Psychology
2012. Vol.3, No.11, 940-946
Published Online November 2012 in SciRes (http://www.SciRP.org/journal/psych) http://dx.doi.org/10.4236/psych.2012.311141
940
Where Have All the Worriers Gone? Temporal Instability of
the Abbreviated Penn State Worry Questionnaire Limits
Reliable Screening for High Trait Worry
Tamara E. Spence1,2*, Terry D. Blumenthal2, Gretchen A. Brenes3
1Neuroscience Program, Wake Forest University Graduate School of Arts & Sciences, Winston-Salem, USA
2Department of Psychology, Wake Forest University, Winston-Salem, USA
3Department of Psychiatry and Behavioral Medicine, Wake Forest School of Medicine, Winston-Salem, USA
Received August 13th, 2012; revised September 15th, 2012; accepted October 11th, 2012
Participant selection is an important step in research on individual differences. If detecting an effect of a
personality variable is predicated on the use of extreme groups, then mistakenly including participants
who are not in the extremes may weaken the ability to see an effect. In this study, changes in trait worry
were evaluated in 68 undergraduate students reporting low or high levels of worry. Participants completed
the abbreviated Penn State Worry Questionnaire (PSWQ-A) three times: 1) at the beginning of the se-
mester; 2) 3 - 13 weeks later; and 3) 1 hr later, following a psychophysiological assessment session.
Test–retest reliability across the three administrations was high, but almost half of the sample no longer
met the pre-defined criteria for classification as low or high worriers at the second administration. That is,
scores were reliable, but not stable, across time. Instability of self-report worry was significantly greater
for high worriers than for low worriers, and this effect was predicted by trait anxiety at the beginning of
the semester. These findings suggest that the PSWQ-A is sensitive to factors other than trait worry, which
may result in dilution of effects when participants are selected for extreme worry scores. This also sug-
gests that screening participants weeks before the actual study should be supplemented by readministra-
tion of the screening questionnaire, to identify participants who no longer meet criteria for inclusion.
Keywords: Reliability; Trait; PSWQ-A; Worry; Anxiety
Introduction
Self-report measures are commonly used to screen for per-
sonality traits in both clinical and research settings. The sta-
bility of these questionnaires is crucial for accurate assessment,
as well as for determining the effectiveness of a treatment or
intervention strategy. Nevertheless, the stability of personality
questionnaires can decrease with increasing length of time
between evaluations (Schuerger, Zarrella, & Hotz, 1989). Mean
shifts in self-report measures of anxiety, for example, can occur
with repeated assessment even in the absence of an external
variable, such that symptoms appear to improve over time
(Knowles, Coker, Scott, Cook, & Neville, 1996; Windle, 1954).
The use of questionnaires to recruit participants may, therefore,
pose a problem for researchers interested in mechanisms
underlying anxiety and its associated features, as instability can
result in misclassification of participants, weakening of effect
sizes, and increased error in the data (Knowles et al., 1996).
The purpose of this paper is to examine the stability of repeated
administrations of trait questionnaires of worry and anxiety
when such measures are used to screen participants for in-
clusion in a study.
The use of self-report measures to screen large groups of
prospective participants for a desired quality or personality trait
is common practice in the social sciences and has been used in
several studies designed to better understand the nature and
function of worry (Delgado, et al., 2009; Ruscio & Borkovec,
2004). However, it is it is not always clear whether study eligi-
bility was determined based on a single assessment of worry/
anxiety or if these questionnaires were readministered in order
to ensure stability of the desired trait over time. The former
scenario is the most convenient, and investigators may often
assume minimal change in the trait over time. Nevertheless, it is
important to recognize that personality traits may not be com-
pletely impervious to influence by environment, context, or
emotional state (Mischel, 1977). Also, just as some individuals
may be more sensitive to state-dependent fluctuations in self-
report assessment of personality traits than others, some pur-
ported trait questionnaires may demonstrate more instability
over time than others.
The temporal instability of personality trait measures is par-
ticularly problematic for studies in which comparison groups
are defined by their level of a particular trait, a problem that
becomes more substantial when the groups represent opposite
ends of the spectrum (Knowles et al., 1996). If the self-report
measures used to select prospective participants do not reflect
the trait of interest in a stable fashion, then individuals who are
initially recruited may no longer actually meet the inclusion
criteria upon study enrollment. Researchers who then fail to
find significant differences between groups may attribute this
failure to the lack of an effect of a specific personality trait on
the dependent variable, when a real effect may have been diluted
or masked by a shift in the trait used to assign participants to
groups. Therefore, the ability to predict which participants will
demonstrate trait stability vs. trait drift on a screening question-
*Corresponding author.

T. E. SPENCE ET AL.
naire is of methodological value. With respect to studies on
pathological worry, advancements in ways to separate stable,
chronic worriers from acutely worried individuals through the
use of self-report measures will facilitate a better understanding
of worry as a personality trait.
Worry is a major cognitive component of anxiety (Mathews,
1990); it is associated with poorly controlled negative thoughts
about uncertain future events (Borkovec, 1994). Chronic,
excessive worry is the defining feature of Generalized Anxiety
Disorder (GAD; Diagnostic and Statistical Manual of Mental
Disorders IV, TR; American Psychiatric Association, 2000),
and may contribute to the both the generation and maintenance
of other forms of anxiety by facilitating the early detection of
danger while preventing the rational processing of potentially
threatening information (Borkovec, 1994). The establishment of
severe worry as the primary diagnostic criterion for GAD
(DSM-III-R, 1987) provided a major impetus for the develop-
ment of psychometric instruments for the accurate and reliable
assessment of trait worry, the most frequently used measure
being the Penn State Worry Questionnaire (PSWQ; Meyer,
Miller, Metzger, & Borkovec, 1990).
The PSWQ is a content-nonspecific (i.e., general) instrument
that is used to assess pathological worry in terms of its per-
ceived excessiveness, uncontrollability, and duration. It has
excellent internal consistency and test-retest reliability and is,
therefore, considered to be highly reliable by conventional
standards (Stober, 1998), a fact that reinforces its continued use
in research studies. Because of the strong association between
pathological worry and GAD, there is an increased incentive to
use the PSWQ in studies designed to better understand this
disorder by targeting participants that closely model it, which
was one goal of the present investigation.
An 8-item abbreviated version of the PSWQ (PSWQ-A) was
proposed by Hopko et al. (2003). It is a shorter, more conve-
nient measure with comparable psychometric properties to the
full-length version, and it has been validated in young adults
(Crittendon & Hopko, 2006). Crittendon and Hopko (2006)
found that the PSWQ-A was strongly correlated with the full-
length PSWQ (r = .83) and showed a similar test-retest
reliability compared with the PSWQ (r = .87 v. r = .74 - .93).
Furthermore, the PSWQ-A demonstrated adequate construct
validity as a measure of general worry and had strong internal
consistency. Taken together, these observations suggest that the
PSWQ-A may be a quick and effective screening tool for
pathological worry.
However, little research has examined the PSWQ-A in a
nonclinical sample of young adults. The aim of this study was
to evaluate the stability of the PSWQ-A among young adults,
classified as either low or high worriers. Based on previous
reports, we expected the PSWQ-A to demonstrate high test-
retest reliability in the present sample; however we refrained
from making any hypotheses concerning the temporal stability
of worry group classification, determined using pre-defined
PSWQ-A cut-off scores, due to the exploratory nature of the
study design. In addition to the PSWQ-A, all participants com-
pleted the State-Trait Anxiety Inventory-Trait Scale (STAI-T;
Spielberger, Gorsuch, Lushene, Vagg, & Jacobs, 1983) as part
of a battery of self-report measures at the beginning of the
academic semester. Because the STAI-T is a well-established
measure of trait anxiety, we did not expect to see much vari-
ation in this measure over time. Consequently, we used the
STAI-T as a gold-standard for comparison in determining the
stability of the PSWQ-A.
Method
This paper reflects part of a larger psychophysiological study
of the impact of trait worry on reactivity to affective stimuli.
Participants
The PSWQ-A was administered to a total of 576 Intro-
ductory Psychology students during a series of Mass Testing
sessions at the beginning of the spring (N = 266) and fall (N =
310) academic semesters. The distributions of PSWQ-A scores
was normal in both the spring (skewness = .06) and fall
(skewness = .12) semesters. Students reported slightly, but not
significantly, lower PSWQ-A scores in the spring (M = 21.73,
SD = 7.67) than in the fall (M = 22.92, SD = 9.00), t(574) =
1.69, p > .05. The mean PSWQ-A scores were consistent with
that reported by Crittendon and Hopko (2006), M = 21.8, SD =
8.2, in a comparable sample of undergraduate students. Stu-
dents scoring one or more standard deviations below or above
the mean for a given semester were classified as low worriers
and high worriers, respectively, and were invited to participate
in a study on the effects of emotional words on information
processing, the results of which will be reported elsewhere. No
reference to worry was included in the invitation to participants.
Study enrollment began 3 - 13 weeks after the initiation of
Mass Testing and continued throughout the semester. Because
the means and standard deviations of PSWQ-A scores differed
slightly between the spring and fall semesters, the cut-off score
for inclusion in the high worry group also changed: ≥30 for the
spring semester and ≥32 for the fall semester. The cut-off score
for inclusion in the low worry group was the same for both
semesters: PSWQ-A score ≤ 13.
Seventy seven students (36 men, 41 women) provided
written informed consent for participation in the psycho-
physiological study, and completed demographic and health
questionnaires. Eight women and one man were excluded due
to hearing loss, the use of psychostimulant medication, experi-
menter error, or requested termination of the testing session.
This left a final sample of 68 students (Nspring = 39 and Nfall = 29)
with a mean age of 19.71 years (SD = 1.01, range = 18.42 -
22.42). No participants reported receiving psychotherapy or
taking mood-enhancing (psychoactive) compounds. Students
received course credit for their participation. All procedures
were approved by the University’s Institutional Review Board.
Measures
The Penn State Worry Questionnaire-Abbreviated (PSWQ-A;
Hopko et al., 2003) is an 8-item self-report trait measure of
pathological worry symptomatology derived from the PSWQ
(Meyer et al., 1990). General worry tendencies are rated on a
5-point Likert-type scale that ranges from “1” (not at all typical
of me) to “5” (very typical of me). Example items include “My
worries overwhelm me” and “I have been worrying about
things.” Internal consistency of the PSWQ-A in the present
sample of nonclinical young adults was excellent (α = .96) and
test-retest reliability was good (r = .88, 3 - 13-week period).
The Spielberger State-Trait Anxiety Inventory (STAI et al.,
1983) is a widely used self-report measure of anxiety that con-
sists of two separate 20-item scales for the assessment of im-
mediate (state) and general (trait) anxious feelings (STAI-S and

T. E. SPENCE ET AL.
STAI-T scales, respectively). The current experience of anxiety
is evaluated using the STAI-S and rated on a 4-point
Likert-type scale that ranges from “1” (not at all) to “4” (very
much so). The frequency of anxious symptomatology is deter-
mined using the STAI-T and rated from “1” (almost never) to
“4” (almost always). Example items from the STAI-S and
STAI-T are “I am tense” and “I feel nervous and restless,” re-
spectively. In the present study, internal consistency of the
STAI was excellent (α = .94 and α = .92 for the state and trait
scales, respectively) and test–retest reliability of the STAI-T
was strong (r = .83, 3 - 13-week period).
The Generalized Anxiety Disorder 7-Item Questionnaire
(GAD-7; Spitzer, Kroenke, Williams, & Lowe, 2006) is a brief
instrument that measures the severity of GAD symptoms ex-
perienced over a 2-week period. The extent to which individu-
als are bothered by a given symptom (e.g., “worrying too much
about different things”) is rated on a 4-pont Likert-type scale
that ranges from “0” (not at all) to “3” (nearly every day).
The Center for Epidemiologic Studies-Depression Scale
(CES-D; Radloff, 1977) is a 20-item self-report measure of
current depressive symptomatology, within a 1-week period.
The frequency of each symptom (e.g., “I felt everything I did
was an effort”) is rated on a 4-point Likert-type scale that
ranges from “0” (rarely or none of the time [<1 day]) to “3”
(most or all of the time [5 - 7 days]).
Procedure
Participants were screened for both trait worry and trait anxi-
ety at the beginning of the academic semester. We were blind to
participants’ worry and anxiety levels until the entire study was
completed. Upon study enrollment, participants were tested
individually in sessions lasting 1 - 1.5 hr.
After informed consent was obtained and study eligibility
was determined, participants completed the above question-
naires prior to a 30 min acoustic startle assessment session1.
Participants then completed a final set of questionnaires that
contained the PSWQ-A and additional measures that are not
germane to this study.
In summary, the PSWQ-A was administered three times in
order to 1) recruit participants with either low or high levels of
trait worry (Mass Testing), 2) obtain a baseline measure of
worry severity upon arrival at the laboratory (pre-session), and
3) evaluate the effects, if any, of the psychophysiological pro-
cedure on worry severity (post-session). To compare the stabil-
ity of trait worry with that of trait anxiety, the STAI-T was
administered in conjunction with the PSWQ-A at both Mass
Testing and pre-session. The time between the first and second
administrations of these questionnaires was 3 - 13 weeks,
whereas the time between the second and third administrations
was approximately 1 hr.
Data Analysis
All data were analyzed using IBM SPSS Statistics version 19.
Pearson’s product-moment correlations (r) were used to com-
pute test-retest reliability estimates for multiple administra-
tions of self-report measures and intercorrelations among pre-
session measures of psychological distress. Effect sizes for all
t-tests and analyses of variance (ANOVA) are reported using
Cohen’s d (Cohen, 1992) and partial eta squared (2 η
p
),
respectively. Greenhouse-Geisser epsilon (ε) corrected degrees
of freedom were used to counteract possible violations of
sphericity in repeated measures tests involving more than two
levels. Although uncorrected degrees of freedom are reported
below, statistical significance was determined using ε corrected
values. All analyses consisted of two-tailed tests, and statistical
significance was determined using an alpha level of .05.
Results
Temporal Instability of Worry
Changes in PSWQ-A scores as a function of repeated assess-
ment were examined using a 2 (semester: spring, fall) by 3
(time: Mass Testing, pre-session, post-session) mixed-model
ANOVA with repeated measures for time. There was no effect
of semester on PSWQ-A scores, p > .3. However, a main effect
of time on self-report worry was observed, F(2,136) = 17.56,
p < .001, ε = .658, 2 η
p
= .21. Tests of pairwise comparisons
were conducted using Bonferroni adjusted alpha levels of .017
per test (.05/3). Results indicated that there were significant
reductions in the average PSWQ-A score from Mass Testing
(M = 20.44, SE = 1.45) to pre-session (M = 18.15, SE = 1.11)
and from pre-session to post-session (M = 16.82, SE = 1.12).
Although the PSWQ-A had strong test–retest reliability
across administrations (r = .88, p < .001, from Mass Testing to
pre-session, and r = .96, p < .001, from pre- to post-session),
further inspection of mean shifts in worry over time indicated
that 41% of the sample failed to retain their original classi-
fication as members of either the low worry group or high
worry group from Mass Testing to pre-session. An indepen-
dent-samples t-test between proportions indicated that a greater
number of high worriers (69%) demonstrated significant drift in
self-report worry during this time than low worriers (20%), t(67)
= 4.09, p < .001.
In an effort to better understand these mean shifts in worry,
we divided the sample into four distinct groups based on
PSWQ-A scores at pre-session (see Figure 1): 1) stable low
worry (n = 31), M = 10.32, SE = .30; 2) unstable low worry,
(n = 8), M = 15.75, SE = .80; 3) unstable high worry (n = 20),
M = 25.00, SE = .84; and 4) stable high worry (n = 9), M =
33.67, SE = 1.18. Independent samples t-tests were used to eva-
luate the average difference (D) in PSWQ-A scores between the
stable and unstable worry groups at the third assessment point
(post-session). Unstable high worriers reported significantly low-
er levels of worry than stable high worriers, t(27) = 6.24, p < .001,
D = 10.71, d = 1.20. However, unstable low worriers reported
only marginally higher levels of worry than stable low worriers,
t(37) = –2.05, p < .1, D = –2.04, d = –.33 (see Figure 1).
1Briefly, two miniature surface recording electrodes were placed on the skin
above the orbicularis oculi muscle on the left side of the face, with a ground
electrode placed on the left temple, and participants wore headphones
through which 50-ms bursts of intense (100 dB) broad
b
and noise were
intermittently presented. During the testing session, participants passively
viewed a series of words of varying emotional valence that were presented
on a computer monitor at a viewing distance of 40 cm. For each word, the
exposure duration was 1 s (average intertrial interval = 20 s). The electro-
myographic activity of facial muscle contractions in response to the loud
noises (i.e., the acoustic startle eyeblink response) was recorded, quantified,
and subsequently analyzed as a function of both worry severity and word
type (those startle data are not included in this paper). Electrodes and head-
phones were removed before the third administration of the PSWQ-A.
Temporal Stability of Trait Anxiety
Similar to the PSWQ-A, the STAI-T demonstrated high test-
942

T. E. SPENCE ET AL.
Figure 1.
Changes in self-report worry in select groups of low and high
worriers as a function of repeated assessment with the abbreviated
Penn State Worry Questionnaire (PSWQ-A). Mean shifts in
PSWQ-A scores from study recruitment (Mass Testing) to enroll-
ment (pre-session) resulted in the reclassification of participants
into four groups: 1) stable low worry (n = 31); 2) unstable low
worry (n = 8); 3) unstable high worry (n = 20); and 4) stable high
worry (n = 9). Error bars represent the SEM.
retest reliability (r = .83, p < .001). Based on changes in self-
report worry in this sample, stability of trait anxiety from Mass
Testing to pre-session was investigated using a 4 (group) by 2
(time) mixed-model ANOVA with repeated measures for time
(see Figure 2). There was a main effect of time on STAI-T
scores, F(1,64) = 4.82, p < .05, 2 η
p
= .07, driven by a reduc-
tion from Mass Testing (M = 39.95, SE = 1.03) to pre-session
(M = 38.03, SE = 1.05). In addition, there was a main effect of
group, F(3,64) = 28.73, p < .001, 2 η
p
= .57, such that the high
worry groups had higher mean STAI-T scores than the low
worry groups. However, the interaction between group and time
was not significant, F(3,64) = 1.16, p > .3. To follow up the
main effects, independent-samples t-tests were performed to
examine differences in Mass Testing trait anxiety between groups
as function of worry stability. Results indicated that participants
in the stable high worry group (M = 51.11, SE = 3.11) reported
significantly higher levels of trait anxiety at the beginning of
the academic semester than those in the unstable high worry
group (M = 44.45, SE = 1.66), t(27) = 2.07, p < .05, d = .83,
despite the fact that members of both groups were originally
recruited for comparable levels of worry severity. By contrast,
Mass Testing trait anxiety did not significantly differ between
participants in the stable low worry group (M = 30.00, SE =
1.22) and those in the unstable low worry group (M = 34.25,
SE = 2.31), t(37) = –1.59, p > .1.
Although trait anxiety and worry severity were highly corre-
lated at both Mass Testing (r = .75, p < .001) and pre-session
(r = .72, p < .001), significant shifts in worry were not paired
with similar shifts in trait anxiety as a function of repeated
assessment. Participants who were both highly worried and
highly anxious showed less variance in self-report worry over
time, providing a rationale to conduct a discriminant analysis of
the predictive ability of trait anxiety at the beginning of the
semester to determine the stability of worry several weeks later.
Because trait anxiety did not differ between the low worry
groups, the analysis was restricted to the high worry groups.
The discriminant function, D = (.125 × trait anxiety) – 5.80,
indicated a significant association between groups and Mass
Testing STAI-T scores. Specifically, the canonical correlation
between trait anxiety and the discriminant function (R = .37),
F(1,16) = 7.46, p < .05, explained approximately 14% of the
Figure 2.
Stability of self-report trait anxiety over time. Groups were defined
by differences in the consistency of worry (PSWQ-A scores) from
Mass Testing to pre-session. The stable high worry group reported
higher levels of trait anxiety than the unstable high worry group at
both time points. By contrast, trait anxiety did not differ between
the stable and unstable low worry groups. Error bars represent the
SEM.
variance between the stable and unstable high worry groups.
Application of the function to group centroids generated mean
scores of .572 and –.258 for the stable and unstable high worry
groups, respectively. The cross-validated classification showed
that overall 72.4% of high worriers were correctly classified as
belonging to either the stable high worry group (33.3%) or the
unstable high worry group (90.0%). The value of tau for the
classification was .355, meaning that 35.5% fewer errors were
made by using the discriminate function to predict worry group
membership compared with random classification, which
supports the conclusion that the probability of observing tem-
poral stability of worry among individuals who report initial
high PSWQ-A scores increases if they also report high STAI-T
scores (odds ratio = 4.5). Visual inspection of the raw data
revealed that a score ≥ 45 on the STAI-T accounted for 89% of
the stable high worry group and 50% of the unstable high worry
group.
Additional Measures of Psychological Distress
Worry, state and trait anxiety, GAD symptomatology, and
depression assessed prior to the acoustic startle test (pre-session)
were found to be significantly positively correlated with one
another at the level of the whole sample (all ps < .001, see
Table 1); however, the strength of these relationships was
diluted by the inclusion of participants in the unstable worry
groups, for whom there were much weaker associations be-
tween pre-session worry and psychological distress. Significant
differences in the strength of correlation coefficients among
pre-session measures between the stable and unstable worry
groups were evaluated using Fisher z-to-r transformations and
are highlighted in Table 1.
In order to better evaluate potential factors contributing to
group differences in stability of self-report worry over time,
each additional measure of psychological distress was examined
as a function of final worry group classification using a series
of one-way ANOVA. A significant main effect of group was
observed for all measures (all ps < .001). F(3,64) values (with
2
η
p
in parenthesis) for the STAI-T, STAI-S, GAD-7, and
CES-D were 20.12 (.49), 9.63 (.31), 18.77 (.47), and 9.81 (.31),
respectively. Tukey’s honestly significant difference (HSD)
tests were then used to compare mean differences in self-report

T. E. SPENCE ET AL.
944
Table 1.
Intercorrelations among pre-session measures.
STAI-T STAI-S GAD-7 CES-D
PSWQ-A .715*** .577*** .704*** .499***
.790***/.439*+ .671***/.419* .798***/.367†++ .656***/−.090+++
STAI-T .568*** .715*** .663***
.635***/.441* .855***/.370†+++ .807***/.311++
STAI-S .604*** .452***
.673***/.479** .645***/.125+
GAD-7 .807***
.854***/.688***
Note: Pearson’s r values. Intercorrelations for the entire sample (N = 68) are presented in bold. Intercorrelations for participants as a function of stability of self-report
worry are presented below those for the whole sample. Left of the slash, stable worry groups (n = 40, 31 stable low worriers and 9 stable high worriers); Right of the slash,
unstable worry groups, (n = 28, 8 unstable low worriers and 20 unstable high worriers). PSWQ-A = Penn State Worry Questionnaire-Abbreviated; STAI-T = State-Trait
Anxiety Inventory-Trait Scale; STAI-S = State-Trait Anxiety Inventory-State Scale; GAD-7 = Generalized Anxiety Disorder 7-Item Questionnaire; CES-D = Center for
Epidemiologic Studies-Depression. †p < .1; *p < .05; **p < .01; ***p < .001, illustrating the strength of relationships within each group; +p < .05; ++p < .01; +++p < .001,
reflecting differences in r values between groups determined using Fisher z-to-r transformations.
measures of psychological distress among worry groups (see
Table 2), with an emphasis on measures that distinguished
stable and unstable worry groups. These results showed that
both trait anxiety and GAD symptomatology significantly dif-
fered between the stable and unstable high worry groups (p < .05
in both cases), with stable high worriers reporting higher levels
of trait anxiety and more GAD symptoms than unstable high
worriers. Only depressive symptoms differed significantly
between the stable and unstable low worry groups (p < .05),
with unstable low worriers reporting a higher incidence of
depression than the stable low worry group. However, this
effect was largely driven by two participants in the unstable low
worry group who had CES-D scores of 23 and 36 (with 36
being the maximum score reported in the present study).
Excluding these two participants from the analysis reduced the
CES-D mean score (from 14.25 to 9.17) for the unstable low
worry group, thereby eliminating the significant difference be-
tween the low worry groups (Tukey’s HSD test, p > .89).
Discussion
Consistent with Crittendon and Hopko (2006), who showed
that the PSWQ-A has good 2-week test-retest reliability (r = .87)
in undergraduate students (N = 183), we found that the strength
of this relationship was maintained throughout a 3 - 13-week
interval in a smaller sample of students (r = .88, N = 68).
However, despite the fact that all participants were originally
classified as either low worriers or high worriers based on
PSWQ-A scores, almost half of the sample no longer met the
criteria for inclusion in the study upon arrival at the laboratory.
Drift in the trait worry scores was greater for high worriers than
for low worriers, an effect that would not have been seen had
we not readministered the PSWQ-A. This suggests that signi-
ficant within-person variation across administrations in non-
clinical young adults may limit the utility of the PSWQ-A as a
trait measure. Also, these results illustrate the fact that
reliability is not synonymous with stability; a relatively con-
sistent change in score across participants will not affect the
correlation between scores at the two times, but the actual
scores will be different, and this may affect the probability of a
particular individual being included in the study for which this
particular questionnaire is a screen.
Table 2.
Characteristics associated with stability of worry over time.
Variable
stable
low worry
n = 31
unstable
low worry
n = 8
unstable high
worry
n = 20
stable
high worry
n = 9
STAI-T29.94 (1.24)a31.88 (2.20)a 41.20 (1.78)b 49.11 (3.03)c
STAI-S27.42 (.97)a27.50 (2.54)a,b 36.40 (2.42)b 42.11 (4.14)b,c
GAD-72.32 (.33)a 5.38 (1.76)a,b 7.20 (.82)b 11.22 (1.75)c
CES-D7.45 (.84)a 14.25 (3.67)b 12.90 (1.09)b 19.33 (3.05)b
Note: Within each row, significant differences between groups are denoted by
distinct superscript letters (Tukey’s HSD post-hoc test after one-way ANOVA).
STAI = State-Trait Anxiety Inventory (STAI-T = Trait Scale, STAI-S = State
Scale); GAD-7 = Generalized Anxiety Disorder 7-Item Questionnaire; CES-D =
Center for Epidemiologic Studies–Depression. Mean scores (SE).
Comparisons among pre-session measures of psychological
distress revealed that all measures were highly intercorrelated
in the stable worry groups; however, inclusion of participants in
the unstable worry groups weakened the strength of these
relationships, suggesting a unique dissociation between trait
worry, as assessed by the PSWQ-A, and other well-established
state and trait measures in these individuals. For personality
researchers who use questionnaire scores dimensionally, rather
than recruiting participants scoring in the extremes of the
measure, this drift may not be as important an issue. In those
cases, a score that shifts from 1.1 to .9 SD above the mean may
not cause significant problems. But when participant inclusion
is based on specific cutoffs, the person scoring 1.1 SD above
the mean would be included in the study, whereas the same
person scoring .9 SD above the mean would not. Therefore, if a
researcher does not know that the trait score is unstable, people
may be included in the study who do not actually meet the
inclusion criteria, weakening the effect size of the personality
factor under study. This problem can be prevented by simply
readministering the screening questionaire again, as close to the
time of the experimental session as possible. Although some
investigators (Hazen, Vasey, & Schmidt, 2009; Krebs, Hirsch,
& Mathews, 2010; Tallis, Eysenck, & Mathews, 1991) report
readministration of screening questionnaires prior to study
enrollment, and the subsequent exclusion of participants who
no longer meet study criteria, many do not. We recommend the

T. E. SPENCE ET AL.
practice of questionnaire readministration and participant exclu-
sion in studies utilizing extreme groups, since failure to do so
may allow unqualified participants into a study, and that can
lead to dilution of the distinction between extreme groups,
increased error, and reduced effect sizes.
There are three potential explanations for our findings. The
first is regression toward the mean. Given the delay between
recruitment and study enrollment, and the selection of parti-
cipants with scores in the extremes of the PSWQ-A distribution,
some regression toward the mean PSWQ-A score was anti-
cipated, but this does not fully explain our results. For these
changes in PSWQ-A score to be due to regression toward the
mean, that regression would have been expected to be greater
for participants further away from the mean, and this was not
the case. Participants with extreme scores of 8 and 40 were as
likely to retain their original worry group classification as they
were to exhibit a shift in worry upon repeated assessment with
the PSWQ-A. This was also true for participants with less
extreme PSWQ-A scores. Furthermore, the majority of high
worriers (69%) demonstrated significant drift in self-report
worry over time, which was in stark contrast to the percentage
of low worriers (20%) who did so. Regression toward the mean
may explain some, but not all, of the shift seen here.
A second possibility is the test-retest effect, in which self-
report anxiety decreases as a function of repeated assessment
(Windle, 1954), possibly due to an increased familiarity with
the test items, such that some participants respond in accor-
dance with what they perceive to be a more socially acceptable
level of negative affect upon reassessment (Goldberg, 1978;
Knowles et al., 1996). While this may partially explain the re-
duction in worry from Mass Testing to pre-session in some
high worriers, it cannot account for the increase in worry scores
across the same time period in some low worriers.
The most likely explanation for these findings may be that
the PSWQ-A is sensitive to state-dependent fluctuations in
worry, probing current experience of worry as opposed to
general worry tendencies. This explanation may seem counter-
intuitive given the content-non-specific nature of the items.
However, misinterpretation of some items as asking about
current state, by some participants, would be sufficient to yield
the shifts in mean worry scores seen in this study.
Trait anxiety as measured with the STAI-T was positively
correlated with worry, but was more stable over time. Parti-
cipants in the stable high worry group reported higher levels of
trait anxiety than those in the unstable high worry group,
suggesting that the stable participants were as worried as, but
more anxious than, the unstable participants at the beginning of
the semester. A discriminant function analysis suggests that
trait anxiety may predict the stability of worry in participants
with greater levels of psychological distress. In order to de-
crease the possibility of recruiting participants with unstable
worry scores, investigators may consider administering the
STAI-T in conjunction with the PSWQ-A and recruiting only
those individuals with high scores on both measures (e.g.,
STAI-T score ≥ 45 and PSWQ-A score ≥ 30).
Stability in a personality measure is especially important for
studies in which change is a primary outcome measures (e.g.,
those involving implementation of an intervention to reduce
worry). For example, if we had not administered the PSWQ-A
when participants arrived at the laboratory, we would not have
known that some of those participants no longer met inclusion
criteria (one standard deviation or more from the mean). Had
we then conducted the experiment and measured worry at the
end of the session, we might have mistakenly attributed shifts
in worry to the psychophysiological assessment session. More
generally, if a pretest is used to select participants, and if a
posttest is then used to evaluate the success of an intervention,
drift in scores between the time of the pretest and the time
immediately before the intervention could be mistaken for
success (or failure) of the intervention. This would be a prob-
lem easily avoided by readministration of the questionnaire.
Limitations of the Present Study
There are several important limitations of our experimental
design. First, our small sample may have had low statistical
power. However, sample size cannot account for the shift of
many participants out of their preliminary screening classifica-
tions. Second, a larger sample would have allowed us to con-
duct an item analysis of the PSWQ-A, which might identify the
items that are less or more stable over time. Third, our inability
to track the exact dates between first and second administra-
tions of the PSWQ-A means that we do not know if the parti-
cipants in the unstable worry groups were those with the great-
est temporal delay (13 weeks) between recruitment and study
enrollment. Given that the majority of participants in the unsta-
ble group were high worriers, it is unlikely that these partici-
pants completed the PSWQ-A in the first week of Mass Testing
and enrolled in the present study (for extra course credit) in the
last week of the academic semester. Nevertheless, future studies
should investigate the relationship between the time between
administrations and the stability of the PSWQ-A. Fourth, it is
possible that the full-length PSWQ may be more stable over
time than the abbreviated version. However, Hazen et al. (2009)
used the PSWQ to screen for pathological worry prior to im-
plementation of an intervention designed to reduce worry se-
verity; they report some degree of instability in this measure
over time (e.g., 8 of 32 high worriers no longer met the cut-off
criterion for study enrollment in the 23 days between PSWQ-A
administrations). Although 25% instability is much lower than
the 79% instability among high worriers that we report, we
recommend that a future study administer both versions of the
PSWQ and directly compare the relative stability of worry as a
function of repeated assessment among participants recruited
for low and high levels of worry.
Conclusion
In summary, our data indicate that some measures of anxiety
may be more stable than others across administrations, and that
participants selected during preliminary screening may no
longer meet inclusion criteria when tested at a later date. This
weakening of the distinction between groups occurs whether
the researcher knows it or not. This can be a significant prob-
lem if high scores are used to select participants for some inter-
vention or treatment that is expected to decrease these scores
(e.g., testing a treatment for high anxiety/worry), since some
“improvement” may be seen simply due to trait drift, although
this shift maybe mistakenly be attributed to the treatment
applied. Therefore, if a questionnaire is used to screen indi-
viduals for high levels of some factor prior to inclusion in a
research study, we recommend that it be given at least two
times in order to identify participants with stable levels of that
factor. By only recruiting participants who will be most likely

T. E. SPENCE ET AL.
946
to maintain their study inclusion criteria upon enrollment, we
may save valuable time and resources, increase effect size, and
reduce experimental error.
Acknowledgements
This work was funded by Wake Forest University Graduate
School of Arts & Sciences and the Department of Psychology,
and reflects research conducted by the first author in partial
fulfillment of the requirements for the degree of Doctor of Phi-
losophy in Neuroscience at Wake Forest University Graduate
School of Arts & Sciences. We would like to thank Dustin
Wood, Mike Furr, Eric Stone, and Daniel Blalock for their
assistance and advice.
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