Is Poor Thought Suppression Integral to Pathological Worry?
THESIS
Presented in Partial Fulfillment of the Requirements for the Degree Master of Arts in the
Graduate School of The Ohio State University
By
Graham E. Cooper, B.A.
Graduate Program in Psychology
The Ohio State University
2014
Master's Examination Committee:
Professor Michael W. Vasey, Ph.D., Advisor
Professor Julian Thayer, Ph.D.
Professor Amelia Aldao, Ph.D.
Copyrighted by
Graham Elliot Rome Cooper
2014
Abstract
Past theory on intrusive worry (e.g., Wells and Carter 1999) suggests that
intrusive worry reflects, in part, the worrier's poor success at, and excessive reliance on,
thought suppression to control their negative thoughts. That is, high worriers should be
expected to experience difficulty in suppressing their unwanted thoughts, and attempts to
nonetheless do so should results in greater accessibility of those thoughts (Wenzlaff &
Wegner, 2000). Yet, empirical evidence supporting this theory has been rare, and recent
research (Iijima & Tanno, 2012) has found that there are some high worriers who are able
to successfully suppress unwanted thoughts, with this success predicting a reduced
likelihood of ironic rebound.
In a sample of 58 college students and using a standard laboratory thought
suppression paradigm, the present study sought to replicate the findings by Iijima and
Tanno (2012). Furthermore, individual differences in effortful control (EC) were
proposed as an explanation for differential suppression success among high worriers.
Specifically, we expected that the association between worry and thought suppression
success is moderated by EC and the association between worry and ironic consequences
of suppression is mediated by initial suppression success. We also tested the hypothesis
that high worriers who suppress well will be less likely than their poor suppressing
ii
counterparts to show evidence of pathological worry (e.g., they should be less likely to
meet diagnostic criteria for Generalized Anxiety Disorder [GAD]).
The results successfully replicated the findings by Iijima and Tanno (2012). However,
contrary to expectations there was no evidence to suggest that individual differences in
EC account for differential suppression success among high worriers. Furthermore,
suppression success did not moderate the association between worry and any indicators
of pathological worry. Thus, high worriers who suppressed successfully in the thought
suppression task were not less likely than their poor suppressing counterparts to meet
GAD criteria or to score high on measures of other indicators of pathological worry. This
suggests that thought suppression difficulties may not play an important role in
pathological worry. However, the fact that success in the thought suppression task did not
predict differences in questionnaire-based measures of thought intrusions frequency and
thought suppression success during the participants' daily lives, raises doubt about the
thought suppression task’s ecological validity. Furthermore, there was evidence to
suggest that self-reported thought suppression success moderates the relationship
between worry and symptoms of pathological worry. Specifically, for those individuals
reporting low levels of suppression success, high worry was more strongly associated
with certain symptoms of pathological worry than it was for individuals reporting high
levels of suppression success. Thus, research on suppression among worriers should not
presuppose that high worriers will experience failure on a laboratory suppression task,
but neither should it presuppose that performance on such a task is an accurate
representation of a worrier's true thought suppression abilities.
iii
Acknowledgments
I would like to thank my advisor, Dr. Michael Vasey, for his incredible patience
and expert input throughout the writing process. I would also like to extend my sincere
gratitude to the other members of my thesis committee, Dr. Julian Thayer and Dr. Amelia
Aldao, for their invaluable contributions.
iv
Vita
June 2008 .......................................................Vintage High School
2012................................................................B.A. Psychology, University of California,
Berkeley
2012-2013 ......................................................University Fellow, The Ohio State
University
2013 to present ..............................................Graduate Teaching Associate, Department
of Psychology, The Ohio State University
Fields of Study
Major Field: Psychology
v
Table of Contents
Abstract ............................................................................................................................... ii
Acknowledgments.............................................................................................................. iv
Vita...................................................................................................................................... v
Table of Contents ............................................................................................................... vi
List of Tables ................................................................................................................... viii
List of Figures ..................................................................................................................... x
Chapter 1: Introduction ...................................................................................................... 1
Chapter 2: Methods ........................................................................................................... 15
Sample ........................................................................................................................... 15
Material ......................................................................................................................... 16
Questionnaires ........................................................................................................... 16
Thought Suppression Task ........................................................................................ 19
Procedure ....................................................................................................................... 20
Analytic Strategy ........................................................................................................... 20
vi
Chapter 3: Results ............................................................................................................. 23
Preliminary Analyses .................................................................................................... 23
Descriptive Statistics ..................................................................................................... 23
Primary Analyses .......................................................................................................... 29
Hypothesis 1 - Worry x Phase 2 Intrusions Predicting Phase 3 Intrusions ............... 29
Hypothesis 2 - Worry x EC Predicting Phase 2 Intrusions........................................ 31
Hypothesis 3 - Worry x Phase 2 Intrusions Interaction Predicting GAD Status and
Correlates of GAD ..................................................................................................... 31
Further Analyses ........................................................................................................... 39
Chapter 4: Discussion ....................................................................................................... 44
Limitations ................................................................................................................. 50
References ......................................................................................................................... 53
vii
List of Tables
Table 1. Descriptive statistics. .......................................................................................... 24
Table 2. Bivariate correlations. ......................................................................................... 27
Table 3. Regression analysis predicting phase 3 intrusions with PSWQ and phase 2
intrusions. .......................................................................................................................... 30
Table 4. Regression analysis predicting phase 2 intrusions with PSWQ and ATQ-EC. .. 31
Table 5. Regression analysis predicting GAD-Q-IV continuous score with PSWQ and
phase 2 intrusions. ............................................................................................................. 32
Table 6. Regression Analysis predicting Newman's dichotomous score from PSWQ and
phase 2 intrusions. ............................................................................................................. 32
Table 7. Regression analysis predicting Fresco's dichotomous score from PSWQ and
phase 2 intrusions. ............................................................................................................. 33
Table 8. Regression analysis predicting SCID diagnosis from PSWQ and phase 2
intrusions. .......................................................................................................................... 34
Table 9. Regression analysis predicting DASS-A from PSWQ and phase 2 intrusions... 34
Table 10. Regression analysis predicting DASS-D from PSWQ and phase 2 intrusions. 35
Table 11. Regression analysis predicting DASS-S from PSWQ and phase 2 intrusions. 36
viii
Table 12. Regression analysis predicting TSI-I from PSWQ and phase 2 intrusions. ..... 36
Table 13. Regression analysis predicting TSI-S from PSWQ and phase 2 intrusions. .... 37
Table 14. Regression analysis predicting TSI-E from PSWQ and phase 2 intrusions. .... 38
Table 15. Regression analysis predicting SF-36 from PSWQ and phase 2 intrusions. .... 38
Table 16. Regression analysis predicting DASS-A from PSWQ and TSI-E. ................... 39
Table 17. Regression analysis predicting DASS-D from PSWQ and TSI-E. ................... 41
Table 18. Regression analysis predicting GAD-Q-IV continuous score from PSWQ and
TSI-E. ................................................................................................................................ 42
ix
List of Figures
Figure 1. Scatter plot of phase 2 intrusions by scores on the PSWQ. .............................. 28
Figure 2. Scatter plot of phase 2 intrusions by GAD status based on Fresco's GAD-Q-IV
cutoff score........................................................................................................................ 29
Figure 3. PSWQ x phase 2 intrusions predicting phase 3 intrusions. ............................... 30
Figure 4. PSWQ x TSI-E predicting DASS-A.................................................................. 40
Figure 5. PSWQ x TSI-E predicting DASS-D.................................................................. 41
Figure 6. PSWQ x TSI-E predicting GAD-Q-IV continuous score.................................. 43
x
Chapter 1: Introduction
Worry is a common phenomenon that is characterized, at a pathological level, by
its intrusive nature (Tallis & Eysenck, 1994). Conventional views of worry have posited
that intrusive worries result, in part, from difficulties suppressing unwanted thoughts and
vulnerability to ironic effects of attempting to do so (Wells & Carter, 1999). However,
finding evidence to support this theory has been difficult (Magee, Harden, & Teachman
2012), and a recent study by Iijima and Tanno (2012) suggests that some high worriers
are capable of suppressing successfully and do not experience ironic consequences of
their suppression. However, because that findings is so at odds with conventional
thinking about worry, it warrants replication. That was the primary aim of the current
study. Furthermore, we sought to clarify the source of such variability in thought
suppression success. Finally, we sought to test whether worriers who suppress well are
less likely to meet criteria for generalized anxiety disorder (GAD) or to show other signs
of pathological worry.
Wells' and Carter's (1999) model of suppression and worry argues that intrusive
worries reflect the worrier’s poor success at, and excessive reliance on, thought
suppression to control unwanted worrisome thoughts. Worriers may also be especially
vulnerable to ironic increases in unwanted thoughts stemming from efforts to suppress
1
them. Such arguments are based on Wegner's ironic process model of thought
suppression (Wegner, 1992). From Wegner's perspective, suppressing a thought requires
both an effortful operating process and an automatic ironic monitoring process. The
effortful operating process involves the active search for alternative thoughts to distract
the suppressor from the unwanted thought. The ironic monitoring process, on the other
hand, involves monitoring one's thought processes for intrusions by the suppressed
thought. This automatic monitoring process is necessary for the suppressor to notice
when there is a need to increase effort toward the operating process. However, by its very
nature this monitoring process ironically allows the suppressed thought to come more
readily to mind (Wenzlaff & Wegner, 2000). Indeed, some evidence suggests that when
the suppressor is under high cognitive load, thereby taking resources away from the
effortful operating process, suppression efforts are more likely to fail (Wegner & Erber,
1992). Thus, worriers will endorse both a tendency to rely on thought suppression and
poor thought suppression success (i.e., a tendency to experience intrusive thoughts).
One common method of assessing thought suppression use and success is the
White Bear Suppression Inventory (WBSI), Wegner's self-report measure of chronic
thought suppression (Wegner & Zanakos, 1994). Although the WBSI was originally
conceived as a unitary measure of suppression, a number of factor analytic studies have
shown that the instrument is not unidimensional (e.g., Blumberg, 2000; Schmidt et al.,
2009 & Wismeijer, 2012). Items comprise at least two factors: one measuring the
tendency to suppress and one measuring the experience of intrusive thoughts. If the ironic
process model is accurate then we would expect both aspects to be elevated among
2
worriers. Because the effortful operating process should be expected to eventually
deplete cognitive resources even when the suppressor is not under cognitive load, most
people should experience a greater accessibility of their unwanted thoughts if they rely
heavily on thought suppression. Thus, the path to a high score on the WBSI is to
frequently attempt to suppress unwanted thoughts and to experience intrusions from
doing so.
Consistent with this view, several self-report studies examining the correlation
between thought suppression and worry have made use of the WBSI total score. For
example, Watkins and Moulds (2009), in a study examining thought control strategies
among depressed individuals, found a significant correlation (r = .62) between
participants' scores on the Penn State Worry Questionnaire (PSWQ; Meyer, Miller,
Metzger, & Borkovec, 1990) and their total score on the WBSI. Similarly, McKay and
Greisberg (2002) found that scores on the PSWQ were highly correlated (r = .54) with
scores on the WBSI in a non-clinical sample of Canadian undergraduates. Furthermore,
in a non-clinical sample of Dutch college students (Muris, Merckelbach, & Horselenberg,
1995), scores on the WBSI were highly associated (r = .57) with trait anxiety scores from
the State Trait Anxiety Inventory (STAI; Spielberger, 1983). Thus, this line of research
suggests that worriers do indeed report a tendency to both rely heavily on suppression
and to experience intrusive thoughts
While we would expect worriers to have a high total score on the WBSI if thought
suppression underlies intrusive worry, studies that examine the relationship between
worry and the individual factors of the WBSI are more informative for understanding
3
how thought suppression relates to worry. Insofar as worriers have been assumed to
score high on a measure of intrusions, such studies have largely tended to focused on the
suppressive tendencies factor. For example, in a sample of normal Canadian adolescents,
Laugesen, Dugas, and Bukowski (2003) found a significant positive correlation (r = .25)
between the suppression factor of the WBSI and the PSWQ-C (Penn State Worry
Questionnaire for Children; Chorpita, Tracey, Brown, Collica, & Barlow, 1997). A
similar study examining gender differences in the relationship between worry and a
variety of cognitive variables replicated these results (r = .49 for females; r = .44 for
males) in a sample of normal Canadian college students (Robichaud, Dugas, & Conway,
2003). Thus, analyses using the suppressive tendencies factor demonstrate that worriers
tend to report significant tendencies to suppress unwanted thoughts. According to
Wegner's ironic process theory, this should predict further intrusive thoughts, insofar as
the effortful operating process depletes the very cognitive resources it requires.
Magee, Harden, and Teachman (2012) suggest two routes by which
psychopathology (and, by extension, GAD or worry) may be particularly linked to
thought suppression. First, it may be that worriers experience more frequent intrusive
thoughts than other individuals simply because they rely more heavily on thought
suppression as a strategy for emotion regulation. In other words, in response to
worrisome thoughts these individuals chronically exert thought suppression effort
resulting, per the ironic process theory, in an increased recurrence of such thoughts. The
other possibility is that worriers are more susceptible to the ironic effects of suppressing
than other individuals. That is, even if worriers suppress no more than anyone else, their
4
attempts at doing so have a higher likelihood of leading to intrusive thoughts. Consistent
with this view, Iijima and Tanno (2012) found that the likelihood of an ironic rebound is,
in part, a function of initial suppression success. This suggests a third, related, path to
more frequent intrusions: poor initial suppression success. Of course, these models are
not mutually exclusive. That is, the pathological worrier likely relies more on
suppression but may also have poor suppression success. Reliance on suppression
consumes the resources required to engage in the effortful operating process, leading to
further intrusions. This prompts the worrier to engage in further suppression efforts,
leading to a downward spiral. Questionnaire studies reveal that worriers report relying
more heavily on suppression. The assumption is that they should therefore experience
more ironic effects, but that can only be examined in laboratory studies.
Although the methods used in laboratory studies of though suppression vary, a
relatively standard design has emerged: Participants are assigned to either an
experimental suppression condition or a control condition and are provided with a target
thought. Each participant completes three phases. The first is a monitoring period in
which participants in both groups are instructed to think about whatever they like, but to
indicate (e.g., by pressing a button on a keyboard) whenever they experience the target
thought. During the second phase, participants in the experimental group are instructed
to suppress the target thought but to also indicate its occurrence if it intrudes. In contrast
the control participants complete a second monitoring period. During the third phase,
participants in both groups complete another monitoring period. Wegner and colleagues
(1987) proposed that participants in the experimental condition may exhibit either or both
5
of two phenomena: 1) an initial enhancement effect (i.e., an increased occurrence of the
target thought during the suppression phase relative to controls) or 2) a rebound effect
(ie., an increased occurrence of the target thought in the third phase following the
cessation of active attempts to suppress after the second period).
Despite the evidence from questionnaire studies, laboratory studies have failed to
find strong evidence for a link between pathological worry and negative consequences of
suppression. For example, Mathews and Milroy (1994) compared the effects of
suppressing worrisome thoughts between high worriers and normal controls. Participants
completed a five minute period in which they were assigned to think about their primary
worry (worry condition), think about anything besides their primary worry (suppression
condition), or think about a non-worrisome thought (non-worry condition). All
participants were then allowed to let their mind wander for fifteen minutes, and were
prompted to record their current thoughts approximately every sixty seconds. At the end
of this period, the participant and the experimenter collaborated to determine how to
categorize the thoughts. The results yielded no evidence to support the view that high
worriers are more likely to experience rebound effects of suppression than control
participants. That is, while worriers on average experienced more negative thoughts than
non-worriers during the free thought period, this was not a function of whether they were
in the worry condition or the suppression condition. In addition, there was evidence that
the worriers experienced an increase in their pleasant and neutral thoughts following
suppression, suggesting that, if anything, their suppression efforts may have had positive
consequences. However, because thoughts were recorded during the free thought phase
6
but not during the first phase, it is impossible to draw conclusions regarding an initial
enhancement effect.
Another study compared suppression effects between patients with GAD, patients
with speech phobia, and normal controls (Becker, Rinck, Roth, & Margraf, 1998).
Participants in this study first completed a five minute think-aloud free thought period.
They then completed two further five minute periods in random order: one in which they
were asked to suppress thoughts of a white bear while thinking aloud, and one in which
they were asked to suppress thoughts of their primary worry while thinking aloud. The
researchers later classified the topic of the thoughts during each period, split into four
second intervals. Results showed that worriers spent more time worrying about their
primary worry during the worry suppression period than other participants. However,
time spent worrying did not change as a function of period for any group. Thus, group
differences during the worry suppression period likely reflected a greater baseline
duration of worrisome thoughts among the worriers. In other words, while Mathews and
Milroy found that suppression led to greater positive thoughts among worriers, Becker
and colleagues found that psychopathological symptoms were unassociated with any
group differences in suppression success. Thus, neither study supported the conclusion
that worriers suffer from particular impairment at thought suppression.
More recently, a study by McLean and Broomfield (2007) examined the effects of
either suppressing or monitoring a worrisome thought over the course of a week among
high worriers. Data from this study demonstrated that, relative to those participants who
simply monitored their worries, those attempting to suppress their thoughts during the
7
week reported fewer intrusions. They also reported their worries to be more controllable
and less distressing. Although the lack of normal controls in this study precludes
conclusions about the relative benefits of suppression for worriers versus non-worriers, it
is clear that, at least on average, suppression was not a maladaptive strategy for worriers.
Taken together, these studies suggest that there is little reason to believe that worriers are,
on average, more likely to experience negative consequences as a result of thought
suppression.
Consistent with this view, a recent meta-analysis by Magee, Harden, and
Teachman (2012) specifically examined the relationship between psychopathology and
thought suppression. The results revealed evidence of only minor suppression
impairment among GAD samples, drawing on data from the three previously cited
studies as well as unpublished data. This was the case for both an initial enhancement
effect and a rebound effect. Thus, contrary to what might be expected, the studies
examined in this meta-analysis did not find strong experimental support for differences
between worriers and normal controls. This is also consistent with the general
conclusions drawn by other reviews regarding the link between worry and thought
suppression (Purdon 1999; Najmi & Wegner, 2008).
Thus, it is not clear if a link exists between worry and impaired suppression.
According to questionnaire studies, worriers do indeed report greater use of thought
suppression, which has generally been taken to imply that they experience a greater
amount of intrusions as a result. Laboratory studies, however, have found weak evidence
for a link between greater use of suppression and more intrusions among high worriers.
8
That is, worriers do not appear to be any more vulnerable to ironic effects than non
worriers, and several studies suggest that worriers may benefit from suppressing.
However, while this may be true on average, it is not necessarily the case that all worriers
are the same. In other words, there may be some worriers who are particularly vulnerable
to ironic effects or have a poor capacity for suppression whereas other worriers may be
able suppress successfully. This would be consistent with the results reported by Magee
et al. (2012). That is, that there is a very small effect in the expected direction, when
averaged across many worriers, due to some studies finding modest deficits among
worriers while others find that suppression carries modest benefits. This would suggest
the presence of an undetected moderating variable, such that individual differences exist
among worriers with regards to their ability to suppress.
Iijima and Tanno (2012) provide evidence for such individual differences in a
nonclinical sample of Japanese undergraduate students. In this study, the researchers
made use of the general three-phase suppression paradigm described earlier. Participants
first completed a free thought baseline period. They were then asked to identify their
primary worry from the past several days (the target thought) and complete a suppression
period followed by a final free thought period. Unlike the general suppression paradigm,
every participant attempted to suppress the target thought during the second period,
leaving no control group. As in Mathews and Milroy (1994), participants were prompted
to record their current thoughts approximately every sixty seconds and later categorized
the nature of the thoughts. On average, no significant association was found between
scores on the PSWQ and intrusions by the target thought during any period of the task.
9
However, individual differences were present. Among high worriers, there was
differential success at suppressing the target thought when asked to do so, and those high
worriers who were relatively successful at initial suppression were less likely to
experience intrusions during a subsequent free thought period. In other words, the
relationship between worry and the rebound effect was moderated by degree of initial
suppression success. Thus, there must be individual differences among high worriers that
contribute to suppression success. The ironic process model suggests that thought
suppression may be effective so long as sufficient cognitive resources are available to
support the effortful operating process (Wegner, 1994). This suggests that individual
differences in available cognitive resources may explain differential suppression success.
Consistent with this viewpoint are data provided by Rosen and Engle (1998) in a
study of working memory capacity and suppression success among undergraduate
students. Participants had their working memory capacity assessed, and were categorized
as either high or low span individuals. In a complex word learning task, in contrast to
low span individuals, high span individuals were both less likely to experience
spontaneous intrusions from earlier lists and were more successful at suppressing such
intrusions when necessary. Similarly, Brewin and Smart (2005) also examined the
relationship between working memory capacity and suppression success. Their data
showed that greater working memory capacity is associated with fewer intrusions when
suppressing personally relevant thoughts. These studies show that pre-existing
differences in cognitive resources are associated with suppression success. Consistent
with a causal view of this relationship, a recent study found that an intervention designed
10
to increase cognitive resources led to greater suppression success (Gailliot, Plant, Butz, &
Baumeister, 2007).
One cognitive resource-related construct that may serve as a moderator of
suppression success is effortful control (EC). EC is a self-regulatory aspect of
temperament that is thought to reflect the capacity to use various executive functions,
including the ability to shift, focus, and sustain attention (i.e., attentional regulation)
(Rothbart & Rueda, 2005). Wegner suggested that the ability to shift one's attention in
the search for distracters is vital to thought suppression (1992). If this is the case, it
should be expected that EC could serve as a moderator of suppression success.
To test the hypothesis that EC contributes to suppression success, we conducted an initial
study to test the moderating effect of self-reported EC on the relationship between selfreported suppressive tendencies and self-reported change in depressive symptoms across
the course of an academic quarter (Cooper, Gillie, Heath, & Vasey, 2014). Consistent
with our hypotheses, EC moderated the relationship between suppression and change in
depressive symptoms, such that the tendency to suppress was only associated with an
increase in depressive symptoms when EC was low. These findings offer support for the
hypothesis that individual differences in EC may contribute to differential success in
using thought suppression as a strategy for emotional regulation. They may also account
for the differential suppression success among worriers reported by Iijima and Tanno
(2012).
One potential problem with this hypothesis is that theoretical accounts of worry
suggest that worriers are likely to be low in EC (Hirsch and Mathews, 2012). Indeed,
11
some past research (e.g., Hayes, Hirsch, & Mathews, 2008) has demonstrated a link
between worry and decreased attentional control. Thus, it might seem that the link
between cognitive resources like attentional control and suppression success is not
relevant to a study of suppression among worriers. However, analysis of data collected
from three samples in our lab have demonstrated that there is significant variation across
worriers with regard to their levels of EC and the narrower construct of attentional
control. Using self-report measures of EC and worry, our data showed relatively small
correlation coefficients between the two constructs (range: r = -0.13 to r = -0.3) (Vasey,
Chriki, & Toh, 2014). A modest correlation such as this makes it clear that there is ample
room for individual differences among worriers with regards to their levels of EC. Thus,
some worriers may possess relatively high levels of such cognitive resources. If so, some
worriers may, counter-intuitively, have the self-regulatory capacity necessary to
successfully make use of emotion regulation strategies such as thought suppression.
The connection between EC and suppression success, as well as the surprising
discovery of high worriers who possess high levels of EC, suggests that individual
differences in effortful control may account for the high worriers found by Iijima and
Tanno (2012) who were able to suppress worry successfully. However, the lack of
information regarding levels of effortful control in Iijima and Tanno's previous study
leaves this hypothesis untested. Furthermore, it remains to be seen if the finding that
some worriers have good ability to suppress unwanted thoughts is replicable. Therefore,
the present study's primary aim was to replicate that finding. Assuming a successful
replication, the study also sought to test the hypothesis that EC will moderate suppression
12
success among high worriers. Specifically, Iijima and Tanno's study suggests that phase
two suppression success moderates the relationship between worry and phase three
intrusions. We propose, however, that phase two suppression is itself an outcome of the
interaction between worry and EC. Consequently, thought suppression success in phase 2
should mediate the effect of worry’s interaction with EC on phase 3 intrusions. . We also
sought to test the hypothesis that pathological worry should be associated with poor
thought suppression success. That is, high levels of worry should be more likely to
predict GAD symptoms, GAD diagnostic status and other correlates of pathological
worry among individuals with low versus high thought suppression ability.
If impaired thought suppression is a significant contributor to GAD, then we
should expect those high worriers who suppress successfully to differ from their poor
suppressing counterparts with regards to a number important correlates of pathological
worry. First, they should report fewer and/or less severe symptoms of GAD and they
should be less likely to meet GAD diagnostic criteria. Evidence shows that not all high
worriers meet criteria for GAD (Ruscio, 2002) and it is reasonable to hypothesize that
those who do not should be more likely to show good thought suppression ability.
Additionally, such good suppressors should differ from poor suppressing worriers in
terms of related symptom dimensions (e.g., symptoms of autonomic arousal and
depression), self-reported thought suppression success, and their level of impairment in
functioning.
In summary, the study tested three hypotheses. First, we expect to replicate Iijima
and Tanno's findings such that some high worriers are able to successfully suppress
13
unwanted thoughts, and that such success predicts an absence of ironic consequences of
suppression (Hypothesis 1). Second, we predict this differential thought suppression
success among high worriers will reflect individual differences in EC (Hypothesis 2).
Finally, we expect that those high worriers who suppress well will be differentiated from
other high worriers by lower scores on measures tapping aspects of pathological worry
(Hypothesis 3).
14
Chapter 2: Methods
Sample
The sample consisted of 58 undergraduate students enrolled in Psychology 1100
who were recruited through the Research Experience Program (REP) and were 18 years
of age or older.1 The mean age of the sample was 19.19 (SD= 2.01) and 62.10% were
female. Approximately 77.6% of the sample was Caucasian, 6.9% were African
American, 5.2% were Asian American, 5.2% were Latino/Latina, and 5.2% were of
mixed ethnic heritage. Approximately 94.83% of participants spoke English as their first
language and 96.55% were single.
Participants were recruited as part of a larger study examining attentional control
and worry. Participants were prescreened and recruited based on their scores on the
Attentional Control Scale (ACS; Derryberry & Reed, 2002) and the Generalized Anxiety
Questionnaire, 4th edition (GAD-Q-IV; Newman et al., 2002). Specifically, participants
were considered to have low attentional control if their score on the ACS was 44 or lower
and high attentional control if their score was 52 or higher. A GAD-Q-IV prescreening
1
A further 10 participants were available through a secondary study, but are not included here. These
participants are not included as a fewer number of questionnaires were available for use in characterizing
the high worriers. The results of the main tests were unchanged when these participants were included.
15
total was created, whereby participants received one point for each of the following GAD
symptoms that they endorsed: excessive worry, excessive distress from worry, difficulty
controlling worry, excessive worry about minor things, and excessive and uncontrollable
worry more days than not during the past six months. Participants were considered to
have low GAD symptoms if their score on the GAD-Q-IV prescreening total was 2 or
lower and high GAD symptoms if their score on the GAD-Q-IV prescreening total was 4
or higher. Thus four groups of participants at the corners of the normal distribution were
identified: those with high GAD symptoms and high attentional control, those with high
GAD symptoms and low attentional control, those with low GAD symptoms and high
attentional control, and those with low GAD symptoms and low attentional control.
Participants were recruited such that we primarily sampled from these four groups, but
some participants were also recruited who were neither high nor low on either measure (a
“middle” group). 31.03% of the current sample was classified within this group.
Material
Questionnaires
Demographic Questionnaire. The demographic questionnaire included items concerning
the participant’s age, gender, ethnicity, and marital status.
Penn State Worry Questionnaire (PSWQ). The PSWQ (Meyer et al, 1990) is a self-report
measure of pathological worry consisting of 16 items rated on a 1 to 5 Likert scale. This
measure has demonstrated good internal consistency (α = 0.93), high test-re-test
reliability (0.92) over a period of 8 – 10 weeks. For the current sample, Chronbach's
alpha was .95.
16
Adult Temperament Questionnaire- Effortful Control (ATQ-EC). The ATQ-EC (Evans
& Rothbart, 2007) is a self-report measurement of three facets of EC, including activation
control, inhibition control, and attentional control. The 19 ATQ-EC consists of 19 items
rated on a 1 to 7 Likert scale. The subscale shows good internal correlations for the three
facets (α > 0.66). For the current sample, Chronbach's alpha was .82.
Generalized Anxiety Disorder Questionnaire IV (GAD-Q-IV). The GADQ-IV (Newman
et. al., 2002) is a self-report questionnaire designed as a screening measure for GAD
diagnostic criteria as defined by the Diagnostic and Statistical Manual (DSM-IV-TR;
American Psychological Association, 2000). The authors found the GAD-Q-IV to have
83% sensitivity and 89% specificity for GAD diagnoses when compared to a structured
clinical interview. Additionally, it demonstrated good test-retest reliability, convergent
and discriminant validity, and good agreement (kappa of 0.67) with a diagnostic
interview. The GAD-Q-IV has an optional skip-out rule in which certain items are
skipped if participants respond negatively to earlier items. Consistent with prior research,
and with Newman and colleague's preferred scoring method, the measure was scored
using the skip-out rule despite the fact that participants were not instructed to follow it.
This allowed for the creation of two separate dichotomous scores that assess GAD
symptoms. The first (hereafter referred to as Newman's dichotomous GAD-Q-IV score)
calls for a cutoff score of 5.7 (Newman et. al., 2002) on the GAD-Q-IV when
administered using the skip-out rule. The second (hereafter referred to as Fresco's
dichotomous GAD-Q-IV score) calls for a cutoff score of 7.67 (Moore et. al., 2014), and
is argued to provide more optimal sensitivity (85%) and specificity (74%).
17
Structured Clinical Interview for DSM IV (SCID). The SCID (First et. al., 1994) is a
structured clinical interview that measures DSM-IV diagnoses (excluding personality
disorders). For the purposes of this study, the entire SCID was not administered.
Instead, participants were only asked questions from the sections pertaining to GAD and
Panic Disorder. These sections were administered by one of two advanced graduate
students. The interrater reliability of GAD diagnosis as defined by the SCI was .86, as
measured by Cohen's kappa coefficient. This was calculated from approximately 17% of
the interviews that were conducted during the course of the study.
Depression, Anxiety, and Stress Scales (DASS). The DASS (Lovibond & Lovibond,
1995) is a 42 item self-report measure designed to yield three scales measuring the
negative emotional states of depression (DASS-D), anxiety (DASS-A) and stress (DASSS). DASS-D taps into the low positive affectivity that characterizes depression, while
DASS-A taps into anxious arousal. DASS-S taps into the heightened negative affectivity
that characterizes both anxiety and depression. The DASS items are rated on a 0 to 3
Likert scale. The authors reported good psychometric properties for the anxiety (DASSA, α = 0.81), depression (α = 0.91), and stress (α = 0.89) subscale. For the current
sample, Chronbach's alphas on the DASS-A, DASS-D, and DASS-S were .82, .93, and
.91, respectively.
Thought Suppression Inventory (TSI). The TSI (Rassin, 2003) is a 15 item self-report
measure of thought suppression that consists of 3 subscales. The first subscale, the TSI-I,
measures the experience of intrusive thoughts. The second subscale, the TSI-S, measures
the use of suppression as a strategy for dealing unwanted thoughts. The final subscale,
18
the TSI-E, taps into success or effectiveness at using thought suppression. The scale
shows good internal reliability (α of .71, .64, and .67). In addition, three-week test-retest
reliability was .80 for TSI-I, .43 for TSI-S, and .83 for TSI-E. For the current sample,
Chronbach's alphas on the TSI-I, TSI-S, and TSI-E were .76, .71, and .70, respectively.
Medical Outcomes Study 36-item Short-Form Health Survey (SF-36). The SF-36 (Ware
& Sherbourne, 1992) is a 36 item self-report measure of 8 health dimensions relevant to
health and functioning, and has been shown to evidence differences in functioning
between those who meet GAD criteria and those who do not (Weisberg et. al., 2010). In
order to reduce the number of analyses to be performed, the total score from the SF-36
was utilized, rather than individual scale scores. For the current sample, Chronbach's
alpha was .83.
Thought Suppression Task
Participants completed the task while seated in front of a computer monitor with a
computer keyboard in their lap. They were then instructed to enter the name of a loved
one. After doing so, their loved one’s name was embedded in the following sentence:
“Now imagine that (loved-one’s name) has been in a car accident.” This sentence was
presented on the computer monitor for 30 seconds and participants were instructed that
the thought would serve as their “target thought” for the remainder of the experiment.
Participants then completed three 5-minute periods. Prior to beginning the first phase
(baseline), all participants saw the baseline monitor instructions. In the second phase,
participants were instructed to suppress the target thought. In the third phase, participants
19
once again received the monitor instructions. The instructions for each of the periods
were originally adapted from Salkovskis and Campbell (1994) and are provided below:
Baseline and monitor period instructions: “During the next few minutes, you may
think about anything you like. You may think about the accident target thought, but you
do not have to. If at any time you think of the accident target thought please press the
“X” key for each occurrence. It is important that you continue in the same way for the
full duration.”
Suppression period instructions: “During the next few minutes, please record
your thoughts as you did before. It is very important that you try as hard as you can to
suppress the accident target thought, but be sure to press the “X” key if you do think of
the accident target thought. It is important that you continue in the same way for the full
duration.”
Procedure
This study was conducted within a larger consisting of three sessions: two
sessions in the laboratory, approximately two weeks apart, plus an online questionnaire
session completed in between the two laboratory sessions. During the first session,
participants completed the PSWQ, the GAD-Q-IV, the DASS, and the ATQ-EC, among a
larger set of questionnaires. Participants were also interviewed using the SCID during
the first session. During the online session, participants completed the SF-36 and TSI.
The thought suppression task was completed during the second laboratory session.
Analytic Strategy
20
To address Hypothesis 1, which was a direct replication of Iijima and Tanno
(2012), we tested whether PSWQ scores interacted with phase 2 intrusions to predict
phase 3 intrusions. Specifically, we conducted a multiple linear regression (MLR)
analysis testing PSWQ, phase 2 intrusions and the PSWQ x phase 2 interaction with
phase 3 intrusions as the dependent variable. Phase 1 intrusions were included as a
covariate.
To test Hypothesis 2, which posited a moderated mediation model, we tested the
interaction of PSWQ and ATQ-EC as a predictor of phase 2 intrusions. Specifically, we
conducted an MLR analysis testing PSWQ, ATQ-EC, and the PSWQ x ATQ-EC
interaction phase 2 intrusions as the dependent variable. Phase 1 intrusions were again
included as a covariate. The moderated mediation model requires that the independent
variable and the moderator interact to predict the mediator. The plan was to test the
overall model if this first test produced significant results.
To test Hypothesis 3, we then conducted a series of analyses in order to determine
whether or not high worriers who suppress successfully differ from other high worriers
with regards to a number of important correlates of worry. First we tested the interaction
of worry and phase 2 intrusions predicting GAD symptoms as measured by the GAD-QIV. Specifically, we conducted an MLR analysis testing PSWQ, phase 2 intrusions, and
the PSWQ x phase 2 intrusions interaction predicting either GAD-Q-IV continuous score,
Newman's dichotomous GAD-Q-IV score, or Fresco's dichotomous GAD-Q-IV score as
the dependent variable. Next we repeated the previous analyses substituting in either
SCID diagnosis status, DASS-A, DASS-D, DASS-S, TSI-I, TSI-S, TSI-E, or SF-36 as
21
the dependent variable. Phase 1 intrusions were again held as a covariate. In models
including a dichotomous dependent variable (i.e., Newman's dichotomous GAD-Q-IV
score, Fresco's dichotomous GAD-Q-IV score, or SCID diagnostic status) binomial
logistic regression analyses were run rather than MLR analyses.
Where significant interactions were found, the interaction was probed using
PROCESS, which is a macro for SPSS for estimating and probing interactions, as well as
and conditional process effects (Hayes, 2013). We probed simple slopes to test the
predictors’ effects on the dependent variable at high (one SD above the mean) and low
(one SD below the mean) levels and examined the regions of significance through the
Johnson-Neyman procedure.
22
Chapter 3: Results
Preliminary Analyses
We initially examined regression diagnostics to determine if there were extreme
cases that exert excessive influence on the overall fit of the model or on individual
regression coefficients. Specifically, we used ±1.0 as a cutoff for standardized DFFITS
and DFBETA values (Cohen, Cohen, West, & Aiken, 2002). Results revealed the
presence of one high influence case. This participant exerted extreme influence on the fit
of the model (DFFITS = -21.42) and on the interaction term (DFBETA = -10.25).
Consequently, this case was excluded from further analyses, bringing the final sample to
57 participants.
Descriptive Statistics
Descriptive statistics for ATQ-EC, PSWQ, DASS-A, DASS-D, DASS-S, TSI-I,
TSI-S, TSI-E, SF-36, GAD-Q-IV continuous score, and reported intrusions by phase are
presented in Table 1. All scores fell within the expected ranges. Regarding dichotomous
scores derived from the GAD-Q-IV, 22 participants met GAD cutoffs using Newman's
dichotomous GAD-Q-IV score while 35 participants did not. Using Fresco's
dichotomous GAD-Q-IV score, 17 participants met GAD cutoffs while 40 participants
23
did not. Based on SCID criteria, 13 participants met criteria for GAD while 44
participants did not.
M
SD

N
ATQ-EC
82.22
15.83
.82
57
PSWQ
53.53
15.48
.95
57
DASS-A
5.86
5.10
.82
57
DASS-D
7.32
7.05
.93
57
DASS-S
12.00
8.10
.91
57
TSI-I
12.63
4.20
.76
56
TSI-S
17.09
3.50
.71
56
TSI-E
15.02
3.34
.70
56
SF-36
72.69
17.04
.83
56
GAD-Q-IV Continuous
4.60
4.23
-
57
Phase 1 Intrusions
10.09
9.51
-
57
Phase 2 Intrusions
7.81
8.22
-
57
Phase 3 Intrusions
5.54
7.20
-
57
Table 1. Descriptive statistics.
Bivariate correlations for all variables are presented in Table 2. Several
correlations are noteworthy. As in past studies, ATQ-EC scores were only modestly
inversely correlated with PSWQ scores (r = -.31). Thus, it is feasible to test their
interaction. Surprisingly, whereas ATQ-EC scores were significantly negatively
correlated with TSI-I scores, phase 1 intrusions, and phase 3 intrusions (r = -.41, -.30, and
-.28, respectively), they were not significantly associated with phase 2 intrusions (i.e.,
intrusions during the suppression period; r = -.20). Additionally, ATQ-EC scores were
positively correlated with TSI-S and SF-36 scores (r = .28 and .45, respectively).
24
Consistent with expectation, PSWQ scores were positively correlated with TSI-I
and TSI-S scores as well as phase 1 intrusions (r = .68, .35, and .27, respectively) and
negatively correlated with TSI-E and SF-36 scores (r = -.41 and-.58, respectively). They
were also significantly positively correlated with the GAD-Q-IV continuous score,
Newman's and Fresco's dichotomous GAD-Q-IV scores, and SCID diagnosis (r = .76,
.62, .55, and .53, respectively).
TSI- I scores were significantly positively correlated with the GAD-Q-IV
continuous score, Newman's and Fresco's dichotomous GAD-Q-IV scores, and SCID
diagnosis (r = .75, .59, .68, and .46, respectively) as well as with TSI-S and phase 1
intrusions (r = .47 and .28, respectively). TSI-I scores were significantly negatively
correlated with TSI-E and SF-36 scores (r = -.37 and -.75, respectively).
TSI-S scores were also significantly positively correlated with the GAD-Q-IV
continuous score and Fresco's dichotomous GAD-Q-IV scores, as well as SCID diagnosis
(r = .37, .34, and .28, respectively). TSI-S scores were significantly negatively correlated
with SF-36 scores (r = .44).
Finally, TSI-E scores were significantly negatively correlated with GAD-Q-IV
continuous scores, Newman's and Fresco's dichotomous GAD-Q-IV scores, and SCID
diagnosis (r = -.47, -.39, -.41, and -.31, respectively). TSI-E scores were significantly
positively correlated with SF-36 scores (r = .41).
As expected, SF-36 scores were significantly negatively correlated with the GADQ-IV continuous score, Newman's and Fresco's dichotomous GAD-Q-IV scores, and
25
SCID diagnosis (r = -.64, -.46, -.55, and -.36, respectively), as well as to phase 1
intrusions (r= -.32).
Phase 1 intrusions were significantly positively correlated with both phase 2 and
phase 3 intrusions (r = .81 and .73, respectively). Phase 2 intrusions were significantly
positively related to phase 3 intrusions (r = .89). As noted above, only phase 1 intrusions
were significantly positively correlated with PSWQ scores (r = .27). Contrary to
expectations no score derived from the GAD-Q-IV was positively related to reported
intrusions in any phase. However, SCID diagnosis was significantly positively correlated
with phase 3 intrusions (r = .34).
The correlation between PSWQ and phase 2 intrusions (r = .09) was very weak.
Therefore, it appears feasible to test the interaction between PSWQ scores and phase 2
intrusions. As shown by the scatter plot in Figure 1, participants scoring high on the
PSWQ were equally likely to report a low number of phase 2 intrusions as those scoring
low on the PSWQ. Indeed, as shown in Figure 2, the same held true for those meeting
GAD criteria based on Fresco's dichotomous GAD-Q-IV score. Thus, it was also feasible
to test the interaction between that score and phase 2 intrusions.
26
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
1. ATQ-EC
27
2. PSWQ
-.31*
3. DASS-A
-.38**
.62**
4. DASS-D
-.33*
.61**
.63**
5. DASS-S
-.41**
.67**
.83**
.69**
6. TSI-I
-.41**
.68**
.68**
.71**
.69**
7. TSI-S
-.17
.35**
.34*
.42**
.34*
.47**
8. TSI-E
.28*
-.41**
-.44**
-.46**
-.49**
-.37**
-.15
9. SF-36
.45**
-.58**
-.67**
-.65**
-.61**
-.75**
-.44**
.41**
10. GAD-Q-IV Continuous
-.33*
.76**
.71**
.76**
.72**
.75**
.37**
-.47**
-.64**
11. GAD-Q-IV Newman
-.26
.62**
.64**
.63**
.59**
.59**
.25
-.39**
-.46**
.90**
12. GAD-Q-IV Fresco
-.32*
.55**
.74**
.78**
.67**
.68**
.34**
-.41**
-.55**
.87**
.82**
13. SCID Diagnosis
-.23
.53**
.60**
.45**
.51**
.46**
.28*
-.31*
-.36**
.54**
.43**
.56**
14. Phase 1 Intrusions
-.30*
.27*
.06
.08
.08
.28*
.18
-.11
-.32*
.07
-.01
-.02
.23
15. Phase 2 Intrusions
-.20
.09
-.03
-.07
-.01
.08
.10
.03
-.14
-.05
-.11
-.09
.21
.81**
16. Phase 3 Intrusions
-.28*
.20
.15
.01
.13
.15
.22
.10
-.26
.05
-.02
.00
.34**
.73**
Table 2. Bivariate correlations.
*p < .05; **p < .01
27
.89**
16
Figure 1. Scatter plot of phase 2 intrusions by scores on the PSWQ.
28
Figure 2. Scatter plot of phase 2 intrusions by GAD status based on Fresco's GAD-Q-IV
cutoff score.
Primary Analyses
Hypothesis 1 - Worry x Phase 2 Intrusions Predicting Phase 3 Intrusions
As shown in Table 3, the conditional effects of PSWQ (p = .88) and intrusions
during phase 2 (p = .09) on phase 3 intrusions were non-significant. However, consistent
with Iijima and Tanno (2012), the interaction term was significant (p = .005). As
expected, PSWQ scores were not associated with phase 3 intrusions when phase 2
intrusions were low (i.e. one SD below the mean; simple slope (B) = .00 p = .88), as
shown in Figure 3. However, PSWQ scores were positively associated with phase 3
intrusions when phase 2 intrusions were high (i.e. one SD above the mean; B = .15 p <
29
.001). Examination of the region of significance revealed that the simple slope for
PSWQ was significant when phase 2 intrusions were greater than 6.27.
B (SE)
sr
R2
.838***
Intercept
-1.44 (1.85)
PSWQ
.00 (.03)
.00
Phase 2 Intrusions
.31 (.18)
.10
Phase 1 Intrusions
-.06 (.08)
-.05
PSWQ x Phase 2 Intrusions
.01 (.00)**
.17**
Table 3. Regression analysis predicting phase 3 intrusions with PSWQ and phase 2
intrusions.
*p< .05; **p< .01; ***p< .001
Figure 3. PSWQ x phase 2 intrusions predicting phase 3 intrusions.
30
Hypothesis 2 - Worry x EC Predicting Phase 2 Intrusions
The hypothesized moderated mediation model first required that scores on the
PSWQ and ATQ-EC interact significantly in predicting phase 2 intrusions. Results of a
regression model testing that hypothesis are presented in Table 4. The individual effects
of ATQ-EC (p = .91) and PSWQ (p = .84) were non-significant. Contrary to expectation,
the PSWQ x ATQ-EC interaction was also non-significant (p = .91).
B (SE)
sr
R2
.665***
Intercept
2.91 (12.39)
PSWQ
-.05 (.23)
-.02
ATQ-EC
.02 (.14)
.01
Phase 1 Intrusions
.73 (.07)***
.79***
PSWQ x ATQ-EC
.00 (.00)
-.01
Table 4. Regression analysis predicting phase 2 intrusions with PSWQ and ATQ-EC.
*p< .05; **p< .01; ***p< .001
Hypothesis 3 - Worry x Phase 2 Intrusions Interaction Predicting GAD Status and
Correlates of GAD
PSWQ x Phase 2 Intrusions Predicting GAD-Q-IV Continuous Score
As shown in Table 5, while the individual effect of PSWQ was significant (p <
.001) predicting the GAD-Q-IV continuous score, the individual effect of phase 2
intrusions was not (p = .42). The regression analysis also revealed that the interaction
between PSWQ and phase 2 intrusions was not significant (p = .34).
31
B (SE)
sr
R2
.60***
Intercept
-7.25 (1.70)***
PSWQ
.24 (.03)***
.65***
Phase 2 Intrusions
.14 (.17)
.07
Phase 1 Intrusions
-.05 (.07)
-.06
PSWQ x ATQ-EC
.00 (.00)
-.09
Table 5. Regression analysis predicting GAD-Q-IV continuous score with PSWQ and
phase 2 intrusions.
*p< .05; **p< .01; ***p< .001
PSWQ x Phase 2 Intrusions Predicting Newman's Dichotomous GAD-Q-IV Score
As shown in Table 6, while the individual effect of PSWQ on Newman’s
dichotomous GAD-Q-IV score was significant (p < .001), the individual effect of phase 2
intrusions was not (p= .21). Furthermore, the regression analysis revealed no significant
interaction between PSWQ and phase 2 intrusions (p= .16).
B (SE)
Exp(B)
R2
.584***
Intercept
-11.1 (3.50)**
.00**
PSWQ
.19 (.06)***
1.21***
Phase 2 Intrusions
.27 (.22)
1.31
Phase 1 Intrusions
-.03 (.07)
.97
PSWQ x Phase 2 Intrusions
-.01 (.00)
1.00
Table 6. Regression Analysis predicting Newman's dichotomous score from PSWQ and
phase 2 intrusions.
Exp(B) = Logs odd ratio. R2 = Nagelkerke's R squared.
*p< .05; **p< .01; ***p< .001
32
PSWQ x Phase 2 Intrusions Predicting Fresco's Dichotomous GAD-Q-IV Score
As shown in Table 7, while the individual effect of PSWQ was significant (p =
.007), the individual effect of phase 2 intrusions was not (p = .73). Furthermore, the
regression analysis revealed no significant interaction between PSWQ and phase 2
intrusions (p = .75).
B (SE)
Exp(B)
R2
.506***
Intercept
-9.85 (3.65)**
.00**
PSWQ
.16 (.06)**
1.18**
Phase 2 Intrusions
.10 (.31)
1.11
Phase 1 Intrusions
-.07 (.08)
.94
PSWQ x Phase 2 Intrusions
.00 (.01)
1.00
Table 7. Regression analysis predicting Fresco's dichotomous score from PSWQ and
phase 2 intrusions.
Exp(B) = Logs odd ratio. R2 = Nagelkerke's R squared.
*p< .05; **p< .01; ***p< .001
PSWQ x Phase 2 Intrusions Predicting SCID Diagnosis
As shown in Table 8, while the individual effect of PSWQ was significant (p =
.04), the individual effect of phase 2 intrusions was not (p = .80). Furthermore, the
regression analysis revealed no significant interaction between PSWQ and phase 2
intrusions (p = .87).
33
B (SE)
R2
Exp(B)
.574***
Intercept
-14.38 (6.51)*
.00*
PSWQ
.20 (.10)*
1.22*
Phase 2 Intrusions
.11 (.41)
1.11
Phase 1 Intrusions
-.06 (.09)
.95
PSWQ x Phase 2 Intrusions
.00 (.01)
1.00
Table 8. Regression analysis predicting SCID diagnosis from PSWQ and phase 2
intrusions.
Exp(B) = Logs odd ratio. R2 = Nagelkerke's R squared.
*p< .05; **p< .01; ***p< .001
PSWQ x Phase 2 Intrusions Predicting DASS-A
As shown in Table 9, while the individual effect of PSWQ was significant (p <
.001), the individual effect of phase 2 intrusions was not (p = .69). Furthermore, the
regression analysis revealed no significant interaction between PSWQ and phase 2
intrusions (p = .60).
B (SE)
sr
R2
.395***
Intercept
-4.20 (2.53)
PSWQ
.20 (.05)***
.46***
Phase 2 Intrusions
-.10 (.25)
-.04
Phase 1 Intrusions
-.09 (.11)
-.09
PSWQ x Phase 2 Intrusions
.00 (.00)
.06
Table 9. Regression analysis predicting DASS-A from PSWQ and phase 2 intrusions.
*p< .05; **p< .01; ***p< .001
34
PSWQ x Phase 2 Intrusions Predicting DASS-D
As shown in Table 10, while the individual effect of PSWQ was significant (p <
.001), the individual effect of phase 2 intrusions was not (p = .51). Furthermore, the
regression analysis revealed no significant interaction between PSWQ and phase 2
intrusions (p = .23).
B (SE)
sr
R2
.361***
Intercept
-9.34 (3.47)**
PSWQ
.33 (.06)***
.54***
Phase 2 Intrusions
.23 (.34)
.07
Phase 1 Intrusions
.07 (.14)
.05
PSWQ x Phase 2 Intrusions
-.01 (.01)
-.13
Table 10. Regression analysis predicting DASS-D from PSWQ and phase 2 intrusions.
*p< .05; **p< .01; ***p< .001
PSWQ x Phase 2 Intrusions Predicting DASS-S
As shown in Table 11, while the individual effect of PSWQ was significant (p <
.001), the individual effect of phase 2 intrusions was not (p = .23). Furthermore, the
regression analysis revealed no significant interaction between PSWQ and phase 2
intrusions (p = .21).
35
B (SE)
sr
R2
.476***
Intercept
-9.61 (3.74)*
PSWQ
.42 (.07)***
.61***
Phase 2 Intrusions
.45 (.37)
.12
Phase 1 Intrusions
-.08 (.16)
-.06
PSWQ x Phase 2 Intrusions
-.01 (.01)
-.13
Table 11. Regression analysis predicting DASS-S from PSWQ and phase 2 intrusions.
*p< .05; **p< .01; ***p< .001
PSWQ x Phase 2 Intrusions Predicting TSI-I
As shown in Table 12, while the individual effect of PSWQ was significant (p <
.001), the individual effect of phase 2 intrusions was not (p = .75). Furthermore, the
regression analysis revealed no significant interaction between PSWQ and phase 2
intrusions (p = .36).
B (SE)
sr
R2
.494***
Intercept
2.14 (1.91)
PSWQ
.19 (.04)***
.53***
Phase 2 Intrusions
.06 (.19)
.03
Phase 1 Intrusions
.13 (.08)
.16
PSWQ x Phase 2 Intrusions
-.01 (.00)
-.09
Table 12. Regression analysis predicting TSI-I from PSWQ and phase 2 intrusions.
*p< .05; **p< .01; ***p< .001
36
PSWQ x Phase 2 Intrusions Predicting TSI-S
As shown in Table 13, neither the individual effect of PSWQ (p = .10) nor the
individual effect of phase 2 intrusions (p = .72) was significant. Furthermore, the
regression analysis revealed no significant interaction between PSWQ and phase 2
intrusions (p = .71).
B (SE)
sr
R2
.067
Intercept
13.32 (2.08)***
PSWQ
.06 (.04)
.22
Phase 2 Intrusions
-.08 (.21)
-.05
Phase 1 Intrusions
.03 (.09)
.05
PSWQ x Phase 2 Intrusions
.00 (.00)
.05
Table 13. Regression analysis predicting TSI-S from PSWQ and phase 2 intrusions.
*p< .05; **p< .01; ***p< .001
PSWQ x Phase 2 Intrusions Predicting TSI-E
As shown in Table 14, while the individual effect of PSWQ was significant (p =
.005), the individual effect of phase 2 intrusions was not (p = .65). Furthermore, the
regression analysis revealed no significant interaction between PSWQ and phase 2
intrusions (p = .32).
37
B (SE)
sr
R2
.201*
Intercept
20.53 (1.91)***
PSWQ
-.10 (.04)**
-.37**
Phase 2 Intrusions
-.09 (.19)
-.06
Phase 1 Intrusions
-.07 (.08)
-.12
PSWQ x Phase 2 Intrusions
.00 (.00)
.13
Table 14. Regression analysis predicting TSI-E from PSWQ and phase 2 intrusions.
*p< .05; **p< .01; ***p< .001
PSWQ x Phase 2 Intrusions Predicting SF-36
As shown in Table 15, while the individual effect of PSWQ was significant (p =
.002), the individual effect of phase 2 intrusions was not (p = .56). Furthermore, the
regression analysis revealed no significant interaction between PSWQ and phase 2
intrusions (p = .84).
B (SE)
sr
R2
.373***
Intercept
104.12 (8.62)***
PSWQ
-.53 (.16)**
-.37**
Phase 2 Intrusions
.51 (.85)
.07
Phase 1 Intrusions
-.57 (.36)
-.17
PSWQ x Phase 2 Intrusions
.00 (.02)
-.02
Table 15. Regression analysis predicting SF-36 from PSWQ and phase 2 intrusions.
*p< .05; **p< .01; ***p< .001
38
Further Analyses
In response to null results for the Hypotheses 2 and 3, we also tested the
interaction between PSWQ scores and TSI-E scores predicting the measures of related
symptoms.
PSWQ x TSI-E Predicting DASS-A
As shown in Table 16, the conditional effect of PSWQ was significant (p = .001)
while the conditional effect of TSI-E was non-significant (p = .20). However, the
interaction term was significant (p = .03). Specifically, PSWQ scores were more strongly
associated with DASS-A scores when TSI-E scores were low (i.e. one SD below the
mean; (B) = .25 p < .001) than when TSI-E scores were high (i.e. one SD above the
mean; B = .13 p = .004), as shown in Figure 4. Examination of the region of significance
revealed that the simple slope for PSWQ was non-significant when TSI-E scores were
greater than 19.97.
B (SE)
sr
R2
.48***
Intercept
-13.02 (7.64)
PSWQ
.45 (.13)**
.35**
TSI-E
.57 (.45)
.13
PSWQ x TSI-E
-.02 (.01)*
-.22*
Table 16. Regression analysis predicting DASS-A from PSWQ and TSI-E.
*p< .05; **p< .01; ***p< .001
39
Figure 4. PSWQ x TSI-E predicting DASS-A.
PSWQ x TSI-E Predicting DASS-A
As shown in Table 17, the conditional effect of PSWQ was significant (p = .005)
while the conditional effect of TSI-E was non-significant (p = .48). Furthermore, the
interaction term was marginally significant (p = .098). However, as shown in Figure 5,
the pattern of results was the same as when DASS-A scores were entered as the
dependent variable. Specifically, PSWQ scores were more strongly associated with
DASS-D scores when TSI-E scores were low (i.e. one SD below the mean; (B) = .31 p <
.001) than when TSI-E scores were high (i.e. one SD above the mean; B = .18 p = .004).
Examination of the region of significance revealed that the simple slope for PSWQ was
non-significant when TSI-E scores were greater than 20.25.
40
B (SE)
sr
R2
.47***
Intercept
-12.85 (10.72)
PSWQ
.53 (.18)**
.29**
TSI-E
.45 (.63)
.07
PSWQ x TSI-E
-.02 (.01)
-.17
Table 17. Regression analysis predicting DASS-D from PSWQ and TSI-E.
*p< .05; **p< .01; ***p< .001
Figure 5. PSWQ x TSI-E predicting DASS-D.
PSWQ x TSI-E Predicting GAD-Q-IV Continuous Score
As shown in Table 18, the conditional effect of PSWQ was significant (p < .001) while
the conditional effect of TSI-E was non-significant (p = .42). Furthermore, the
41
interaction term was only marginally significant (p = .098). However, as shown in Figure
6, the pattern of results was the same as when DASS-A scores were entered as the
dependent variable. Specifically, PSWQ scores were more strongly associated with
GAD-Q-IV continuous scores when TSI-E scores were low (i.e. one SD below the mean;
(B) = .22 p < .001) than when TSI-E scores were high (i.e. one SD above the mean; B =
.16 p < .001). Examination of the region of significance revealed that the simple slope
for PSWQ was non-significant when TSI-E scores were greater than 23.78.
B (SE)
sr
R2
.62***
Intercept
-9.67 (5.43)
PSWQ
.34 (.09)***
.31***
TSI-E
.26 (.32)
.07
PSWQ x TSI-E
-.01 (.01)
-.14
Table 18. Regression analysis predicting GAD-Q-IV continuous score from PSWQ and
TSI-E.
*p< .05; **p< .01; ***p< .001
42
Figure 6. PSWQ x TSI-E predicting GAD-Q-IV continuous score.
Additional Analyses
Further analyses entered DASS-S, SCID diagnosis status, SF-36, Newman's
dichotomous GAD-Q-IV score, or Fresco's dichotomous GAD-Q-IV score as the
dependent variable, yet revealed no significant interactions (p > .25). However, with the
exception of the model in which SF-36 was entered as the dependent variable, the pattern
of results in each case was as expected. Similarly, when these results were graphed, they
produced patterns consistent with the fan-shaped interactions depicted in Figures 4, 5,
and 6. When SF-36 was entered as the dependent variable, there was no hint of any
interaction.
43
Chapter 4: Discussion
The current study aimed first (Hypothesis 1) to replicate the surprising finding by
Iijima and Tanno (2012) that some high worriers are capable of successfully suppressing
negative thoughts without experiencing any ironic consequences from doing so. That is,
we expected that high worriers will vary in the number of intrusions experienced during a
thought suppression period (i.e., phase 2), and their degree of success during that period
will predict intrusions during a subsequent thought monitoring period (i.e., phase 3).
Additionally the study aimed to extend the findings of Iijima and Tanno (Hypothesis 2)
by testing the hypothesis that differences in thought suppression success among high
worriers can be explained by individual differences in self-regulatory capacity (i.e., EC).
Specifically, we hypothesized a moderated mediation model in which worry interacts
with EC to predict intrusions during thought suppression (i.e., phase 2), which in turn
predicts intrusions during phase 3. Finally, we hypothesized that individual differences in
thought suppression success among high worriers should predict various indicators of
pathological worry. Specifically, if poor thought suppression ability is indeed a
contributor to pathological worry, those high worriers who can suppress successfully
should score lower than their poor suppressing counterparts on measures of GAD
symptoms and related correlates of pathological worry as well as on self-reports of
44
thought suppression success and overall adaptive functioning. However, whereas the
study yielded a successful replication of Iijima and Tanno’s (2012) original finding, we
found no evidence that it reflected individual differences in EC. Furthermore, contrary to
expectation, differences in thought suppression success among high worriers were
unrelated to any indicator of pathological worry nor were they related to self-reports of
thought suppression success. On the other hand, self-reported thought suppression
success moderated the relationship between worry and certain symptoms of pathological
worry.
Like Iijima and Tanno (2012), we found a significant interaction between worry
and intrusions during a thought suppression period predicting subsequent intrusions. The
pattern of this interaction was such that worry was significantly positively associated with
phase 3 intrusions when intrusions were high during thought suppression but not when
they were low. High worriers who experienced high intrusions while suppressing were
significantly more likely to experience high phase 3 intrusions than low worriers who
similarly experienced high intrusions while suppressing. In contrast, those high worriers
who experienced low intrusions while suppressing during phase 2 were no more likely to
experience phase 3 intrusions than low worriers who reported low intrusions during phase
2.
To reiterate, Iijima and Tanno's (2012) original findings are surprising. Extant
theories of worry make it clear that high worriers should suffer from limited thought
suppression success, and be particularly vulnerable to the ironic consequences of thought
suppression. Yet despite the surprising nature of those findings, a successful replication
45
shows that they were not simply a result of random chance. Given this successful
replication, we can be confident that this study provides a valid context in which to test
hypotheses concerning differences between those high worriers who can suppress
successfully and those who cannot.
However, it may not be surprising that some high worriers can suppress
successfully. Past research has demonstrated that not all individuals who endorse high
levels of worry meet diagnostic criteria for GAD (Ruscio, 2002). Thus, high worriers
who report few intrusions while suppressing should be expected to demonstrate lower
severity on indicators of pathological worry and its correlates than their poor suppressing
counterparts. This also includes that they should report higher levels of general selfregulatory capacity. It is this self-regulatory capacity that is predicted to account for their
successful suppression.
We proposed a model in which those high worriers who suppress well are able to
do so by virtue of having higher levels of EC than their poor suppressing counterparts
(Hypothesis 2). That is, we predicted that EC should moderate the relationship between
worry and phase 2 intrusions, and that phase 2 intrusions should in turn mediate that
interaction’s association with phase 3 intrusions. Thus, high worriers who experience
low intrusions during thought suppression should do so because they have higher EC than
their poor suppressing counterparts. If so, this success during thought suppression should
predict lower intrusions during a subsequent thought monitoring period. Among those
with high EC, worry should be unrelated to phase 2 intrusions and therefore worry should
be unrelated to intrusions during phase 3. In contrast, among those low in EC, worry
46
should be significantly positively associated with phase 2 intrusions and hence to phase 3
intrusions.
The viability of this model hinges first on finding that EC does indeed moderate
the association between worry and intrusions during the phase 2 thought suppression
period. However, contrary to expectations, that hypothesized interaction was not found.
Indeed, EC was unrelated to thought suppression success, regardless of a person’s level
of worry. Consequently, the hypothesized model of moderated mediation was not tested
since it was not viable.
The fact that individual differences in EC were unrelated to thought suppression
success among worriers is surprising. In a variety of samples and differential research
contexts, past studies have shown that cognitive resources play a role in determining
suppression success. Our findings suggest that self-report measures of EC do not index
this aspect of cognitive resources. Thus, it may be premature to discard the hypothesis
that cognitive resources account for differential suppression success among high
worriers. Indeed, it is possible that a study utilizing a performance-based measure of
cognitive resources (as in Rosen & Engle, 1998) would find support for our initial
hypothesis. Additionally, heart rate variability (HRV) is a physiological measure
associated with self-regulatory capacity (Thayer et. al., 2009). Future research should
examine HRV as a moderator or the link between worry and thought suppression success.
Given that poor thought suppression success and vulnerability to its ironic
consequences are thought to play a role in the development of GAD, worriers who are
able to suppress unwanted thoughts successfully and avoid ironic consequences of
47
suppression in a laboratory thought suppression paradigm should score lower than their
poor suppressing counterparts on indicators of pathological worry. However, contrary to
this prediction, intrusions during thought suppression did not moderate the association
between worry and any of the pathological worry features examined. These included
GAD symptoms and GAD status as measured by questionnaire and structured diagnostic
interview, symptoms of depression and physiological hyperarousal, self-reported thought
suppression ability, and general level of functioning. Thus, not only were high worriers
who suppressed well no less likely to meet diagnostic criteria for GAD, but they did not
differ from their poor suppressing counterparts on any of the correlates that were tested.
It is particularly troubling that worry did not interact with phase 2 intrusions to
predict self-reports of intrusions or thought suppression success on the TSI. The TSI-I
serves as an index of self-reported experience of intrusions by participants in their daily
lives whereas the TSI-E provides an index of their experience of thought suppression
success. Insofar as some high worriers are able to successfully suppress during a thought
suppression task, they should also be expected to report that they experience fewer
intrusions and greater thought suppression success than those who experience high
intrusions during phase 2 of the thought suppression task. The fact that they did not
suggests that a person’s ability to suppress successfully during a laboratory task may
have poor ecological validity as a measure of thought suppression success in one's daily
life. This is further supported by the fact that self-reported suppression success
moderated the relationship between worry and symptoms of pathological worry.
Specifically, we found a significant interaction between worry and self-reported
48
suppression success predicting physiological hyperarousal. The pattern of this interaction
was such that at low levels of suppression success high worry was more strongly
associated with hyperarousal than it was at high levels of suppression success. Similar
tests produced marginally significant results for self-reported depressive symptoms and
self-reported symptoms of GAD. Furthermore, although non-significant, the pattern of
results was the same when the interaction predicted questionnaire- and interview-based
GAD status, as well as self-reported negative affectivity. These results suggest that while
there are high worriers who experience success while suppressing in their daily lives and
are less likely to experience the pathological symptoms of worry than other high worriers,
these are not necessarily the same individuals who experience suppression success in a
laboratory setting.
There are numerous explanations for why success on a laboratory suppression
task may not be a good index of true suppression abilities. For example, while some high
worriers may indeed perform well on a brief thought suppression task in the laboratory, it
is possible that such success has little bearing on the success or consequences of thought
suppression efforts during daily life, which are likely to occur for longer periods of time.
Furthermore, such efforts are likely to occur while the person is under greater stress and
cognitive load. Both of these factors could contribute to some high worriers suppressing
well on an in-session suppression task while simultaneously reporting difficulty with
intrusions in their daily life. It is possible that a laboratory suppression task that accounts
for such factors would produce a greater degree of overlap between task success and selfreported suppression success.
49
Limitations
As noted above, the present study was limited by the use of a self-report measure
to assess cognitive resources. Future studies should utilize performance-based measure
of cognitive resources (e.g., working memory capacity) or a physiological measure of
self-regulatory capacity. Doing so may provide support for the hypothesis that some high
worriers are successful at thought suppression as a function of their cognitive resources.
Additionally, this study may have been limited by the fact that worriers were
instructed to suppress a standard emotionally charged thought rather than a personal
worry. This is in contrast to the paradigm utilized by Iijima and Tanno (2012), in which
worriers were instructed to suppress a current and personally relevant worry. While
instructing every participant to suppress the same thought provides a greater degree of
control to the thought suppression task, it is possible that doing so reduces the ecological
validity of the task when performed by worriers. That is, when the thought is one that is
relatively easy to suppress (e.g., a worry that is not personally relevant) it may be harder
to characterize the traits that contribute to differential suppression success among high
worriers. However, it should be noted that the pattern of results reported in this study is
almost identical to the pattern reported by Iijima and Tanno (2012). Thus, while future
research should further assess the importance of instructing worriers to suppress a
personally relevant thought, it should not be assumed that doing so is likely to provide
stronger evidence for any of the hypotheses tested here.
Despite the potential limitations to the present study, it suggests several important
conclusions regarding the relationship between worry and thought suppression success.
50
First, simply replicating Iijima and Tanno's (2012) findings is an important step in further
understanding this relationship. Existing theories of intrusive worry posit that it reflects
poor thought suppression ability. Our successful a replication of Iijima and Tanno's
(2012) finding suggests that may not always be the case. Some high worriers are, in a
laboratory setting, capable of both successfully suppressing negative thoughts and
avoiding the ironic consequences of doing so. Thus, studies on this relationship must not
presuppose that high worriers will experience difficulty on a standard thought
suppression task.
However, it is also apparent that the typical laboratory thought suppression task
likely lacks ecological validity. The fact that some worriers appear to suppress well in a
laboratory setting and yet receive similar scores on countless measures indexing various
correlates of pathological worry is very troubling. It is equally problematic that selfreported suppression success moderates the relationship between worry and the
symptoms of pathological worry. These findings suggest that there is a significant piece
of the thought suppression experience, at least for worriers, that is not captured in a
laboratory task. Thus, it also should not be presupposed that success on such tasks is
revealing any flaws in extant theories of worry.
It is important that future research on worry and thought suppression further
assess the extent to which worriers are capable of suppressing successfully. Methods for
doing so include the use of longer suppression periods, personally relevant target
thoughts, and cognitive load manipulations. Future research should also continue to
explore the potential role of cognitive resources in determining suppression success
51
through the use of task based measures of cognitive resources and physiological measures
of self-regulatory capacity.
The present study failed to support any hypotheses beyond a replication of Iijima
and Tanno's (2012) previous results. Nonetheless, our conclusions regarding the
laboratory thought suppression task are significant. By better understanding what it is
that such tasks measure, researchers will be better able to design studies in an informed
manner so as to better assess the suitability of our current theories of worry and its
associated problems.
52
References
American Psychiatric Association. (2000). Diagnostic and statistical manual of mental
disorders: DSM-IV-TR. Washington, DC: American Psychiatric Association.
Becker, E. S., Rinck, M., Roth, W. T., & Margraf, J. (1998). Don’t Worry and Beware of
White Bears: Thought Suppression in Anxiety Patients. Journal of Anxiety
Disorders, 12(1), 39–55. doi:10.1016/S0887-6185(97)00048-0
Blumberg, S. J. (2000). The white bear suppression inventory: revisiting its factor
structure. Personality and Individual Differences, 29(5), 943–950.
doi:10.1016/S0191-8869(99)00245-7
Brewin, C. R., & Smart, L. (2005). Working memory capacity and suppression of
intrusive thoughts. Journal of Behavior Therapy and Experimental Psychiatry,
36(1), 61–68. doi:10.1016/j.jbtep.2004.11.006
Chorpita, B. F., Tracey, S. A., Brown, T. A., Collica, T. J., & Barlow, D. H. (1997).
Assessment of worry in children and adolescents: An adaptation of the Penn State
Worry Questionnaire. Behaviour Research and Therapy, 35(6), 569–581.
doi:10.1016/S0005-7967(96)00116-7
Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied Multiple
Regression/Correlation Analysis for the Behavioral Sciences. Routledge.
Cooper, G. E., Gillie, B. L., Heath, J. H., & Vasey, M. W. (2014). Thought suppression
isn't always bad: Suppressive tendencies predict increases in depressive symptoms
only when self-regulatory capacity is low. Manuscript in preparation.
Derryberry, D., & Reed, M. A. (2002). Anxiety-related attentional biases and their
regulation by attentional control. Journal of Abnormal Psychology, 111(2), 225–
236. doi:10.1037/0021-843X.111.2.225
Evans, D. E., & Rothbart, M. K. (2007). Developing a model for adult temperament.
Journal of Research in Personality, 41(4), 868–888.
doi:10.1016/j.jrp.2006.11.002
53
First, M. B., Spitzer, R. L., Gibbon, M., & Williams, J. B. (1994). Structured clinical
interview for Axis I DSM-IV disorders. New York: Biometrics Research.
Gailliot, M. T., Plant, E. A., Butz, D. A., & Baumeister, R. F. (2007). Increasing SelfRegulatory Strength Can Reduce the Depleting Effect of Suppressing Stereotypes.
Personality and Social Psychology Bulletin, 33(2), 281–294.
doi:10.1177/0146167206296101
Hayes, A. F. (2013). Introduction to mediation, moderation, and conditional process
analysis: A regression-based approach. Guilford Press.
Hayes, S., Hirsch, C., & Mathews, A. (2008). Restriction of working memory capacity
during worry. Journal of Abnormal Psychology, 117(3), 712–717.
doi:10.1037/a0012908
Hirsch, C. R., & Mathews, A. (2012). A cognitive model of pathological worry.
Behaviour Research and Therapy, 50(10), 636–646.
doi:10.1016/j.brat.2012.06.007
Iijima, Y., & Tanno, Y. (2012). The rebound effect in the unsuccessful suppression of
worrisome thoughts. Personality and Individual Differences, 53(3), 347–350.
doi:10.1016/j.paid.2012.03.023
Laugesen, N., Dugas, M. J., & Bukowski, W. M. (2003). Understanding Adolescent
Worry: The Application of a Cognitive Model. Journal of Abnormal Child
Psychology, 31(1), 55–64. doi:10.1023/A:1021721332181
Lovibond, P. F., & Lovibond, S. H. (1995). The structure of negative emotional states:
Comparison of the Depression Anxiety Stress Scales (DASS) with the Beck
Depression and Anxiety Inventories. Behaviour research and therapy,33(3), 335343.
Magee, J. C., Harden, K. P., & Teachman, B. A. (2012). Psychopathology and thought
suppression: A quantitative review. Clinical Psychology Review, 32(3), 189–201.
doi:10.1016/j.cpr.2012.01.001
Mathews, A., & Milroy, R. (1994). Effects of priming and suppression of worry.
Behaviour Research and Therapy, 32(8), 843–850. doi:10.1016/00057967(94)90164-3
McKay, D., & Greisberg, S. (2002). Specificity of Measures of Thought Control. The
Journal of Psychology, 136(2), 149–160. doi:10.1080/00223980209604146
54
McLean, A., & Broomfield, N. M. (2007). How does thought suppression impact upon
beliefs about uncontrollability of worry? Behaviour Research and Therapy,
45(12), 2938–2949. doi:10.1016/j.brat.2007.08.005
Meyer, T. J., Miller, M. L., Metzger, R. L., & Borkovec, T. D. (1990). Development and
validation of the penn state worry questionnaire. Behaviour Research and
Therapy, 28(6), 487–495. doi:10.1016/0005-7967(90)90135-6
Moore, M. T., Anderson, N. L., Barnes, J. M., Haigh, E. A., & Fresco, D. M. (2014).
Using the GAD-Q-IV to identify generalized anxiety disorder in psychiatric
treatment seeking and primary care medical samples. Journal of anxiety
disorders, 28(1), 25-30.
Muris, P., Merckelbach, H., & Horselenberg, R. (1996). Individual differences in thought
suppression. The White Bear Suppression Inventory: Factor structure, reliability,
validity and correlates. Behaviour Research and Therapy, 34(5–6), 501–513.
doi:10.1016/0005-7967(96)00005-8
Najmi, S., & Wegner, D. M. (2009). Hidden Complications of Thought Suppression.
International Journal of Cognitive Therapy, 2(3), 210–223.
doi:10.1521/ijct.2009.2.3.210
Newman, M. G., Zuellig, A. R., Kachin, K. E., Constantino, M. J., Przeworski, A.,
Erickson, T., & Cashman-McGrath, L. (2002). Preliminary reliability and validity
of the generalized anxiety disorder questionnaire-IV: A revised self-report
diagnostic measure of generalized anxiety disorder. Behavior Therapy, 33(2),
215–233. doi:10.1016/S0005-7894(02)80026-0
Purdon, C. (1999). Thought suppression and psychopathology. Behaviour Research and
Therapy, 37(11), 1029–1054. doi:10.1016/S0005-7967(98)00200-9
Rassin, E. (2003). The White Bear Suppression Inventory (WBSI) focuses on failing
suppression attempts. European Journal of Personality, 17(4), 285-298.
Robichaud, M., Dugas, M. J., & Conway, M. (2003). Gender differences in worry and
associated cognitive-behavioral variables. Journal of Anxiety Disorders, 17(5),
501–516. doi:10.1016/S0887-6185(02)00237-2
Rosen, V. M., & Engle, R. W. (1998). Working Memory Capacity and Suppression.
Journal of Memory and Language, 39(3), 418–436. doi:10.1006/jmla.1998.2590
Rothbart, M. K., & Rueda, M. R. (2005). The development of effortful control.
Developing individuality in the human brain: A tribute to Michael I. Posner, 167188.
55
Ruscio, A. M. (2002). Delimiting the boundaries of generalized anxiety disorder:
differentiating high worriers with and without GAD. Journal of Anxiety
Disorders, 16(4), 377-400.
Salkovskis, P. M., & Campbell, P. (1994). Thought suppression induces intrusion in
naturally occurring negative intrusive thoughts. Behaviour research and
therapy, 32(1), 1-8.
Schmidt, R. E., Gay, P., Courvoisier, D., Jermann, F., Ceschi, G., David, M., … Van der
Linden, M. (2009). Anatomy of the White Bear Suppression Inventory (WBSI): A
Review of Previous Findings and a New Approach. Journal of Personality
Assessment, 91(4), 323–330. doi:10.1080/00223890902935738
Spielberger, C. D. (1983). Manual for the State-Trait Anxiety Inventory STAI (Form Y)
(“Self-Evaluation Questionnaire”).
Tallis, F., & Eysenck, M. W. (1994). Worry: Mechanisms and Modulating Influences.
Behavioural and Cognitive Psychotherapy, 22(01), 37–56.
doi:10.1017/S1352465800011796
Thayer, J. F., Hansen, A. L., Saus-Rose, E., & Johnsen, B. H. (2009). Heart rate
variability, prefrontal neural function, and cognitive performance: the
neurovisceral integration perspective on self-regulation, adaptation, and health.
Annals of Behavioral Medicine, 37(2), 141-153.
Vasey, M. W., Chriki, L. S., & Toh, G. Y. (2014). [Effortful control and worry].
Unpublished raw data.
Ware Jr, J. E., & Sherbourne, C. D. (1992). The MOS 36-item short-form health survey
(SF-36): I. Conceptual framework and item selection. Medical care, 473-483.
Watkins, E. R., & Moulds, M. L. (2009). Thought Control Strategies, Thought
Suppression, and Rumination in Depression. International Journal of Cognitive
Therapy, 2(3), 235–251. doi:10.1521/ijct.2009.2.3.235
Wegner, D. M. (1992). You can’t always think what you want: Problems in the
suppression of unwanted thoughts. In Advances in experimental social psychology
(Vol. 25, pp. 193–225).
Wegner, D. M. (1994). Ironic processes of mental control. Psychological Review, 101(1),
34–52. doi:10.1037/0033-295X.101.1.34
56
Wegner, D. M., & Erber, R. (1992). The hyperaccessibility of suppressed thoughts.
Journal of Personality and Social Psychology, 63(6), 903–912. doi:10.1037/00223514.63.6.903
Wegner, D. M., Schneider, D. J., Carter, S. R., & White, T. L. (1987). Paradoxical effects
of thought suppression. Journal of Personality and Social Psychology, 53(1), 5–
13. doi:10.1037/0022-3514.53.1.5
Wegner, D. M., & Zanakos, S. (1994). Chronic Thought Suppression. Journal of
Personality, 62(4), 615–640. doi:10.1111/j.1467-6494.1994.tb00311.x
Weisberg, R. B., Beard, C., Pagano, M. E., Maki, K. M., Culpepper, L., & Keller, M. B.
(2010). Impairment and functioning in a sample of primary care patients with
generalized anxiety disorder: results from the primary care anxiety
project. Primary care companion to the Journal of clinical psychiatry, 12(5).
Wells, A., & Carter, K. (1999). Preliminary tests of a cognitive model of generalized
anxiety disorder. Behaviour Research and Therapy, 37(6), 585–594.
doi:10.1016/S0005-7967(98)00156-9
Wenzlaff, R. M., & Wegner, D. M. (2000). Thought Suppression. Annual Review of
Psychology, 51(1), 59–91. doi:10.1146/annurev.psych.51.1.59
Wismeijer, A. A. J. (2012). Dimensionality Analysis of the Thought Suppression
Inventory: Combining EFA, MSA, and CFA. Journal of Psychopathology and
Behavioral Assessment, 34(1), 116–125. doi:10.1007/s10862-011-9246-5
57