 1.00 12426.28 .001 .69 .10 .55 .73 .01 .83 1.0 [.1, 2.7]
Job satisfaction T2, Model 8 18.72 (20) 1.00 1.00 12296.42 .001 .69 .15 .41 .73 .12 .89 2.3 [.7, 4.9]
Job satisfaction T3, Model 9 16.52 (20) 1.00 1.00 10570.00 .001 .69 .16 .41 .73 .05 .90 2.6 [.8, 5.6]
Note. Sample sizes were 463 and 249 twin pairs for monozygotic and dizygotic twins, respectively. AE models were used in Cholesky decomposition.
Parameters a
11
,a
21
,a
22
,e
11
,e
21
, and e
22
denote paths presented in Figure 2. Path coefficient estimates below .10 are not significant at the .05 level; estimates
within the range between .11 and .26 are significant at the .05 level; and estimates larger than .27 are significant at the .001 level. T1 Time 1 (Age 21);
T2 Time 2 (Age 25); T3 Time 3 (Age 30). df degrees of freedom; CFI comparative fit index; TLI Tucker–Lewis index; AIC Akaike’s
information criterion; RMSEA root mean square error of approximation; CI confidence interval; GMA general mental ability.
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13
JOB SATISFACTION, GENETICS, ENVIRONMENTS, & CHANGE
at work significantly affected job satisfaction assessed at corre-
sponding time points (95% CIs for e
21
in Models 4, 5, and 6 were
[.22, .11], [.28, .17], and [.26, .14]). Those factors
explained an appreciable amount of variance (from 2.6% to 4.9%)
in job satisfaction over time, though the differences did not appear
significant. Similar results were obtained in additional analyses
with four variables included simultaneously in one model (i.e., the
three job satisfaction variables plus each of the two work environ-
mental variables). Taken together, the analyses indicated that
environmental influences on job satisfaction related to the two
work environmental variables did not seem to change over time.
Discussion
Due to its pivotal role in predicting work behaviors, job satisfaction
has been one of the most widely studied constructs in applied psy-
chology (Brief & Weiss, 2002; Dalal, 2013; Judge & Kammeyer-
Mueller, 2012; Locke, 1976; Spector, 1996). Researchers have de-
voted a great deal of attention to examining the antecedents of job
satisfaction, primarily from two contrasting perspectives—the dispo-
sitional and situational approaches. Mirroring this line of inquiry,
behavioral genetics research has contributed greatly to the nature-
versus-nurture debate, which is a variation of the person-versus-
environment debate. As a result, twin studies have been increasingly
adopted in applied psychology research to study how the person plays
an indispensable role in shaping work experiences as reflected by their
genetic influences, and more interestingly, the causal effects of envi-
ronmental influences after genetic influences are partialed out (e.g.,
Arvey et al., 1989; Ilies et al., 2006; Judge, Ilies, et al., 2012; Shane
et al., 2010).
With the emerging trend of developmental behavioral genetic
research (e.g., Briley & Tucker-Drob, 2013; Plomin & Deary,
2015; Turkheimer et al., 2014), applied psychology research has
lagged behind by not incorporating the role of time in examining
genetic influences on job satisfaction. In their recent review,
Bleidorn et al. (2014) pointed out that one important question that
developmental behavioral genetic research should tackle is, “Do
the relative contributions of genetic and environmental influences
on individual differences . . . change across the adult life span?” (p.
248). Responding to this call, the present research used a three-
wave longitudinal twin design to examine how genetic influences
on job satisfaction change over time and the role of individual
differences and work environment variables in those processes.
Our findings have important theoretical and practical implications
for job satisfaction research.
Theoretical Implications
Change of genetic influences on job satisfaction over time.
An important assumption in previous dispositional research on job
satisfaction was that the influences of individual characteristics on
job satisfaction are stable over time (Bouchard et al., 1992; Dor-
mann & Zapf, 2001; George, 1992; Staw & Ross, 1985). Such an
assumption of static influences of individual characteristics has
also been shared in other areas, such as job performance (Sturman,
2007). This is probably one important reason why researchers
examined or called for more research on dynamic job performance
(e.g., Dalal, Bhave, & Fiset, 2014; Lievens, Ones, & Dilchert,
2009; Sturman, Cheramie, & Cashen, 2005; Thoresen, Bradley,
Bliese, & Thoresen, 2004; Zyphur, Chaturvedi, & Arvey, 2008).
Such a focus on change over time has also been emerging in other
areas of applied psychology research, such as justice (e.g.,
Hausknecht, Sturman, & Roberson, 2011), extrinsic career success
(e.g., Judge, Klinger, & Simon, 2010), and job satisfaction (e.g.,
Chen, Ployhart, Thomas, Anderson, & Bliese, 2011; Liu, Mitchell,
Lee, Holtom, & Hinkin, 2012). Similarly, in this study, we directly
examined the assumption of stable genetic influences on job sat-
isfaction. We found that when participants were approximately 21
Table 6
Results of Bivariate Biometric Analyses for Work Environmental Variables and Job Satisfaction Over Time
Bivariate biometric models
Model fit indices Path coefficients estimates
Variance explained
by work
environmental
factors with genetic
influences controlled
for (%)
2
(df)CFI TLI AIC RMSEA a
11
a
21
a
22
e
11
e
21
e
22
Estimate 95% CI
Occupational status at
corresponding time point with
Job satisfaction T1, Model 1 10.01 (20) 1.00 1.00 11878.90 .001 .53 .03 .56 .85 .11 .82 1.2 [.3, 2.6]
Job satisfaction T2, Model 2 15.19 (20) 1.00 1.00 12080.83 .001 .64 .05 .43 .77 .07 .90 .4 [.1, 1.4]
Job satisfaction T3, Model 3 21.05 (20) .99 1.00 8536.42 .011 .68 .02 .44 .74 .12 .89 1.5 [.2, 3.5]
Interpersonal conflict at work at
corresponding time point with
Job satisfaction T1, Model 4 11.89 (20) 1.00 1.02 12383.04 .001 .48 .21 .52 .88 .16 .81 2.6 [1.2, 4.6]
Job satisfaction T2, Model 5 18.92 (20) 1.00 1.00 12070.47 .001 .39 .30 .33 .92 .22 .87 4.9 [3.0, 8.0]
Job satisfaction T3, Model 6 38.15
ⴱⴱ
(20) .87 .92 8525.54 .046 .43 .18 .40 .91 .20 .87 4.2 [2.1, 6.9]
Note. Sample sizes were 463 and 249 twin pairs for monozygotic and dizygotic twins, respectively. AE models were used in Cholesky decomposition.
Parameters a
11
,a
21
,a
22
,e
11
,e
21
, and e
22
denote paths presented in Figure 2. Path coefficient estimates below .06 are not significant at the .05 level; estimates
within the range between .17 and .13 are significant at the .05 level; and estimates larger than .14 are significant at the .001 level. T1 Time 1 (Age 21);
T2 Time 2 (Age 25); T3 Time 3 (Age 30). df degrees of freedom; CFI comparative fit index; TLI Tucker–Lewis index; AIC Akaike’s
information criterion; RMSEA root mean square error of approximation; CI confidence interval.
ⴱⴱ
p.01.
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14 LI, STANEK, ZHANG, ONES, AND MCGUE
years old, genetic influences explained 31.2% of the variance in
job satisfaction. However, genetic influences on job satisfaction
measured at Ages 25 and 30 were markedly smaller than those at
Age 21. The results did not support the prediction by Bouchard et
al. (1992) that genetic factors typically accounted for 30% of the
variance in job satisfaction across life span, nor the statement by
Dormann and Zapf (2001) that dispositional influences on job
satisfaction are stable over time. Furthermore, the findings did not
appear to provide support to the prediction from the gravitational
hypothesis (McCormick et al., 1972) or from the corresponsive
principle of personality development (Roberts et al., 2003), both of
which suggested increased genetic influences on job satisfaction
over time. However, the results were consistent with Staw’s (2004)
assertion that over time, genetic influences may be “diluted by
strong work situations” (p. 169). Work characteristics (e.g., Hack-
man & Oldham, 1975; Morgeson et al., 2012; Parker, 2014),
macroeconomic situations and labor market conditions (Judge,
Hulin, et al., 2012), and individual differences in perceptions of
objective environments may be possible explanations for such
environmental influences during early adulthood.
Although general mental ability has been suggested as an im-
portant mediator for genetic influences on job satisfaction (Arvey
& Bouchard, 1994; Ilies & Judge, 2003), previous research has not
examined this issue explicitly. Our findings suggest that general
mental ability measured at Age 17 played a very small role in
mediating genetic influences on job satisfaction at Ages 21, 25,
and 30. On the contrary, genetic factors associated with PA and
NA measured at Age 25 seemed to be more important in trans-
mitting the overall genetic influences on job satisfaction. The
differential mediation roles of general mental ability, PA, and NA
might be related to the fact that they were assessed at distinct
measurement time points. However, genetic influences mediated
by the three individual characteristics did not change substantially
over time while the overall genetic influences on job satisfaction
decreased with age. The results suggest that other individual dif-
ferences may play a mediating role in accounting for the decrease
in genetic influences on job satisfaction (Judge, Locke, & Durham,
1997; McCartney et al., 1990).
Environmental influences on job satisfaction over time.
The current study is characterized by an important feature, that is,
the effects of environmental factors were examined with genetic
influences controlled for. In investigating environmental influ-
ences on work attitudes, it is crucial to control for influences due
to selection (e.g., self selection and organization selection). This is
because individuals select, and/or are selected into, work environ-
ments based on their individual characteristics (Holland, 1996;
McCormick et al., 1972; Schneider, 1987; Wrzesniewski & Dut-
ton, 2001). In behavioral genetics, Plomin and colleagues called
the influences of selection on measured environmental factors “the
nature of nurture” (Plomin & Asbury, 2005, p. 89). It challenges
the assumption of environmental causation, which researchers
have typically tried to demonstrate by finding significant relation-
ships between measured environmental factors and outcomes of
interest (Judge, Ilies, et al., 2012). Also challenging the environ-
mental assumption are findings in many fields, including industrial
and organizational psychology, in which researchers have reported
significant genetic influences on what appear to be environmental
factors, such as leadership experiences (Arvey et al., 2007), work
stress (Judge, Ilies, et al., 2012), organizational climate (Hersh-
berger, Lichtenstein, & Knox, 1994), and work characteristics (Li,
Zhang, Song, & Arvey, in press). Put differently, research indi-
cates that differences in measured work environments are associ-
ated with differences in individuals’ genetic makeup. Indeed, at all
three measurement times, occupational status and interpersonal
conflict at work were influenced significantly by genetic factors.
At the same time, genetically informative research designs afford
a more refined examination of environmental causation. Because
influences from the person are separated from environmental in-
fluences in behavioral genetics approaches, we can more clearly
understand the influence of environmental factors by controlling
for influences from the person (Johnson et al., 2009). In this vein,
as Plomin et al. (1994) contended, “Genetics research provides the
best available evidence for the importance of nonheritable [envi-
ronmental] factors” (p. 1735).
After controlling for genetic influences, “pure” environmental
influences related to interpersonal conflict accounted for a signif-
icant amount of the variance in job satisfaction over time. Further,
the amount of explained variance did not significantly differ over
time. Environmental influences related to occupational status also
explained significant variance in job satisfaction, though to a lesser
extent. The difference may be related to the fact that occupational
status is deemed as an omnibus work characteristic that cuts across
positions one holds, whereas interpersonal conflict at work is more
job, organization, and even work-group specific (Johns, 2006).
Relevant specific and concrete work features may be more perti-
nent to job satisfaction than broad and general features. The
magnitude of variance explained by occupational status also did
not differ significantly across time. Put differently, environmental
influences from occupational status and interpersonal conflict at
work on job satisfaction seemed stable over the time points exam-
ined in this study.
These stable influences from occupational status and interpersonal
conflict at work are seemingly inconsistent with previous research,
which has shown that the effects of job redesign on job satisfaction
dissipated over time (Campion & McClelland, 1993; Champoux,
1978; Griffin, 1991). One critical reason for the disparity may be that
in this study, we measured the two work environmental variables and
job satisfaction repeatedly and at the same time points, whereas
previous research used work characteristics measured at only one time
point to predict job satisfaction over time. Indeed, inspection of
zero-order correlations in Table 1 suggested that the two work envi-
ronment factors had the highest correlations with job satisfaction
measured at the same time, and their correlations with job satisfaction
measured at later time points were smaller. This pattern of correlations
seems consistent with previous work design research on long-term
effects of job characteristics.
Similar to previous research on adult personality traits, intelli-
gence (Bouchard & Loehlin, 2001; Loehlin, 2007), and work-
related outcomes (Arvey et al., 2007; Judge, Ilies, et al., 2012;
Shane et al., 2010; Zhang, Zyphur, et al., 2009), we found negli-
gible influences of shared environmental factors in this study.
However, this does not necessarily mean that family environments
are not important.
1
As pointed out by Hoffman (1991), children of
the same family may experience and interpret the same objective
environment differently, resulting in an idiosyncratic, “subjective
1
We thank our Action Editor, Michael Sturman, for pointing this out.
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15
JOB SATISFACTION, GENETICS, ENVIRONMENTS, & CHANGE
family experience” (p. 187). As such, family environments are
critical to individuals’ development, which may be captured by
unique environmental (i.e., E) factors.
It is also noteworthy that we found negative relationships of
general mental ability with job satisfaction. Previous research has
shown both positive (e.g., Judge et al., 1999) and negative (e.g.,
Lounsbury, Gibson, Steel, Sundstrom, & Loveland, 2004) corre-
lations between the two variables. Researchers also proposed that
this relationship may even be nonlinear (Ganzach, Gotlibobski,
Greenberg, & Pazy, 2013). Future research should tackle this
question in greater depth.
Practical Implications
Findings from behavioral genetics research are often misinter-
preted, and we caution against any simplistic interpretation of our
findings. Results of the current research may have implications for
both organizations and employees. First, the change of genetic influ-
ences on job satisfaction over time provided an explicit demonstration
that we should not simply interpret genetics-related findings as indi-
cating that because a variable is highly genetically influenced, it is not
malleable (Gerhart, 2005). Our results suggest that genetic factors
explained less individual differences in job satisfaction at Age 25 and
Age 30 compared with Age 21. Put differently, the person seems to
play a less important role in shaping job satisfaction as employees
age. This finding also suggests that during early adulthood, employ-
ees’ job satisfaction levels may be more shaped by environmental
factors such as organizational practices, and that these external factors
may become increasingly important over time. In other words, em-
ployees appeared to become gradually more adaptive during this
period of time. Organizations may therefore have more room to
influence employees’ job satisfaction as employees progress through
early adulthood.
Do the diminishing influences of genetic factors on job
satisfaction mean that organizations can disregard individual
differences over time? The literature on person– environment fit
(Edwards, 2008; Kristof-Brown & Guay, 2010) suggests not.
The current results do not suggest so either. Instead, our find-
ings show that genetic influences on job satisfaction remained
significant over time. Researchers have called for more indi-
vidualized or personalized management practices that capitalize
on person– environment correlations (Judge, Ilies, et al., 2012;
Lawler, 1974; Rousseau, 2005). Along this line, our findings
suggest that organizations need to consistently consider em-
ployees’ individual characteristics when they strive to enhance
employee job satisfaction over time during early adulthood. Our
findings also suggest that PA was more important in mediating
genetic influences on job satisfaction over time than general
mental ability during early adulthood. In other words, PA
played a more important role in reflecting the influences of the
person. Thus, it seems that when organizations consider adopt-
ing individualized practices, they should pay more attention to
the importance of personality (e.g., PA) in customizing their
practices, rather than to general mental ability.
The finding that interpersonal conflict consistently explained
an important amount of the variance in job satisfaction suggests
that organizations and employees should endeavor to eliminate
negative interactions and improve positive relationships in or-
ganizations as a means of improving employees’ job satisfac-
tion. Given that effects of work design practices oftentimes do
not endure across time (Griffin, 1981), our finding has partic-
ular implications for organizations to enhance employee job
satisfaction from a longer period of time. That is, maintaining
lower levels of interpersonal conflict appears to be a useful
approach for fostering enduring, high levels of job satisfaction
across time. Among individual-differences variables, PA seems
most important in shaping job satisfaction over time. Previous
research drawing this conclusion often suggested utilizing per-
sonnel selection methods to hire individuals with elevated trait
levels of PA. This seems to be a reasonable recommendation for
organizations. On the other hand, recent personality research
has shown that levels of personality traits may change over time
(Judge, Simon, Hurst, & Kelley, 2014; Li, Fay, Frese, Harms, &
Gao, 2014; Roberts et al., 2003; Wu & Griffin, 2012). In this
vein, if organizations implement interventions to tune employ-
ees’ personality traits, organizations might better attract and
retain their talented members, which in turn may contribute to
long term organizational effectiveness.
Study Strengths, Limitations, and Future Research
The most important strength of our study was that we used a
three-wave longitudinal twin study design with data collected from
multiple sources (e.g., self-report, proctored assessment, interview,
and expert coding). Capitalizing on a longitudinal, natural quasi-
experiment of two types of twins (Plomin et al., 1994) enabled us
to address critical research questions about genetic and environ-
mental influences on job satisfaction over time, which have not
been addressed previously.
Still, this study was limited in several ways. First, using the
longitudinal twin database allowed us to examine only a limited
number of important predictors of job satisfaction. Although we
studied important individual difference variables (e.g., general
mental ability, PA, and NA) as well as omnibus and specific
work contextual variables, as suggested by previous research,
future research should examine other individual characteristics
(e.g., core self-evaluations [Judge et al., 1997] and the Big Five
factors and facets) as well as additional work characteristics
(e.g., job autonomy; Humphrey et al., 2007). Second, we only
studied overall job satisfaction in this study. Future research
could examine other job satisfaction variables (e.g., intrinsic
job satisfaction, satisfaction with pay). Third, in this study, PA
and NA were measured before the first job satisfaction variable
was assessed, and we examined them as antecedents of job
satisfaction. Although personality traits, especially their rank-
order stability, are unlikely to change dramatically over time
(Caspi et al., 2005; McCrae et al., 2000; Roberts & DelVecchio,
2000), we cannot rule out the possibility of reciprocal relation-
ships between individual characteristics and job satisfaction (Li
et al., 2014; Scollon & Diener, 2006; Wu & Griffin, 2012).
Fourth, as with any other research methodology, although the
twin methodology has its strengths in estimating the relative
potency of genetic and environmental influences on observed
variables, and has been embraced by many disciplines (e.g.,
Briley & Tucker-Drob, 2013; Cesarini, Johannesson, Magnus-
son, & Wallace, 2012; Fowler & Schreiber, 2008; Freese, 2008;
Ilies et al., 2006; P. Miller, Mulvey, & Martin, 1995), it also has
limitations. For example, the equal environments assumption in
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16 LI, STANEK, ZHANG, ONES, AND MCGUE
twin studies assumes that the similarity caused by environmen-
tal factors (including both family and societal influences) is
generally the same for both identical and fraternal twins. Em-
pirical research has shown that this is a reasonable assumption
for most observed variables (Bouchard & Propping, 1993; also
see Plomin et al., 2013). In addition, prenatal and neonatal
differences may somewhat affect estimates of genetic influ-
ences, because identical twins share the same chorion and thus
may experience different environmental differences than frater-
nal twins before they are born. However, such differences
would lead to the genetic influences identified in twin studies to
be underestimates (Plomin et al., 2013). Estimates of unique
environmental influences (E factors) often include measure-
ment error. In addition, it is possible that nonsignificant, shared
environmental effects observed in this study were caused by
range restriction (i.e., there was not sufficient variance in the
family environments of participants).
2
Acknowledging those
limitations, Zyphur, Zhang, Barsky, and Li (2013) nevertheless
concluded that “if researchers are interested in understanding a
broad sense of genetic influences on observed variables, tradi-
tional twin models are adequate” (p. 572). A fifth limitation of
the current study is that the participants were only in their 20s
and early 30s, and the findings may be specific to the economic
situations that the participants encountered (Hulin, Roznowski,
& Hachiya, 1985; Judge, Hulin, et al., 2012). Future research
should examine whether the same findings hold for employees
in other life stages and in other economic situations. Sixth,
although we outlined two mechanisms through which PA and
NA may affect job satisfaction, we were unable to distinguish
them empirically. Seventh, the measure of occupational status
in this study (Hollingshead, 1975; also see D. C. Miller, 1991),
although widely adopted in applied psychology and manage-
ment research (Judge et al., 1999; Lykken & Tellegen, 1996;
Zhang, Ilies, et al., 2009), is based on relatively broad catego-
ries of occupations, and thus does not provide fine distinctions
in socioeconomic status. Eighth, as pointed by one of our
anonymous reviewers, we were unable to study potential
changes of the genetic influences on the three individual dif-
ference variables at the three time points when job satisfaction
was measured. This prevented us from examining whether the
changes in genetic influences on job satisfaction over time were
related to possible changes in the genetic and environmental
influences on the three individual difference variables at the
same time points. This is an important direction for future
research. Lastly, we did not study specific genes related to job
satisfaction over time, as done in previous research (Chi, Li,
Wang, & Song, in press; Li et al., 2015; Song, Li, & Arvey,
2011). Future research can tackle this issue using a molecular
genetics approach.
Conclusions
Investigating how time affects the relationships studied in
applied psychology is a critical step toward building better
theories (George & Jones, 2000; Mitchell & James, 2001).
Given that time is considered as a “surrogate for environmental
stimuli” (Johns, 2006, p. 392), examining the changes of ge-
netic and environmental influences on job satisfaction at dif-
ferent points in time represents an initial step toward investi-
gating a form of gene– environment interaction on job
satisfaction. By examining both genetic and environmental an-
tecedents of job satisfaction from a temporal perspective, we
hope that this study can stimulate future longitudinal research
on more nuanced interplays between nature and nurture in
shaping job satisfaction and employee well-being.
2
We thank one of our anonymous reviewers for pointing this out.
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Received July 7, 2014
Revision received August 3, 2015
Accepted September 3, 2015
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22 LI, STANEK, ZHANG, ONES, AND MCGUE
... This perspective has received support from research on the changing genetic influences on some personality traits (Bleidorn et al. 2014, Eaves et al. 1986, Plomin & Spinath 2004). Using a three-wave longitudinal twin study, Li et al. (2015a) found that genetic influences on job satisfaction decreased from explaining 31.2% of the variance at age 21 to 18.7% and 19.8% of variance at ages of 25 and 30. Moreover, the same genetic factors were found to influence job satisfaction at each of the three time points, although with different magnitudes. ...
... This is because (a) genetic factors affect both job demands and well-being, and (b) work design research (or personality theories ) has suggested the causal direction from job demands (or the personality trait) to well-being (or job satisfaction). Similarly, Li and colleagues [Li et al. 2015a [Li et al. ,b, 2016; D. Li, Z. Song, Z. Zhang, and R. Arvey (submitted manuscript)] reported that genetic factors were mainly responsible for shaping the relationships of core self-evaluation with job demands, job control, and job complexity. Such genetic influences reflect influences from the person as a whole, probably through multiple processes of selection ( Johnson et al. 2009), including occupational selection (Holland 1996), organizational selection (Schneider 1987, Schneider et al. 1995), and/or job crafting (Wrzesniewski & Dutton 2001). ...
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Work design research typically views employee work characteristics as being primarily determined by the work environment and has thus paid less attention to the possibility that the person may also influence employee work characteristics and in turn accounts for the work characteristics–well-being relationships through selection. Challenging this conventional view, we investigated the role of a fundamental individual difference variable—people’s genetic makeup—in affecting work characteristics (i.e., job demands, job control, social support at work, and job complexity) and in explaining why work characteristics relate to subjective and physical well-being. Our findings based on a national US twin sample show sizable genetic influences on job demands, job control, and job complexity, but not on social support at work. Such genetic influences were partly attributed to genetic factors associated with core self-evaluations. Both genetic and environmental influences accounted for the relationships between work characteristics and well-being, but to varying degrees. The results underscore the importance of the person, in addition to the work environment, in influencing employee work characteristics and explaining the underlying nature of the relationships between employee work characteristics and their well-being.
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