Signature Patterns of Emotion Regulation and Their Relationship

Signature Patterns of Emotion Regulation and Their Relationship

Signature Patterns of Emotion Regulation and Their Relationship to Depression and Anxiety
Michael T. Moore & David M. Fresco, Kent State University, Robert M. Holaway, Temple University, and Douglas S. Mennin, Yale University

INTRODUCTION
Borkovecs avoidance theory of worry (Borkovec, Ray, &
Stber, 1998) posits that worry confers temporary benefit to the
individual worrier by distancing them from their own anxietyprovoking mental images, and that this negative reinforcement
explains the maintenance of the activity, and ultimately the
development of pathological forms of worry as exemplified in
Generalized Anxiety Disorder (GAD). However, what is left
unexplained is why emotion, particularly anxiety, is experienced
as so toxic to the worrier that worry can become an inflexible
means of avoidance coping, occasionally to such an extent that a
diagnosis of GAD is given?
The emotion dysregulation model of GAD (Mennin,
Heimberg, Turk, & Fresco, 2002) posits that GAD is associated
with four key deficits in how individuals influence, control,
experience, and express their emotions (Gross, 1998, pg. 275)
and explains the process by which emotions are regarded as
increasingly aversive. Specifically, the pathological worrier has
emotional reactions that occur more easily, quickly, and intensely
than for most other people (heightened intensity) and have
difficulty identifying their emotions, instead experiencing them as
confusing and overwhelming (poor understanding). As a result,
the worrier experiences emotions as aversive and may become
anxious when they occur (negative reactivity), potentially leading
to difficulty knowing when or how to diminish their emotional
experience (maladaptive management).
Research examining this theory has found that both college
students and patients seeking treatment for GAD endorsed more
deficits in emotion regulation than participants without GAD
(Fresco, Armey, Mennin, Turk, & Heimberg, 2005a; Mennin,
Heimberg, Turk, & Fresco, 2005). In addition, individuals with
GAD, but not controls, displayed greater increases in anxiety and
rigidity in their thinking after listening to sadness- or anxietyinducing music (Fresco et al., 2005b; Mennin et al., 2005).
The emotion regulation measures used in these studies
consisted of theoretically-defined composites that have not been
empirically evaluated as to their internal consistency.
Subsequent research has identified empirically-defined emotion
regulation factors that replicated the original theoretical
conceptualization of emotion dysregulation in GAD as being
composed of the original four factors: negative reactivity,
heightened intensity, poor understanding, and maladaptive
management (Mennin, Fresco, Holaway, Moore, & Heimberg,
2005).
In the current investigation, cluster analysis was utilized to
identify distinct emotion dysregulation profiles, while differences
in symptoms of psychopathology among these profiles were
investigated using ANOVA in a sample of college
undergraduates (n = 457) at a large university in the Midwestern
USA.

DISCUSSION

ABSTRACT
The present study utilized cluster analysis procedures to empirically identify subgroups of four factors (negative reactivity, heightened intensity, poor understanding, and
maladaptive management) thought to underlie various forms of psychopathology characterized by emotional dysregulation. Self-report measures were administered to 457
undergraduate students. In order to produce a stable and robust solution, the four factors were submitted to a two-stage clustering procedure consisting of an agglomerativehierarchical clustering method followed by an iterative non-hierarchical clustering method. Two clusters were identified, with Cluster 1 thought to be characterized by emotional
dysregulation and increased risk for psychopathology and Cluster 2 by emotional regulation and relative psychological well-being. This description was verified by comparing
these two clusters on various measures of psychopathology and psychological well-being. Cluster 1 was found to have higher scores on measures of anxiety, worry, depression,
experiential avoidance, pessimistic attributional style, and ruminative brooding. Cluster 2 was found to have higher scores on need for cognition, a measure of psychological wellbeing.

RESULTS

METHODS
Participants
457 undergraduate students
40% Male, 60% Female
12% African American, 84% Caucasian, 4%
Other
Participants ranged from 17-57 years of age (M = 22.73,
SD = 9.1)
Emotion Regulation Measures
Affective Control Scale (ACS; Williams et al., 1997)
Berkeley Expressivity Questionnaire (BEQ; Gross & John, 1997)
Toronto Alexythymia Scale (TAS; Bagby et al., 1994a, Bagby et al.,
1994b)
Trait Meta-Mood Scale (TMMS; Salovey et al., 1995)
Construct Validity Measures
Acceptance and Action Questionnaire (AAQ; Hayes, 1996)
Attributional Style Questionnaire (ASQ; Seligman et al., 1979)
Mood and Anxiety Symptom Questionnaire (MASQ; Watson & Clark, 1991)
Need for Cognition Scale (NFCS; Cacioppo, Petty, & Kao, 1984)
Penn State Worry Questionnaire (PSWQ; Meyer et al., 1990)
Response Styles Questionnaire (RSQ; Nolen-Hoeksema & Morrow, 1991)

Fig. 1

The four factors (negative reactivity, heightened intensity, poor
understanding, and maladaptive management) were submitted to an
agglomerative, hierarchical cluster analysis. Examination of the
agglomeration schedule was utilized to determine the appropriate number
of clusters (Hair et al., 1995), which indicated that a 2 cluster solution was
superior to a 3 cluster solution (see Fig. 1). Participants in Cluster 1
evidenced relatively high negative reactivity and maladaptive
management, and poor clarity of their emotions. Cluster 2 was
characterized by low scores on these three emotion regulation factors.
However, the two clusters did not differ on emotional intensity. Next, the
scores were subjected to a non-hierarchical/K-Means cluster analysis, and
agreement between the two, as judged by calculation of Cohens (1960)
Kappa (), was used as a more objective determinant of the relative ), was used as a more objective determinant of the relative
stability of the two solutions (Hartigan, 1975; Milligan, 1980). This analysis
suggested that the two-cluster solution (), was used as a more objective determinant of the relative = .70) was significantly more
stable than the 3-cluster solution (), was used as a more objective determinant of the relative = -.17).
MANOVA was then used as an exploratory technique to determine
psychopathology symptom profiles specific to the two clusters (see Table
1 for means and SDs by cluster). The Cluster 1 was found to have
significantly higher mean scores on all measures of psychopathology,
including: anxiety [F(1, 442) = 31.22, p < .001, f = .27], worry [F(1, 442) = 31.60, p < .001, f = .27], depression [F(1, 442) = 51.00, p < .001, f = .34], experiential avoidance [F(1, 442) = 99.33, p < .001, f = .47], pessimistic explanatory style [F(1, 442) = 7.34, p = .007, f = .13], and ruminative brooding [F(1, 442) = 48.02, p < .001, f = .33]. The Cluster 2 was found to have significantly higher mean scores on the measures of psychological well-being, such as need for cognition [F(1, 442) = 15.02, p < .001, f = .18]. The findings provide further evidence supporting the dimensions of emotion regulation articulated in Mennin et al.s emotion dysregulation model of GAD as they relate more broadly to indices of psychopathology. The emotion regulation scales seemed to agglomerate into emotional regulation and emotional dysregulation clusters. Interestingly, heightened intensity of emotions did not feature prominently in the cluster solution. Although only anecdotal, this finding complements previous results suggesting that heightened intensity of emotions may in fact be a signature characteristic of GAD. Extra-test measures lent confidence to the claims that Cluster 1 is indicative of emotional dysregulation and Cluster 2 is indicative of emotional regulation. Limitations Participants consisted of relatively highfunctioning college students, resulting in uncertain generalizability to the general public The cluster analysis and follow-up tests were exploratory in nature. Thus, replication studies will be needed before strong conclusions can be drawn from these results. Future Studies Exploring the agglomerative nature of the four emotion regulation factors in a clinical sample to determine the external validity of the current results Examining the emotional reactivity of the emotion regulation clusters in a mood priming paradigm REFERENCES Table 1 Bagby, R. M., Parker, J. D. A., & Taylor, G. J. (1994a). The twenty item Toronto Alexithymia Scale: I. Item selection and cross validation of the factor structure. Journal of Psychosomatic Research, 38, 23-32. Variable Anxiety (MASQ) Cluster 1 M (SD) 26.93 (9.19)*** Cluster 2 M (SD) 22.74 (5.26) Bagby, R. M., Taylor, G. J., & Parker, J. D. A. (1994b). The twenty-item Toronto Alexithymia Scale: II. Convergent, discriminant, and concurrent validity. Journal of Psychosomatic Research, 38, 33-40. Borkovec, T. D., Ray, W. J. & Stber, J. (1998). Worry: A cognitive phenomena intimately linked to affective, physiological, and interpersonal behavior processes. Cognitive Therapy & Research, 22, 561-576. Cacioppo, J. T., Petty, R. E., & Kao, C. F. (1984). The efficient assessment of need for cognition. Journal of Personality Assessment, 48, 306-307. Fresco, D., M., Armey, M. A., Mennin, D. S., Turk, C. L., & Heimberg, R. G. (2005a). Brooding and Pondering: Isolating the active ingredients of depressive rumination with confirmatory factor analysis. 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