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WAYS OF COPING

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Validity Studies
The Ways of Coping Scale
A Reliability Generalization Study
Educational and
Psychological Measurement
Volume 68 Number 2
April 2008 262-280
Ó 2008 Sage Publications
10.1177/0013164407310128
http://epm.sagepub.com
hosted at
http://online.sagepub.com
Kathryn R. Rexrode
Philhaven Behavioral Healthcare Services
Suni Petersen
Siobhan O’Toole
Alliant International University
For more than 20 years, the Ways of Coping Scale (WOCS) has been used extensively
to measure coping. Yet beyond the original psychometric data, few studies have reexamined its properties utilizing the enormous body of research generated on the WOCS.
Reliability has been assumed to be consistent as an attribute of the test. This study used
reliability generalization to identify (a) the variability in reliability estimates for the
WOCS scores across studies, (b) the typical score reliability for the WOCS, and (c) the
salient features across studies that relate to the variability in reliability estimate scores
for the WOCS. Typical reliability across subscale scores ranged from .60 to .75 with
Positive Reappraisal showing the least variability and Self-Controlling showing the
most. Factors related to this variability were age and format of administration.
Keywords: Ways of Coping Scale; reliability generalization; reliability; reliability
coefficients; coping measures
S
cores are reliable, not measures (Vacha-Haase, 1998), making statements about
the reliability of a measure inappropriate and potentially misleading (Pedhazur
& Schmelkin, 1991). Using test manuals or prior studies to determine the reliability
of measures for a proposed study is risky. Reliability is affected by variability in
sample characteristics and successive administrations of an instrument. Unreliable
scores can lead to attenuation of the reported effect size (Reinhardt, 1996) and affect
statistical power (Onwuegbuzie & Daniel, 2002). Recently, reliability generalization
has been used as a method to determine the reliability likely to be achieved by a particular instrument given certain sample or instrument characteristics (Henson, Kogan,
& Vacha-Haase, 2001).
Many areas of psychological research focus on coping as an important variable, yet
the investigation of psychometric properties have not kept pace with the proliferation
Authors’ Note: The authors thank Dr. Lois Benishek and Pilar Gonzalez for their assistance with data
collection and Todd Snyder for his assistance with coding. Please address correspondence to Kathryn
Rexrode, Philhaven Behavioral Healthcare Services, 283 South Butler Road, P.O. Box 550, Mount
Gretna, PA 17064; e-mail: [email protected]
262
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of studies. Relying on its popularity, researchers have placed an enormous amount
of confidence in the most popular instrument of coping, the Ways of Coping Scale
(WOCS; Folkman & Lazarus, 1988b). This article discusses the theoretical basis and
properties of the WOCS, reports on reliability generalization results, and makes recommendations for its use in future studies.
Coping as a Construct
Some researchers view coping as an ego state (Vaillant, 1977), others as stable
personality traits (Costa & McCrae, 1989), and still other researchers focus on
coping as a transactional process (Folkman & Lazarus, 1980; Lazarus, 1993). Each
conceptualization has limitations. Although a thorough discussion of models of
coping are beyond the scope of this article, it is essential to provide an overview of
the conceptualization of coping underlying the WOCS.
Beginning in the 1970s, Richard Lazarus developed the cognitive phenomenological theory of stress, which incorporates a transactional explanation of the stress process (Folkman & Lazarus, 1980). There are three types of stress: challenge, threat,
and harm-loss (Folkman & Lazarus, 1980). Challenge involves potentially gaining
something by overcoming obstacles through mastery. Whereas threat occurs when
someone anticipates potential harm-loss, harm-loss is the actual loss of something.
During a stressful encounter, an individual makes appraisals of the particular situation (Folkman & Lazarus, 1980). First, the person conducts a primary appraisal to
evaluate what is at stake in the situation and then engages in a secondary appraisal to
assess his or her coping resources and alternatives to manage the situation. Once
coping efforts affect or do not affect the stressful encounter, new appraisals are made
and new coping efforts are ensued.
When stress occurs, individuals cope by using cognitive or behavioral strategies
to deal with internal and external demands as well as conflicts between these two
things (Folkman & Lazarus, 1980). Typically, they are either problem focused or
emotion focused. Whereas problem-focused coping alters the source of stress in
the person–environment relationship, emotion-focused coping serves to decrease
distressful emotions.
Measurement of Coping
Instruments used to measure the coping construct are plagued with psychometric
problems (De Ridder, 1997). Issues surrounding such problems include whether
coping is a process or style, the various dimensions of coping, and the validity and
reliability of scores from coping scales (De Ridder, 1997; Stone, Kennedy-Moore,
Newman, Greenberg, & Neale, 1992). Although many researchers agree that
coping is a process and varies depending on the situation, researchers frequently
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Educational and Psychological Measurement
conduct their research in a way that suggests that people have consistent ways of
coping that they use in practically all situations (De Ridder, 1997). Another problem
with the current realm of coping research involves the large number of identified
coping responses. To reduce this number, coping responses have been categorized
into various dimensions, a term that is conceptually problematic as a result of
researchers failing to distinguish between coping strategies (i.e., distancing and positive reappraisal) and metastrategies that are groups of strategies (i.e., approach and
avoidance coping; De Ridder, 1997). Some researchers contend that only behavioral
responses constitute coping. Others challenge this thinking by stating that cognitions
are a form of coping as well (De Ridder, 1997).
The WOCS
The WOCS is one of the most widely used coping measures to date (De Ridder,
1997; Parker & Endler, 1992; Stone et al., 1992). The WOCS reflects transactional
theory in several ways (Stone et al., 1992). It is meant to be used with a specific
stressful encounter in mind, measuring what a person actually does in that situation
rather than what he or she typically does or thinks he or she should do. The person is
expected to report all coping efforts, not just those that were helpful. The measure
categorizes multiple dimensions of coping into either emotion-focused or problemfocused efforts. According to the manual, the WOCS asks respondents to think of the
most stressful event that has occurred in the past week, to write a description of it,
and to identify all of the coping responses used to deal with the situation.
The manual, however, allows the reader to determine certain aspects of administration. For example, some researchers use elaborate interviewing techniques to assist
the participant in focusing on an event whereas others simply use self-administration
and open-ended referent events (Charlton & Thompson, 1996; Folkman & Lazarus,
1988b; Miller, Gordon, Daniele, & Diller, 1992). Both the way at arriving at the trigger event and the process of administration can be modified. Although the manual
designates a 1-week time frame, researchers have varied this feature from proximal
to distal events. This flexibility is one of the advantages of the WOCS; however,
it also makes it susceptible to changes that may compromise the reliability of scores,
particularly in the absence of examining the impact of such deviations. Thus, this
study tested bona fide adaptations to compare reliabilities. Adaptations include version of test, Likert scale (4 or 5 points), format (self-report, interview), recall time
window, target stressor, translation, gender, ethnicity, age, and population type
(clinical, nonclinical, medical).
The wide variability in WOCS reliability scores could be due to the leeway
afforded the researcher. It is also possible that the variability in reliability estimates
may be due to the instability of the factor structure across populations and settings.
Parker, Endler, and Bagby (1993) conducted a confirmatory factor analysis in two
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studies with college students. Results yielded the following factors: Distancing/
Avoidance, Confrontive/Seeking Social Support, Problem Focused, and Denial.
Edwards and O’Neill (1998) conducted another confirmatory factor analyses. Unfortunately, they found little support for the 1988 factor structure, which they attributed
to a lack of domain sampling procedures. Smyth and Yarandi (1996) conducted a
factor analysis using African American workers and found that Active Coping,
Avoidance, and Minimizing the Situation accounted for 67% of the variance. Instead
of constructing items based on a priori constructs, the items on the WOCS appeared
to be gathered because they were related to a particular coping strategy, which may
never result in a stable and meaningful factor structure. Such differences in factor
structures could also account for the variability in reliability.
Reliability Generalization
Reliability induction refers to researchers’ use of reliability coefficients from the
manual or other studies (Vacha-Haase, Henson, & Caruso, 2002). Such reliability
induction occurs frequently in scientific articles because authors fail to report reliability coefficients for their particular sample (Shields & Caruso, 2004; VacheHaase, Ness, Nilsson, & Reetz, 1999). Reliability generalization is a method used
to determine the factors that relate to the reliability of scores across studies by calculating the mean measurement error variances and exploring factors related to
such score error variance (Vacha-Haase, 1998). Reliability generalization can identify both the typical score reliability and the variability in reliability estimates for
an instrument across studies. The value of the method lies in its ability to identify
how and why score reliability varies across studies for a particular instrument (Yin
& Fan, 2000). The purpose of this study was to examine the reliability generalization of the WOCS.
Method
Archival Data Collection
Three sets of literature searches were completed (i.e., February 2001, targeting a
time window of 1986-2001; May 2002, targeting a time window of 2000-2002; and
June 2004, targeting a time window of 2002-2004). Five databases (PsycINFO,
LexusNexus, Web of Science, Medline, and PsycARTICLES) were searched. The
reference sections from the articles were scanned for other potential articles that used
the WOCS. The search criterion was any article that gave one of six versions of the
WOCS to participants. The researchers eliminated articles that were duplicated in
different databases, written in non-English language, or used a modification of the
WOCS that did not comply with any of the published and validated versions.
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Then we categorized the articles into (a) articles reporting coefficient alpha estimates for the eight subscales of the WOCS, (b) articles reporting less than the possible eight coefficient alpha estimates, (c) articles citing coefficient alpha estimates
from another source/article, and (d) articles reporting no coefficient alpha estimates. Authors of articles were contacted to obtain the coefficient alpha estimates
in cases where alphas were not reported for current scores on each of the eight
subscales.
Coding of Article Characteristics
A template was developed to code the key characteristics associated with each
study. Variables were dummy coded as needed. Coding resulted in 93.33% agreement between two independent raters. Remaining discrepancies were the product of
oversight and were resolved. Predictor variables were coded as follows: type of article (substantive or measurement), version (1988/1986 Folkman–Vitaliano–LaPoint–
Folkman variation, or cancer version collapsed to 1988/1986 Folkman, or other),
Likert scale (4 or 5 points); format (self-report or interview), recall time window
(past week, past 2 weeks, past month, past 6 months, past year, future, or other collapsed to past week/current or all others), target stressor (self-selected, vocational
academic, caregiving relationship, physical compromise, or other collapsed to selfselected, vocational/academic, caregiving relationship, or physical compromise), translation (English, non-English, or combination collapsed to English or other), gender
(mixed, female, or male samples collapsed to mixed gender or one gender), ethnicity
(mixed, all White, all Black, or international collapsed to national or international),
sample source (adolescents, college age, adults, seniors, or mixed collapsed to adults
only or other), and population type (nonclinical, medically compromised, psychopathological, cancer, or mixed collapsed to nonclinical or other).
Data Analysis: Data Reduction Phase
Given that the sample size of coefficient alpha estimates were small and varied
depending on the subscale (i.e., Confrontive Coping [CC] = 80, Distancing [D] =
83, Self-Controlling [SC] = 83, Seeking Social Support [SS] = 106, Accepting
Responsibility [AR] = 78, Escape-Avoidance [EA] = 86, Planful Problem Solving
[PP] = 83, and Positive Reappraisal [PR] = 81), the number of levels for the predictor variables were collapsed as noted above to increase the sample size within each
level. Levels within variables that contained less than 15% of the sample or were
conceptually related were collapsed. For example, the time recall window consisted
of seven levels. The percentage of data points associated with each of these levels
was 68.8%, 1.8%, 4.5%, 1.8%, 0%, 0%, and 16.1%, respectively. These seven categories were then collapsed into two categories (i.e., past week/current and other),
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with 68.8% and 24.1% of the data, respectively. Any two-level predictors that had
statistically significant differences in percentages were deleted because a significant imbalance in the distribution of data would not have been an accurate representation of the category and may not have allowed any differences to be found.
After using these procedures, the 10 variables retained were version, format, recall
time window, target stressor, translation, sample size, sample gender, sample ethnicity, sample source, and population type.
Results
Reliability Induction and Range of Coefficients
This study of the WOCS confirmed that although some researchers report reliability coefficient estimates for their sample and data, others continue to use reliability induction as evidence that their data are reliable. Of the 661 citations found
during the data collection process, 92 were usable. Because some articles provided
reliability estimates for multiple samples, the 92 articles yielded 112 data points
for this study. Of the 92 articles, 23% of the authors had to be contacted for coefficient alpha estimates. Another 16% (n = 15) of the articles reported reliability coefficient estimates from the WOCS manual or previous research studies and also had
to be contacted. In all, before any authors were contacted, 36 of the 92 articles
(39%) failed to report reliability estimate information for the sample data. Figure 1
presents box and whiskers plots to display the variability across scale reliability
coefficients and to determine typical reliability for each scale of the WOCS.
Predictors of Reliability Estimates
ANOVAs were used to identify variables that predicted reliability estimates for
the different subscales. Version, sample gender, and population type were not statistically significantly related to reliability estimates for any subscales.
Four variables were statistically significantly related to only a single subscale.
Recall time window predicted the AR subscale, F(1, 69) = 4.75, p < :05, Z2 = :06,
with current/past week demonstrating lower reliability estimates. Format was statistically significant only for the EA subscale, F(1, 83) = 4.87, p < :05, Z2 = :06.
Self-report format produced higher reliability estimates than interviews. Translation was statistically significant only for the SC subscale, F(1, 80) = 6.18, p < :05,
Z2 = :07, with higher coefficient alphas related to the English version. Overall,
there was a statistically significant difference in reliability estimates for the SS subscale across target stressor, F(3, 93) = 3.12, p < :05, Z2 = :09. Utilizing a Scheffé
post hoc comparison, the only statistically significant difference was between
caregiving/relationship and physical compromise (p < :05), with the caregiving/
relationship category related to higher coefficient alpha levels.
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Educational and Psychological Measurement
Figure 1
Box and Whisker Plot of Subscale Reliability Coefficients
Note: Outliers (O) are values more than 1.5 box lengths from the 25th percentile, and extremes (∗ ) are
values more than 3 box lengths from the 25th percentile. AR = Accepting Responsibility; CC = Confrontive Coping; D = Distancing; EA = Escape-Avoidance; PP = Planful Problem Solving; PR = Positive
Reappraisal; SC = Self-Controlling; SS = Seeking Social Support.
Sample source and sample ethnicity were predictive of reliability estimates for more
than one subscale. Sample source was statistically significant for AR, F(1, 76) = 6.24,
p < :05, Z2 = :08; PR, F(1, 79) = 14.15, p < :001, Z2 = :15; and SC, F(1, 80) = 7.40,
p < :01, Z2 = :09. For each of these subscales, an all adult sample source produced
higher coefficient alpha levels than other samples. Sample ethnicity reached statistical
significance for PR, F(1, 60) = 12.55, p < :001, Z2 = :17, and SC, F(1, 62) = 6.11,
p < :05, Z2 = :09. For both the PR and SC subscales, a U.S.-based sample was predictive of higher reliability estimates than an international sample. Tables 1 through 5
provide details for the ANOVAs.
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Table 1
Means, Standard Deviations, and One-Way ANOVAs for Effects
of Translation on WOCS Subscale Score Coefficient Alpha Estimates
Translated
English
ANOVA
Variable
M
SD
M
SD
df
F
Z2
AR
CC
D
EA
PP
PR
SC
SS
.60
.59
.60
.66
.71
.72
.55
.75
.13
.14
.17
.09
.06
.08
.18
.06
.63
.62
.65
.70
.70
.76
.65
.74
.12
.12
.12
.09
.08
.08
.11
.08
1, 74
1, 76
1, 79
1, 82
1, 79
1, 77
1, 79
1,102
0.80
0.21
1.91
2.55
0.08
3.19
6.18∗
0.26
.01
.00
.02
.03
.00
.04
.07
.00
Note: WOCS = Ways of Coping Scale; ANOVA = analysis of variance; Z2 = partial eta-squared
estimated effect size; AR = Accepting Responsibility; CC = Confrontive Coping; D = Distancing;
EA = Escape-Avoidance; PP = Planful Problem Solving; PR = Positive Reappraisal; SC = SelfControlling; SS = Seeking Social Support.
∗
p < .05.
Table 2
Means, Standard Deviations, and One-Way ANOVAs for Effects of
Recall Time Window on WOCS Subscale Score Coefficient Alpha Estimates
Current/Past Week
Other
ANOVA
Variable
M
SD
M
SD
df
F
Z2
AR
CC
D
EA
PP
PR
SC
SS
.61
.61
.64
.70
.70
.76
.62
.74
.12
.12
.12
.08
.08
.07
.13
.07
.68
.62
.63
.69
.72
.73
.65
.75
.09
.14
.16
.11
.08
.09
.13
.09
1, 69
1, 72
1, 75
1, 77
1, 75
1, 73
1, 75
1, 96
4.75∗
0.10
0.08
0.30
0.82
3.45
0.83
0.77
.06
.00
.00
.00
.01
.05
.01
.01
Note: See note to Table 1.
∗
p < .05.
Finally, a set of Pearson product–moment correlations were used to assess
whether sample size was related to the reliability estimates of any of the subscale
scores. The correlations ranged from r = −.15 (p > .05) for the SC subscale to
r = :04 (p > .05) for the D subscale. Sample size was not a statistically significant
predictor of coefficient alpha estimates for any of the subscale scores.
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Educational and Psychological Measurement
Table 3
Means, Standard Deviations, and One-Way ANOVAs for Effects
of Format on WOCS Subscale Score Coefficient Alpha Estimates
Self-Report
Interview
ANOVA
Variable
M
SD
M
SD
df
F
Z2
AR
CC
D
EA
PP
PR
SC
SS
.63
.62
.64
.71
.71
.75
.63
.74
.12
.12
.13
.09
.08
.08
.13
.07
.60
.55
.60
.65
.68
.74
.61
.75
.13
.14
.12
.05
.07
.08
.10
.10
1, 75
1, 78
1, 81
1, 83
1, 81
1, 79
1, 81
1, 103
0.38
2.34
1.09
4.87∗
0.96
0.24
0.28
0.06
.01
.03
.01
.06
.01
.00
.00
.00
Note: See note to Table 1.
∗
p < .05.
Table 4
Means, Standard Deviations, and One-Way ANOVAs for Effects of Sample
Ethnicity on WOCS Subscale Score Coefficient Alpha Estimates
International
United States
ANOVA
Variable
M
SD
M
SD
df
F
Z2
AR
CC
D
EA
PP
PR
SC
SS
.62
.60
.63
.67
.70
.71
.58
.75
.13
.13
.16
.09
.07
.08
.16
.07
.62
.62
.65
.71
.70
.78
.67
.74
.14
.13
.14
.08
.10
.08
.12
.09
1, 57
1, 59
1, 61
1, 62
1, 60
1, 60
1, 62
1, 74
0.01
0.27
0.16
2.46
0.03
12.56∗∗∗
6.11∗
0.39
.00
.01
.00
.04
.00
.17
.09
.01
Note: See note to Table 1.
∗
p < .05. ∗∗∗ p < .001.
Discussion
The purpose of this reliability generalization study was to identify (a) the amount
of variability in reliability estimate scores for the WOCS subscales, (b) the typical
reliability of scores for the subscales, and (c) the instrument and sample-related characteristics that are related to the reliability of scores that are obtained on the WOCS.
Only 61% of the WOCS articles used in this study attributed reliability to the scores
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Table 5
Means, Standard Deviations, and One-Way ANOVAs for Effects of Sample
Source (Age Group) on WOCS Subscale Score Coefficient Alpha Estimates
Adults
Other
ANOVA
Variable
M
SD
M
SD
df
F
Z2
AR
CC
D
EA
PP
PR
SC
SS
.64
.62
.65
.71
.70
.77
.65
.75
.11
.13
.13
.08
.09
.08
.12
.08
.57
.57
.60
.67
.69
.69
.57
.72
.15
.11
.12
.10
.07
.06
.09
.08
1, 76
1, 77
1, 81
1, 83
1, 80
1, 79
1, 80
1, 103
6.24∗
2.50
1.68
2.85
0.31
14.15∗∗∗
7.40∗∗
3.03
.08
.03
.02
.03
.00
.15
.09
.03
Note: See note to Table 1.
∗
p < .05. ∗∗ p < .01. ∗∗∗ p < .001.
rather than the actual instrument, and another 39% of the studies did not report reliability. The range of reliability coefficients across studies was dramatic. This study
demonstrates the dire need for emphasizing to researchers the importance of reporting reliability for their particular samples and supporting the recent initiatives to standardize research reporting. Clearly, there are no fixed reliability coefficient estimates
for the WOCS subscales.
Characteristics Related to Reliability
A few of the instrument and sample-related characteristics examined in this
reliability generalization study explained a moderate proportion of the variance in
coefficient reliability estimates (AR, PR, and SC subscales), and these variances
were influenced by different factors. In analyzing data from the WOCS, researchers
might consider those factors that should be statistically controlled in order to ensure
reliability of the WOCS with their sample.
AR subscale. The AR subscale refers to acknowledging one’s own role in the
problem with a concomitant theme for trying to put things right. The standard
administration method calls for a time window within 1 week, but the results of this
study suggest that for this subscale a greater time window leads to higher reliability
coefficients. With stressors, a person often goes through a reactive process that
includes shock, anger, denial, or sadness, which may last longer with the increasing
severity of the stressor (Courtois, 2004) and make it premature for a person to
examine his or her own role and take responsibility for that role within a 1-week
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time window. Furthermore, coping may vary week to week for extraneous reasons.
Consequently, a larger time window affords the opportunity to engage in more strategies and observe one’s own process.
PR subscale. The PR subscale ‘‘describes efforts to create positive meaning by
focusing on personal growth’’ (Folkman & Lazarus, 1988b, p. 11). The age of participants was a statistically significant predictor with greater reliability when used
with adults than when used with younger age groups. Children have less time in
their lives to build a repertoire of coping skills. Their expressions of distress are
much more likely to be unfocused than adults’ (McCown & Davies, 1995) and
dependent on the quality of caregiving (Broderick & Blewitt, 2006). At the same
time, there is more danger of a shift in attachment to the parents during times of
severe family stress (Broderick & Blewitt, 2006) as parents struggle to maintain
their own equilibrium. In the absence of guidance and a possible shift in attachment, children’s distress is sometimes stalled and emerges in behavioral problems
that appear unrelated to the stressor (Worden, Davies, & McCown, 1999).
Adolescents and college students may encompass a wider spread of moral development and developmental stages leading to a more heterogeneous group than
adults. Adults may have a more practiced repertoire of coping skills and as a result
are more likely to focus on personal growth as a goal and source of pride in dealing
with distress. In so doing, adults may have more of a tendency than younger individuals to reframe stressful situations (Faust & Katchen, 2004; Shelby, 1997).
Another possibility that may account for these population differences is that the PR
subscale contains a spiritual dimension (Folkman & Lazarus, 1988b), and as people
age, they increasingly use spirituality as a form of coping (Martin, Rott, Poon,
Courtenay, & Lehr, 2001). Therefore, it is likely that the WOCS will result in more
reliable scores when the PR scale is administered to adults than to younger people.
The study began with assessing ethnicity (meaning racial identification, immigrant identification, and different nationalities). Reliabilities were rarely reported
on different ethnic groups. Therefore, these categories were collapsed into one, and
because it was predominantly international, this designation was juxtaposed with
national. Therefore, as the scale is used on larger diverse samples, differential
statistical analyses should be conducted.
SC subscale. The SC subscale of the WOCS describes ‘‘efforts to regulate one’s
feelings and actions’’ (Folkman & Lazarus, 1988b, p. 11). Factors predicting reliability for the SC subscale were source and translation. However, of the predictors,
age of participants was the only statistically significant predictor, again with adults
producing scores with higher reliability. Young people tend to decrease impulsivity
and increase self-control as they move toward adulthood (Greene et al., 2000).
The prediction models included factors that were not statistically significant yet
did account for a portion of the variance. This outcome could be explained by the
lack of reporting in many articles. Gender also accounted for variance, yet most
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studies included both genders without analyzing reliabilities independently for
each. The few studies that included only women or only men showed more variability; therefore, it is likely that there are differences based on gender. Again, it
would be extremely beneficial for researchers to conduct independent reliability
analyses on segments of their sample.
EA subscale. The EA subscale ‘‘describes wishful thinking and behavioral
efforts to escape or avoid the problem’’ (Folkman & Lazarus, 1988b, p. 11). The
reliability of the WOCS on the EA subscale appears to be sensitive to the administration format. Greater reliability will be achieved on this scale with self-report
rather than interview, which may be due to the sensitive nature of some of the questions, such as use of substances.
SS subscale. The targeted stressor was related to the reliability variance on the
SS subscale. Although social support is a factor in the adjustment to many situations, there are some situations in which social support may not be as necessary.
With serious illness, a person’s tendency to seek social support diminishes in order
to accommodate the physical demands of the illness. When faced with disasters
that have compromised basic needs, practical support is far more important than
social support (Massey, 1997).
The remaining WOCS subscales. Based on the results of this study, reliabilities
on CC, D, and PP were not predicted by any of the variables in this study and
therefore are less likely to be compromised with different populations or ways of
administering the WOCS.
Recommendations for the Use of the WOCS
There are multiple recommendations regarding the use of the WOCS. First,
researchers need to report reliability estimates, paying particular attention to attributing the reliability statistics to their sample scores. Calculating reliability coefficients separately for gender, ethnicity, and age groups will make a considerable
contribution to the literature. Researchers should go beyond reporting reliability
coefficients by addressing the effect on resulting reliabilities in their interpretation
of results (Vacche-Hasse et al., 2002). Second, researchers should more adequately describe their samples. Third, it is recommended that researchers use a
time window greater than 1 week for the AR subscale. Fourth, samples should be
limited to adults or controlled for age with the AR, SC, and PR subscales. Fifth,
until further information is gleaned through future studies, it may be helpful for
researchers to use the standardized approach via the manual to administer the
WOCS because, in our study, many researchers deviated from scale administration instructions.
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Educational and Psychological Measurement
Limitations of the Study and Directions for Future Research
The major limitation of this study is the fact that it is underpowered for a complete exploration of the variables related to the reliability scores, which was due to
the underreporting and misreporting problems cited previously. Some researchers
have proposed that the lack of reporting of sample reliability estimates is due to the
‘‘file drawer’’ problem of not publishing studies with low reliabilities (Shields &
Caruso, 2003, p. 410). Other researchers who find that their reliability estimates are
too low may opt not to report the statistics or may report reliability estimates from
the manual. Therefore, possible studies with low reliability were not likely a part of
the sample of data used in this study. There was also an underreporting of the predictor variables investigated.
Test-retest reliability was intentionally excluded from analysis in this study for
reasons other than the lack of data. Folkman and Lazarus (1988b) state that testretest reliability is in direct conflict with their transactional theory of stress. Healthy
individuals make adjustments in coping as time passes, and such changes render
test-retest reliabilities meaningless.
The standard deviation of scores is another frequently coded variable in reliability
generalization studies (Caruso, Witkiewwitz, Belcourt-Dittloff, & Gottlieb, 2001). It
is fairly well known that higher variance in scores is related to higher reliability
(Crocker & Algina, 1986; Kieffer & Reese, 2002). Based on classical test theory, ‘‘if
error variance remains constant and observed score variance increases, then true
score variance must increase to exactly the same extent’’ (Shields & Caruso, 2003,
p. 409). Reliability, which is the ratio of true variance to total variance, will also
increase as a result. Unfortunately, the standard deviation of scores is not always
reported by researchers in the literature.
Many articles described their participants by source of sample (such as college
students) without reporting age; therefore, the study did not discriminate between
ages but rather between broad categories of people. It would be helpful to specify
specific age ranges of both adults and young people (Martin et al., 2001).
In summary, the results suggest that the reliability of the WOCS varies based on
subscales, administration, and sample variables. Reporting problems have led to a
misunderstanding of where, when, and with whom to use this instrument. This
situation is likely to improve as researchers heed Wilkinson and the American
Psychological Association Task Force on Statistical Inference’s (1999) recommendation that all researchers report reliability coefficient estimate information for their sample data, even if the study is not a psychometric study (Henson & Thompson, 2002).
The WOCS has more data gathered than any other coping instrument. Although it was
originally developed using solid test construction strategies, the ongoing investigatory
work has been limited. Future research should focus on strengthening the instrument
and understanding the factors related to obtaining adequate reliability across studies.
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275
Most important, researchers using the instrument should report more specific population descriptions, means, standard deviations, and reliabilities on each subscale.
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