Coping and mental health
Running head: COPING AND MENTAL HEALTH
Differing Effects of Coping Strategies on Mental Health
during Prolonged Unemployment: A Longitudinal Analysis
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Coping and mental health
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Abstract
A three-wave longitudinal design was used to examine the causal effects of two coping
strategies in the context of prolonged unemployment, namely job search and distancing (i.e.,
emotional detachment from unemployment), on mental health and the duration of these
effects. Two situational appraisals, namely economic hardship and unemployment negativity
(i.e., perceived negativity of unemployment life) were found to influence both coping
strategies and mental health. We confirmed that the negative effect of job search on mental
health was primarily due to the two situational appraisals, especially unemployment
negativity. In contrast, the positive effect of distancing on mental health was significant, even
after the situational appraisals were taken into account. In addition, the positive effect of
distancing on mental health lasted longer than the negative effect of job search.
Coping and mental health
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Differing Effects of Coping Strategies on Mental Health
during Prolonged Unemployment: A Longitudinal Analysis
Unemployment has a profound, negative influence on individuals, including decreased
psychological and physical well-being (see McKee-Ryan, Song, Wanberg, & Kinicki, 2005;
Prussia, Fugate, & Kinicki, 2001). Considerable research has examined how people cope with
unemployment, and the effectiveness of different coping strategies (e.g., Bennett, Martin,
Bies, & Brockner, 1995; Leana & Feldman, 1992; Wanberg, 1997). According to Lazarus and
Folkman (1984), coping serves two important functions: managing or tackling the problem,
and regulating the emotional distress experienced. Correspondingly, reemployment and
mental health constitute perhaps the two most important outcomes for evaluating coping
strategies for unemployment. Previous research has documented that problem-focused coping
(e.g., job search activities) rather than emotion-focused coping (e.g., distancing from
unemployment) enhances the probability of being reemployed (e.g., Kinicki, Prussia, &
McKee-Ryan, 2000; Wanberg, 1997). However, the number of chronically unemployed
people is large in many societies, and their mental health deserves attention and concern.
Unemployed people are more susceptible to mental health problems than their employed
counterparts (e.g., Lai, Chan, & Luk, 1997), which can add significant pressure to the health
care system of a society (Üstün, 1999).
The present study is primarily concerned with the effects of coping strategies on the
mental health of people in a state of prolonged unemployment in Hong Kong. McKee-Ryan
et al. (2005) have conducted a meta-analysis of the effects of different coping strategies on
mental health. Most studies involving coping strategies and mental health in an
unemployment context are based on a cross-sectional design, and causal directions cannot be
firmly ascertained. The first objective of the present study is to extend this literature by the
use of a longitudinal design to evaluate the causal effects of coping strategies.
Coping and mental health
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The second objective is to explore the duration of the effects of coping strategies on
mental health. The dynamic nature of coping is well recognized (Folkman & Lazarus, 1985;
Kinicki & Latack, 1990; McKee-Ryan et al., 2005), but it is not clear how long the effects of
different coping strategies may last in a prolonged unemployment context, and whether the
duration varies across different coping strategies. The present study also sheds light on these
important questions.
The Transactional Model of Stress and Coping (DeLongis, Folkman, & Lazarus, 1988;
Lazarus & Cohen, 1977; Lazarus & Folkman, 1984) provides an overarching theoretical
framework for the study. The model posits that how people appraise the environment
influences their mental health as well as their choice of coping strategies, which in turn
affects mental health. Although this model implies the mediating effects of coping strategies
on the relationship between situational appraisals and mental health, it also raises the
possibility that the effects of coping strategies on mental health may be partially explainable
by situational appraisals. Thus, the present study examined whether two situational
appraisals, economic hardship and unemployment negativity, may account for some of the
effects of coping strategies on mental health in the prolonged unemployment context. This
question is important because a better understanding of the interplay of situational appraisals,
coping and mental health is pivotal to theoretical advances and the design of effective
intervention strategies to help unemployed individuals.
Finally, we also consider the possibility of reciprocal causal relationships between coping
strategies and mental health, because reciprocal relationships between coping and mental
health in the context of unemployment are plausible (Kinicki & Latack, 1990; Latack,
Kinicki, & Prussia, 1995; McKee-Ryan et al., 2005). Our longitudinal design provides an
effective way to test such reciprocal relationships (Cole & Maxwell, 2003; Zapf, Dormann, &
Frese, 1996).
Coping and mental health
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Effects of Coping Strategies on Mental Health
Coping is defined as the cognitive and behavioural effort an individual makes to
contend with events appraised as stressful (Lazarus & Folkman, 1984). Coping strategies are
usually categorized into problem- or emotion-focused (Lazarus & Folkman, 1984). Problemfocused coping strategies represent the active attempts people make to reduce the stress they
face, whereas emotion-focused coping strategies direct at regulating emotional problems and
emotionally escaping from or avoiding stressful situations. Kinicki and Latack (1990)
identified a five-factor model of coping in the unemployment context, including proactive
search (i.e., job search activities), nonwork organization, positive self-assessment, distancing
from loss (i.e., distancing from unemployment in our study), and job devaluation. Job search
activities (or job search in short) and distancing from unemployment (or distancing in short)
represent two important strategies that have been typically used to operationalize problemfocused and emotion-focused coping strategies respectively in the context of unemployment
(Gowan, Riordan, & Gatewood, 1999). Job search refers to attempts to find a job and to focus
time and energy on job-seeking activities to seek reemployment. In contrast, distancing
represents a cognitive effort to escape the unpleasant reality of unemployment by trying not
to think about it, believing that time will take care of the situation, and the like. The present
research also focuses on these two coping strategies: One involving the proactive search for a
solution and the other entailing the avoidance of the stressor.
In the context of unemployment, distancing shows positive effect and job search shows
negative effect on mental health. For instance, Julkunen (2001) found that emotion-focused
coping including distancing was positively related to mental health across six-European
samples. The positive effect of distancing on mental health was also found in a Hong Kong
Chinese sample (Lai & Chan, 2002). In fact, the positive relationship between distancing and
Coping and mental health
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mental health was confirmed in McKee-Ryan et al.’s (2005) meta-analysis. In contrast,
several studies reported a negative relationship between job search and mental health (Leana
& Feldman, 1992; Wanberg, 1997). McKee-Ryan et al.’s (2005) meta-analysis also confirmed
the negative effect of job search on mental health.
The typical explanation for this pattern of effects is that job search requires time and
financial cost, and often results in rejection and frustration (Barber, 1998; Moynihan,
Roehling, Lepine, & Boswell, 2003). More often than not, job seeking brings discouragement
and a new source of stress for individuals in a state of prolonged unemployment. In contrast,
the distancing strategy may reflect an attempt of the unemployed people to adapt to their
current life, which may result in emotional stability.
While the above explanation is sensible, a methodological problem needs to be
addressed before its validity can be firmly established. Most previous findings concerning
coping strategies and mental health in an unemployment context are based on cross-sectional
designs (Grossi, 1999; Julkunen, 2001; Morrison, O'Connor, Morrison, & Hill, 2001; Pearlin,
Menaghan, Lieberman, & Mullan, 1981). Some studies (Gowan et al., 1999; Kinicki &
Latack, 1990; Lai & Chan, 2002) adopted a longitudinal design, but they did not test the
lagged effect of coping on mental health. Wanberg’s (1997) longitudinal study is an important
exception for establishing the causal effects of coping strategies. In a two-wave study,
Wanberg examined the effects of five coping strategies (job search, nonwork organization,
positive self-assessment, distancing from loss, and job devaluation) on synchronous mental
health and three-month lagged mental health. The results revealed that job search was
negatively related to synchronous mental health, but not significantly to three-month lagged
mental health when previous mental health was controlled for. In the absence of significant
lagged effects, the causal effect of job search cannot be firmly established. With regard to
distancing, it surprisingly did not show any significant effects on both synchronous and three-
Coping and mental health
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month lagged mental health. Her results deviated from the findings of most other studies, but
we note that her results for lagged effects were based on both unemployed as well as
reemployed individuals. In the present study, we used a longitudinal design to examine
whether or not the positive effect of distancing and the negative effect of job search would be
confirmed among individuals with prolonged unemployment.
Hypothesis 1: The use of distancing shows a positive causal effect on mental health
among people with prolonged unemployment.
Hypothesis 2: The use of job search shows a negative causal effect on mental health
among people with prolonged unemployment.
Duration of Coping Effects
Coping is widely viewed as a dynamic process that changes over time, which calls for a
longitudinal approach to examine how coping behaviours influence outcome variables over
time (Folkman & Lazarus, 1985; Kinicki & Latack, 1990; McKee-Ryan et al., 2005). Some
researchers argued that the effects of coping strategies on mental health are transient (Brenner,
Sorbom, & Wallius, 1985), because their long-term effects are hard to detect (Leana &
Feldman, 1995; Wanberg, 1997). In fact, as noted before, Wanberg (1997) did not find any
lagged effect of coping on mental health measured three months later in an unemployment
context. A major objective of the present research is to evaluate the duration of the effects of
coping with a three-wave longitudinal design.
Based on a comprehensive review of psychological and physiological studies, Taylor
(1991) concluded that negative events prompt people to exert more efforts to dampen their
impact than positive or neutral events. Taylor suggested several explanations for this pattern,
including: (1) when people are confronted with negative events that may threaten their selfconceptions, they try to reinterpret, distort, or minimize the information so as to maintain
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their self-esteem; (2) people resist negative moods, and spontaneously make an effort to get
out of bad mood; and (3) negative events elicit intensive causal and analytic reasoning, which
in turn helps a person take action to minimize or end the negative events.
Coming back to the unemployment context, the detrimental effect of job search on
mental health is commonly attributed to failure experiences and rejection during the jobseeking process (Lai & Chan, 2002; Wanberg, 1997; Warr, Jackson, & Banks, 1988). In
contrast, distancing helps unemployed people avoid such negative experiences and adapt to
their current life, which contributes to good mental health (Julkunen, 2001; Lai & Chan, 2002;
McKee-Ryan et al., 2005). Based on Taylor’s (1991) argument, because job search involves
unpleasant events, people should try to actively minimize the negative consequences of
rejection and critical feedback during job-seeking. Thus, the negative effect of job search
should be short-lived because of the active efforts that people make to minimize its impact. In
contrast, distancing is less likely to cause such kind of unpleasant events, and people are less
inclined to moderate and diminish its effects. As a consequence, the positive effect of
distancing on mental health should be more long-lasting than the negative effect of job search.
This reasoning suggests that the effect of job search is more likely to be contemporaneous,
whereas a lagged effect of distancing is more likely to be found.
Hypothesis 3: The positive effect of distancing on mental health lasts longer than the
negative effect of job search.
The Roles of Situational Appraisals
Although individuals with prolonged unemployment may suffer from their taxing
circumstances, they are not entirely passive and can react to unemployment in different ways
(Latack et al., 1995; Leana & Feldman, 1988; Leana, Feldman, & Tan, 1998). Transactional
model of stress and coping (DeLongis et al., 1988; Lazarus & Cohen, 1977; Lazarus &
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Folkman, 1984) provides an important theoretical framework for understanding the processes
involved. The model construes stressful experiences as person-environment transactions, and
when environmental demands tax or exceed people’s coping resources and give rise to an
imbalance, people will appraise the situation as negative and stressful. In the model, appraisal
is a core transactional variable and reflects the specific way an individual appraises an
environmental condition, which may be influenced by his/her personal characteristics
(Lazarus & Folkman, 1984). Situational appraisals are related to a problem’s severity and
extent of threat, and affect the mental health and well-being of individuals (Folkman, Lazarus,
Gruen, & De Lange, 1986). The model also theorizes that situational appraisals can influence
the coping strategies individuals choose to adopt (Folkman, Lazarus, Dunkel-Schetter,
DeLongis, & Gruen, 1986; Lazarus & Folkman, 1984). Because the transactional model
suggests that situational appraisals influence both mental health and coping behaviours, it
opens up the possibility that some relationships between coping strategies and mental health
may be caused by situational appraisals.
In the study, we operationalized situational appraisals as the appraisals of the severity
of the problems in the economic and psychological aspects of unemployment life, namely
economic hardship and unemployment negativity. Economic hardship arises when financial
resources cannot meet daily needs, which is a common experience for unemployed people,
especially the chronically unemployed (Kessler, Turner, & House, 1988; Liem & Liem, 1988).
Even if a person receives unemployment benefits from government, economic hardship is
still likely because the amount usually covers basic needs only. However, unemployed people
may feel different levels of economic hardship, depending on the extent of the imbalance
between their financial demands and the resources available to them.
Unemployment negativity is defined as how upset an individual is about being
unemployment by Wanberg and Marchese (1994). It can be viewed as a situational appraisal
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of the negative experiences associated with unemployment in psychological terms. People
who suffer from prolonged unemployment not only have to face job loss, but also everyday
life events. For unemployed people, many routine life events, such as quarrels among family
members and small conflict with neighbours, may turn into daily hassles that lower mental
health (e.g. Lazarus, 1984). As with economic hardship, unemployed people may experience
different levels of unemployment negativity depending on their specific circumstances. In
summary, economic hardship and unemployment negativity are salient and important
appraisals in a prolonged unemployment context. As a first attempt to explore the effects of
situational appraisals on the relationships between coping strategies and mental health, it
makes good sense to focus on these two constructs.
Negative situational appraisals are typically related to lower mental health (DeLongis et
al., 1988; Folkman, Lazarus, Gruen et al., 1986; Frese, 1987; Lazarus & Cohen, 1977;
Lazarus & Folkman, 1984; Vinokur & Schul, 2002). With regard to economic hardship,
Kessler, House, and Turner (1987) found that among three immediate consequences of
unemployment (marital conflict, loss of work relationship, and economic hardship),
economic hardship accounted for 90% of the explainable variance in mental health problems
related to unemployment. Price et al. (2002) found that economic hardship mediated the
relationship between job loss and poor health. The negative relationship between economic
hardship and mental health has been confirmed in the meta-analysis of McKee-Ryan et al.
(2005).
The negative effect of unemployment negativity is also well-known, as people who feel
negative about unemployment are likely to live an unhappy life and suffer from poor mental
health. Both Wanberg and Marchese (1994) and Wanberg et al. (1999) reported a negative
relationship between unemployment negativity and mental health. To replicate the wellknown effects of economic hardship and unemployment negativity on mental health, the
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following hypothesis is proposed:
Hypothesis 4. Economic hardship and unemployment negativity negatively affect
mental health during prolonged unemployment.
In transactional model of stress and coping, there is a general assumption that unless a
threat is clearly seen as insurmountable, the more severe a problem brought about by a
stressful encounter, the more likely that people would adopt problem-focused coping
strategies, i.e., face it and fight it (Folkman, Lazarus, Dunkel-Schetter et al., 1986). Economic
hardship should propel unemployed people to engage in intensive job search because they
want to escape from the economic stress associated with unemployment (Kinicki et al., 2000;
Leana & Feldman, 1995; Vinokur & Caplan, 1987). In a similar vein, unemployment
negativity raises people’s need for a job, and intensifies job seeking activities (Feather &
O’Brien, 1986). Seeking reemployment is an obvious way to put an end to the negativity
associated with unemployment.
Following Lazarus and Folkman’s (1984) theorizing, Gowan et al. (1999) proposed
that people with coping resources, e.g., social support and financial resources, are more likely
to emotionally distance themselves from the negative events, because they have no
imperative needs to solve the unemployment problem immediately. Although only the
positive effect of social support on distancing was statistically significant in their study, the
correlation between financial resource and distancing was in the expected direction. Similarly,
Kinicki et al. (2000) found that coping resources (including self-esteem, life satisfaction and
social support) were positively related with distancing. Coping resources are associated with
situational appraisals (Lazarus & Folkman, 1984): The more the coping resources, the less the
perceived severity of problems. In line with Gowan et al.’s (1999) theorizing, we predict
negative effects of economic hardship and unemployment negativity on distancing, because
these negative appraisals reflect the severity of the unemployment problem and the
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inadequacies of coping resources. In other words, low economic hardship and unemployment
negativity increase the tendency of distancing from unemployment.
Hypothesis 5a. Economic hardship and unemployment negativity negatively influence
the adoption of distancing during prolonged unemployment.
Hypothesis 5b. Economic hardship and unemployment negativity positively influence
the adoption of job search during prolonged unemployment.
The above arguments suggest that both economic hardship and unemployment
negativity are negatively related to both distancing and mental health, which raises the
possibility that the positive relationship between distancing and mental health documented in
many previous studies may be due to economic hardship and unemployment negativity. In
other words, these two variables function as third variables that can inflate the relationship of
two variables. In a similar vein, the two situational appraisals may also inflate the negative
relationship between job search and mental health frequently reported in previous studies
because they are negatively related to mental health, but positively to job search. Obviously,
mental health and coping strategies are determined by many factors, and it is unlikely that
these two situational appraisals can provide a full explanation for the effects of coping on
mental health. This reasoning is summarized in the following hypothesis.
Hypothesis 6a: The positive effect of distancing on mental health is partially due to
economic hardship and unemployment negativity.
Hypothesis 6b: The negative effect of job search on mental health is partially due to
economic hardship and unemployment negativity.
Methods
Participants and Procedures
The data reported in this study were part of a research program to examine the
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psychological, behavioural and social profiles of unemployed individuals receiving assistance
from the Comprehensive Social Security Assistance (CSSA) Scheme of the Hong Kong
Government. CSSA helps unemployed people meet their basic needs, and in order to
encourage self-reliance, the Hong Kong government introduced the “Support for Selfreliance” scheme, which made the granting of CSSA conditional on their engagement in job
search activities. In other words, the scheme required the unemployed recipients to participate
in work-like activities or training schemes for them to continue to receive CSSA, which was
intended to motivate CSSA recipients to achieve self-reliance through reemployment.
A three-wave longitudinal design with about three months between two waves was
adopted. Participation was voluntary, and they either filled out the survey at home or in an
unemployment services centre that they frequently visited. Because some participants’
education levels were low, administrators of the survey would provide an explanation of the
statements in the questionnaire if participants were unclear about their meaning. A total of
2,224 participants were surveyed in the first wave. The number of participants dropped to
1,446 in Wave 2, and 1,070 in Wave 3. Attrition was caused by various reasons, including
unwillingness to participate in the survey, loss of contact, and reemployment. Note that our
study context was primarily concerned with prolonged unemployment, and only those who
remained unemployed continued to be surveyed.
To evaluate the extent of the non-response bias, demographic characteristics of the
participants who responded to all the three waves were compared with those of the attrition
cases, i.e., those who only responded to the first or the first two waves. Chi-square tests
showed that there was no significant group difference in gender composition, χ2 (df = 1) = 1,
ns, but there was a significant difference in marital status, χ 2 (df = 3) = 11.92, p < .01,
showing a relatively lower proportion of married people in the attrition cases. The group
difference in education level was also significant, χ 2 (df = 6) = 29.21, p < .001, with the
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education level of the attrition cases being higher. In addition, one-way ANOVAs were used
to test the group differences in the continuous variables. Compared with the respondents, the
attrition cases were younger, F (1, 2222) = 62.79, p < .001 (mean age = 38.69 vs. 42.45), and
had a shorter duration of unemployment, F (1, 2156) = 6.51, p < .05 (mean length in years =
2.36 vs. 2.69), and hence a shorter duration of receiving unemployment benefits, F (1, 2222)
= 7.64, p < .01, (mean length in years = 1.32 vs. 1.55).
Although there were significant differences in some demographic characteristics
between respondents and the attrition cases, the problem of self-selection should not be
serious. Based on Goodman and Blum (1996), we used multiple logistic regression to assess
the presence of non-random sampling. The dichotomous variable (1 = respondents; 0 =
attrition cases) was regressed on all the variables of interest (i.e., mental health, job search
activities, distancing from unemployment, economic hardship and unemployment negativity)
measured in wave 1. No statistically significant logistic regression coefficients were found,
indicating no non-random sampling problem.
Because of the repetitive nature of a panel design, we were concerned that the tedious
process may have discouraged some participants from responding to all of the three waves of
the survey carefully. The first step in the analysis was to screen out problematic cases. We
dropped the cases that provided the same response to 25 or more consecutive items in a
survey (about 20% of the total number of items), because they were unlikely to be motivated,
and their responses were likely to be problematic. This criterion was used because it provided
a balance between maintaining the quality of the data and retaining a large portion of the
respondents. Note that the full questionnaire was quite long, including not only the
measurement items of the focal variables for the present study, but also items for evaluating
the CSSA programs which were not relevant to the current study. In the questionnaire, nearly
all constructs had both positively and negatively worded items. So we are quite confident that
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the deleted cases were problematic cases. All three waves of a respondent were assessed
independently, and if a respondent was problematic in one wave, we had to drop this case
because of the longitudinal design, resulting in a relatively high percentage of non-inclusion.
129 cases were screened out by this procedure, and 941 valid cases were available for further
analyses.
Among the valid unemployed cases, the majority were male (81%), and only 19% were
female. With regard to education level, 44.6% attended secondary school, 47.5% attended
primary school, 0.2% only attended kindergarten, and 6.1% had no schooling at all. Only
1.6% had some tertiary education. In terms of marital status, 51.8% were married, 34% single,
12.5% divorced, and 1.7% widowed. In Wave 1, the mean age was 42.53, the mean length of
unemployment was 2.59 years, and the mean length of receipt of unemployment benefits was
1.51 years. We also conducted a comparison of demographic characteristics between the
excluded (N = 129) and valid cases (N= 941). Chi-square tests showed that there was no
significant group difference due to gender, χ2 (df = 1) = 2.10, ns, education level, χ2 (df = 6)
= 10.87, ns, and marital status, χ2 (df = 3) = 6.24, ns. One-way ANOVAs showed that the
two groups were not different in age, F (1, 1068) = .17, ns, nor in length of unemployment, F
(1, 1040) = .79, ns.
Measures
Mental health. To keep the survey short, we randomly selected five items from
Goldberg’s (1972) 12-item general health questionnaire. They were “I have lost much sleep
over worry ”, “I have being thinking of myself as a worthless person”, “I have felt constantly
under stain”, “I have been feeling unhappy and depressed”, and “I have been feeling
reasonably happy, all things considered (reverse)”. Participants were asked to report how
frequently they experienced the conditions described in the statements in the past three
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months on five point scales (1 = always and 5 = never). A higher score represented better
mental health, and the Cronbach’s alphas of the mental health scale for the three waves were
acceptable: 69, .72, and .72.
Coping. The measures for the coping strategies, job search and distancing, were
adopted from the corresponding subscales of the coping scale developed by Kinicki and
Latack (1990). Two items were used to measure the active attempt to find a job: “focus my
time and energy on job search activities” and “talk with people who can help me find a job”.
The correlations of the two items were .42 (p < .01), .42 (p < .01), .38 (p < .01) for the three
waves respectively. Distancing was assessed by four items: “try not to think about
unemployment”, “tell myself that time usually takes care of situations like this”, “remind
myself that other people have been in this situation and that I can probably do as well as they
did”, and “remind myself that it isn’t the end of the world”. The Cronbach’s alphas of the
distancing scale for the three waves were acceptable, except for Wave 1: .60, .67, and .71,
respectively. Respondents were asked to rate the frequency of using each strategy to cope
with unemployment on 5-point scales, ranging from 1 (always) to 5 (never). We reversed the
scoring so that a higher score indicated a higher frequency of using each coping strategy.
Situational appraisals. Our study involved two situational appraisals. Economic
hardship was measured by one general item, “how do you evaluate your financial situation of
you and your family, if you cannot find a job in a short time and continue to receive
assistance from the Comprehensive Social Security Assistance (CSSA) Scheme”. Participants
responded on a five-point scale (1 = extremely bad; 5 = extremely good). A single item scale
is deemed adequate because the notion of economic hardship is simple and we only need a
summary judgment (Sackett & Larson, 1990). One item was also used to measure
unemployment negativity, which was adopted from Wanberg and Marchese (1994).
Participants were asked how much being unemployed was upsetting on a five-point scale (1=
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extremely upsetting; 5 = not at all). Again, because we only need a summary judgment on
unemployment negativity, and this construct seems straightforward, a single item scale
should be adequate (e.g., Sackett & Larson, 1990; Wanous, Reicheres, & Hudy, 1997).
Furthermore, the narrow scope of this construct is supported by Wanberg and Marchese
(1994), who used three items to measure this construct. They found that the Cronbach alpha
was above .90, suggesting high item redundancy (Boyle, 1991). We reversed the scoring of
the two variables so that a higher score indicated higher economic hardship and
unemployment negativity.
Confirmatory factor analysis. We first tested the measurement model based on all the
items of the focal variables in Wave 1 (13 items representing 5 variables: job search,
distancing, mental health, unemployment negativity, and economic hardship). AMOS 6 was
used, and the results showed satisfactory model fit: 2 = 250.58, df = 57, goodness-of-fit
index (GFI) = .96, comparative fit index (CFI) = .90, incremental fit index (IFI) = .90, root
mean square error of approximation (RMSEA) = .060. All items loaded significantly and
substantially on their intended constructs. The measurement models for Wave 2 and Wave 3
also provided a good fit to the data, supporting the subsequent tests of the structural models.
Analytic Strategies
Model specification. The models adopted in this study were based on Burkholder and
Harlow (2003) and Finkel (1995), which have the following features: (1) independent and
dependent variables are measured at all time points; (2) the measurement errors of the same
indicators across different waves are set as correlated; (3) the auto-regression of each
construct is taken into account. When predicting a dependent variable at Time t+1 by an
independent variable at Time t, the dependent variable at Time t is controlled for. The
unidentified variables that may affect the dependent variable at Time t+1 are likely to be
operative at Time t, and the controlling for the dependent variable at Time t is an indirect way
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54
to control for and reduce the effects of unidentified variables on the dependent variable at
Time t+1 (Cole & Maxwell, 2003; Zapf et al., 1996). (4) The model also controlls for the
contemporaneous (occasion-specific) covariance between the predictor and the outcome
variables by allowing the disturbance errors for different latent variables at the same time
point to correlate. Taking this covariance into account is important because it modells
unobserved occasion-specific influences, which may be present in each wave and inflate
time-lagged parameter estimates (Wiesner, 2003). If time-lagged effects emerge in the
presence of such stringent controls, they are indeed very robust.
Figure 1 shows the baseline model to test Hypotheses 1 to 3, and explore the possibility
of reciprocal relationships between two coping strategies (job search and distancing) and
mental health. Economical hardship and unemployment negativity were then added to the
model to test Hypothesis 4 to Hypothesis 5b. Hypotheses 6a and 6b were tested by
comparing the models before and after economical hardship and unemployment negativity
were included.
Note that causal language is used in the paper to simplify the presentation of the
findings, but we emphasize that our longitudinal design only provides strong, but not
definitive, evidence for the causal claims made.
__________
________________
INSERT FIGURE 1 ABOUT HERE
______________
____________
Time invariance. Before evaluating the hypotheses, we first tested the time invariance
of the measurement model in terms of factor loadings across the three waves. A model with
no constraints was compared with progressively more restrictive models. Results showed that
the model fit did not change significantly after the constraints were imposed, indicating that
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55
time-invariance of the measurement model was assumed. The final model provided a
satisfactory fit to the data, 2 = 1241.09, df = 463, GFI = .92, CFI = .90, IFI = .90, RMSEA
= .04. Therefore, all the models in the subsequent analyses included the time-invariance
constrains of factor loadings.
Results
Descriptive Statistics
_______________________
INSERT TABLE 1 ABOUT HERE
_______________________________
Table 1 presents the means and intercorrelations of mental health, job search, distancing,
economic hardship, and unemployment negativity across the three waves. Note that the
correlations between job search and mental health were all negative and significant and the
contemporaneous correlations were generally larger than correlations across waves. Thus, the
synchronous relationship between job search and mental health was stronger than their lagged
relationships, which was consistent with previous research (e.g., Wanberg, 1997). In contrast,
the correlations between distancing and mental health were all positive, but the
contemporaneous correlations were not larger than the lagged relationships. Economic
hardship and unemployment negativity were both negatively associated with mental health,
and positively with job search in each wave, confirming the necessity to control for these two
important situational appraisals in order to gauge the genuine relationship between job search
and mental health. However, the relationships between distancing and economic hardship as
well as unemployment negativity were trivial.
Note that although the demographic variables (gender, age, education level, length of
unemployment) were correlated with some focal variables, their influence did not change the
Coping and mental health
56
pattern of our main findings to be presented below1. We therefore did not include them in
subsequent analyses.
Hypothesis Testing
A set of models were compared, and the results of model comparison were used to
evaluate the hypotheses. Hypotheses 1 and 2, which are about the causal effects of job search
and distancing on mental health, and Hypothesis 3, which is about the duration of the effects,
were tested by a series of nested models shown in Figure 2. Following Frese, Garst, and Fay
(2007), small figures were presented to show models tested. First, the baseline model (Model
I-0 in Table 2 and Figure 2) included autoregressive paths, constrains of time invariance of
the factor loadings, within-occasion covariance between job search, distancing and mental
health, but no cross-lagged relationships between coping strategies and mental health. Second,
we tested models that included time-lagged effects based on these two hypotheses. Model I-1
added two time-lagged paths from distancing to mental health to the baseline model (i.e.,
distancing at Time 1 to mental health at Time 2, and distancing at Time 2 to mental health at
Time 3). Model I-2 added two time-lagged paths from job search to mental health to the
baseline model. Model I-3 included the time-lagged effects of both distancing and job search
on mental health. Third, to explore the possibility of reciprocal effects, we first evaluated
models with only the time-lagged effects that are opposite to those specified in our
hypotheses, i.e., from mental health to the two coping strategies. Model I-4 added the 3month lagged effects of mental health on distancing to the baseline model. Model I-5 added
the 3-month lagged effects of mental health on job search to the baseline model. Model I-6
included the 3-month lagged effects of mental health on both coping strategies. Finally, we
tested the reciprocal model in Model I-7, i.e., the two coping strategies and mental health
exerted cross-lagged effects on each other. To test whether the effects of coping strategies on
Coping and mental health
57
mental health can last 6 months, Model I-8 added the path from distancing at Time 1 to
mental health at T3 to the baseline model, and Model I-9 added the path from job search at
Time 1 to mental health at Time 3.
__________________
______________
INSERT FIGURE 2 and TABLE 2 ABOUT HERE
_______________
_________________
Table 2 shows that all the models fitted the data reasonably well, but only Model I-1,
which specifies 3-month lagged effects of distancing on mental health, and the reciprocal
model (Model I-7) yielded significantly better model fit than the baseline model (Model I-1:
△2 = 10.78, df = 2, p < .01; Model I-7: △2 = 19.47, df = 8, p < .05). However, because
Model I-7 was not significantly better than Model I-1, △2 = 8.69, df = 6, ns, Model I-1 is
preferred according to the parsimony principle (Kelloway, 1998). The comparison based on
the Akaike Information Criterion (AIC) between the two models also showed that Model I-1
is better than Model I-7 (AIC: Model I-1 = 1431.3; Model I-7 = 1433.6). In fact, in the
reciprocal model, only the 3-month lagged paths from distancing to mental health were
significant, and other time-lagged paths were not, thus yielding no support for reciprocal
causality. Since both Models I-8 and I-9 did not improve model fit significantly as compared
to the baseline model, the 6-month lagged effects of distancing and job search on mental
health were not supported.
Given that Model I-1 was the optimal model, we further tested whether the 3-month
lagged paths were equivalent across the two time lags (Time 1 to Time 2, and Time 2 to Time
3) in Model I-10. Results showed that the model with the invariance constraint (Model I-10)
did not differ from Model I-1 significantly, △2 = .63, df = 1, ns. The AIC of Model I-10
(1429.9) was also smaller than that of Model I-1 (1431.3), which supports the equivalence of
the causal effects across time.
Coping and mental health
58
In summary, Hypothesis 1 was supported: Distancing showed a time-lagged, positive
effect on mental health. This effect was only found in a three-month lag (i.e., from Time 1 to
Time 2, and from Time 2 to Time 3), but not in a six-month lag (i.e., from Time 1 to Time 3).
The causal effect of job search on mental health (Hypothesis 2) was not confirmed in
the time-lagged analysis. Because we hypothesize that the effects of job search are likely to
be short-lasting (Hypothesis 3), we explored its synchronous effects. We deleted the
contemporaneous correlations between job search and other latent variables in Model I-10,
which was the best model among Models I-1 to I-10, and added three synchronous paths from
job search to mental health (Model II). Note that those contemporaneous correlations were
deleted because in structural equation model, the disturbance of an endogenous variable
should be uncorrelated with that of its antecedent variables. This model yielded an acceptable
fit, 2 = 1378.26, df = 465, GFI = .91, CFI = .89, IFI = .89, RMSEA = .05, AIC = 1570.26.
The coefficients for the 3 month lagged effects of distancing and the synchronous effects of
job search on mental health were all significant. Distancing influenced 3 month-lagged
mental health, but not 6 month-lagged mental health. Job search did not affect time-lagged
mental health, but showed synchronous effects on mental health. Therefore, Hypothesis 3 was
supported in that the effect of distancing lasted longer than that of job search. The results for
model II are presented in Figure 3. For the sake of clarity, only the structural paths are shown.
________________________________
INSERT FIGURE 3 ABOUT HERE
________________________________
To test Hypotheses 4 to 6b, which are about the effects of economic hardship and
unemployment negativity, these two variables were added to Model II, which included threemonth lagged effects of distancing and synchronous effects of job search on mental health.
We first tested the 3-month lagged effects of these two variables on job search, distancing,
Coping and mental health
59
and mental health, and all the effects were found to be insignificant. We then tested models
with synchronous effects only. Figure 4 shows all the models we used to test Hypotheses 4 to
6b. The baseline model (Model III-0) includes all the variables without specifying any
relationship between the two situational appraisals (economic hardship and unemployment
negativity) and other variables. Model III-1 adds the paths from economic hardship and
unemployment negativity to mental health. To explore the effects of situational appraisals on
coping strategies, Model III-2 adds the paths from economic hardship and unemployment
negativity to distancing to the baseline model, and Model III-3 adds the paths from economic
hardship and unemployment negativity to job search to the baseline model. Finally, Model
III-4 includes all the paths from the two situational appraisals to coping strategies and mental
health.
The results are summarized in Tables 3. As shown, Models III-1 to III-4 all
significantly improved model fit relative to the baseline model. However, only Models III-4
yielded reasonably acceptable model fit, 2 = 1844.86, df = 653, GFI = .90, CFI = .89, IFI
= .88, RMSEA = .044. Its 2 was significantly smaller than the other models and its AIC
(2098.86) was the smallest. The results for the path coefficients of Model III-4 are shown in
Figure 5. Again, for the sake of clarity, we only present the structural paths. Figure 5 shows
that economic hardship and unemployment negativity were both negatively and significantly
related to mental health, which supports Hypothesis 4. Hypothesis 5a was not supported,
because the two situational appraisals did not show any significant effect on distancing.
Unemployment negativity, but not economic hardship, was positively related to job search,
providing partial support to Hypothesis 5b.
______________
_________________
INSERT TABLE 3 AND FIGURES 4 AND 5 ABOUT HERE
________________
________________
Coping and mental health
60
A comparison of Figures 3 and 5 suggests that Hypothesis 6b, but not Hypothesis 6a,
was confirmed. In support of Hypothesis 6b, two of the three paths from job search to mental
health became insignificant after economic hardship and unemployment negativity were
included. The third path, while still significant, was reduced drastically. Therefore, the
relationships between job search and mental health were primarily due to the two situational
appraisals, particularly unemployment negativity. The 3-month lagged effects of distancing
on mental health remained significant after economic hardship and unemployment negativity
were added, thus rejecting Hypothesis 6a. In other words, although economic hardship and
unemployment negativity influenced mental health, they did not change the relationship
between distancing and mental health significantly.
Discussion
Causal Effects of Distancing and Job Search on Mental Health
Although the effects of coping strategies on mental health during unemployment have
been consistently demonstrated in many studies, a few important issues have not been settled
because of some methodological ambiguities, such as the causality of the relationships and
whether the relationships are caused by factors that correlated with both coping strategies and
mental health. The longitudinal design and the stringent modelling approach of the present
study have provided some robust findings for settling these important issues. In a prolonged
unemployment context, the positive causal effect of distancing on mental health was
confirmed in a time-lagged manner. With respect to job search, only synchronous effects were
found, and lagged effects were absent. Furthermore, the synchronous effect was mostly
attributable to the two negative situational appraisals studied (economic hardship and
unemployment negativity), especially unemployment negativity.
The negative relationship between job search and mental health has frequently been
Coping and mental health
61
reported (Lai & Chan, 2002; McKee-Ryan et al., 2005; Wanberg, 1997; Warr et al., 1988).
The leading explanation for this relationship is that job search involves rejection and
frustration, which is deleterious to unemployed people’s mental health. Our results suggest
that this explanation is only part of the story, and the detrimental effects of job search during
unemployment are over-estimated. The negative relationship between job search and mental
health is inflated by the presence of negative situational appraisals, notably by unemployment
negativity in our case. Consistent with transactional model of stress and coping (DeLongis et
al., 1988; Lazarus & Cohen, 1977; Lazarus & Folkman, 1984), our results showed that
situational appraisals influenced both coping strategies and mental health. Appraisals about
the difficult life situations associated with unemployment trigger intensive job search, since
people in these predicaments have the immediate need to reduce the hardship quickly
(Feather & O'Brien, 1986; Leana & Feldman, 1995; Vinokur & Caplan, 1987). At the same
time, negative situational appraisals associated with unemployment also lower the mental
health of the unemployed (Kessler et al., 1987; Price et al., 2002; Wanberg et al., 1999;
Wanberg & Marchese, 1994). Our findings suggest that these relationships lead to the overestimation of the negative relationship between job search and mental health.
We note, however, that after taking into account economic hardship and
unemployment negativity, job search still showed some significant effect on mental health in
one of the three waves. This result suggests that we cannot completely rule out the possibility
that job search may partially mediate the effects of unemployment negativity and economic
hardship on mental health. In other words, it is possible that difficult life situations appraised
by unemployed individuals not only damage mental health directly, but also lower mental
health indirectly through job search. The mediating role of coping in the relationship between
appraisal and mental health has been implied in the transactional model of stress and coping
(e.g., Lazarus & Folkman, 1984), and more research is needed to see if coping can indeed
Coping and mental health
62
mediate the effects of negative situation appraisal on mental health.
The present study only included the situational appraisals of two negative aspects of the
unemployment experience (economic and psychological aspects). Other types of situational
appraisals may account for the residual relationship between job search and mental health.
For example, appraisals about the social aspect of the unemployment experience, such as
normative pressure from significant others, may further weaken or nullify the relationship
between job search and mental health. Besides appraisals about the severity of problems or
the extent of threat in different aspects of unemployment life, the appraisals about the
causality of unemployment and about the reversibility of unemployment may also play a
major role in the process. Further research should continue to explore other appraisals that
may inflate the relationship between job search and mental health of unemployed people.
Previous results generally support the positive effect of distancing on mental health in
the unemployment context (e.g., Julkunen, 2001; Lai & Chan, 2002), although some mixed
results have been reported (Kinicki & Latack, 1990; Wanberg, 1997). Our results concerning
the positive effect of distancing on mental health for people with prolonged unemployment
are robust, because of the use of a rigorous modelling approach. We note that there were
small positive correlations between job search and distancing, which suggests that the two
coping strategies may coexist. In other words, individuals may adopt the two coping
strategies simultaneously to cope with prolonged unemployment. Distancing has the adaptive
function of decreasing or assuaging the negative emotions evoked by unemployment, which
may in turn facilitate efforts to solve or manage the problems of unemployment. In fact, we
regard distancing as a relatively adaptive way to regulate negative emotions in out context,
and it should be distinguished from other dysfunctional emotion-focused coping strategies.
For example, Folkman, Lazarus, Dunkel-Schetter et al. (1986) identified two conceptually
similar emotion-focused coping strategies: Distancing and escape-avoidance. Their
Coping and mental health
63
“distancing” measure was consistent with ours and refers to efforts to detach oneself from the
situation and look at the bright side of things. In contrast, “escape-avoidance” is concerned
with wishful thinking (e.g., “wished that the situation would go away or somehow be over
with”) and efforts to escape from or avoid the situation (e.g., “tried to make myself feel better
by eating, drinking, smoking, using drugs or medication, etc.”; “slept more than usual”;
“avoided being with people in general”). It seems that in the unemployment context, the
effect of escape-avoidance on mental health is likely to be negative, which is worth further
exploration.
In addition, we found that appraisals related to difficult life situations (economic
hardship and unemployment negativity) were related to job search, but did not affect
distancing. Given the adaptive function of distancing in the context of prolonged
unemployment, the results do not seem surprising. We note that little research has examined
the antecedents of the distancing strategy during unemployment, with the exception of the
study by Gowan et al. (1999). They explored several antecedents of distancing, including
education level, financial hardship, and social support. As mentioned before, only social
support showed a positive relationship with distancing. In addition, gender differences in
coping with unemployment reported by Leana and Feldman’s (1991) suggest that distancing
may be related to personal dispositions. Future research should continue to explore the
antecedents of the distancing strategy in an unemployment context.
Taking advantage of our panel data, we explored the possibility of reciprocal
relationships between coping and mental health during prolonged unemployment. This idea
has been raised, but not tested (Kinicki & Latack, 1990; Latack et al., 1995; McKee-Ryan et
al., 2005). Our time-lagged results clearly support the unidirectional effect of distancing on
mental health. Regarding the relationship between job search and mental health, the lagged
effect in neither direction was not confirmed. Perhaps unemployed people’s coping styles are
Coping and mental health
64
relatively stable, which are not susceptible to fluctuations in their mental health. It is possible,
however, that drastic changes in mental health may alter one’s coping styles, which deserves
attention in future research.
Duration of Effects of Distancing and Job Search on Mental Health
As reviewed earlier, the relationship between coping and mental health is dynamic
(Kinicki & Latack, 1990; McKee-Ryan et al., 2005). Our results revealed that the effect of
distancing on mental health was identifiable after a three month lag, but not in six months. In
contrast, only synchronous, but not lagged, effect of job search was found. The results
support our prediction that the positive effect of distancing on mental health is more longlasting than the negative effect of job search.
The pattern of results makes sense, according to Taylor’s (1991) conclusion that people
are more active in reducing the impact of negative experiences than the impact of positive or
neutral events. The rejection and other negative experiences during the process of job seeking
will stimulate people to actively minimize the negative impact and duration of these
experiences. On the contrary, the distancing strategy implies that unemployed people try to
adapt to their current life, which is cushioned by the unemployment welfare benefits. Thus,
the positive effect of distancing on mental health is relatively long-lasting because it decays
naturally without any effortful intervention.
The results shed some light on the issue of “true time lag”. Several scholars (e.g., De
Lange, Taris, Kompier, Houtman, & Bongers, 2004; Taris, 2000) observed that most
longitudinal studies pay little attention to an important issue, the appropriate time lag between
a cause and an effect, which may cause biased results. If the time lag is too long, it is possible
that the effect of a cause does not last long enough for its effect to be detected. If the time lag
is too short, however, the predictors may have insufficient time to exert their influence on
Coping and mental health
65
outcome variables. The present study has addressed the issue in the context of prolonged
unemployment by having three waves of measurement, and suggested that coping effects do
not last for six months. In the future, shorter durations, such as one to two months, should be
examined to track the duration of the coping effects more precisely.
Practical Implications
Chronic unemployment is common among unemployed people. For instance, among
the unemployed population, those with unemployment for more than 52 weeks amounted to
10% according to the data provided by the Bureau of Labour Statistics in US in 2006. In our
study, less than 15% of unemployed individuals got reemployed during the study period.
Obviously, prolonged unemployment is highly undesirable, but relevant research points to a
worry that an active search for reemployment may subject chronically unemployed people to
more rejection and frustration, thus damaging their already fragile mental health. However,
the present study shows that job search per se does not exert a heavy toll on mental health,
and that its negative impact is short-lasting. The results also show that perceived difficulties
associated with unemployment, namely, economic hardship and unemployment negativity,
contribute to the deterioration of mental health. Given that job search can improve the
possibility of reemployment (e.g., Kinicki et al., 2000; Wanberg, 1997), it should be
promoted. Social institutions that try to help unemployed people should motivate them to
engage in job search, but our findings suggest that to reduce the small and fleeting negative
impact of job search on mental health, these social institutions should find ways to reduce
their unemployment negativity and economic hardship.
Our findings show that distancing has a salutary effect on mental health, confirming the
usefulness of this emotion-focused coping strategy for people to adapt to prolonged
unemployment life. However, distancing does not help reemployment, and unemployed
Coping and mental health
66
people may be trapped in unemployment if they only emotionally distance themselves from
unemployment. Social institutions for helping the unemployed people need to find ways to
drive people to actively seek for a job, such as providing training in interview skills and job
training. In addition, for programs designed to help chronically unemployed people to seek
reemployment to be effective, it is important to provide emotional support to help them
overcome the transient stress associated with job search.
Limitations and Suggestions for Further Research
The limitations of the present research and potential topics for future research are
discussed as follows. First, self-report measures used in the study may create the concern for
the potential threat of common method variance. Fortunately, the use of a longitudinal design
and the control of contemporaneous correlations in the assessment of time-lagged effects
should have reduced the problem of common method variance drastically. Nonetheless,
multiple data sources should be included in future research to minimize this problem. Second,
some of the scales are short, and economic hardship and unemployment negativity were
measured by single-item scales. Although they gave rise to meaningful results, future
research should use longer and hence more reliable scales to replicate our findings. In
addition, the use of time-limited retrospective coping reports may have some limitations
(Folkman & Moskowitz, 2004; Todd, Tennen, Carney, Armeli, & Affleck, 2004), such as
distortion caused by confounding factors. Future studies may use the method of daily reports
to measure coping strategies more accurately. Third, the high attrition rate in the present
research may be a problem. Although the problem of non-random sampling seems small, our
findings need to be replicated with other samples to ensure their generalizability. Fourth, we
conducted our research in a prolonged unemployment context, and our findings may not be
generalizable to people who face short-term unemployment. Future research should contrast
Coping and mental health
67
these two unemployment contexts. Finally, the research was conducted in Hong Kong, and it
is desirable to evaluate the findings in different cultural contexts. We believe that the
relationships studied involve basic processes that are less affected by cultural forces, and that
a similar pattern of findings should emerge in diverse cultural contexts. However, we note
that the positive effect of distancing on mental health seems to be more consistent in Chinese
societies (Lai & Chan, 2002; Lai & Wong, 1998) than in the West (e.g., Kinicki et al., 1990).
Perhaps the positive effect of distancing may be accentuated in cultures where the use of
distancing and forbearance is acceptable, such as the Chinese culture (e.g., Lai & Chan, 2002;
Philips & Pearson, 1996). More research is definitely needed to explore how national culture
moderates the positive effect of distancing on mental health in an unemployment context.
Coping and mental health
68
References
Austenfeld, J. L., & Stanton, A. L. (2004). Coping through emotional approach: A new look
at emotion, coping, and health-related outcomes. Journal of Personality, 72(6), 13351363.
Barber, A. E. (1998). Recruiting employees: Individual and organizational perspectives.
Thousand Oaks: Sage Publication.
Bennett, N., Martin, C. L., Bies, R. J., & Brockner, J. (1995). Coping with a layoff: A
longitudinal study of victims. Journal of Management, 21(6), 1025-1040.
Boyle, G. J. (1991). Does item homogeneity indicate internal consistency or item redundancy
in psycho- metric scales? Personality and Individual Differences, 12, 291-294.
Brenner, S.-O., Sorbom, D., & Wallius, E. (1985). The stress chain: A longitudinal
confirmatory study of teacher stress, coping and social support. Journal of
Occupational Psychology, 58(1), 1-13.
Burkholder, G. J., & Harlow, L. L. (2003). An Illustration of a longitudinal cross-lagged
design for larger structural equation models. Structural Equation Modeling, 10(3),
465-486.
Cole, D. A., & Maxwell, S. E. (2003). Testing mediational models with longitudinal data:
Questions and tips in the use of structural equation modeling. Journal of Abnormal
Psychology, 112, 558-577.
De Lange, A. H., Taris, T. W., Kompier, M. A. J., Houtman, I. L. D., & Bongers, P. M. (2004).
The relationships between work characteristic and mental health: Examining normal,
reversed and reciprocal relationship in a 4-wave study. Work & Stress, 18, 149-166.
DeLongis, A., Folkman, S., & Lazarus, R. S. (1988). The impact of daily stress on health and
mood: Psychological and social resources as mediators. Journal of Personality and
Social Psychology, 54(3), 486-495.
Coping and mental health
69
Feather, N. T., & O'Brien, G. W. (1986). A longitudinal study of the effects of employment
and unemployment on school-leavers. Journal of Occupational Psychology, 59, 121144.
Finkel, S. E. (1995). Causal analysis with panel data. Thousand Oaks, CA: Sage.
Folkman, S., & Lazarus, R. S. (1985). If it changes it must be a process: A study of emotion
and coping during three stages of a college examination. Journal of Personality and
Social Psychology, 48, 150-170.
Folkman, S., Lazarus, R. S., Dunkel-Schetter, C., DeLongis, A., & Gruen, R. J. (1986).
Dynamics of a stressful encounter: Cognitive appraisal, coping, and encounter
outcomes. Journal of Personality and Social Psychology, 50(5), 992-1003.
Folkman, S., Lazarus, R. S., Gruen, R. J., & De Lange, A. H. (1986). Appraisal, coping,
health status, and psychological symptoms. Journal of Personality and Social
Psychology, 50(3), 571-579.
Folkman, S., & Moskowitz, J. T. (2004). Coping: Pitfalls and promise. Annual Review of
Psychology, 55(745-774).
Frese, M. (1987). Alleviating depression in the unemployed: Adequate financial support,
hope and early retirement. Social Science and Medicine, 25, 213-215.
Frese, M., Garst, H., & Fay, D. (2007). Making things happen: Reciprocal relationships
between work characteristics and personal initiative in a four-wave longitudinal
structural equation model. Journal of Applied Psychology, 92, 1084-1102.
Goodman, J. S. D., & Blum, T. C. (1996). Assessing the nonrandom sampling effects of
subject attrition in longitudinal research. Journal of Management, 22, 627-652.
Gowan, M. A., Riordan, C. M., & Gatewood, R. D. (1999). Test of a model of coping with
involuntary job loss following a company closing. Journal of Applied Psychology,
84(1), 75-86.
Coping and mental health
70
Grossi, G. (1999). Coping and emotional distress in a sample of Swedish unemployed.
Scandinavian Journal of Psychology, 40, 157-165.
Julkunen, I. (2001). Coping and mental well-being among unemployed youth-A Northern
European perspective. Journal of Youth Studies, 4(3), 261-178.
Kelloway, E. K. (1998). Using LISREL for structural equation modeling: A researcher's
guide. Thousand Oaks, CA: Sage Publications.
Kessler, R. C., House, J. S., & Turner, J. B. (1987). Unemployment and health in a
community sample. Journal of Health and Social Behavior, 28, 51-59.
Kessler, R. C., Turner, J. B., & House, J. S. (1988). Effects of unemployment on health in a
community survey: Main, modifying, and mediating effects. Journal of Social Issues,
44(4), 69-85.
Kinicki, A. J., & Latack, J. C. (1990). Explication of the construct of coping with involuntary
job loss. Journal of Vocational Behavior, 36, 339-360.
Kinicki, A. J., Prussia, G. E., & McKee-Ryan, F. M. (2000). A panel study of coping with
involuntary job loss. Academy of Management Journal, 43, 90-100.
Lai, J. C. L., & Chan, R. K. H. (2002). The effects of job-search motives and coping on
psychological health and re-employment: A study of unemployed Hong Kong Chinese.
International Journal of Human Resource Management, 13(3), 465-483.
Lai, J. C. L., & Wong, W. S. (1998). Optimism and Coping with Unemployment among Hong
Kong Chinese Women. Journal of Research in Personality, 32, 454-479.
Latack, J. C., Kinicki, A. J., & Prussia, G. E. (1995). An integrative process model of coping
with job loss. Academy of Management Review, 20(2), 311-342.
Lazarus, R. S. (1984). Puzzles in the study of daily hassles. Journal of Behavioral Medicine,
7(4), 375-389.
Lazarus, R. S., & Cohen, J. B. (1977). Environmental Stress. In I. Altman & J. F. Wohlwill
Coping and mental health
71
(Eds.), Human Behavior and Environment (Vol. 2). New York: Plenum.
Lazarus, R. S., & Folkman, S. (1984). Stress, appraisal, and coping. New York: Springer.
Leana, C. R., & Feldman, D. C. (1988). Individual responses to job loss: Perceptions,
reactions, and coping behaviors. Journal of Management, 14(3), 5-19.
Leana, C. R., & Feldman, D. C. (1991). Gender differences in responses to unemployment.
Journal of Vocational Behavior, 38, 65-77.
Leana, C. R., & Feldman, D. C. (1995). Finding new jobs after a plant closing: Antecedent
and outcomes of the occurrence and quality of reemployment. Human Relations, 48,
1381-1401.
Leana, C. R., Feldman, D. C., & Tan, G. Y. (1998). Predictors of coping behavior after a
layoff. Journal of Organizational Behavior, 19, 85-97.
Leana, C. R., & Feldman, D. D. (1992). Coping with job loss. New York: Lexington Books.
Liem, R., & Liem, J. (1988). Psychological effects of unemployment on workers and their
families. Journal of Social Issues, 44(4), 87-105.
McKee-Ryan, F. M., Song, Z., Wanberg, C. R., & Kinicki, A. J. (2005). Psychological and
physical well-being during unemployment: A meta-analytic study. . Journal of
Applied Psychology, 90, 53-76.
Morrison, T. G., O'Connor, W. E., Morrison, M. A., & Hill, S. A. (2001). Determinants of
psychological well-being among unemployed women and men. Psychology and
Education: An Interdisciplinary Journal, 38(1), 34-41.
Moynihan, L. M., Roehling, M. V., Lepine, M. A., & Boswell, W. R. (2003). A longitudinal
study of the relationship among job search self-efficacy, job interviews, and
employment outcomes. Journal of Business and Psychology, 18, 207-233.
Pearlin, L. I., Menaghan, E. G., Lieberman, M. A., & Mullan, J. T. (1981). The stress process.
Journal of Health and Social Behavior, 22(4), 337-356.
Coping and mental health
72
Philips, M. R., & Pearson, V. (1996). Coping in Chinese Communities: The Need for a New
Research Agenda. In M. H. Bond (Ed.), The Handbook of Chinese Psychology (pp.
429-440). Hong Kong: Oxford University Press.
Price, R. H., Choi, J. N., & Vinokur, A. D. (2002). Links in the chain of adversity following
job loss: How financial strain and loss of personal control lead to depression, impaired
functioning, and poor health. Journal of Occupational Health Psychology, 7(4), 302312.
Prussia, G. E., Fugate, M., & Kinicki, A. J. (2001). Explication of the coping goal construct:
Implications for coping and reemployment. Journal of Applied Psychology, 86, 11791190.
Sackett, P. R., & Larson, J. R. (1990). Research strategies and tactics in industrial and
organizational psychology. In M. D. Dunnette & L. M. Hough (Eds.), Handbook of
industrial and organizational psychology (2 ed., Vol. 1, pp. 419-489). Palo Alto, CA:
Consulting Psychologists Press.
Taris, T. W. (2000). A primer in longitudinal data analysis. London: Sage.
Taylor, S. E. (1991). Asymmetrical effects of positive and negative events: The mobilizationMinimization hypothesis. Psychological Bulletin, 110(1), 67-85.
Todd, M., Tennen, H., Carney, M. A., Armeli, S., & Affleck, G. (2004). Do we know how we
cope? Relating daily coping reports to global and time-limited retrospective
assessments. Journal of Personality and Social Psychology, 86, 310-319.
Vinokur, A., & Caplan, R. D. (1987). Attitudes and social support: Determinants of jobseeking behavior and well-being among the unemployed. Journal of Applied Social
Psychology, 17, 1007-1024.
Vinokur, A. & Schul, Y. (2002). The web of coping resources and pathways to reemployment
following a job loss. Journal of Occupational Health Psychology, 7(1), 68-83.
Coping and mental health
73
Wanberg, C. R. (1997). Antecedents and outcomes of coping behaviors among unemployed
and reemployed Individuals. Journal of Applied Psychology, 82(5), 731-744.
Wanberg, C. R., Carmichael, H. D., & Downey, R. G. (1999). Satisfaction at last job and
unemployment: A new look. Journal of Organizational Behavior, 20, 121-131.
Wanberg, C. R., & Marchese, M. C. (1994). Heterogeneity in the unemployment experience:
A cluster analytic investigation. Journal of Applied Social Psychology, 24(6), 473-488.
Wanous, J. P., Reicheres, A. E., & Hudy, M. J. (1997). Overall job satisfaction: How good are
single-item measures? Journal of Applied Psychology, 82, 247-252.
Warr, P., Jackson, P., & Banks, M. H. (1988). Unemployment and mental health: Some
British studies. Journal of Social Issues, 44, 47-68.
Wiesner, M. (2003). A longitudinal latent variable analysis of reciprocal relations between
depressive symptoms and delinquency during adolescence. Journal of Abnormal
Psychology, 112, 633-645.
Zapf, D., Dormann, C., & Frese, M. (1996). Longitudinal studies in organizational stress
research: A review of the literature with reference to methodological issues. Journal
of Occupational Health Psychology, 1, 145-169.
Coping and mental health
74
Footnote
1
We added five demographic variables (gender, age, length of unemployment, length of
receipt of CSSA, and education level) to model II shown in Figure 3, and the results remained
similar. The model fit was acceptable, 2 = 1539.90, df = 589, GFI =.91, CFI = .89, IFI = .89,
RMSEA = .04. The path coefficients for the synchronous effects of job search were all
negative and significant (Wave 1: Beta = -.38, p < .001; Wave 2: Beta = -.19, p < .001; Wave
3: Beta = -.23, p < .001), and the path coefficients for the cross-lagged effects of distancing
were both positive and significant (both Beta = .12, p <.001).
Coping and mental health
75
Table 1
Means, Standard Deviations, and Intercorrelations of Variables across the Three Waves
Mean
-42.53
2.59
SD
-10.86
2.82
1
--.13**
.09**
2
3
5
6
-.12**
--
1.51
1.88
.06
.04
.51**
--
-2.85
-.83
.06
-.01
-.40**
-.13**
-.09**
-.03
-.04
-.01
-.11**
(.69)
7. Mental health T2
2.89
.77
-.02
-.13**
.05
.03
.08*
.50**
8. Mental health T3
2.91
.72
-.08*
-.08*
.03
.02
.04
.46**
9. Job search T1
3.47
.97
-.13**
-.05
-.24**
-.17**
.08*
-.17**
10. Job search T2
3.4
.85
-.05
-.02
-.16**
-.16**
.06
-.14**
11. Job search T3
3.41
.83
-.07*
-.04
-.22**
-.18**
.06
-.09**
12. Distancing T1
2.91
.86
-.04
.00
-.03
-.02
.03
.07*
13. Distancing T2
3.00
.80
.02
.01
.04
.01
.03
.08*
14. Distancing T3
3.01
.75
-.01
.04
-.08*
-.07*
.02
.06
15. Economic
hardship T1
16. Economic
hardship T2
17. Economic
hardship T3
18. Unemployment
negativity T1
19. Unemployment
negativity T2
20. Unemployment
negativity T3
3.85
.80
-.01
.06
-.02
-.04
-.03
-.21**
3.74
.78
-.03
.02
.01
.00
.02
-.17**
3.73
.75
-.07*
.02
.02
-.01
.00
-.15**
4.11
1.09
-.03
.03
-.14**
-.13**
-.05
-.38**
3.99
1.03
.01
.04
-.11**
-.10**
-.00
-.24**
3.93
.97
-.01
.01
-.15**
-.12**
-.01
-.25**
a
1. Gender
2. Age
3.
Length
of
unemployment
4. Length of receipt
of CSSA
5. Education level b
6. Mental health T1
4
Coping and mental health
76
Table 1 (Continued)
7
8
9
10
11
12
13
14
1. Gender a
2. Age
3.
Length
of
unemployment
4. Length of receipt
of CSSA
5. Education level b
6. Mental health T1
7. Mental health T2
(.72)
8. Mental health T3
9. Job search T1
10. Job search T2
11. Job search T3
12. Distancing T1
13. Distancing T2
14. Distancing T3
15. Economic
hardship T1
16. Economic
hardship T2
17. Economic
hardship T3
18. Unemployment
negativity T1
19. Unemployment
negativity T2
20. Unemployment
negativity T3
.50**
-.07*
-.18**
-.12**
.09**
.00
.04
-.17**
(.72)
-.11**
-.13**
-.21**
.06
.08*
-.03
-.12**
.45**
.37**
.17**
.05
.10**
.14**
.48**
.07*
.19**
.14**
.07*
.07*
.05
.27**
.06
(.60)
.19**
.18**
-.05
(.67)
.30**
-.10**
(.71)
-.08*
-.25**
-.18**
.09**
.10**
.07*
-.02
-.07*
-.03
-.15**
-.23**
.07*
.02
.10**
-.02
-.08*
-.06
-.27**
-.22**
.36**
.21**
.19**
.03
-.09**
-.03
-.39**
-.25**
.26**
.31**
.22**
-.01
-.07*
-.05
-.27**
-.34**
.21**
.27**
.41**
.01
-.04
.02
Coping and mental health
77
Table 1 (Continued)
15
16
17
18
19
20
1. Gender a
2. Age
3.
Length
of
unemployment
4. Length of receipt
of CSSA
5. Education level b
6. Mental health T1
7. Mental health T2
8. Mental health T3
9. Job search T1
10. Job search T2
11. Job search T3
12. Distancing T1
13. Distancing T2
14. Distancing T3
15. Economic
hardship T1
16. Economic
hardship T2
17. Economic
hardship T3
18. Unemployment
negativity T1
19. Unemployment
negativity T2
20. Unemployment
negativity T3
.35**
.29**
.32**
.22**
.12**
.08*
.20**
.28**
.15**
.37**
.14**
.14**
.30**
.28**
.37**
Note: * p<.05; ** p<.01.
a
nominal variable, 0-male, 1-female.
b
ordinal variable. The higher the score, the higher the education level.
T1 – Time 1; T2 – Time 2; and T3 – Time 3. Coefficient alphas are given on the
diagonal.
Coping and mental health
78
Table 2
A Series of Models for Distancing, Job Search and Mental Health
2
df
GFI
CFI
IFI
RMSEA
AIC
Model
Comparison
△2
△ df
Model I-0. Baseline model
1241.09 463
.92
.90
.90
.04
1437.09
Model I-1. Three-month lagged effects of distancing on
1231.31 461
.93
.90
.91
.04
1431.31
9.78**
2
mental health
Model I-2. Three-month lagged effects of job search on
1240.73 461
.92
.90
.90
.04
1440.73
.36
2
mental health
Model I-3. Three-month lagged effects of both distancing
1230.12 459
.93
.90
.91
.04
1434.12
10.97
4
and job search on mental health
Model I-4. Three-month lagged effects of mental health
1236.11 461
.93
.90
.90
.04
1436.11
4.98
2
on distancing
Model I-5. Three-month lagged effects of mental health
1238.39 461
.92
.90
.90
.04
1438.39
2.7
2
on job search
Model I-6. Three-month lagged effects of mental health
1234.55 459
.93
.90
.90
.04
1438.55
6.54
4
on both distancing and job search
Model I-7- Reciprocal model
1221.62 455
.93
.91
.91
.04
1433.62
19.47*
8
Model I-8. Six-month lagged effect of distancing on
1240.07 462
.92
.90
.90
.04
1438.07
1.02
1
mental health
Model I-9. Six-month lagged effect of job search on
1237.77 462
.92
.90
.90
.04
1435.77
3.32
1
mental health
Model I-10. Equivalence constraint of causal paths added
1231.94 462
.93
.90
.91
.04
1429.94
.63
1
to Model I-1
Model II. Synchronous effect of job search and 3-month
1378.26 465
.91
.89
.89
.05
1570.26
lagged effect of distancing on mental health
Note. * p < .05; ** p <.01. For model comparison results, Model I-1 to Model I-9 were compared with Model I-0; Model I-10 was compared
with Model I-1.
Coping and mental health
79
Table 3
A Series of Models for the Effects of Situational Appraisals on Coping Strategies and Mental Health
Model Comparison

2
Df
GFI
CFI
IFI
RMSEA
AIC
△2
△ df
Model III-0. Baseline model
2453.58
671
.88
.81
.82
.053
1437.09
Model III-1. Effects of economic hardship and
unemployment negativity on mental health
Model III-2. Effects of economic hardship and
unemployment negativity on distancing
Model III-3. Effects of economic hardship and
unemployment negativity on job search
Model III-4. Effects of economic hardship and
unemployment negativity on mental health,
distancing, and job search
2190.87
665
.89
.84
.84
.049
2420.87
262.71**
6
2433.24
665
.88
.82
.82
.053
2663.24
20.34**
6
2046.50
665
.89
.86
.86
.047
2276.50
407.08**
6
1844.86
653
.90
.88
.88
.044
2098.86
608.72**
18
Note. * p < .05; ** p <.01. For model comparison results, Models III-1 to III-4 were compared with Model III-0.
Coping and mental health
80
Figure Captions
Figure 1. Baseline Model for Distancing, Job search and Mental Health.
Figure 2. Different Structural Models for Testing Hypotheses 1-3
Figure 3. Path Coefficients of Model II for the Effects of Distancing and Job search on Mental Health.
Figure 4. Different Structural Models for Testing Hypotheses 4-6b
Figure 5. Path Model (Model III-4) for Distancing, Job search and Mental Health with the Inclusion of Economic Hardship and Unemployment
Negativity
Coping and mental health
Figure 1. Baseline Model for Distancing, Job search and Mental Health.
S11
S12
S21
D11
H12
H13
S31
Job Search
Time 2
Job Search
Time 1
H11
S22
H14
H15
H21
H22
H23
S32
Job Search
Time 3
H24
H25
H31
H32
H33
Mental Health
Time 1
Mental Health
Time 2
Mental Health
Time 3
Distancing
Time 1
Distancing
Time 2
Distancing
Time 3
D12
D13
D14
D21
D22
D23
D24
D31
D32
D33
H34
D34
H35
81
Coping and mental health
82
Figure 2. Different Structural Models for Testing Hypotheses 1-3
Model I-0. Baseline model
Model I-4. Three-month lagged
effects of mental health on
distancing
Model I-8. Six-month lagged effect
of distancing on mental health
Model I-1. Three-month lagged
effects of distancing on mental
health
Model I-5. Three-month lagged
effects of mental health on job
search
Model I-9. Six-month lagged effect
of job search on mental health
Model I-2. Three-month lagged
effects of job search on mental
health
Model I-6. Three-month lagged
effects of mental health on both
distancing and job search
Model I-3. Three-month lagged
effects of both distancing and job
search on mental health
Model I-7- reciprocal model
Model II. Synchronous effect of job
search and 3-month lagged effect of
distancing on mental health
Note. The first row of latent variables in each model pertains to job search, the middle row, mental health, and the bottom row, distancing. The
first column of latent variables refers to time 1, the second column, time 2, and the third column, time 3.
Coping and mental health
Figure 3. Path Coefficients of Model II for the Effects of Distancing and Job search on Mental Health.
Job Search
Time 1
.61***
-.31.***
Mental Health
Time 1
.08
Job Search
Time 2
.64***
-.19***
.56***
.11***
Mental Health
Time 2
Job Search
Time 3
-.18***
.59***
Mental Health
Time 3
-.21***
-.17***
.11***
Distancing
Time 1
.21***
Note. * p <.05; ** p < .01.
Distancing
Time 2
.40***
Distancing
Time 3
83
Coping and mental health
84
Figure 4. Different Structural Models for Testing Hypotheses 4-6b
Model III-0. Baseline model
Model III-3. Effects of economic hardship
and unemployment negativity on job search
Model III-1. Effects of economic hardship and
unemployment negativity on mental health
Model III-2. Effects of economic hardship
and unemployment negativity on distancing
Model III-4. Effects of economic hardship
and unemployment negativity on mental
health, distancing, and job search
Note. The first row of variables in each model pertains to unemployment negativity, the second row, job search, the third row, mental health, the
fourth row, distancing, and the bottom, economic hardship. The three columns for the squares and circles (not including disturbances), represent,
from left to right, Time 1 to Time 3, respectively.
Coping and mental health
85
Figure 5. Path Model (Model III-4) for Distancing, Job search and Mental Health with the Inclusion of Economic Hardship and Unemployment
Negativity
Unemployment
Negativity
Time 1
Unemployment
Negativity
Time 2
.36***
Unemployment
Negativity
Time 3
.36***
.27***
.43***
Job Search
Time 1
.34***
Job Search
Time 2
.47***
-.17***
-.25***
-.37***
-.08
Job Search
Time 3
.50***
-.09*
-.06
-.05
.06
Mental Health
Time 1
.50***
.05
Mental Health
Time 2
.54***
.07
Mental Health
Time 3
.03
-.00
.06
.09***
Distancing
Time 1
.21***
.09***
-.23***
Distancing
Time 2
.39***
-.24***
Distancing
Time 3
-.10***
-.14***
-.07
-.09**
-.06
Economic
Hardship
Time 1
.33***
-.06
Economic
Hardship
Time 2
.31***
Economic
Hardship
Time 3
Note. * p < .05; ** p < .01. The correlation between economic hardship and unemployment negativity in Wave 1 and correlations between their
disturbances in Waves 2 and 3 are included, but not shown for the sake of clarity. Dotted lines represent insignificant paths.