Cross-Lagged Effects of Voluntary Job Changes and Well-Being: A Continuous Time Approach

Abstract

Well-being plays an important role in organizational entry and exit processes. However, longitudinal research on the relationship between voluntary job change and well-being is still sparse, and focuses on rather short time intervals (max. 3 years). Using 12 waves of the Household, Income, and Labour Dynamics in Australia (HILDA) survey, the present study extends previous research by examining whether and how well-being is affected by a voluntary external job change, and vice versa. We tested cross-lagged effects between voluntary job change and well-being (job satisfaction, vitality, sense of belonging) with a sample of 2,565 workers, and between job change and work–family conflicts as another indicator for well-being with a sample of 1,574 working parents. Results of continuous time modeling revealed that job change predicted decreased job satisfaction and vitality and increased work–family conflicts. Job change had no significant effect on the sense of belonging. The strongest relations between job change and well-being were observed in the first 5 years after an organizational entry (job satisfaction 1 year 2 months; vitality 4 years 4 months; work–family strains 3 years 5 months; sense of belonging 3 years 8 months). Job change had no significant effect on the sense of belonging. We also found partial support for reverse effects: Increased job satisfaction made a job change less likely (strongest effect after 2 years) and higher work–family conflicts more likely (strongest effect after 4 years). Thus, the results indicate when it is especially important to support newcomers to improve adjustment and prevent quitting.

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Running head: VOLUNTARY JOB CHANGES AND WELL-BEING
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Cross-lagged effects of voluntary job changes and well-being: A continuous time approach
Meike Sons and Cornelia Niessen
Friedrich-Alexander University Erlangen-Nürnberg
©"2021,"American"Psychological"Association."This"paper"is"not"the"copy"of"record"and"
may"not"exactly"replicate"the"final,"authoritative"version"of"the"article."Please"do"not"
copy"or"cite"without"authors'"permission."The"final"article"will"be"available,"upon"
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Author Note
Meike Sons and Cornelia Niessen, Work and Organizational Psychology, Friedrich-
Alexander University Erlangen-Nürnberg, Naegelsbachstr. 49c, 91052 Erlangen, Germany.
Correspondence concerning this article should be addressed to Meike Sons, Work and
Organizational Psychology, Naegelsbachstr. 49c, 91052 Erlangen, Germany. Phone: +49 9131
27448, E-Mail: meike.sons@fau.de
Acknowledgement: we sincerely thank Dr. Charles Driver for his helpful work on continuous
time modeling and his advice on the statistical analysis.

VOLUNTARY JOB CHANGES AND WELL-BEING 1
Abstract
Well-being plays an important role in organizational entry and exit processes. However,
longitudinal research on the relationship between voluntary job change and well-being is still
sparse, and focuses on rather short time intervals (max. three years). Using 12 waves of the
Household, Income and Labour Dynamics in Australia (HILDA) survey, the present study
extends previous research by examining whether and how well-being is affected by a voluntary
external job change, and vice versa. We tested cross-lagged effects between voluntary job
change and well-being (job satisfaction, vitality, sense of belonging) with a sample of 2565
workers, and between job change and work-family conflicts as another indicator for well-being
with a sample of 1574 working parents. Results of continuous time modeling revealed that job
change predicted decreased job satisfaction and vitality and increased work-family conflicts. Job
change had no significant effect on sense of belonging. The strongest relations between job
change and well-being were observed in the first five years after an organizational entry (job
satisfaction one year two months; vitality four years four months; work-family strains three years
five months; sense of belonging three years eight months). Job change had no significant effect
on sense of belonging. We also found partial support for reverse effects: increased job
satisfaction made a job change less likely (strongest effect after two years) and higher work-
family conflicts more likely (strongest effect after four years). Thus, the results indicate when it
is especially important to support newcomers to improve adjustment and prevent quitting.
Keywords: organizational socialization, turnover, continuous time modeling, well-being, work-
family conflicts

VOLUNTARY JOB CHANGES AND WELL-BEING 2
Cross-lagged effects of voluntary job changes and well-being: A continuous time approach
Job change has become a common feature of working life as career paths have shifted
from long-term employment in a single organization to career paths with greater job mobility
(Biemann, Zacher, & Feldman, 2012; Rigotti, Korek, & Otto, 2014). Researchers distinguish
between external (employees change organizations) and internal job changes (lateral moves
within the organization; Dunford, Shipp, Boss, Angermeier, & Boss, 2012), and between
voluntary movement across the membership boundaries of organizations and involuntary job
changes caused by anything other than the employee’s own initiative (e.g., termination by the
company; Hausknecht & Trevor 2011; Latzke, Kattenbach, Scheidhofer, Schramm, &
Meyrhofer, 2016). The present study focuses on external, voluntary job change and its
relationship with well-being across varying time intervals of the organizational entry and exit
process over a period of 12 years.
After entering a new job, job demands and uncertainties increase (Bauer & Truxillo,
2000; Bordia, Hunt, Paulsen, Tourish, & DiFonza, 2004; Bravo, Peiró, Rodriguez, & Whitely,
2003; Ellis, Bauer, Mansfield, Erdogan, Truxillo, & Simon, 2015), with negative consequences
for workers’ well-being (Bauer, Bodner, Erdogan, Truxillo, & Tucker, 2007; Ellis et al., 2015;
Dunford, et al., 2012). Over time, newcomers also gain resources (e.g., knowledge, skills, social
relations) that help them cope with the increased job demands (Ellis et al., 2015). Dunford and
colleagues (2012), for example, showed for a period of two years that burnout trajectories
increase after job change and then level off. Many socialization studies have examined
trajectories of newcomers’ outcomes over shorter time intervals, assuming that the largest
changes take place within the first four to six months (Cooper-Thomas & Anderson, 2005;
Lance, Vandenberg, & Self, 2000; Valero & Hirschi, 2019; Vandenberghe, Panaccio, Bentein,
Mignonac, & Roussel, 2011). Only a few studies have examined longer time periods up to two

VOLUNTARY JOB CHANGES AND WELL-BEING 3
years (Boswell, Boudreau, & Tichy, 2005; Dunford et al., 2012; Jokisaari & Nurmi, 2009;
Kammeyer-Mueller & Wanberg, 2003). Thus, previous studies have examined changes in
resources and of outcomes over rather short time intervals after a job change, therefore failing to
obtain a more complete picture of these trajectories over longer periods. Researchers are often
not able to choose the optimal time lag due to a lack of knowledge about temporal aspects
(Ashforth, 2012). Thus, our understanding of well-being trajectories and the underlying
processes of resource loss and gain after a job change is limited and fragmented, which is
problematic for two reasons. Firstly, it seems likely that the direction and strength of the
relationship between job change and well-being vary considerably according to the chosen time
period. Consequently, depending on which time period is selected in a study, well-being can
decrease, increase and or not change at all after job change. This can contribute to misleading
conclusions. Secondly, understanding when exactly well-being suffers is crucial for effective
prevention and on-boarding programs. Moreover, previous studies did not take possible reverse
effects into account, in that workers with impaired well-being might be more likely to quit their
jobs (Chen, Ployhart, Thomas, Anderson & Bliese, 2011, Jiang, Liu, McKay, Lee, & Mitchell,
2012). To examine the causal relationship between job change and well-being, we followed the
recommendation to test cross-lagged effects (e.g. Zapf, Dormann, & Frese, 1996) and build on
studies which have used this design (e.g., Meier & Spector, 2013; Mathieu, Kukenberger,
D'Innocenzo, & Reilly, 2015; Lang, Bliese, Lang, & Adler, 2011). This allows us to investigate
the contribution of both possible causal directions of the relationship between voluntary job
change and various indicators of well-being in a single model over a long period of time, again
providing a more complete picture of when it is especially necessary to support individuals’
well-being to prevent turnover.

VOLUNTARY JOB CHANGES AND WELL-BEING 4
The present study contributes to the literature on organizational socialization and
turnover in at least three ways. First, by analysing up to 12 annual waves of the Household,
Income and Labour Dynamics in Australia survey (HILDA, Wooden & Watson, 2002; 2012), we
aim to advance research on socialization by modeling changes in different indicators of well-
being (job satisfaction, vitality, work-family conflict and social belonging) at any given time
over 12 years with a continuous time approach (Oud & Jansen, 2000; Oud & Voelkle, 2014),
thus providing a more nuanced and complete picture about whether these indicators change in
sync over time or not.
Second, we aim to identify the specific timeframes of change, specifically, when a job
change has a positive and negative relation with well-being, and when job change stops having a
significant effect on different aspects of well-being. Providing insight into these trajectories over
a long period of time in the socialization process helps to identify the critical time periods and
key peaks of the process, so that future research can address more specific questions related to
specific time frames in their study design. Further, at least in terms of well-being, we can
observe if and when the socialization or adaptation process is completed after the change.
Third, we propose that the relationship between voluntary job change and well-being is
bidirectional. Studies on these two issues have typically focused exclusively on relationships in
one direction (Ellis et al., 2015; Hom, Lee, Shaw, & Hausknecht, 2017; Lee, Hom, Eberly, &
Mitchell, 2017; Rubenstein, Eberly, Lee, & Mitchell, 2018). In our study, we consider
bidirectional effects in a single model and control for the reverse relationship to obtain more
valid results about the direction and strength of the relationships.
Given the dynamic nature of organizational socialization and turnover processes and the
call to study changes in variables over time (Ellis et al., 2015; Halbesleben, Neveu, Paustian-
Underdahl, & Westman, 2014; Lee et al., 2017; Rubenstein et al., 2018), we chose continuous

VOLUNTARY JOB CHANGES AND WELL-BEING 5
time modeling (Oud & Voelkle, 2014; Voelkle, Gische, Driver, & Lindenberger, 2018). Well-
being develops continuously over time and is therefore not well described by discrete time
intervals (Ryan, Kuiper, & Hamaker, 2018), but by continuous time approaches which provides a
more natural representation of the processes, thus avoiding oversimplifications (Oud & Delsing,
2010). Moreover, continuous time approach allows the exploration of the unfolding of cross-
lagged effects across different time intervals without being forced to choose a priori exactly the
right time interval at which an effect occurs (Driver et al., 2017).
The relationship between voluntary job change and well-being
Meta-analyses over the last 20 years have consistently shown that voluntary job change is
related to experiencing stress (
!" = " .16
, Griffeth, Hom, & Gaertner, 2000;
"!
= .21, Rubenstein
et al., 2018;
!
= .23, Podsakoff, Whiting, Podsakoff, & Blume, 2009), and that well-being is
associated with turnover (
!" = " −.10;Bauer"et"al.,2007
), which is often explained with
resource theories, specifically conservation of resources (COR) theory (Hobfoll, 1989; 2001;
Ellis et al., 2015). A primary assumption of COR theory is that people strive to obtain, retain,
and protect resources. Resources are defined as “objects, conditions, personal characteristics, or
energies that are valued by the individual” (Hobfoll, 1989; p. 516) and contribute to success in
goal attainment (Halbesleben et al., 2014). Accordingly, stress occurs when individuals (a) lose
resources (e.g., competence as a result of role ambiguity), (b) fail to gain resources following an
investment of resources (e.g., self-esteem after putting effort into accomplishing new task
demands), or (c) are threatened with resource loss (e.g., social recognition by colleagues). COR
theory also includes a momentum component, which assumes that resource losses and gains have
a spiraling nature and change over time, albeit with a different pace, impact and duration
(Hobfoll, 1989; Halbesleben, et al, 2014; Hobfoll, Halbesleben, Neveu, & Westman, 2018).
Resource losses should accelerate more over time than resource gains. COR theory offers a

VOLUNTARY JOB CHANGES AND WELL-BEING 6
unifying theoretical framework to explain changes in the (bidirectional) relationships between
voluntary job change and well-being over time (Ellis et al., 2015) and integrate the literature on
organizational socialization and turnover. Building on these two bodies of literature, we
examined the following indicators of well-being: job satisfaction (a person’s affective reaction to
their job; Locke, 1976), vitality (the subjective experience of energy and liveliness; Ryan &
Frederick, 1997), work-family conflicts (mutually incompatible role pressures from the work and
family domains; Greenhaus & Beutell, 1985), and sense of belonging (desire for interpersonal
connection; Marshall & Barnett, 1993).
Voluntary job change (organizational entry)
à
job satisfaction
There is evidence that newcomers’ job satisfaction increases in the first year after a job
change (Boswell et al., 2005; Dunford et al, 2012) due to the contrast experienced between the
new job and previous one (the honeymoon effect, Boswell et al., 2005). However, soon after
organizational entry, job satisfaction starts to decline as the negative aspects of the new job,
which less noticeable immediately after entry, become more apparent - the so-called hangover
effect (Boswell et al., 2005; Boswell, Shipp, Payne, & Culbertson, 2009; Chadi & Hetschko,
2017; Georgellis, & Yusuf, 2016). After the hangover period, learning processes start, which,
according to COR theory (Hobfoll, 1989), can result in resource gains (e.g., knowledge, skills,
social relationships) and raise job satisfaction. We aim to replicate the honeymoon-hangover
pattern using a continuous time approach, which allows us to estimate the effects over 12 years.
Hypothesis 1a: After a voluntary job change, job satisfaction exhibits a positive linear
trend and a negative quadratic trend (increasing after entry, then decreasing and levelling out
over time).
Job satisfaction
à
voluntary job change (organizational exit)

VOLUNTARY JOB CHANGES AND WELL-BEING 7
Freeman (1978) suggested that job satisfaction might play a double role for job changes, as both
predictor and result. This is in line with meta-analytic findings showing that less satisfied
workers leave their organizations more often (e.g., Griffeth et al., 2000; Rubenstein et al., 2018)
and with turnover process models that propose a lack of job satisfaction as a major antecedent of
turnover (e.g., Lee & Mitchell, 1994; Hom & Griffeth, 1995; Lee, Mitchell, Wise, & Fireman,
1996; Steel, 2002). According to COR theory (e.g., Hobfoll, 1989), low job satisfaction can be
the result of a lack of resources (e.g., social support, work control), whereas high job satisfaction
is an expression of abundant resources and a good person-environment fit (e.g., Cooper-Thomas,
van Vianen, & Anderson; 2004, Louis, 1980). When individuals are less satisfied with their
work, they might invest less effort in protecting and gaining important resources, such as skills
or social relations, making a voluntary job change more likely. The present study aims to
replicate this relationship and proposes the following hypothesis.
Hypothesis 1b: Higher job satisfaction makes a voluntary job change less likely.
Voluntary job change (organizational entry)
à
vitality
Vitality refers to the subjective experience of energy and liveliness; individuals with high vitality
are enthusiastic, lively and attentive (Peterson & Seligman, 2004; Ryan & Frederick, 1997).
Vitality is often seen as a scarce energetic resource (Ten Brummelhuis & Bakker, 2012; Hobfoll
& Shirom, 2001). During socialization, employees need to invest effort to deal with new
challenges at work, which might drain their vitality. Moreover, through energy-consuming
proactive behaviours like information-seeking and feedback-seeking (e.g., Anseel, Beatty, Shen,
Lievens & Sackett, 2015; Ashford & Black, 1996; Bauer et al., 2007; Morrison, 2002)
newcomers aim to increase their resources, such as knowledge about appropriate role behaviors
that will help them adjust to their new job and role (Halbesleben, et al., 2014; Hobfoll, 1989).
Therefore, we propose that vitality should decrease during the early stages of the organizational

VOLUNTARY JOB CHANGES AND WELL-BEING 8
socialization process. With increasing organizational tenure, the newcomer gains more resources,
such as knowledge about tasks, roles, and organizational culture (Bauer & Erdogan, 2012; Ellis
et al., 2015), which can be seen as caravan passageway (Hobfoll, 1989), in that possession of one
main resource (e.g., knowledge) enables access to other resources (e.g., self‐efficacy). Thus, the
initial decrease in vitality after organizational entry should be followed by an increase in vitality.
Hypothesis 2a: After a voluntary job change, vitality exhibits a negative linear trend and
a positive quadratic trend (decreasing after entry, then increasing and levelling out over time).
Vitality
à
voluntary job change (organizational exit)
We also propose that workers’ vitality predicts voluntary job change. Vitality is
associated with career success (Baruch, Grimland, & Vigoda-Gadot, 2014), higher job
performance (Carmeli, 2009) and better mental health (Ryan & Frederick, 1997). As persons
strive to protect their resources and avoid potential strains (Hobfoll, 1989), workers with high
vitality should be less likely to leave their resource-rich organization. A recent meta-analysis by
Kleine, Rudolph, and Zacher (2019) found a small negative correlation between thriving, which
included vitality, and job changes (rc = -.29). Therefore, it seems reasonable that higher vitality
should make a voluntary job change less likely.
However, there is also evidence for the opposite effect, that vitality makes voluntary job
change more likely. In particular, workers with high vitality have high levels of energy resources
that they are willing to invest in job improvement and development (Fredrickson, 2001; Op den
Kamp, Tims, Bakker, & Demerouti, 2018). Moreover, a high amount of vitality is particularly
important for dealing with heightened job demands after a job change (Ellis et al., 2015).
Therefore, we assume that workers with high vitality have the energy to search for a new, even
better job and anticipate being able to deal with the new and challenging demands they will face

VOLUNTARY JOB CHANGES AND WELL-BEING 9
in their new job. Considering these effects in both directions, we formulate our assumption for a
positive effect of vitality on voluntary job change as a research question:
Research question: Does higher vitality make a voluntary job change more likely?
Voluntary job change (organizational entry)
à
work-family conflicts
Incompatible demands between work and family are a main cause of stress (Amstad,
Meier, Fasel, Elfering, Semmer, 2011; Gilboa, Shirom, Fried, & Cooper, 2008). Work-family
conflicts or strains are defined as a form of inter-role conflict involving mutually incompatible
role pressures from the work and family domains (Greenhaus & Beutell, 1985). Work-family
conflicts are positively related to strain (Nohe, Meier, Sonntag, & Michel, 2015, Amstad et al.,
2011) and to negative employee-related and work-related outcomes (Amstad et al., 2011), such
as lower job satisfaction, more strain at work and at home, and turnover intention. Interference
between work and family is often described in terms of role theory (Katz & Kahn, 1966), with
one’s role requirements at work incompatible with those in the family or vice versa. According
to COR theory (Hobfoll, 1989), a voluntary job change should make role interference more
likely, as work demands and family requirements compete for the same limited resource pool
(Greenhaus & Beutell, 1985). Studies of the investment of time resources have shown that
individuals are more likely to reduce family time to meet work demands than vice versa (e.g.,
Eagle, Miles, & Icenogle, 1997). This should be especially true at early stages of organizational
socialization, where time and energy resources must be invested to cope with the demands of the
job change. These resources are then not available for family demands such as housework or
childcare. As work pressures such as long working hours, role overload, extensive time demands,
overtime and evening work are associated with conflicts between the work and family roles (e.g.,
Byron, 2005; Michel, Kotrba, Mitchelson, Clark, & Baltes, 2011), a voluntary job change should
result in higher work-family conflicts. However, we also propose that with increasing

VOLUNTARY JOB CHANGES AND WELL-BEING 10
organizational tenure and more routine in one’s new job, this loss should reverse and level out
over time.
Hypothesis 3a: After a voluntary job change, work-family conflicts exhibit a positive
linear trend and a negative quadratic trend (increasing after entry, then decreasing and levelling
out over time).
Work-family conflicts
à
voluntary job change (organizational exit)
We also propose that higher work-family conflicts should make a voluntary job change
more likely. According to Greenhaus and colleagues (2001), individuals strive to reduce conflicts
between the work and family domains. One way to reduce such conflicts and conserve resources
is to change to a more family-friendly work environment (Boyar, Maertz, Pearson, & Keough,
2003). Metanalytic results provide evidence for the positive relation between work-family
conflicts and intention to leave an organization (Amstad et al., 2011). Since intention to leave is
the strongest predictor of job change (Griffeth et al., 2000), we assume that work-family
conflicts make a voluntary job change more likely.
Hypothesis 3b: Higher work-family conflicts make a voluntary job change more likely.
Voluntary job change (organizational entry)
à
sense of belonging
Sense of belonging is defined as a unique and subjective experience related to the need
for connection with others, the need for positive appreciation and the desire for interpersonal
connection, and arises from a perception of quality, meaning and satisfaction regarding one’s
social connections (Marshall & Barnett, 1993). Sense of belonging has been described as a
fundamental human motivation (Baumeister, DeWall, Ciarocco, & Twenge, 2005; Deci & Ryan,
2008) and is one of the most frequently mentioned resources within COR theory (Hobfoll et al.,
2018). Job change might also alter workers’ social system, which should be especially true
during the early stages of organizational socialization. Changing organizations often necessitates

VOLUNTARY JOB CHANGES AND WELL-BEING 11
a move, which might cause workers to lose contact with friends or colleagues from their old job.
Thus, employees must rebuild their social networks at work and in their private lives and gain
insight into the organization’s communication and interaction rules (Bauer & Erdogan, 2012;
Ellis et al., 2015; Fang, Duffy, & Shaw, 2011). Rebuilding networks is challenging for
newcomers and requires resource input (e.g., Ashford & Black, 1996), leaving workers with
fewer resources available to nurture their private social relations (e.g., time to meet with friends).
Therefore, we propose that a voluntary job change should be related to a decreased sense of
belonging at early stages of organizational socialization, but should diminish with increasing
organizational tenure as one adapts to the new environment and builds a social network within
the organization.
Hypothesis 4a: After a voluntary job change, sense of belonging exhibits a negative
linear trend and a positive quadratic trend (decreasing after entry, then increasing and levelling
out over time).
Sense of belonging
à
voluntary job change (organizational exit)
Sense of belonging is an important resource that workers seek to protect (Hobfoll et al., 2018;
Hobfoll, Freedy, Lane, & Geller, 1990) and should therefore make a voluntary job change less
likely. Individuals who care about their social ties thus tend to prefer stable personal
environments. They are afraid that a job change might have negative consequences for their
social network (Kirchmeyer, 2006). Furthermore, studies have shown that a high sense of
belonging at work is associated with lower intention to leave the organization (e.g., Rhoades,
Eisenberger, & Armeli, 2001). Organizational cultures with a high sense of belonging are
associated with high-quality social relationships characterized by mutual respect (Kahn &
Mentzer, 1998) and helping behaviors (e.g., Mossholder, Richardson, & Settoon, 2011).

VOLUNTARY JOB CHANGES AND WELL-BEING 12
Consequently, workers become more embedded in the organizational system, making job change
less likely (Mitchell, Holtom, Lee, Sablynski, & Erez, 2001).
Hypothesis 4b: Higher sense of belonging makes a voluntary job change less likely.
Method
Sample
The hypotheses were tested with longitudinal panel data from the Household, Income and
Labour Dynamics in Australia (HILDA, Wooden & Watson, 2002; 2012) survey. HILDA is an
ongoing survey of Australian households and persons (first wave in 2001). We selected 12
measurement waves from 2005 to 2016 and included all persons who participated at least in the
first and last measurement occasions and worked more than 20 hours per week. We excluded all
involuntary job changes from our analysis (job was temporary or seasonal; holiday job; got laid
off/no work available/retrenched/made redundant/employer went out of business/dismissed; self-
employed business closed down for economic reasons; Hausknecht & Trevor, 2011). In addition,
only working parents were included in the analyses investigating the relationship between job
change and work-family conflicts (Sample II, see Table 1). The size of Sample I was N = 2565,
with n = 974 women, an average age of 49.11 years (SD = 10.09) and an average of 0.80 job
changes (Min = 0; Max = 8) from 2005 to 2016 (Sample I, see Table 1). Work-family conflicts
were only measured in Sample II of working parents (N = 1574, of whom n = 614 were female,
average age of 50.10 years (SD = 9.27), with 2.09 children (SD =1.29; Min = 1; Max = 8) in
2016, with a mean number of job changes of 0.58 (Min = 0; Max = 6).
Measures and Testing for Longitudinal Measurement Invariance
The measures are presented in Table 2. Longitudinal measurement invariance tests were
used to examine the measures’ comparability across time (e.g., Finkel, 1995). In order to deal
with missing values in our dataset and the potential nonnormality of the measurement

VOLUNTARY JOB CHANGES AND WELL-BEING 13
instruments, we used robust full-information maximum likelihood estimation for model fitting
(Muthén & Muthén, 2010). At least configural (items assigned to their theoretical underlying
factor) and metric measurement invariance (relations between latent variable and items are
constant over time) are required to adequately test cross-lagged effects (Finkel, 1995; Hu &
Cheung, 2008; Little, Preacher, Selig, & Card, 2007). The configural invariance model serves as
the baseline model. Increasingly restrictive assumptions of longitudinal measurement invariance
are estimated using χ2 difference tests to determine whether the factor structure remains the same
over time (Byrne, Shavelson, & Muthén, 1989; Vandenberg & Lance, 2000). Table 3 displays
the results of the measurement invariance tests. Taken together, the results provide compelling
evidence for configural and metric invariance and sufficient evidence for full scalar invariance
for all measures across time. As a note on sense of belonging: model fit was improved by an
indicator-specific factor for the reverse-coded items, which encompasses stable item-specific
components that are not shared with the reference indicator and are stable across time (Crayen,
Geiser, Scheithauer, & Eid, 2011; Eid, 2000).
Data analysis strategy
We fitted an SEM-based continuous time (CT) cross-lagged panel model to our data
(Driver et al., 2017; Voelkle, Oud, Davidov, & Schmidt, 2012; Voelkle & Oud, 2013; Dormann,
Guthier, & Cortina, 2019), schematically illustrated in Figure 1. Continuous time models use
stochastic differential equations to account for unequal time intervals caused by different time
spans between measures due to unequal time intervals between measurement points or missing
data (Oud & Voelkle, 2014). The estimated effects are independent of the time lag (Driver et al.,
2017) and continuous time models are therefore ideally suited to handle complex data structures
like panel data with potentially some amount of missing data (Oud & Jansen, 2000). The time
independent estimates contribute to a better comparability and generalizability of the results

VOLUNTARY JOB CHANGES AND WELL-BEING 14
between studies (Driver et al., 2017) and provides information about the maximum impact values
and location in the cross-regression function (Ryan et al., 2018). Moreover, this analytical
approach also makes it possible to examine more dynamic associations among variables, like
cross-lagged panel effects. Examining how cross-effects evolve and vary as a function of the
time interval leads to a more dynamic and realistic representation of the reality and avoids
oversimplifications (Dormann et al., 2019). This makes the continuous time approach especially
well-suited for the examination of psychological phenomena and our research questions: how job
change relates to well-being over time and how well-being affects the likelihood of job change
(for details see Supplement). We fit the data using the open-source software R (version 3.2.2; R
Development Core Team, 2015) and the package ctsem (version 1.1.6, Driver et al., 2017),
which interfaces to OpenMx (Neale et al., 2016).
Results
Table 4 (Sample I) and Table 5 (Sample II) provides means, standard deviations,
intercorrelations, and sample sizes for the study variables. The results of the continuous time
models are summarized in Tables 6 to 9. Estimates of the auto- and cross-effects for both
processes are displayed in the drift matrix. The auto-effects of the drift matrix reflect the
processes’ temporal stability; negative values for auto-effects are typical in the continuous time
approach. The direction of the cross-effects in continuous time models is the same as in discrete
time (DT) models and may be positive or negative (Angraini, Toharudin, Folmer, & Oud, 2014).
Of primary interest for our hypotheses are the cross-effects of the drift matrix, which reflect the
interrelatedness of the processes (e.g., an increase in X leads to a decrease/increase in Y). We
analyse the significance of the effects using 95% likelihood-based confidence intervals. The drift
coefficients make it possible to estimate the auto-regressive and cross-effects for any chosen
time interval ∆t between 0 and 12 years. The cross-effects as a function of the time interval

VOLUNTARY JOB CHANGES AND WELL-BEING 15
between observations (0 <
∆
t < 12 years) are shown in Figures 2 to 5. Note that the continuous
time parameter estimates are reported only in the tables, whereas discrete time parameter
estimates are reported in the text. In order to predict the relationships between variables over a
given time period, the continuous time parameters are used to calculate the discrete time
parameters (Dormann et al., 2019). In Hypothesis 1a, we assumed that after a job change, job
satisfaction exhibits a positive linear trend and a negative quadratic trend. However, contrary to
our assumptions, we found the opposite effect: the negative effect of job change on job
satisfaction (coef. = -1.20) reached its maximum at a time interval of one year and two months
(coef. = -0.47) and weakened at time intervals greater two years on the job (cross-effect was
coef. = -0.26 for a time interval of three years, see Table 6, and Figure 2). Hypothesis 1b, that
higher job satisfaction makes a job change less likely, was supported, as the 95% confidence
interval did not include zero (see Table 6). For a discrete interval of 1 year, the cross-effect of
job satisfaction on job change was coef. = -0.04. As can be seen from Figure 2 (dotted line),
when job satisfaction increases, job changes become less likely, but this effect does not last long:
After two years, the likelihood of a job change increases again (coef. for three years = -0.02).
Hypothesis 2a proposed that after a job change, workers’ vitality exhibits a negative linear trend
and a positive quadratic trend. In line with this hypothesis, we found a significant cross-effect,
with a job change predicting decreased vitality after organizational entry, followed by a later
increase and flattening out as organizational tenure increases (Table 7, Figure 3). The cross-
effect for a discrete time interval of one year indicates a negative effect of coef. = -0.14, meaning
that a job change leads to decreased vitality across a one-year time lag. The negative cross-effect
increases further for time intervals of up to five years, before beginning to decline at even longer
time intervals (solid line in Figure 3). The value reaches its maximum of coef. = -0.30 after a
time interval of four years and four months. Thus, workers’ vitality decreases in the first four

VOLUNTARY JOB CHANGES AND WELL-BEING 16
years after a job change and then begins to rise slowly. Concerning our research question on
whether higher vitality makes a job change more likely, we found no significant effect (Table 7,
Figure 3). We examined Hypothesis 3a and Hypothesis 3b with Sample II of working parents. In
line with Hypothesis 3a, we found that job change predicts an initial increase in perceived work-
family conflicts and a decrease in perceived work-family conflicts over longer time intervals
(Table 8). Figure 4 shows that for a discrete interval of one year, the cross-effect of job change
on subsequent changes in work-family conflicts was substantially positive, with coef. = 0.36.
The increase reached its maximum at the discrete time interval of three years and five months
(coef. = 0.57) and decreased with longer time intervals (solid line in Figure 4). In line with the
assumptions of Hypothesis 3b, we found that increases in work-family conflicts were
significantly related to later job changes (Table 8). The cross-effect of work-family conflicts on
job change was significant, with coef. = 0.01 for a discrete time interval of one year (dotted line
in Figure 4). This cross-effect initially increased at larger time intervals (coef. = 0.021 with a
discrete time interval of three years) and started to decrease again for intervals greater than five
years (coef. = 0.020). Hypothesis 4a assumed that an initial increase in sense of belonging after a
job change is followed by a decrease, which levels out over time. The effect of job change on
subsequent changes in sense of belonging was negative, coef. = -0.12, for a discrete time interval
of one year. This effect was larger for longer time intervals, and reached its peak value at a time
interval of three years and eight months, with coef. = -0.16, before starting to decrease again
(solid line in Figure 5). However, the 95% confidence interval includes zero; therefore,
Hypothesis 4a was not supported by the data (see Table 9). Hypothesis 4b, that an increase in
sense of belonging is negatively related to a later job change, was also not supported by the data
(see Table 9).

VOLUNTARY JOB CHANGES AND WELL-BEING 17
Further, analyses revealed that the fit of the more complex (CT) cross-lagged panel model
improved over a standard (DT) cross-lagged panel model, and is thus the more appropriate
approach for this kind of data. As displayed in Table 10 the minus2LL for the fit of the more
complex (CT) model was better than the minus2LL for the fit of the standard (DT) model.
Discussion
The goal of the current study was to examine when the relationship between job change
and well-being is positive, negative and stop to develop. Processes as well-being develop over
time and are not well described by discrete time intervals (Ryan et al., 2018). Therefore, we
choose a continuous time approach, which makes it possible to explore the unfolding of cross-
lagged effects across different time intervals and provides information about the maximum
impact values and location in the cross-regression function (Driver et al., 2017). Specifically, we
investigated how a voluntary job change relates to different indicators of well-being and vice
versa at any given time over 12 years. Continuous time modeling revealed (1) the strongest
relations between job change and well-being in the first five years after an organizational entry
(job satisfaction one year two months; vitality four years four months; work-family strains three
years five months; sense of belonging three years eight months). (2) Divergent fluctuations in job
satisfaction, vitality and work-family conflicts across employees’ organizational tenure, and (3)
evidence for a partially bidirectional relationship between voluntary job change and well-being.
Theoretical and Practical Implications
Our study has several implications. First, a theoretical implication of our findings is that
impaired well-being after organizational entry, that is, a decline in vitality and increase in work-
family conflicts, lasts longer than assumed and studied in previous research (Bauer et al., 2007;
Rubenstein et al., 2018; Valero & Hirschi, 2019), up to five years. This can indicate that beyond
the actual socialization leading a newcomer to become an insider (Bauer et al., 2007; Feldman,

VOLUNTARY JOB CHANGES AND WELL-BEING 18
1981), a new job continues to pose challenges for the development of employees' professional
expertise, requiring the use of energetic resources, triggering further resource loss and leading to
conflicts with private life. Therefore, the reasons for the drop in well-being might change over
time. For example, in the beginning, dealing with new job demands and uncertainty about how to
fill one’s new role may affect well-being negatively. However, as socialization progresses,
insiders may be given new responsibilities that challenge their professional development, require
energy, and again conflict with their personal lives. Research on expertise emphasizes that
acquiring professional expertise (knowledge, skills) can take years (Sonnentag, Niessen, Volmer,
2006), and COR theory (Hobfoll, 1989) propose that resource gains build up more slowly than
the resource losses at the beginning. Future research should therefore investigate the specific
mediators in the relationship between job change and vitality and work-family conflicts in the
different time periods.
Second, we found that the indicators of well-being examined in this study showed
different dynamics over time. Unlike vitality and work-family conflicts, job satisfaction
decreased sharply after the first year, but started then to recover fast, while employees were still
reporting higher strain (decreased vitality, increased work-family strain). Although increasing
job satisfaction may indicate that newcomers have gained new resources (caravan passageway),
it remains important to maintain support opportunities from supervisors and organizations as
employees continue to experience strain. Furthermore, our results contradict previous research
on the honeymoon-hangover effect of job satisfaction. One reason might be that previous studies
examined specific transitions (public service sector, Boswell et al., 2009; self-employment,
Georgellis & Yusuf, 2016) or focused on specific samples such as high-level managers (Boswell
et al., 2005) that are more strongly associated with benefits such as job security, opportunities for
development, training, or a higher salary, affecting job satisfaction in the beginning positively

VOLUNTARY JOB CHANGES AND WELL-BEING 19
rather than negatively. Therefore, the link between voluntary job change and job satisfaction
might be more complex than previously portrayed, and the investigation of moderator variables
such as change in status (upwards, lateral, downwards; Louis, 1980; Nicholson & West, 1988)
seems important.
Third, our study was the first to also investigate the reverse effects of well-being on
voluntary job change (turnover) in a single model. In line with the resource conservation
principle within COR theory (Hobfoll, 1989), we found that low job satisfaction and high work-
family conflicts increase the likelihood of a voluntary job change. Thus, programs promoting job
satisfaction seem most promising to reduce voluntary job changes for at least a year, while the
time frame for reducing work-family conflicts is longer (up to five years). The fact that job
satisfaction and work-family conflicts are not only outcomes, but also antecedents of voluntary
job changes underscore the importance of supporting self-initiated behaviours (e.g., speaking up
if dissatisfied with aspects of one’s job). Further, we found no predictive effect of vitality on
voluntary job change. It might be that not only one’s energy level, but also motivational factors
(e.g., achievement motivation) and attitudes (e.g., career orientation) play a role in the decision
to quit one’s job.
Voluntary job change was not associated with sense of belonging. One explanation could
be the operationalisation of sense of belonging, which was not specific to the work context.
Meta-analytic findings by Jiang and colleagues (2012) that on-the-job characteristics play a more
important role than off-the-job characteristics in the job change process provide support for this
explanation. In sum, from a practical perspective, the results indicate that organizational
awareness about particularly “risky” time spans for declines in well-being makes it possible to
optimally position organizational support, support from leadership, and onboarding programs.
Limitations

VOLUNTARY JOB CHANGES AND WELL-BEING 20
This study has both strengths and limitations. With respect to the former, we used
extensive longitudinal data to examine the temporal dynamics of well-being in organizational
entry and exit as well as continuous time models, which have the advantage of accounting for
unequal time intervals within the data. Our approach provided a rare opportunity to study
individuals’ organizational socialization over time and across different industries and
professions, supporting the generalisability of the study results. At the same time, we
acknowledge several limitations. One limitation refers to the measurement of job satisfaction
using a single item, as space for comprehensive scales in panel surveys is limited. However, the
single-item measure has good face validity and is generally accepted by participants (Nagy,
2002). Despite the fact that the data were collected at different points in time, common method
bias might still be a concern, as the data were collected exclusively via a self-report
questionnaire (Podsakoff, MacKenzie, & Podsakoff, 2003). Although cross-lagged panel designs
are typically considered the optimal way to understand causality in field settings, they are less
able to establish causality than experimental designs. The possible influence of a nonconstant
third variable on the parameter estimates (common factor explanation) is still present (Finkel,
1995; Zapf et al., 1996). Although alternative explanations are possible, they should not lead one
to undervalue the evidence from cross-lagged panel models (Lang et al., 2011). Furthermore, the
application of a continuous time framework produces parameter estimates that are independent
of time lag, which circumvents the problem of time interval dependency in discrete cross-lagged
panel models (e.g., Kuiper & Ryan, 2018). The chosen continuous time model fits our data better
than a discrete time model. As a further limitation of the study design, we note that the
assessment intervals of approximately one year were rather long. While we provide some
insights into temporal dynamics by considering time as a continuous variable, future research
may benefit from conducting and integrating assessments at shorter time intervals, such as days,

VOLUNTARY JOB CHANGES AND WELL-BEING 21
weeks or months. In addition, future research might extend our efforts by also considering time-
varying covariates that could potentially influence the dynamics between voluntary job change
and well-being (e.g., changes in the labour market). In the present study, it was not possible to
investigate the most important underlying mechanisms (e.g., uncertainty, role ambiguity; Ellis et
al., 2015) of the relationship between voluntary job change and well-being because no
corresponding information was available in the HILDA dataset. Future research is needed to
systematically investigate the mechanisms, preferably using a continuous time approach.
However, to demonstrate how mediation can be tested in the continuous time modeling
approach, we have analysed, as an example, whether job complexity mediates the relationship
between job change and work-family strains (see supplement). In addition, future research
should consider age, gender, and personality as possible moderating variables. It is possible that
specific subgroups are less likely to experience declines in well-being.
Concluding, applying a continuous time modeling approach, our study highlights the
importance of time when examining organizational entry and exit. We found that the direction
and magnitude of the effects between voluntary job change and different outcomes of well-being
vary over time.

VOLUNTARY JOB CHANGES AND WELL-BEING 22
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VOLUNTARY JOB CHANGES AND WELL-BEING 36
Figures and Tables
Figure 1. Conceptual model. A continuous time perspective on a cross-lagged panel model. The
first three of the twelve time points for the two-process continuous time structural equation
model are illustrated. Occasions at which no observations are made are represented by latent
variables (circles). The processes influence one another continuously overtime.
Note. X = job change; Y = well-being variables (job satisfaction, vitality, work-family conflicts,
sense of belonging).

VOLUNTARY JOB CHANGES AND WELL-BEING 37
Figure 2. Cross-effects derived from the continuous time model for job change and work
satisfaction. The plot shows the cross-lagged parameters as a function of a time interval of 0 <
∆
t
< 12 years. Job change predicting subsequent changes in job satisfaction (solid) and changes in
job satisfaction predicting subsequent job change (dotted).
job satisfaction à job change
job change à job satisfaction
Years
job change – job satisfaction

VOLUNTARY JOB CHANGES AND WELL-BEING 38
Figure 3. Cross-effects derived from the continuous time model for job change and vitality. The
plot shows the cross-lagged parameters as a function of a time interval of 0 <
∆
t < 12 years. Job
change predicting subsequent changes in vitality (solid) and changes in vitality predicting
subsequent job change (dotted).
vitality à job change
job change à vitality
Years

VOLUNTARY JOB CHANGES AND WELL-BEING 39
Figure 4. Cross-effects derived from the continuous time model for job change and work-family
strains. The plot shows the cross-lagged parameters as a function of a time interval of 0 <
∆
t < 12
years. Job change predicting subsequent changes in work-family stains (solid) and changes in
work-family strains predicting subsequent job change (dotted).
work-family strains à job change
job change à work-family strains
Years

VOLUNTARY JOB CHANGES AND WELL-BEING 40
Figure 5. Cross-effects derived from the continuous time model for job change and sense of
belonging. The plot shows the cross-lagged parameters as a function of a time interval of 0 <
∆
t <
12 years. Job change predicting subsequent changes in sense of belonging (solid) and changes in
sense of belonging predicting subsequent changes in job change (dotted).
sense of belonging à job change
job change à sense of belonging
Years

VOLUNTARY JOB CHANGES AND WELL-BEING 41
Table 1
Description of the general sample (Sample I) and sample of working parents (Sample II):
Measurement waves from 2005 to 2016
Characteristics
Sample I
Sample II
N = 2565
Percent (%)
N = 1574
Percent (%)
Gender
Male
Female
1591
974
62.03
37.97
960
614
60.99
39.01
Mean age (2016)
49.11
50.10
Children
(Sample I 2016; Sample II 2005)
Yes
No
1900
665
74.07
25.93
1574
100
Measurement occasions
12
10 to 5
< 5
956
1563
46
37.27
60.94
1.79
647
901
26
41.11
57.24
1.65
Education
Master’s degree
Diploma
Bachelor’s degree
Advanced Diploma
Cert III or IV
12 years of school
11 years or fewer years of school
183
230
421
298
705
297
431
7.13
8.97
16.41
11.62
27.49
11.58
16.80
116
160
266
176
426
171
259
7.37
10.17
16.90
11.19
27.06
10.86
16.45

VOLUNTARY JOB CHANGES AND WELL-BEING 42
Industries
Electricity, gas, water and waste services
Retail trade
Transport, postal and warehousing
Administrative and support services
Healthcare and social assistance
Agriculture, forestry and fishing
Public administration and public safety
Manufacturing
Education and training
Arts and recreation services
Professional, scientific, technical services
Construction
Other services and or not stated
43
133
150
100
326
113
285
97
408
63
663
81
103
1.68
5.19
5.85
3.90
12.71
4.41
11.11
3.78
15.91
2.45
25.84
3.15
4.02
25
67
94
58
210
64
174
50
267
39
397
36
93
1.59
4.26
5.97
3.68
13.34
4.07
11.05
3.18
16.96
2.48
25.22
2.29
5.91
Types of job change
Not satisfied with job
867
42.32
436
47.18
Obtain a better job
974
47.54
389
42.65
Too much travel time
55
2.68
29
3.18
Self-employed
59
2.88
15
1.64
Own sickness, disability or injury
30
1.46
12
1.32
Change of lifestyle
26
1.27
22
2.41
Travel/have a holiday
38
1.85
9
0.99
Total number of job changes
2049
912
Note. The information on children in Sample I refers to the year 2016, whereas in Sample II it refers to
the year 2005. The sample of working parents overlap with the sample of working adults.

VOLUNTARY JOB CHANGES AND WELL-BEING 43
Table 2
Overview of the measures.
Measures
Assessment
Scale
Items
Reliability
Voluntary job change
Annually
(12 times)
0 = no change
1 = job change
“Do you still work for the same employer?”
“What was the reason for this change?” a)
(1) Not satisfied with job
(2) To obtain a better job/ wanted a change/ start a
new business
(3) Too much travel time/too far from public transport
(4) Self employed
(5) Own sickness, disability or injury
(6) Change of lifestyle
(7) Travel/ have a holiday
Job satisfaction
Annually
(12 times)
0 = totally
dissatisfied
10 = totally
satisfied
“How satisfied or dissatisfied you are with the work itself?”
Vitality SF-36 Health Survey
(Ware, 2000)
Annually
(12 times)
1 = all of the time
6 = none of the
time
“Do you feel full of life?”
“Do you have a lot of energy?”
“Do you feel worn out?” (revers coded)
α = .79
to
α = .81

VOLUNTARY JOB CHANGES AND WELL-BEING 44
Work-family strains
(Marshall & Barnett, 1993)
Annually
(12 times)
1 = strongly
disagree
7 = strongly
agree
“Because of requirements of your job, you miss out on
home/family activities.”
“Because of requirements of your job, family time is less
enjoyable/ more pressured.”
rkk* = .71
to
rkk* = .81
Sense of belonging
(Marshall & Barnett, 1993;
Weiss, 1974)
Annually
(12 times)
1 = strongly
disagree
7 = strongly
agree
“I often feel very lonely.” (revers coded)
“I have no one to lean on in times of trouble.” (revers coded)
“I dont have anyone that I can confide in.” (revers coded)
“I seem to have a lot of friends.”
“There is someone who can always cheer me up when Im
down.”
“When somethings on my mind, just talking with the people I
know can make me feel better.”
“People dont come to visit me as often as I would like.” (revers
coded)
α = .77
to
α = .81
Note. a) The following involuntary job changes were excluded: (1) Job was temporary or seasonal. (2) Got laid off/no work
available/retrenched/made redundant/employer went out of business/dismissed. (3) Migrated to a new country. (4) Self employed: business closed
down for economic reasons (went broke/liquidated/no work/not enough business). (5) Spouse/partner transferred. (6) To stay at home to look after
children/house/someone else. (7) Pregnancy/to have children. (8) Retired/did not want to work any longer. (9) Returned to study/started
study/needed more time to study. (10) Holiday job.

VOLUNTARY JOB CHANGES AND WELL-BEING 45
* Spearman-Brown coefficient.

VOLUNTARY JOB CHANGES AND WELL-BEING 46
Table 3
Maximum Likelihood CFA Model Fit (N = 2565 for vitality and sense of belonging; N = 1574 for work-family strains)
Invariance types
χ2
df
CFI
SRMR
RMSEA
Contrast
SB-Δχ2
Δdf
ΔCFI
ΔSRMR
ΔRMSEA
p
Vitality
Configural
2626.116
516
.960
.022
.041
Metric
2646.373
538
.960
.024
.040
2 vs. 1
20.321
22
.000
.002
.001
.563
Full Scalar
2824.517
560
.957
.023
.040
3 vs. 2
189.162
22
.003
.001
.000
.000
Sense of belonging
Configural
9681.794
3266
.938
.079
.028
Metric
9788.766
3332
.938
.079
.028
2 vs. 1
82.417
66
.000
.000
.000
.083
Full Scalar
10022.744
3398
.936
.079
.028
3 vs. 2
236.27
66
.002
.000
.000
.000
Work-family strains
Configural
1192.991
181
.905
.045
.069
Metric
1201.479
192
.906
.046
.067
2 vs. 1
09.6705
11
.001
.001
.002
.560
Full Scalar
1216.635
203
.905
.046
.065
3 vs. 2
17.7942
11
.001
.001
.002
.086
Note: df = degree of freedom; CFI = comparative fit index; SRMR = standardized root mean square residual; RMSEA = root mean square error of
approximation; SB-Δχ2 = differences in fit according to Satorra-Bentler. Due to the sensitivity of χ2 statistics to large sample sizes (Steenkamp &
Baumgartner, 1998), we examined model deterioration using CFI, SRMR and RMSEA in addition to global fit statistics (Hu & Bentler, 1999). As

VOLUNTARY JOB CHANGES AND WELL-BEING 47
indications of when measurement invariance is not given, we follow the criteria for fit statistics developed by Chen (2007) for N < 300. According
to these criteria, changes in RMSEA should be less than .015, changes in SRMR should be less than .010, and changes in CFI less than .010.

VOLUNTARY JOB CHANGES AND WELL-BEING 48
Table 4
Means, standard deviations, and correlations with confidence intervals N = 2565 (Sample I general sample)
Variable
M
SD
1
2
3
4
5
6
7
8
1. Sex
0.38
0.49
2. Age_T1
38.48
10.14
.02
3. JC_T1
0.10
0.31
.00
-.18**
4. Jobsat_T1
7.70
1.67
.04*
.08**
-.04
5. Vitality_T1
4.16
0.92
-.07**
.02
-.01
.24**
6. SenseBel_T1
5.28
1.05
.15**
-.07**
.01
.19**
.40**
7. Age_T2
39.92
9.94
.03
1.00**
-.16**
.08**
.03
-.05*
8. JC_T2
0.09
0.28
.00
-.14**
.14**
-.12**
-.01
.01
-.14**
9. Jobsat_T2
7.72
1.66
.04
.07**
-.00
.54**
.22**
.19**
.07**
.02
10. Vitality_T2
4.20
0.92
-.07**
.03
-.04
.17**
.65**
.33**
.03
-.01
11. SenseBel_T2
5.30
1.00
.17**
-.05*
-.02
.19**
.32**
.67**
-.05*
-.00
12. Age_T3
41.11
9.84
.04
1.00**
-.16**
.08**
.03
-.06**
1.00**
-.16**
13. JC_T3
0.08
0.27
-.01
-.14**
.10**
-.11**
-.04
-.03
-.14**
.12**
14. Jobsat_T3
7.73
1.66
.04
.09**
-.04*
.47**
.19**
.19**
.09**
.02
15. Vitality_T3
4.20
0.90
-.07**
.05*
-.05*
.19**
.61**
.31**
.05*
-.03
16. SenseBel_T3
5.31
1.02
.14**
-.06*
-.01
.18**
.33**
.64**
-.05*
.00
17. Age_T4
42.17
9.81
.06*
1.00**
-.18**
.07**
-.01
-.07**
1.00**
-.14**
18. JC_T4
0.08
0.28
-.02
-.17**
.14**
-.02
.02
.00
-.17**
.11**
19. Jobsat_T4
7.80
1.58
.02
.07**
-.04
.42**
.22**
.18**
.07**
-.00
20. Vitality_T4
4.17
0.90
-.06**
.02
-.06**
.20**
.59**
.32**
.03
.01
21. SenseBel_T4
5.32
1.00
.13**
-.05*
-.06**
.19**
.30**
.64**
-.03
-.01
22. Age_T5
43.25
9.91
.08**
1.00**
-.17**
.08**
.00
-.05*
1.00**
-.13**
23. JC_T5
0.05
0.21
-.01
-.13**
.06**
-.04
-.01
.00
-.15**
.04
24. Jobsat_T5
7.70
1.60
.02
.09**
-.04
.39**
.20**
.19**
.10**
.02
25. Vitality_T5
4.19
0.89
-.08**
.04
-.06*
.19**
.59**
.33**
.06*
-.04
26. SenseBel_T5
5.34
1.01
.14**
-.05*
-.05*
.20**
.31**
.63**
-.03
-.02
27. Age_T6
44.17
9.88
.05*
1.00**
-.17**
.07**
.01
-.05*
1.00**
-.14**
28. JC_T6
0.06
0.23
-.03
-.10**
.13**
-.01
.01
.00
-.09**
.08**
29. Jobsat_T6
7.69
1.60
.04
.09**
-.04
.36**
.17**
.17**
.08**
-.01
30. Vitality_T6
4.16
0.92
-.07**
.05*
-.03
.19**
.59**
.31**
.05*
.01
31. SenseBel_T6
5.30
1.00
.14**
-.04
-.01
.19**
.31**
.61**
-.03
-.03
32. Age_T7
45.24
9.86
.05*
1.00**
-.15**
.06**
.01
-.07**
1.00**
-.13**

VOLUNTARY JOB CHANGES AND WELL-BEING 49
Variable
M
SD
1
2
3
4
5
6
7
8
33. JC_T7
0.05
0.22
-.01
-.14**
.11**
-.02
.02
.00
-.13**
.13**
34. Jobsat_T7
7.75
1.61
.03
.08**
-.01
.34**
.18**
.19**
.09**
-.03
35. Vitality_T7
4.16
0.91
-.09**
.05*
-.05*
.18**
.56**
.31**
.05*
-.01
36. SenseBel_T7
5.36
1.03
.13**
-.01
-.02
.16**
.30**
.57**
-.00
-.02
37. Age_T8
46.00
9.93
.04
1.00**
-.17**
.09**
.01
-.07**
1.00**
-.13**
38. JC_T8
0.06
0.24
-.01
-.19**
.08**
-.06*
-.02
-.02
-.19**
.07**
39. Jobsat_T8
7.78
1.55
.05*
.11**
-.06**
.31**
.17**
.18**
.10**
.01
40. Vitality_T8
4.18
0.92
-.09**
.06**
-.04
.17**
.55**
.28**
.08**
-.02
41. SenseBel_T8
5.39
1.01
.15**
-.01
-.03
.17**
.32**
.62**
-.00
.00
42. Age_T9
46.80
9.97
.03
1.00**
-.17**
.07**
.02
-.06*
1.00**
-.14**
43. JC_T9
0.05
0.21
.00
-.15**
.06**
.00
-.00
-.02
-.15**
.10**
44. Jobsat_T9
7.77
1.56
.04
.10**
-.01
.33**
.18**
.17**
.10**
-.02
45. Vitality_T9
4.17
0.92
-.10**
.08**
-.04
.17**
.54**
.28**
.09**
.03
46. SenseBel_T9
5.36
1.04
.14**
.01
-.01
.19**
.29**
.59**
.02
-.01
47. Age_T10
48.01
9.87
.05*
1.00**
-.16**
.08**
.02
-.07**
1.00**
-.13**
48. JC_T10
0.04
0.20
-.01
-.12**
.04
-.04
-.03
-.00
-.12**
.08**
49. Jobsat_T10
7.80
1.53
.05*
.08**
-.03
.31**
.16**
.18**
.09**
-.00
50. Vitality_T10
4.13
0.94
-.08**
.08**
-.02
.13**
.52**
.24**
.09**
-.01
51. SenseBel_T10
5.30
1.01
.14**
.02
-.02
.17**
.29**
.60**
.03
-.01
52. Age_T11
48.85
10.04
.03
1.00**
-.18**
.07**
.01
-.07**
1.00**
-.13**
53. JC_T11
0.04
0.19
-.01
-.13**
.07**
-.03
-.04
.01
-.10**
.05*
54. Jobsat_T11
7.81
1.52
.02
.06**
-.01
.31**
.16**
.18**
.08**
.06*
55. Vitality_T11
4.13
0.94
-.09**
.05*
-.03
.16**
.54**
.26**
.07**
-.00
56. SenseBel_T11
5.36
1.05
.14**
.02
-.01
.15**
.29**
.57**
.04
-.02
57. Age_T12
49.11
10.14
.02
1.00**
-.18**
.08**
.02
-.07**
1.00**
-.14**
58. JC_T12
0.04
0.20
.00
-.13**
.04*
-.01
-.03
-.01
-.12**
.02
59. Jobsat_T12
7.83
1.50
.03
.08**
-.02
.32**
.17**
.19**
.09**
.03
60. Vitality_T12
4.12
0.95
-.11**
.08**
-.05**
.16**
.52**
.25**
.09**
-.02
61. SenseBel_T12
5.35
1.05
.13**
.03
-.01
.17**
.30**
.58**
.04*
-.02

VOLUNTARY JOB CHANGES AND WELL-BEING 50
Variable
M
SD
9
10
11
12
13
14
15
16
1. Sex
0.38
0.49
2. Age_T1
38.48
10.14
3. JC_T1
0.10
0.31
4. Jobsat_T1
7.70
1.67
5. Vitality_T1
4.16
0.92
6. SenseBel_T1
5.28
1.05
7. Age_T2
39.92
9.94
8. JC_T2
0.09
0.28
9. Jobsat_T2
7.72
1.66
10. Vitality_T2
4.20
0.92
.24**
11. SenseBel_T2
5.30
1.00
.18**
.33**
12. Age_T3
41.11
9.84
.07**
.03
-.06*
13. JC_T3
0.08
0.27
-.16**
-.04
-.01
-.14**
14. Jobsat_T3
7.73
1.66
.51**
.19**
.20**
.09**
-.00
15. Vitality_T3
4.20
0.90
.21**
.65**
.28**
.05*
.00
.24**
16. SenseBel_T3
5.31
1.02
.18**
.33**
.68**
-.06*
.00
.20**
.39**
17. Age_T4
42.17
9.81
.06**
.02
-.06*
1.00**
-.15**
.08**
.05*
-.05
18. JC_T4
0.08
0.28
-.10**
-.02
.02
-.16**
.17**
-.11**
-.01
.05
19. Jobsat_T4
7.80
1.58
.48**
.21**
.19**
.09**
-.06*
.55**
.24**
.19**
20. Vitality_T4
4.17
0.90
.19**
.63**
.28**
.04
-.01
.21**
.65**
.33**
21. SenseBel_T4
5.32
1.00
.21**
.32**
.66**
-.03
-.04
.20**
.33**
.70**
22. Age_T5
43.25
9.91
.06**
.04
-.05
1.00**
-.15**
.08**
.04
-.04
23. JC_T5
0.05
0.21
-.08**
-.03
-.02
-.14**
.13**
-.05
-.02
.00
24. Jobsat_T5
7.70
1.60
.42**
.18**
.20**
.09**
-.02
.46**
.22**
.20**
25. Vitality_T5
4.19
0.89
.19**
.61**
.31**
.05*
-.05*
.17**
.63**
.37**
26. SenseBel_T5
5.34
1.01
.20**
.30**
.65**
-.04
-.02
.21**
.33**
.68**
27. Age_T6
44.17
9.88
.07**
.03
-.04
1.00**
-.12**
.07**
.04
-.04
28. JC_T6
0.06
0.23
.01
.03
.03
-.09**
.10**
.01
.03
.02
29. Jobsat_T6
7.69
1.60
.37**
.15**
.18**
.09**
-.02
.45**
.20**
.19**
30. Vitality_T6
4.16
0.92
.19**
.60**
.28**
.05*
-.03
.19**
.61**
.34**
31. SenseBel_T6
5.30
1.00
.19**
.33**
.64**
-.05*
-.04
.18**
.31**
.67**
32. Age_T7
45.24
9.86
.06**
.04
-.04
1.00**
-.15**
.09**
.05*
-.04

VOLUNTARY JOB CHANGES AND WELL-BEING 51
Variable
M
SD
9
10
11
12
13
14
15
16
33. JC_T7
0.05
0.22
-.02
-.01
-.03
-.14**
.09**
-.02
-.03
-.01
34. Jobsat_T7
7.75
1.61
.38**
.13**
.20**
.08**
-.03
.43**
.19**
.21**
35. Vitality_T7
4.16
0.91
.18**
.58**
.26**
.05*
-.04
.17**
.61**
.32**
36. SenseBel_T7
5.36
1.03
.19**
.30**
.60**
-.00
-.06*
.18**
.28**
.63**
37. Age_T8
46.00
9.93
.08**
.03
-.05*
1.00**
-.14**
.09**
.05*
-.04
38. JC_T8
0.06
0.24
-.04
-.02
.01
-.18**
.07**
-.07**
-.02
-.00
39. Jobsat_T8
7.78
1.55
.35**
.16**
.21**
.10**
-.03
.41**
.22**
.20**
40. Vitality_T8
4.18
0.92
.18**
.57**
.25**
.06*
-.02
.19**
.59**
.31**
41. SenseBel_T8
5.39
1.01
.19**
.30**
.62**
.00
-.08**
.20**
.30**
.64**
42. Age_T9
46.80
9.97
.07**
.05*
-.04
1.00**
-.14**
.09**
.06*
-.05*
43. JC_T9
0.05
0.21
-.01
.02
-.02
-.14**
.12**
.02
.02
.03
44. Jobsat_T9
7.77
1.56
.34**
.14**
.20**
.10**
-.04
.40**
.18**
.16**
45. Vitality_T9
4.17
0.92
.19**
.57**
.27**
.08**
-.05*
.22**
.56**
.31**
46. SenseBel_T9
5.36
1.04
.19**
.30**
.61**
.01
-.06*
.22**
.26**
.63**
47. Age_T10
48.01
9.87
.07**
.04
-.04
1.00**
-.13**
.10**
.04
-.05*
48. JC_T10
0.04
0.20
-.06*
-.02
-.01
-.10**
.11**
.01
-.01
.03
49. Jobsat_T10
7.80
1.53
.32**
.14**
.23**
.08**
-.06*
.41**
.19**
.22**
50. Vitality_T10
4.13
0.94
.16**
.55**
.24**
.08**
-.01
.17**
.57**
.31**
51. SenseBel_T10
5.30
1.01
.20**
.29**
.60**
.03
-.05*
.23**
.27**
.63**
52. Age_T11
48.85
10.04
.07**
.03
-.06*
1.00**
-.14**
.09**
.04
-.05*
53. JC_T11
0.04
0.19
.04
-.00
-.00
-.10**
.03
.01
.00
.03
54. Jobsat_T11
7.81
1.52
.32**
.15**
.20**
.07**
-.03
.36**
.20**
.20**
55. Vitality_T11
4.13
0.94
.20**
.53**
.23**
.05*
-.05*
.18**
.56**
.30**
56. SenseBel_T11
5.36
1.05
.15**
.27**
.58**
.04
-.04
.17**
.27**
.59**
57. Age_T12
49.11
10.14
.07**
.03
-.05*
1.00**
-.14**
.09**
.05*
-.06*
58. JC_T12
0.04
0.20
-.05*
-.03
.02
-.11**
.03
-.01
-.03
-.01
59. Jobsat_T12
7.83
1.50
.33**
.14**
.21**
.08**
-.06**
.35**
.17**
.20**
60. Vitality_T12
4.12
0.95
.17**
.54**
.24**
.08**
-.04
.18**
.56**
.28**
61. SenseBel_T12
5.35
1.05
.17**
.29**
.60**
.02
-.05*
.19**
.30**
.61**

VOLUNTARY JOB CHANGES AND WELL-BEING 52
Variable
M
SD
17
18
19
20
21
22
23
24
1. Sex
0.38
0.49
2. Age_T1
38.48
10.14
3. JC_T1
0.10
0.31
4. Jobsat_T1
7.70
1.67
5. Vitality_T1
4.16
0.92
6. SenseBel_T1
5.28
1.05
7. Age_T2
39.92
9.94
8. JC_T2
0.09
0.28
9. Jobsat_T2
7.72
1.66
10. Vitality_T2
4.20
0.92
11. SenseBel_T2
5.30
1.00
12. Age_T3
41.11
9.84
13. JC_T3
0.08
0.27
14. Jobsat_T3
7.73
1.66
15. Vitality_T3
4.20
0.90
16. SenseBel_T3
5.31
1.02
17. Age_T4
42.17
9.81
18. JC_T4
0.08
0.28
-.17**
19. Jobsat_T4
7.80
1.58
.07**
-.00
20. Vitality_T4
4.17
0.90
.02
.02
.25**
21. SenseBel_T4
5.32
1.00
-.05*
.00
.19**
.39**
22. Age_T5
43.25
9.91
1.00**
-.18**
.07**
.02
-.03
23. JC_T5
0.05
0.21
-.14**
.12**
-.13**
-.06**
-.01
-.13**
24. Jobsat_T5
7.70
1.60
.09**
-.01
.56**
.23**
.20**
.09**
-.01
25. Vitality_T5
4.19
0.89
.04
.01
.23**
.67**
.38**
.04
-.03
.26**
26. SenseBel_T5
5.34
1.01
-.04
.01
.20**
.34**
.71**
-.05*
-.01
.21**
27. Age_T6
44.17
9.88
1.00**
-.16**
.06*
.01
-.03
1.00**
-.12**
.08**
28. JC_T6
0.06
0.23
-.06**
.04
-.05*
.01
-.02
-.11**
.12**
-.03
29. Jobsat_T6
7.69
1.60
.08**
-.00
.50**
.19**
.16**
.10**
-.00
.58**
30. Vitality_T6
4.16
0.92
.04
-.01
.22**
.65**
.34**
.05*
.00
.23**
31. SenseBel_T6
5.30
1.00
-.04
-.00
.20**
.34**
.71**
-.03
.01
.21**
32. Age_T7
45.24
9.86
1.00**
-.18**
.07**
.03
-.04
1.00**
-.13**
.09**

VOLUNTARY JOB CHANGES AND WELL-BEING 53
Variable
M
SD
17
18
19
20
21
22
23
24
33. JC_T7
0.05
0.22
-.13**
.11**
-.03
-.02
-.03
-.14**
.06*
-.06**
34. Jobsat_T7
7.75
1.61
.07**
-.04
.46**
.18**
.19**
.08**
-.04
.51**
35. Vitality_T7
4.16
0.91
.04
-.03
.19**
.63**
.30**
.05*
-.04
.20**
36. SenseBel_T7
5.36
1.03
-.01
-.03
.19**
.31**
.65**
-.00
-.03
.20**
37. Age_T8
46.00
9.93
1.00**
-.17**
.07**
.03
-.04
1.00**
-.15**
.08**
38. JC_T8
0.06
0.24
-.16**
.08**
-.02
-.00
-.03
-.17**
.11**
.02
39. Jobsat_T8
7.78
1.55
.10**
-.02
.41**
.21**
.20**
.09**
-.03
.46**
40. Vitality_T8
4.18
0.92
.06*
-.04
.19**
.61**
.33**
.06**
-.03
.20**
41. SenseBel_T8
5.39
1.01
-.01
-.01
.18**
.34**
.68**
-.01
.01
.19**
42. Age_T9
46.80
9.97
1.00**
-.18**
.07**
.02
-.04
1.00**
-.14**
.08**
43. JC_T9
0.05
0.21
-.13**
.07**
-.01
-.01
.01
-.14**
.09**
.03
44. Jobsat_T9
7.77
1.56
.10**
-.01
.42**
.18**
.18**
.10**
-.02
.43**
45. Vitality_T9
4.17
0.92
.08**
-.03
.20**
.60**
.33**
.08**
-.03
.19**
46. SenseBel_T9
5.36
1.04
.01
.01
.19**
.31**
.63**
.02
.01
.21**
47. Age_T10
48.01
9.87
1.00**
-.17**
.07**
.02
-.04
1.00**
-.13**
.09**
48. JC_T10
0.04
0.20
-.09**
.09**
-.03
-.03
.02
-.09**
-.01
.02
49. Jobsat_T10
7.80
1.53
.08**
-.01
.40**
.20**
.20**
.07**
.01
.46**
50. Vitality_T10
4.13
0.94
.07**
-.02
.15**
.58**
.28**
.07**
-.03
.16**
51. SenseBel_T10
5.30
1.01
.02
-.01
.20**
.31**
.63**
.02
-.01
.21**
52. Age_T11
48.85
10.04
1.00**
-.18**
.07**
.02
-.04
1.00**
-.15**
.09**
53. JC_T11
0.04
0.19
-.11**
.07**
-.04
-.01
.02
-.11**
.05*
-.01
54. Jobsat_T11
7.81
1.52
.06*
-.03
.39**
.18**
.20**
.06**
-.03
.40**
55. Vitality_T11
4.13
0.94
.05
-.00
.19**
.57**
.31**
.04
-.03
.20**
56. SenseBel_T11
5.36
1.05
.03
.00
.17**
.31**
.62**
.04
.00
.16**
57. Age_T12
49.11
10.14
1.00**
-.17**
.07**
.02
-.05*
1.00**
-.13**
.09**
58. JC_T12
0.04
0.20
-.11**
.08**
-.06**
.00
.03
-.13**
.03
-.01
59. Jobsat_T12
7.83
1.50
.07**
.02
.38**
.15**
.17**
.08**
.01
.41**
60. Vitality_T12
4.12
0.95
.07**
-.01
.19**
.58**
.29**
.07**
-.03
.19**
61. SenseBel_T12
5.35
1.05
.02
-.00
.18**
.30**
.63**
.04
-.02
.20**

VOLUNTARY JOB CHANGES AND WELL-BEING 54
Variable
M
SD
25
26
27
28
29
30
31
32
1. Sex
0.38
0.49
2. Age_T1
38.48
10.14
3. JC_T1
0.10
0.31
4. Jobsat_T1
7.70
1.67
5. Vitality_T1
4.16
0.92
6. SenseBel_T1
5.28
1.05
7. Age_T2
39.92
9.94
8. JC_T2
0.09
0.28
9. Jobsat_T2
7.72
1.66
10. Vitality_T2
4.20
0.92
11. SenseBel_T2
5.30
1.00
12. Age_T3
41.11
9.84
13. JC_T3
0.08
0.27
14. Jobsat_T3
7.73
1.66
15. Vitality_T3
4.20
0.90
16. SenseBel_T3
5.31
1.02
17. Age_T4
42.17
9.81
18. JC_T4
0.08
0.28
19. Jobsat_T4
7.80
1.58
20. Vitality_T4
4.17
0.90
21. SenseBel_T4
5.32
1.00
22. Age_T5
43.25
9.91
23. JC_T5
0.05
0.21
24. Jobsat_T5
7.70
1.60
25. Vitality_T5
4.19
0.89
26. SenseBel_T5
5.34
1.01
.41**
27. Age_T6
44.17
9.88
.03
-.04
28. JC_T6
0.06
0.23
.03
.04
-.10**
29. Jobsat_T6
7.69
1.60
.23**
.20**
.09**
.01
30. Vitality_T6
4.16
0.92
.67**
.34**
.05*
.03
.26**
31. SenseBel_T6
5.30
1.00
.36**
.71**
-.04
.00
.21**
.40**
32. Age_T7
45.24
9.86
.05
-.03
1.00**
-.08**
.08**
.04
-.04

VOLUNTARY JOB CHANGES AND WELL-BEING 55
Variable
M
SD
25
26
27
28
29
30
31
32
33. JC_T7
0.05
0.22
-.04
-.03
-.13**
.06**
-.07**
-.01
-.00
-.14**
34. Jobsat_T7
7.75
1.61
.20**
.22**
.08**
-.03
.57**
.21**
.24**
.08**
35. Vitality_T7
4.16
0.91
.62**
.31**
.04
.00
.20**
.67**
.32**
.05*
36. SenseBel_T7
5.36
1.03
.33**
.69**
-.02
.00
.19**
.35**
.72**
-.01
37. Age_T8
46.00
9.93
.04
-.05*
1.00**
-.10**
.10**
.04
-.05*
1.00**
38. JC_T8
0.06
0.24
-.02
-.03
-.18**
.15**
-.06**
-.00
-.01
-.18**
39. Jobsat_T8
7.78
1.55
.23**
.22**
.10**
.00
.49**
.20**
.21**
.10**
40. Vitality_T8
4.18
0.92
.61**
.33**
.06*
.04
.22**
.64**
.33**
.06*
41. SenseBel_T8
5.39
1.01
.35**
.70**
-.00
-.00
.20**
.34**
.73**
-.02
42. Age_T9
46.80
9.97
.03
-.05
1.00**
-.09**
.09**
.05*
-.05
1.00**
43. JC_T9
0.05
0.21
-.04
.01
-.15**
.10**
.00
-.01
.02
-.14**
44. Jobsat_T9
7.77
1.56
.19**
.21**
.09**
.02
.47**
.20**
.21**
.08**
45. Vitality_T9
4.17
0.92
.61**
.32**
.07**
.02
.20**
.63**
.32**
.07**
46. SenseBel_T9
5.36
1.04
.33**
.66**
.02
.01
.20**
.33**
.68**
.01
47. Age_T10
48.01
9.87
.02
-.05*
1.00**
-.11**
.09**
.05*
-.04
1.00**
48. JC_T10
0.04
0.20
-.03
.02
-.10**
.10**
-.00
-.06*
-.01
-.11**
49. Jobsat_T10
7.80
1.53
.20**
.24**
.07**
.03
.46**
.22**
.24**
.09**
50. Vitality_T10
4.13
0.94
.57**
.28**
.08**
.01
.18**
.61**
.29**
.07**
51. SenseBel_T10
5.30
1.01
.32**
.64**
.04
.01
.21**
.33**
.67**
.02
52. Age_T11
48.85
10.04
.04
-.06*
1.00**
-.09**
.09**
.05*
-.04
1.00**
53. JC_T11
0.04
0.19
.01
.00
-.12**
.06*
-.05*
-.04
-.01
-.13**
54. Jobsat_T11
7.81
1.52
.18**
.22**
.06*
-.02
.37**
.20**
.19**
.06**
55. Vitality_T11
4.13
0.94
.58**
.30**
.04
.03
.18**
.61**
.30**
.04
56. SenseBel_T11
5.36
1.05
.31**
.63**
.05*
.01
.19**
.32**
.67**
.03
57. Age_T12
49.11
10.14
.04
-.05*
1.00**
-.10**
.09**
.05*
-.04
1.00**
58. JC_T12
0.04
0.20
-.01
.01
-.11**
.09**
-.04
-.02
-.01
-.10**
59. Jobsat_T12
7.83
1.50
.15**
.20**
.08**
.03
.37**
.20**
.19**
.07**
60. Vitality_T12
4.12
0.95
.54**
.27**
.07**
.01
.17**
.57**
.28**
.07**
61. SenseBel_T12
5.35
1.05
.32**
.64**
.03
.01
.18**
.35**
.67**
.03

VOLUNTARY JOB CHANGES AND WELL-BEING 56
Variable
M
SD
33
34
35
36
37
38
39
40
1. Sex
0.38
0.49
2. Age_T1
38.48
10.14
3. JC_T1
0.10
0.31
4. Jobsat_T1
7.70
1.67
5. Vitality_T1
4.16
0.92
6. SenseBel_T1
5.28
1.05
7. Age_T2
39.92
9.94
8. JC_T2
0.09
0.28
9. Jobsat_T2
7.72
1.66
10. Vitality_T2
4.20
0.92
11. SenseBel_T2
5.30
1.00
12. Age_T3
41.11
9.84
13. JC_T3
0.08
0.27
14. Jobsat_T3
7.73
1.66
15. Vitality_T3
4.20
0.90
16. SenseBel_T3
5.31
1.02
17. Age_T4
42.17
9.81
18. JC_T4
0.08
0.28
19. Jobsat_T4
7.80
1.58
20. Vitality_T4
4.17
0.90
21. SenseBel_T4
5.32
1.00
22. Age_T5
43.25
9.91
23. JC_T5
0.05
0.21
24. Jobsat_T5
7.70
1.60
25. Vitality_T5
4.19
0.89
26. SenseBel_T5
5.34
1.01
27. Age_T6
44.17
9.88
28. JC_T6
0.06
0.23
29. Jobsat_T6
7.69
1.60
30. Vitality_T6
4.16
0.92
31. SenseBel_T6
5.30
1.00
32. Age_T7
45.24
9.86

VOLUNTARY JOB CHANGES AND WELL-BEING 57
Variable
M
SD
33
34
35
36
37
38
39
40
33. JC_T7
0.05
0.22
34. Jobsat_T7
7.75
1.61
.01
35. Vitality_T7
4.16
0.91
.01
.22**
36. SenseBel_T7
5.36
1.03
-.03
.19**
.37**
37. Age_T8
46.00
9.93
-.14**
.07**
.03
-.02
38. JC_T8
0.06
0.24
.11**
-.14**
-.04
-.03
-.19**
39. Jobsat_T8
7.78
1.55
-.01
.58**
.21**
.21**
.11**
.00
40. Vitality_T8
4.18
0.92
.00
.21**
.70**
.34**
.06**
-.01
.26**
41. SenseBel_T8
5.39
1.01
-.01
.23**
.35**
.74**
-.01
.01
.23**
.39**
42. Age_T9
46.80
9.97
-.17**
.06*
.03
-.02
1.00**
-.17**
.10**
.07**
43. JC_T9
0.05
0.21
.07**
-.04
-.01
-.01
-.16**
.21**
-.12**
-.00
44. Jobsat_T9
7.77
1.56
.01
.51**
.19**
.19**
.10**
-.02
.59**
.24**
45. Vitality_T9
4.17
0.92
.01
.17**
.67**
.31**
.07**
.00
.21**
.69**
46. SenseBel_T9
5.36
1.04
-.02
.21**
.34**
.71**
.01
.00
.21**
.34**
47. Age_T10
48.01
9.87
-.14**
.06*
.04
-.02
1.00**
-.18**
.10**
.05*
48. JC_T10
0.04
0.20
.07**
.01
-.03
.00
-.12**
.13**
-.02
-.03
49. Jobsat_T10
7.80
1.53
.00
.48**
.20**
.22**
.09**
.00
.53**
.22**
50. Vitality_T10
4.13
0.94
.03
.17**
.66**
.29**
.08**
-.02
.19**
.66**
51. SenseBel_T10
5.30
1.01
-.01
.22**
.33**
.68**
.03
-.02
.22**
.35**
52. Age_T11
48.85
10.04
-.14**
.08**
.05*
-.01
1.00**
-.21**
.10**
.07**
53. JC_T11
0.04
0.19
.13**
-.08**
-.04
-.01
-.12**
.13**
-.04
-.00
54. Jobsat_T11
7.81
1.52
.00
.41**
.18**
.22**
.06**
-.01
.45**
.21**
55. Vitality_T11
4.13
0.94
-.00
.20**
.64**
.32**
.05*
-.02
.23**
.63**
56. SenseBel_T11
5.36
1.05
-.05*
.19**
.29**
.66**
.02
-.03
.19**
.32**
57. Age_T12
49.11
10.14
-