Intolerance of uncertainty mediates the relation between generalized anxiety disorder symptoms and anger.

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Intolerance of Uncertainty Mediates the Relation Between Generalized Anxiety Disorder Symptoms and Anger a

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Katie Fracalanza , Naomi Koerner , Sonya S. Deschênes & Michel J. Dugas

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Department of Psychology, Ryerson University, Toronto, Ontario, Canada b

Department of Psychology, Concordia University, Montreal, Quebec, Canada c

Département de Psychoéducation et de Psychologie, Université du Québec en Outaouais, Gatineau, Quebec, Canada d

Hôpital du Sacré-Coeur de Montréal, Montreal, Quebec, Canada Published online: 28 Feb 2014.

To cite this article: Katie Fracalanza, Naomi Koerner, Sonya S. Deschênes & Michel J. Dugas (2014) Intolerance of Uncertainty Mediates the Relation Between Generalized Anxiety Disorder Symptoms and Anger, Cognitive Behaviour Therapy, 43:2, 122-132, DOI: 10.1080/16506073.2014.888754 To link to this article: http://dx.doi.org/10.1080/16506073.2014.888754

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Cognitive Behaviour Therapy, 2014 Vol. 43, No. 2, 122–132, http://dx.doi.org/10.1080/16506073.2014.888754

Intolerance of Uncertainty Mediates the Relation Between Generalized Anxiety Disorder Symptoms and Anger Katie Fracalanza1, Naomi Koerner1, Sonya S. Descheˆnes2 and Michel J. Dugas3,4


1

Department of Psychology, Ryerson University, Toronto, Ontario, Canada; 2Department of Psychology, Concordia University, Montreal, Quebec, Canada; 3De´partement de Psychoe´ducation et de Psychologie, Universite´ du Que´bec en Outaouais, Gatineau, Quebec, Canada; 4Hoˆpital du Sacre´-Coeur de Montreál, Montreal, Quebec, Canada

Abstract. Previous research has shown that individuals with generalized anxiety disorder (GAD) report elevated anger compared with nonanxious individuals; however, the pathways linking GAD and anger are currently unknown. We hypothesized that negative beliefs about uncertainty, negative beliefs about worry and perfectionism dimensions mediate the relationship between GAD symptoms and anger variables. We employed multiple mediation with bootstrapping on cross-sectional data from a student sample (N ¼ 233) to test four models assessing potential mediators of the association of GAD symptoms to inward anger expression, outward anger expression, trait anger and hostility, respectively. The belief that uncertainty has negative personal and behavioural implications uniquely mediated the association of GAD symptoms to inward anger expression (confidence interval [CI] ¼ .0034, .1845, PM ¼ .5444), and the belief that uncertainty is unfair and spoils everything uniquely mediated the association of GAD symptoms to outward anger expression (CI ¼ .0052, .1936, PM ¼ .4861) and hostility (CI ¼ .0269, .2427, PM ¼ .3487). Neither negative beliefs about worry nor perfectionism dimensions uniquely mediated the relation of GAD symptoms to anger constructs. We conclude that intolerance of uncertainty may help to explain the positive connection between GAD symptoms and anger, and these findings give impetus to future longitudinal investigations of the role of anger in GAD. Key words: generalized anxiety disorder; anger; intolerance of uncertainty; negative metacognitive beliefs; perfectionism. Received 10 October 2013; Accepted 26 January 2014 Correspondence address: Naomi Koerner, PhD, Department of Psychology, Ryerson University, 350 Victoria Street, Toronto, Ontario, Canada, M5B 2K3. Tel: þ1-416-979-5000 ext. 2151. Fax: þ1-416979-5273. E-mail: [email protected]

Introduction Generalized anxiety disorder (GAD) is a prevalent condition characterized by excessive and uncontrollable worry, and associated symptoms, such as irritability (American Psychiatric Association, 2013). GAD has been connected with aggressive behaviour (Posternak & Zimmerman, 2002), anger overexpression and greater anger experience (Hawkins & Cougle, 2011). Individuals with GAD report higher levels of internal anger expression, external anger expression, and trait anger, as well as lower levels of anger control than do individuals without GAD q 2014 Swedish Association for Behaviour Therapy

(Descheˆnes, Dugas, Fracalanza, & Koerner, 2012; Erdem, Çelik, Yetkin, & O¨zgen, 2008). Descheˆnes and colleagues also found that individuals with GAD report higher levels of hostility, physical aggression and aggressive affect compared to individuals without GAD and that internalized anger expression and hostility were significantly associated with greater GAD severity. Overall, prior research suggests that various aspects of the anger experience are elevated in people with GAD; however, potential pathways connecting the aforementioned anger dimensions and GAD symptoms are not yet understood. It is


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possible that cognitive factors that are known to be heightened in people with GAD may contribute to several forms of anger in high worriers. Although this hypothesis has not been explored empirically, there are theoretical reasons to suspect that negative beliefs about uncertainty, negative beliefs about worry and beliefs about the importance of high standards (i.e. perfectionism) may account for some of the association between GAD symptoms and anger. Intolerance of uncertainty (IU) is a dispositional characteristic that results from a set of negative beliefs about uncertainty and its implications (Dugas & Robichaud, 2007). Studies have reliably demonstrated a strong positive correlation between IU and GAD (Gentes & Ruscio, 2011). In the GAD literature, IU is commonly assessed using the Intolerance of Uncertainty Scale (IUS; Buhr & Dugas, 2002). The IUS consists of two factors: IU-1, which reflects the belief that uncertainty has negative behavioural and self-referent implications, and IU-2, which assesses the belief that uncertainty is unfair and spoils everything (Sexton & Dugas, 2009). A connection between IU beliefs and anger is postulated based on several prior findings. First, the belief that uncertainty is unfair, one aspect of IU-2, may be linked to anger because attributions of unfairness can elicit anger (Horan, Chory, & Goodboy, 2010; Smith & Ellsworth, 1985). Since uncertainty is an inevitable part of daily life, individuals who experience uncertainty as unfair may be more prone to anger. Second, the notion that uncertainty “spoils everything”, another aspect of IU-2, implies that uncertainty thwarts progress towards goals, and being blocked from goals can also provoke anger (Smith & Ellsworth, 1985). In addition, given that individuals with GAD attempt to avoid or reduce uncertainty in their lives (Beesdo-Baum et al., 2012), they may have stronger expectations of certainty. Research in non clinical samples suggests that having one’s expectations violated can produce anger (Crossman, Sullivan, Hitchcock, & Lewis, 2009). Thus, individuals with GAD may be more vulnerable to experiencing anger when uncertain events inevitably occur despite the best of planning. In sum, high IU may connect GAD symptoms and anger, as IU may make individuals more prone to perceiv-

IU Mediates the GAD-Anger Connection

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ing unfairness, goal blockage and expectancy violations. Another worry-related belief that may help account for the association between GAD symptoms and anger is the belief that worry is uncontrollable and dangerous, which is considered central to the development and maintenance of GAD (Wells, 2004). Negative beliefs about worry are higher in people with GAD compared to other anxious individuals and non anxious controls (Wells & Carter, 2001). Wells’ (2004) cognitive model of worry posits that negative beliefs about worry lead to efforts to control worry, and we propose that failed efforts to control worry may provoke anger. This idea is based on findings that failed attempts to attain goals (such as controlling worry) can lead to anger (Smith & Ellsworth, 1985), and that the need to control thoughts is associated with anger (Radomsky, Ashbaugh, & Gelfand, 2007). Individuals with GAD also report using self-directed anger to control thoughts more often than individuals with depression and healthy controls (Wells & Carter, 2009). Taken together, these results suggest that the belief that worry is uncontrollable and dangerous may be another factor that helps to explain the GAD – anger connection. A third worry-related belief that may account for the association between GAD symptoms and anger is perfectionism. Several forms of perfectionism have been described in the literature, including beliefs about the importance of setting extremely high standards for oneself (self-oriented perfectionism [SOP]), and perceptions that others have unrealistically high expectations that one cannot meet (socially prescribed perfectionism [SPP]; Hewitt & Flett, 1991b). Numerous studies have demonstrated that SOP and SPP have a moderate positive association with both worry (Buhr & Dugas, 2006; Short & Mazmanian, 2013; Stoeber, Feast, & Hayward, 2009) and anger (Blankstein & Lumley, 2008; Saboonchi & Lundh, 2003). Theoretically, having unrealistically high expectations of oneself could make worriers more susceptible to anger when these standards cannot be met (e.g., Smith & Ellsworth, 1985). In addition, perceiving that others hold one to unreasonably high standards may elicit anger as this may activate a sense of unfairness or failure to accomplish goals (e.g., Horan et al.,


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2010). Overall, we speculate that SOP and SPP may mediate the connection between GAD symptoms and anger. The purpose of the current study was to assess whether cognitive factors related to worry mediate the relationship between GAD symptoms and anger variables. The anger constructs that we examined in the present student were: inward anger expression, outward anger expression, trait anger and hostility, based on prior research suggesting that these forms of anger are most strongly associated with GAD (Descheˆnes et al., 2012). On the basis of the literature described above, we hypothesized that negative beliefs about uncertainty (IU-1 and IU-2), negative beliefs about worry, SOP and SPP would each account for significant unique variance in the relationship between GAD symptoms and anger dimensions.

Method Participants Since the current study examined variables that exist along a continuum in the population (e.g. GAD symptoms; Olatunji, BromanFulks, Bergman, Green, & Zlomke, 2010) or are not disorder-specific (e.g. hostility), we elected to use an undifferentiated student sample to test our hypotheses. In addition, student samples allow for the investigation of novel research questions in a time-efficient and cost-effective manner (see Clark-Carter, 2010). Participants (N ¼ 233) were students from Ryerson University between the ages of 17 and 30 (M ¼ 20.60, SD ¼ 3.08), and most (80%) were female. The majority of participants self-identified as White (40%), followed by Asian (31%), Black (13%), Middle Eastern (8%), Multi-Racial (5%), Other (an ethnicity not listed; 2%) and Latin American (1%).

Measures Generalized anxiety disorder screening. The Generalized Anxiety Disorder Questionnaire (GAD-Q-IV; Newman et al., 2002) is a widely used self-report screening tool that assesses GAD symptoms and the severity of the distress and impairment associated with these symptoms. The GAD-Q-IV can be scored using a continuous approach in which responses are summed to create a score reflecting the severity of GAD symptoms, or a

COGNITIVE BEHAVIOUR THERAPY

categorical approach in which individuals who score 5.7 or above are said to meet GAD criteria (Newman et al., 2002). The continuous scoring system was used in the present study because we examined an undifferentiated sample with varying GAD symptom levels, and sought to understand how this range of GAD symptoms relate to anger constructs. The GAD-Q-IV has shown convergent and discriminant validity, and good test –retest reliability (Newman et al., 2002). Worry-related cognitive factors. The 27-item IUS assesses negative beliefs about uncertainty. The IUS consists of two factors: IU-1 and IU-2 (see “Introduction” section; Sexton & Dugas, 2009). The IUS total and subscale scores have demonstrated construct validity, excellent internal consistency (total a ¼ .95; IU-1 a ¼ .92; and IU-2 a ¼ .91) and good test –retest reliability (rs ¼ .74 to .92; Buhr & Dugas, 2002; Sexton & Dugas, 2009). The 30-item Metacognitions Questionnaire (MCQ-30; Wells & Cartwright-Hatton, 2004) contains five subscales that assess beliefs about thoughts and cognitive processes. Only the beliefs about the uncontrollability and dangerousness of worry subscale (MCQ-UD) was used in the present study because this variable is central to GAD (Wells, 2004), and has been linked to anger (Whiteside & Abramowitz, 2005). As a subscale, the MCQ-UD has shown convergent validity, good internal consistency (a ¼ .91) and acceptable test – retest reliability (r ¼ .59; Wells & Cartwright-Hatton, 2004). The 45-item Multidimensional Perfectionism Scale (MPS; Hewitt & Flett, 1991b) contains subscales that assess SOP and SPP (see “Introduction” section). The MPS also assesses other-oriented perfectionism, although this subscale was not included in the present study as it has not been associated with worry or anger (e.g., Buhr & Dugas, 2006; Hewitt & Flett, 1991a). The SOP and SPP subscales of the MPS have shown convergent and divergent validity, excellent internal consistency (as ¼ .89 and .86) and adequate stability (rs ¼ .69 and .60; Hewitt & Flett, 1991a; Hewitt, Flett, Turnbull-Donovan, & Mikail, 1991). Anger dimensions. The State-Trait Anger Expression Inventory-II (STAXI-II; Spielberger, 1999) includes scales that assess anger expression, trait anger and state anger.


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To meet the goals of the current study, the anger expression-in (AX-I), anger expression-out (AX-O) and trait anger (T-ANG) subscales were used. The AX-I and AX-O subscales each contain eight items; the AX-I measures the inward experience of anger and the AX-O measures the outward expression of anger. The 10-item T-ANG subscale assesses the general tendency to react to situations with anger. The STAXI-II subscales have shown adequate construct validity and internal consistency (as ¼ .70 to .85; Spielberger, 1999). The Aggression Questionnaire (AQ; Buss & Perry, 1992) assesses tendencies towards four specific types of aggression. Only the hostility subscale (AQ-HOST) was used in the present study because scores on this subscale are most elevated in analogue GAD samples (Descheˆnes et al., 2012). The eight-item AQ-HOST assesses the cognitive aspect of aggression, including thoughts of injustice and bitterness towards others. The AQ has shown construct validity, good internal consistency (a ¼ .89) and good test–retest reliability (r ¼ .80; Buss & Perry, 1992). Depressive symptoms. The 20-item Center for Epidemiologic Studies Depression Scale (CES-D; Radloff, 1977) measures symptoms of depression experienced over the past week. The CES-D has demonstrated construct validity, high internal consistency (a ¼ .85) and adequate test – retest reliability (r ¼ .67; Radloff, 1977).

indirect effects, and a confidence interval (CI) for the indirect effects is calculated based on the range of estimates. Bootstrapping allows for more accurate and powerful analyses than traditional mediation approaches (William & MacKinnon, 2008). Preacher and Hayes’ (2008) PROCESS macro for SPSS was used to test study hypotheses. We performed four mediation analyses. GAD severity was entered as the initial variable in each model; however, a different angerrelated construct was entered as the outcome in each analysis, resulting in four models. We used PROCESS to examine mediators simultaneously, allowing for an estimation of the total indirect effect of the set of mediators in each model and the unique contribution of each mediator in the set, while controlling for statistical overlap among mediators. For each analysis, we used 10,000 bootstrap samples and 95% bias corrected CIs to evaluate the significance of the indirect effects (Williams & MacKinnon, 2008). An indirect effect is significant if its CI does not include zero. Effect sizes were calculated based on the mediation proportion (PM), which is the ratio of the indirect effect to the total effect (Mackinnon, 2008).

Procedure

See Table 1 for the means and standard deviations of each measure. All scales showed high internal consistencies (as ¼ .79 to .91), and were normally distributed (skew ¼ 2 .60 to .88, kurtosis ¼ 2 1.27 to 1.09). Pearson correlations were computed between GAD symptoms, anger variables and worry-related processes. The relations of depression, age and sex to all variables were also calculated to identify potential covariates (see Table 2). There were significant positive correlations between all measures, with the following exceptions: SOP and SPP were not significantly correlated with AX-O, and SOP was not significantly correlated with AQ-HOST. For each model, a worry-related process was earmarked for inclusion as a potential mediator only if it was found to have a significant correlation with the anger variable in that particular model. Depression was

Individuals were recruited from Ryerson University through the psychology department participant pool. Participants provided informed consent and completed the aforementioned questionnaires, which were administered in a counterbalanced order. Following this, participants were debriefed and awarded course credit for completing this study.

Data analytic strategy Intercorrelations among all study variables were performed, after which mediation analyses were conducted using a bootstrapping approach. Bootstrapping involves taking a large number of random samples with replacement from an original data set to create pseudo data sets with estimates of the indirect effects for each data set. This creates an approximation of the sampling distribution of the

Results Descriptive statistics and correlational analyses

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Table 1. Descriptive statistics of study measures (N ¼ 233) Measure

Mean

Standard deviation

GAD-Q-IV AX-I AX-O T-ANG AQ-HOST IU-1 IU-2 MCQ-UD SOP SPP CES-D

4.29 18.43 16.21 19.80 21.89 32.29 30.29 13.81 69.08 52.47 17.33

4.09 4.77 4.25 5.48 6.69 10.94 9.52 4.68 15.67 13.07 9.96

Note. GAD-Q-IV, Generalized Anxiety Disorder Questionnaire (dimensionally scored); AX-I, STAXIII—anger expression-in subscale; AX-O, STAXI-II— anger expression-out subscale; T-ANG, STAXI-II— trait anger subscale; AQ-HOST, Aggression Questionnaire—hostility subscale; IU-1, IUS—Factor 1; IU-2, IUS—Factor 2; MCQ-UD, Metacognitions Questionnaire—uncontrollability/danger subscale; SOP, MPS—self-oriented perfectionism subscale; SPP, MPS—socially prescribed perfectionism subscale; CES-D, Center for Epidemiologic Studies Depression Scale.

positively associated with every variable except SOP, and age was negatively associated with AX-I and MCQ-UD, therefore depression and age were entered as covariates in each model. Sex was not significantly associated with any of the study variables.

Mediation analyses Figures 1– 4 provide a visual depiction of the variables entered as potential mediators in each analysis. The figures also display the unstandardized regression coefficients of the direct effects in each model. Unlike traditional mediation approaches, modern mediation approaches such as bootstrapping do not require statistically significant direct effects to interpret indirect effects (Hayes, 2013). The indirect effects (i.e., mediation effects) and total effects for each model are described below. The first mediation analysis examined the indirect effects of GAD symptoms on AX-I through IU-1, IU-2, MCQ-UD, SOP and SPP, controlling for depression and age (see Figure 1). The total effect of GAD symptoms on AX-I was not significant, b ¼ .14, SE ¼ .09, t ¼ 1.66, p ¼ .10; however, 20% of the effect of GAD symptoms on AX-I was


mediated by the worry-related cognitive factors examined in this model, R2adj ¼ :20, F (3, 229) ¼ 22.50, p , .001. GAD symptoms had a significant total indirect effect on AX-I through the set of cognitive factors (CI ¼ .0548, .2387, PM ¼ .9563), and a significant specific indirect effect on AX-I through IU-1 (CI ¼ .0034, .1845, PM ¼ .5444), but not through the other worry-related cognitive factors (see Table 3). The second mediation analysis examined the association between GAD symptoms and AX-O through IU-1, IU-2 and MCQ-UD, controlling for depression and age (see Figure 2). The total effect of GAD symptoms on AX-O was not significant, b ¼ .17, SE ¼ .09, t ¼ 1.91, p ¼ .06; however, 6% of the effect of GAD symptoms on AX-O was mediated by the worry-related cognitive factors examined in this model, R2adj ¼ :06, F (3, 229) ¼ 3.91, p , .01. The total indirect effect of GAD symptoms on AX-O through the set of cognitive factors was not significant (see Table 3), although the specific indirect effect of GAD symptoms on AX-O through IU-2 was significant (CI ¼ .0052, .1936, PM ¼ .4861). The third mediation analysis examined the association between GAD symptoms and T-ANG through IU-1, IU-2, MCQ-UD, SOP and SPP, controlling for depression and age (see Figure 3). The total effect of GAD symptoms on T-ANG was significant, b ¼ .33, SE ¼ .11, t ¼ 3.07, p , .01, and 16% of the effect of GAD symptoms on T-ANG was mediated by the worry-related cognitive factors examined in this model, R2adj ¼ :16, F (3, 229) ¼ 11.85, p , .01. GAD symptoms had a significant total indirect effect on T-ANG through the worry-related cognitive processes taken together as a set (CI ¼ .0889, .3232, PM ¼ .5937); however, none of the worry-related cognitive processes had a significant specific indirect effect on T-ANG (see Table 3). The fourth mediation analysis examined the indirect effects of GAD symptoms on AQHOST through IU-1, IU-2, MCQ-UD and SPP, controlling for depression and age (see Figure 4). The total effect of GAD symptoms on AQ-HOST was significant, b ¼ .33, SE ¼ .10, t ¼ 3.28, p , .01, and 36% of the effect of GAD symptoms on AQ-HOST was mediated by the worry-related cognitive

1.00

.32** 1.00

2

.22** .12 1.00

3 .36** .29** .73** 1.00

4 .46** .44** .28** .50** 1.00

5 .52** .43** .17** .38** .55** 1.00

6 .48** .38** .25** .40** .53** .76** 1.00

7 .47** .26** .19** .35** .31** .47** .49** 1.00

8 .24** .18** .10 .24** .09 .21** .36** .25** 1.00

9 .24** .23** .06 .22** .38** .39** .34** .13* .35** 1.00

10

.56** .42** .20** .35** .57** .52** .44** .40** .07 .29** 1.00

11

13 2 .10 2 .05 .01 2 .04 2 .05 .02 2 .03 .03 .08 2 .02 2 .13 .32** 1.00

12 2 .11 2 .19** 2 .01 2 .05 2 .11 2 .10 2 .04 2 .13* .08 .22** .11 1.00

Note. GAD-Q-IV, Generalized Anxiety Disorder Questionnaire (dimensionally scored); AX-I, STAXI-II—anger expression-in subscale; AX-O, STAXI-II—anger expression-out subscale; T-ANG, STAXI-II –trait anger subscale; AQ-HOST, Aggression Questionnaire—hostility subscale; IU-1 ¼ IUS—Factor 1; IU-2, IUS— Factor 2; MCQ-UD, Metacognitions Questionnaire—uncontrollability/danger subscale; SOP, MPS—self-oriented perfectionism subscale; SPP, MPS—socially prescribed perfectionism subscale; CES-D, Center for Epidemiologic Studies Depression Scale; *p , .05. **p , .01. a Point-biserial correlation, 0 ¼ female, 1 ¼ male.

1. GAD-Q-IV 2. AX-I 3. AX-O 4. T-ANG 5. AQ-HOST 6. IU-1 7. IU-2 8. MCQ-UD 9. SOP 10. SPP 11. CES-D 12. Age 13. Sexa

1

Table 2. Correlations between GAD symptoms, anger dimensions, and worry-related cognitive processes

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Figure 1. Unstandardized regression coefficients between GAD symptoms, potential mediators and inward anger expression. *p , .05, **p , .01.

Figure 2. Unstandardized regression coefficients between GAD symptoms, potential mediators and outward anger expression. *p , .05, **p , .01.

Figure 3. Unstandardized regression coefficients between GAD symptoms, potential mediators and trait anger. *p , .05, **p , .01.

Figure 4. Unstandardized regression coefficients between GAD symptoms, potential mediators, and hostility. *p , .05, **p , 0.01.

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Table 3. Specific and total indirect effects of GAD symptoms on anger variables through worry-related cognitive factors controlling for depression and age Bootstrapped 95% BC CIs Anger variable

Mediator variables

AX-I

IU-1* IU-2 MCQ-UD SOP SPP Total* IU-1 IU-2* MCQ-UD Total IU-1 IU-2 MCQ-UD SOP SPP Total* IU-1 IU-2* MCQ-UD SPP Total*


AX-O

T-ANG

AQ-HOST

Point estimate

SE

Lower

Upper

PM

.0785 .0315 2 .0065 .0322 .0022 .1379 2 .0452 .0824 .0196 .0568 .0265 .0627 .0593 .0397 .0053 .1936 .0654 .1166 2 .0314 .0269 .1775

.0446 .0392 .0330 .0260 .0122 .0467 .0432 .0471 .0342 .0450 .0547 .0621 .0421 .0328 .0130 .0592 .0474 .0536 .0375 .0214 .0573

.0034 2 .0407 2 .0713 2 .0095 2 .0139 .0548 2 .1415 .0052 2 .0440 2 .0251 2 .0798 2 .0445 2 .0114 2 .0119 2 .0119 .0889 2 .0162 .0269 2 .1098 2 .0039 .0733

.1845 .1157 .0612 .0959 .0371 .2387 .0298 .1936 .0919 .1564 .1397 .2032 .1549 .1158 .0465 .3232 .1755 .2427 .0394 .0841 .3010

.5444 .2184 2.0451 .2233 .0153 .9563 2.2667 .4861 .1156 .3351 .0813 .1923 .1818 .1217 .0163 .5937 .1956 .3487 2.0939 .0804 .5308

Note. AX-I, STAXI-II—anger expression-in subscale; AX-O, STAXI-II—anger expression-out subscale; T-ANG, STAXI-II –trait anger subscale; AQ-HOST, Aggression Questionnaire—hostility subscale; IU-1, IUS—Factor 1; IU-2, IUS—Factor 2; MCQ-UD, Metacognitions Questionnaire—uncontrollability/danger subscale; SOP, MPS—self-oriented perfectionism subscale; SPP, MPS—socially prescribed perfectionism subscale; SE, standard error; BC, bias corrected; PM, mediation proportion; 10,000 bootstrap samples. *p , .05.

factors examined in this model, R2adj ¼ :36, F (3, 229) ¼ 41.68, p , .01. GAD symptoms had a significant total indirect effect on hostility through the set of cognitive factors (CI ¼ .0733, .3010, PM ¼ .5308), and a significant specific indirect effect on AQHOST through IU-2 (CI ¼ .0269, .2427, PM ¼ .3487), but not through the other worry-related cognitive factors (see Table 3).

Discussion There is mounting evidence that individuals with GAD report elevated anger compared to nonanxious individuals (Erdem et al., 2008). The present study explored whether ways of thinking that are typical of people with GAD, such as having negative beliefs about uncertainty, negative beliefs about worry and perfectionistic beliefs, might account for some of the variance in the connection

between GAD symptoms and anger. In order to investigate this, we ran four separate mediation analyses, each examining a different form of anger. The results can be summarized as follows: (1) the belief that uncertainty has negative implications for oneself and one’s behaviour (IU-1) independently mediated the relation between GAD symptoms and inwardly expressed anger; (2) the belief that uncertainty is unfair and spoils everything (IU-2) independently mediated the association of GAD symptoms to outwardly expressed anger and hostility; (3) IU-1, IU-2, negative beliefs about worry, SOP and SPP collectively mediated the relationship between GAD symptoms and trait anger; and (4) negative beliefs about worry, SOP and SPP did not emerge as independent mediators of the relationship between GAD symptoms and any anger construct that we examined. Overall, the results of the current study suggest that


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IU may be a factor that uniquely connects GAD symptoms and various manifestations of anger. The notion that negative beliefs about uncertainty may help to explain the association between GAD symptoms and anger is novel. We hypothesized this based on the idea that having high intolerance for uncertainty, as people with GAD do (Gentes & Ruscio, 2011), may lead to more frequent feelings that situations are unfair because of uncertainty, that one’s goals are being thwarted by uncertainty and that one’s expectations are being violated by uncertainty. These attributions are all associated with experiencing anger (e.g., Horan et al., 2010). Given that negative beliefs about uncertainty mediated the connection between GAD symptoms and several anger dimensions, it is of note that anger-related emotions are not currently included in the IU theory of GAD (Dugas & Robichaud, 2007). Understanding the place of anger in GAD models is an important future research direction. In addition, our findings showed that IU-1 mediated the association of GAD symptoms to internalized anger, whereas IU-2 mediated the relation of GAD symptoms to externalized anger and hostility. In line with this, Sexton and Dugas (2009) found that IU-1 had significantly larger associations with anxiety and depression than did IU-2, and like inwardly expressed anger, anxiety and depression are internalizing emotions (ZahnWaxler, 2000). It seems that the belief that uncertainty has negative personal and behavioural implications (IU-1) may lead to internalized emotions, including anger that is felt but not expressed to others, whereas the belief that uncertainty is unfair and spoils everything (IU-2) may lead to externalized emotions, such as anger that others can readily observe. These findings provide hints at possible reasons why GAD has been associated with different anger dimensions in prior research (Descheˆnes et al., 2012), as they suggest that the form of anger that worriers experience may depend on the type of beliefs activated by a particular situation. In addition, our results imply that there may be merit in distinguishing between IU-1 and IU-2 theoretically and clinically, as these beliefs seem to have different emotional consequences.


It should be considered that other factors that were not measured in the present study may also explain the GAD –anger association. For example, low distress tolerance and emotion dysregulation have been associated with both pathological worry (Huang, Szabo´, & Han, 2009; Salters-Pedneault, Roemer, Tull, Rucker, & Mennin, 2006) and anger (Hawkins, Macatee, Guthrie, & Cougle, 2013; Herts, McLaughlin, & Hatzenbuehler, 2012), and could contribute to elevated anger in GAD populations. Future research should expand on the present work by investigating additional constructs that might account for the relation between GAD and problematic anger. The present study had a number of strengths. It tested original hypotheses, providing novel information about how GAD symptoms and anger are related. This study also employed multiple mediation analyses, allowing for the unique contribution of each mediator to be tested while the effects of other mediators were controlled for. In addition, the present study examined the connection between GAD symptoms and a number of different anger variables, which is informative given that various anger constructs have been linked to GAD. These strengths notwithstanding, the present study also had limitations. Although cross-sectional designs are commonly used to explore mediation hypotheses (e.g., Schmertz, Masuda, & Anderson, 2012), cross-sectional studies cannot be used to infer causal relationships between variables, and future studies should extend our results using longitudinal designs. In addition, the present study examined a young, predominantly female student sample, which may limit the generalizability of the findings. Further, although we found significant associations between GAD symptoms and all of the anger dimensions examined, the current results must be interpreted in light of the fact that these correlations were modest (rs ¼ .22 to .46). In sum, this investigation suggests that negative beliefs about uncertainty may be a factor that helps explain why individuals with GAD report higher levels of anger than do non anxious individuals. The results showed that the belief that uncertainty has negative behavioural and self-referent implications (IU-1) linked GAD symptoms to internally expressed anger, whereas the belief that

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uncertainty is unfair and spoils everything (IU-2) linked GAD symptoms to externally expressed anger and hostility. These findings imply that specific negative beliefs about uncertainty relate to different manifestations of anger. Future longitudinal studies using nonstudent samples are needed to clarify how and why GAD and anger are associated, although the present study provides an initial step towards understanding this relationship.


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