Psychological Assessment 2015, Vol. 27, No. 2, 605-620

© 2015 American Psychological Association 1040-3590/15/$ 12.00 http://dx.doi.org/10.1037/pas0000074

Further Clarifying Prospective and Inhibitory Intolerance of Uncertainty: Factorial and Construct Validity of Test Scores From the Intolerance of Uncertainty Scale Ryan Y. Hong and Stephanie S. M. Lee National University of Singapore The Intolerance to Uncertainty Scale (IUS) was developed to measure a dispositional tendency to react negatively to uncertain events, regardless of the occurrence probability of those events. Recent evidence suggests a 2-factor structure underlying the IUS; 1 factor measuring a prospective aspect (i.e., desire for predictability) and the other assessing an inhibitory aspect (i.e., uncertainty paralysis). The factorial and construct validity of the IUS test scores among undergraduate students in Singapore were examined in the present research using exploratory (n = 565) and confirmatory (n = 898) factor analyses. Results indicated that a 2-factor model was preferred over a unitary-factor model. The construct validity of the IUS (and subscale) scores was examined using a comprehensive nomological network of psychopathol­ ogy and personality/affectivity variables. Differential relations were observed for the prospective and inhibitory components, providing support that the 2 subscales assessed unique aspects of the intolerance of uncertainty construct. An 18-item modified version of IUS was also proposed and its test scores had stronger validity evidence than scores from the widely used 12-item version. Keywords: intolerance of uncertainty, factor structure, construct validity, prospective, inhibitory

Intolerance of uncertainty (IU) refers to a dispositional tendency to view the occurrence of negative events as unacceptable and threatening, regardless of the probability of those events actually happening (Carleton, Sharpe, & Asmundson, 2007; Dugas, Gosselin, & Ladouceur, 2001). Individuals high on IU hold a set of negative beliefs about uncertainty in everyday situations (e.g., being uncertain is frustrating) and its implications on the self (e.g., I cannot act under uncertainty) (Freeston, Rheaume, Letarte, Dugas, & Ladouceur, 1994; Koerner & Dugas, 2008). More broadly, these beliefs may stem from a fundamental fear of the unknown (Carleton, 2012). These negative beliefs are thought to increase the susceptibility of individuals experienc­ ing unwanted and excessive worry, a hallmark feature of gen­ eralized anxiety disorder (Buhr & Dugas, 2002; Dugas, Gagnon, Ladouceur, & Freeston, 1998; van der Fleiden et ah, 2010; see also Gentes & Ruscio, 2011). As IU beliefs appear to precede worry (Dugas & Ladouceur, 2000) and experimental manipulation of IU results in corresponding changes in worry symptoms (Ladouceur, Gosselin, & Dugas, 2000), researchers increasingly see IU as a causal vulnerability factor for gener­ alized anxiety disorder (Koerner & Dugas, 2008). Although IU is originally conceptualized as a vulnerability factor for worry and generalized anxiety disorder, recent studies suggest that IU has robust relations with other psychopathological

symptoms such as social anxiety (Boelen & Reijntjes, 2009; Car­ leton, Collimore, & Asmundson, 2010), panic and agoraphobia (Carleton et al., 2014), posttraumatic stress (Bardeen, Fergus, & Wu, 2013; Fetzner, Florswill, Boelen, & Carleton, 2013), obsessive-compulsive symptoms (Flolaway, Heimberg, & Coles, 2006; Jacoby, Fabricant, Leonard, Riemann, & Abramowitz, 2013) and depression (Hong & Paunonen, 2011; Sexton & Dugas, 2009; van der Heiden et al., 2010). The meta-analytic review by Gentes and Ruscio (2011) shows that IU not only relates to symptoms of generalized anxiety disorder (mean r = .57), but also to symptoms of depression (mean r = .52) and obsessive-compulsive disorder (mean r = .52); with associations stronger for student than for treatment-seeking samples. Even after controlling for constructs like neuroticism and overlapping symptoms, IU retains its predict­ ability of a variety of symptoms (Fergus & Wu, 2011; McEvoy & Mahoney, 2011), further suggesting that IU may be a transdiag­ nostic vulnerability factor (Carleton, 2012).

Factorial Validity The 27-item Intolerance of Uncertainty Scale (IUS) is the most widely used instrument in IU research; originally developed in French (Freeston et al., 1994) and later translated to English (Buhr & Dugas, 2002). Although the IUS has demonstrated excellent psychometric properties (e.g., internal consistency reliabilities greater than .90), there has been a considerable debate about its factorial validity. Exploratory factor analytic procedures on largely student samples had yielded two- (Carleton, Norton, & Asmund­ son, 2007; Sexton & Dugas, 2009), four- (Berenbaum, Bredemeier, & Thompson, 2008; Buhr & Dugas, 2002), and five-factor (Freeston et al., 1994) solutions (see Birrell, Meares, Wilkinson, & Freeston, 2011, for a review). This inconsistency has been attrib­ uted to the use of less-than-ideal factor extraction methods such as

This article was published Online First January 19, 2015. Ryan Y. Hong and Stephanie S. M. Lee, Department of Psychology, National University of Singapore. Correspondence concerning this article should be addressed to Ryan Y. Hong, Department of Psychology, National University of Singapore, 9 Arts Link, Singapore 117570. E-mail: [email protected] 605

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Kaiser’s (1970) eigenvalue-greater-than-one criterion and inade­ quate sample sizes (Birrell et al., 2011). Carleton, Norton et al. (2007) proposed a 12-item short version of the IUS and reported a two-factor model, which has since received considerable support in confirmatory factor analysis studies with student (Helsen, Van den Bussche, Vlaeyen, & Goubert, 2013) and clinical samples (Carleton et al., 2012; Jacoby et al., 2013; McEvoy & Mahoney, 2011). Following McEvoy and Mahoney’s (2011) suggestion, the two factors are labeled Prospective IU and Inhibitory IU for the purpose of the current study. Although the two-factor IUS-12 model has gained prominence in the literature, the way the scale items were selected in Carleton, Norton et al. (2007) has been criticized. Specifically, those authors had taken one factor each from a four-factor and a five-factor model (with no overlapping items) and then refined the items to form the IUS-12. This method of item selection was atypical as scale dimensionality was determined on the basis of nonoverlap­ ping item sets across different models and not via established criteria like a scree test (Cattell, 1966) within a single model. Also, as Sexton and Dugas (2009) had noted, this strategy had excluded other items for consideration at the outset and that might have compromised content validity. Although Sexton and Dugas had demonstrated an excellent fit of the two-factor IUS-27 model to their data, subsequent studies showed less favorable fit indices for this model compared to the two-factor IUS-12 model (Helsen et al., 2013; McEvoy & Mahoney, 2011). As a result, most recent studies examining the construct validity of IUS test scores have relied on the IUS-12 rather than the IUS-27. However, Sexton and Dugas’ cautionary note on scale content validity and how this might impact on the construct validity of IUS test scores should not be dismissed. Instead, this issue should be examined more closely.

Construct Validity Little research has been conducted to systematically evaluate the relative merits of IUS-27 versus IUS-12 in terms of construct validity. Using a large sample of 818, Carleton, Norton et al. (2007) found that the total IUS score of the two versions correlated equally with measures of depression, anxiety, and worry. In a mixed sample of clinical and nonclinical participants (n = 106), Khawaja and Yu (2010) found that IUS-27 conferred slight ad­ vantages over the IUS-12 in reliability and discriminant validity between the two subscale scores. However, one limitation was that the validity coefficients associated with the two IUS versions were not statistically tested for differences in magnitude. Even though these studies suggested that both IUS versions were strongly correlated with each other and the patterns of associations with other measures were similar, the limited number of other con­ structs used for validation diminished the confidence of validity inferences. At the same time, given the increasing focus on deter­ mining the nature of prospective and inhibitory IU, it is imperative that more comparative studies be done using a wider nomological net. The nature of prospective and inhibitory IU has been elaborated in recent efforts (Birrell et al., 2011). Prospective IU seems to represent a desire for predictability of future events, triggered by anxious apprehension about uncertainty, and prompting engage­ ment in strategies (e.g., seeking more information) to reduce

uncertainty. Conversely, inhibitory IU appears to measure paraly­ sis and impaired functioning arising from uncertainty (Berenbaum et al., 2008). Birrell et al. (2011) proposed that prospective IU might reflect an approach orientation to uncertainty where intol­ erant individuals try to reduce uncertainty anxiety by gathering information and planning ahead. Conversely, inhibitory IU might entail an avoidance-based strategy (Borkovec, Ray, & Stober, 1998; Stapinski, Abbott, & Rapee, 2010) in which such individuals “freeze up” under uncertainty and engage in some form of mal­ adaptive cognitive perseveration (e.g., thinking of possible threats and delaying making decisions; Dugas & Robichaud, 2007). Differential associations with other constructs have been pro­ posed for the two IU facets and some support has been found. Specifically, prospective IU seems to be more correlated with worry and symptoms of obsessive-compulsive disorder whereas inhibitory IU is more strongly implicated in symptoms of social anxiety, panic disorder, and agoraphobia, posttraumatic stress dis­ order, and depression (Berenbaum et al., 2008; Fetzner et al., 2013; Helsen et al., 2013; McEvoy & Mahoney, 2011, 2012; Sexton & Dugas, 2009). These recent studies documented the differential relations the two aspects of IU have with symptoms of anxiety and mood disorders. However, much less is known about how pro­ spective and inhibitory IU might have differential relations with (a) other vulnerability factors and (b) personality and affectivity factors. Various vulnerability factors to psychopathology have been proposed and some of them are related to IU. Fear of negative evaluation, a tendency to think that others are critical of oneself, is a vulnerability factor implicated in social anxiety (Rapee & Heimberg, 1997) and it has shown a strong association with IU (Boelen & Reijntjes, 2009). Anxiety sensitivity is the tendency to construe anxiety-related bodily arousal as undesirable and harmful, which further evokes more fearful responses to one’s own anxiety, po­ tentially leading to panic disorder (McNally, 1994) and other symptoms like depression and social phobia (Naragon-Gainey, 2010). This vulnerability factor has been found to be associated with IU (Carleton, Sharpe et al., 2007). Riskind, Williams, Gessner, Chrosniak, and Cortina (2000) have proposed the looming cognitive style as a generalized vulnerability factor for various anxiety disorder syndromes. Individuals high on this style are likely to generate mental scenarios that an impending and escalat­ ing threat is approaching rapidly, and preliminary analyses show that this style is positively related to IU (Riskind, Tzur, Williams, Mann, & Shahar, 2007). Depressive rumination is a vulnerability factor defined as the tendency to focus passively on one’s negative moods and shortcomings when one is feeling sad (NolenHoeksema, Wisco, & Lyubomirsky, 2008). Although originally conceptualized as a risk factor to depression, recent empirical evidence suggests that rumination also predicts anxiety, eating, and substance use disorders (Aldao, Nolen-Hoeksema & Schweizer, 2010). Rumination has been found to be associated with IU (de Jong-Meyer, Beck, & Riede, 2009; Yook, Kim, Suh, & Lee, 2010). In addition, a recent meta-analysis demonstrated that some of these vulnerabilities (including IU) showed moderate to strong associations (rs between .35 and .60) with one another (Hong & Cheung, 2014). Although the extant literature suggests that IU is linked to several vulnerability factors of anxiety and mood disorders, less is known about these relations with reference to IU facets. Recent

INTOLERANCE OF UNCERTAINTY

research suggests that inhibitory, more than prospective, IU is related to fear of negative evaluation (Whiting et al., 2014) and anxiety sensitivity (Fetzner et al., 2013). However, research has yet to investigate the relations between the IU facets and other vulnerability factors like looming cognitive style and rumination. It was hypothesized that the associations between the IU facets and vulnerabilities should mirror the associations found for symptoms. For example, inhibitory IU has stronger relations with anxiety and depression and thus was expected to have stronger relations with the corresponding vulnerability factors of those symptoms. There­ fore, fear of negative evaluation, anxiety sensitivity, looming cog­ nitive style, and rumination were expected to be more correlated to inhibitory IU than to prospective IU. Several studies have documented IU's links with personality and affectivity variables. In particular, major personality dimen­ sions like neuroticism (or negative affectivity) and extraversion (or positive affectivity) are implicated in a wide spectrum of anxiety and mood disorders (Watson, Clark, & Harkness, 1994; Kotov, Gamez, Schmidt, & Watson, 2010). IU has a strong positive association with neuroticism/negative affectivity but its negative relation with extraversion/positive affectivity is weaker (Berenbaum et al., 2008; Fergus & Wu, 2011; Hong & Paunonen, 2011; McEvoy & Mahoney, 2012; Norton & Mehta, 2007; van der Heiden et al., 2010). Establishing these links are important as dimensions like neuroticism/negative affectivity are postulated as broad and distal variables that influence psychopathological symp­ toms with IU (as a more specific and proximal variable) playing a mediating role (see Fergus & Wu, 2011; Hong & Paunonen, 2011; Norton & Mehta, 2007). With the recent focus on the two facets of IU, it is timely to examine if the facets have discriminant associ­ ations with these dimensions, which might help delineate their nature. McEvoy and Mahoney (2011, 2012) reported that prospec­ tive and inhibitory IU had equal relations with neuroticism (rs around .50) and extraversion (rs around -.1 6 ). However, Berenbaum et al. (2008) found that extraversion was negatively related to inhibitory IU (or uncertainty paralysis, using the authors’ label) but not to prospective IU. Beyond neuroticism/negative affectivity and extraversion/posi­ tive affectivity, relations between IU and general motivational systems should also be examined. Gray’s (1991) reinforcement sensitivity theory posits two neurobiological systems that underlie important individual differences in personality. The behavioral activation system (BAS) is appetitive and oriented toward stimuli that signal reward (or nonpunishment), giving rise to individual differences in impulsivity. Conversely, the behavioral inhibition system (BIS) organizes behavior in response to cues associated with punishment (or nonreward), and is posited to underlie indi­ vidual differences in anxiety.1 Anxiety appears to be related to BIS sensitivity but not reliably associated with BAS sensitivity (Bijttebier. Beck, Claes, & Vandereycken, 2009), and this pattern of results was also obtained for generalized anxiety disorder (Maack, Tull, & Gratz, 2012). No study has examined how IU might be related to the BIS/BAS sensitivities. It was hypothesized that IU and its facets should be positively correlated with BIS sensitivity. In view of Birrell et al.’s (2011) suggestion that prospective IU might represent an approach strategy to reduce uncertainty, it was speculated here that individuals with high (but not extreme) BAS sensitivity might strive to reduce possible punishment arising from uncertainty by planning ahead (e.g., seeking information).

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The Present Study The first goal of this research was to further clarify the factor structure underlying the IUS using two large samples of Singa­ porean participants. To date, no previous research has examined the factorial validity of IUS test scores in a non-Westem sample, and this research was conducted to address this gap in the litera­ ture. Consistent with recent factor analytic studies (Carleton et al., 2007, 2012; Helsen et al., 2013; Jacoby et al., 2013; McEvoy & Mahoney, 2011; Sexton & Dugas, 2009), it was hypothesized that two correlated dimensions of IU (i.e., prospective and inhibitory) would best represent IUS’s factor space. A second goal was to reexamine the psychometric properties of the full version of the IUS relative to its short-version counterpart. As mentioned earlier, the current preference for the short IUS-12 over the full IUS-27 might be premature and this might limit subsequent validation efforts to clarify the nature of the two aspects of IU. Upon confirming the IUS factor structure, the third research goal was to situate prospective and inhibitory IUS test scores within a comprehensive nomological network of theoretically rel­ evant constructs to examine their convergent and discriminant validities. Other than attempting to replicate previous findings on the associations between prospective/inhibitory IU and symptoms associated with anxiety and mood disorders, the current research hoped to examine IU’s relations with other variables. These vari­ ables included psychopathology vulnerability factors (e.g., fear of negative evaluation, anxiety sensitivity, looming cognitive style, and rumination) and personality/affectivity variables (e.g., neurot­ icism/negative affectivity, extraversion/positive affectivity, and BIS/BAS sensitivities). The general prediction was that inhibitory IU, being the more toxic and debilitating component of IU, would be more strongly associated with the psychopathology vulnerabil­ ity factors, neuroticism/negative affectivity, extraversion/positive affectivity (negatively), and BIS sensitivity than prospective IU. In contrast, given its approach-orientation, prospective IU was pre­ dicted to be positively related to BAS sensitivity.

Method Participants and Procedure Data used in this research came from several independent stud­ ies conducted with undergraduate students at the National Univer­ sity of Singapore. All participants were recruited through a re­ search participation program maintained by the Department of Psychology where they participated for course credits. The explor­ atory sample (« = 565) comprised participants from four studies whereas the confirmatory sample (n = 898) consisted of partici­ pants from a single study. The latter sample with more participants was designated the confirmatory sample because a large sample

1 The revised reinforcement sensitivity theory (Gray & McNaughton, 2000) expanded on the role of a third component, the fight-flight-freeze system (FEES), and refined its role and that of the BIS. The FFFS is an avoidance system that motivates escape behaviors in response to punish­ ment whereas the revised BIS acts as a mediator resolving conflicts between the BAS and FFFS. However, available measures of this theory, such as Carver and White’s (1994) BIS/BAS scales, do not distinguish FFFS from BIS. Hence, conceptualization of the BIS in this study follows the original (e.g., Gray, 1991) rather than the revised theory.

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size was needed for reliable parameter estimates in confirmatory factor analyses. Data from two studies of the exploratory sample had been previously published addressing a different research question (see Hong, 2013). Although participants were not randomly assigned into the two samples, there was little reason to suspect that sample character­ istics would differ drastically as they were all undergraduate students from the same university. Participants in the exploratory sample (73.6% female) had a mean age of 20.4 years (SD = 1.65; range = 18 to 28). Ethnic composition was as follows: Chinese (88.3%). Malay (2.3%), Indian (6.0%), and others (3.4%). Simi­ larly, participants in the confirmatory sample (72.4% female) had a mean age of 20.0 years (SD = 1.37; range = 18 to 25). Ethnic composition was as follows: Chinese (91.0%), Malay (2.9%), Indian (4.3%), and others (1.8%). No differences were found for gender, x2(l, N = 1463) = .27, p = .60, and ethnic composition, X2(3, N = 1463) = .6.31, p = .10, across the samples. Although a significant difference in mean age was detected, r(1036) = 4.84, p < .001, the effect size (d = .26) could be considered as trivial. In the exploratory sample, a subset (n = 335) of participants also had informants who provided independent personality ratings on the participants. The majority of these informants identified themselves as friends to the participants (75.5%); the remaining were dating partners (18.2%) and family members or others (5.7%). Two informants did not indicate their relationship to the participants. The informants reported (a) knowing the participants for an average duration of 4.46 years (SD = 4.44) and (b) a strong degree of acquaintanceship with the participants (M = 7.12, SD =

1.38, on a 1-9-point acquaintanceship rating scale). Informants were asked to provide ratings on personality but not other con­ structs like psychopathological vulnerabilities and symptoms be­ cause the latter constructs were primarily participants’ private subjective thoughts and emotions that were less accessible to informants. Participants (and informants) completed questionnaire batteries, either via paper-and-pencil or online survey methods, in classroom settings. Although different variables were assessed in the different studies, IUS was one of the questionnaires administered. All measures were administered in English as the general Singapore population is competent in the language.

Measures Intolerance of Uncertainty Scale. The English version of the IUS (Buhr & Dugas, 2002) comprised 27 items assessing negative beliefs about uncertainty and its perceived undesirable conse­ quences. Respondents indicated on a 5-point Likert-type scale ranging between 1 (not at all characteristic o f me) to 5 (entirely characteristic o f me). Its excellent psychometric properties, in­ cluding validity evidence, have been reported in numerous studies using nonclinical (e.g., Buhr & Dugas, 2002; Sexton & Dugas, 2009) and clinical samples (e.g.. McEvoy & Mahoney, 2011). The short IUS-12 version scale scores can be derived from the full scale and the two versions are highly correlated at r = .96 (Carleton, Norton, et al., 2007). The IUS items are presented in Table 1.

Table 1 Factor Loadings and Extracted Communality Estimates o f the IUS-27 (n = 565) No.

Item

5 8 18 7 10 19

My mind can’t be relaxed if I don't know what will happen tomorrow. It frustrates me not having all the information I need. I always want to know what the future has in store for me. Unforeseen events upset me greatly. One should always look ahead so as to avoid surprises. I can’t stand being taken by surprise. Uncertainty makes me uneasy, anxious, or stressed. I should be able to organize everything in advance. A small unforeseen event can spoil everything, even with the best planning. The ambiguities in life stress me. It’s unfair having no guarantees in life. I can’t stand being undecided about my future. I must get away from all uncertain situations. Uncertainty makes life intolerable. Uncertainty keeps me from living a full life. I think it’s unfair that other people seem to be sure about their future. Uncertainty keeps me from sleeping soundly. When I am uncertain. 1 can’t go ahead. When it’s time to act, uncertainty paralyses me. When I am uncertain, I can’t function very well. Being uncertain means that I am not first rate. Being uncertain means that I lack confidence. Unlike me, others seem to know where they are going with their lives. Uncertainty makes me vulnerable, unhappy, or sad. The smallest doubt can stop me from acting. Uncertainty stops me from having a strong opinion. Being uncertain means that a person is disorganized.

6

21 11 26 4 27 25 3 9 23 24 14 12 15 13 22 16 17 20 1 2

Factor 1 .74 .73 .68 .66 .66 .64 .62 .59 .56 .53 .52 .50 .46 .44 .41

.35 .32 -.1 4 -.1 2 .03 -.0 1 -.0 1 -.0 9 .21 .10 - .0 9 .07

Factor 2

k2

-.0 4 -.0 3 -.0 5 .10 -.2 5 .03 .15 -.1 5 .02 .24 .09 .16 .29 .18 .35 .29 .27

.51 .50 .41 .55 .25 .44 .54 .24 .32 .53 .35 .40 .49 .35 .51 .35 .30 .59 .52 .68 .50 .45 .35 .60 .34 .17 .16

.87 .81 .80 .71 .68 .65 .60 .51 .47

.34

Note. Factor loadings were obtained from the pattern matrix of a principal axis factor analysis with promax (oblique) rotation. Loadings with magnitude greater than .40 are in boldface. Factor 1 = Prospective IU; Factor 2 = Inhibitory IU.

INTOLERANCE OF UNCERTAINTY

Measures of psychopathological symptoms. Symptoms of generalized anxiety disorder, social anxiety, panic disorder, posttraumatic stress disorder, and depression were assessed. The ten­ dency to experience excessive and intense worrying was measured by the 16-item Penn State Worry Questionnaire (PSWQ; Meyer, Miller, Metzger, & Borkovec, 1990). The PSWQ has exhibited good test-retest (r = .92 over 8-10 weeks) and high internal consistency reliability (e.g., Meyer et al., 1990; Molina & Bork­ ovec, 1994), including in a Singaporean sample (a = .94; Hong, 2007). The 21-item Beck Anxiety Inventory (BAI; Beck, Epstein, Brown, & Steer, 1988) and the 21-item Beck Depression Inventory-II (BDI-II; Beck, Steer, & Brown, 1996) were used to measure general symptoms of anxiety and depression, respec­ tively. Both measures have demonstrated good to excellent internal consistency and test-retest reliabilities (Beck et al., 1988, 1996) and construct validity (Creamer, Foran, & Bell, 1995; Dozois, Dobson, & Ahnberg, 1998). The 64-item Inventory of Depression and Anxiety Symptoms (IDAS; Watson et al., 2007) was used to assess a range of anxiety and depression symptoms. Across college student, community, and psychiatric samples, the IDAS subscales have shown excellent internal consistency reliabilities (alphas around .80-.90) and good validity evidence with established mea­ sures of anxiety and depression. For the purpose of this research, four relevant subscales were used: social anxiety, panic, traumatic intrusions, and general depression. The PSWQ (a = .86), BAI (a = .88), BDI-II (a = .85), and IDAS subscales were available in the exploratory sample whereas only the IDAS subscales were available in the confirmatory sample. Coefficient alphas for the IDAS subscales across the two samples were as follows: social anxiety (.82, .82), panic (.76, .83), traumatic intrusions (.76, .76), and general depression (.88, .87). Measures of psychopathological vulnerabilities. Several vulnerability factors including the fear of negative evaluation, anxiety sensitivity, looming cognitive style, and rumination were assessed. The 12-item Brief Fear of Negative Evaluation scale (BFNE; Leary, 1983) was used to measure individuals’ anxious apprehension about being negatively evaluated socially by others. Rodebaugh et al. (2004) proposed that four reversed-coded items be dropped because they formed a separate factor than those straightforwardly worded items. As such, the current study used the 8-item version that included the straightforwardly worded items only. The 18-item Anxiety Sensitivity Index-3 (Taylor et al., 2007) was used to assess the extent to which people dread their own arousal sensations that arise from beliefs that such sensations are signals of impending threat (e.g., heart attack). The Looming Maladaptive Style Questionnaire (LMSQ; Riskind et al., 2000) was used to measure individuals’ looming cognitive style, which is a tendency to see potentially dangerous situations as rapidly in­ creasing in threat. Participants were presented with six vignettes depicting impending stressful situations and they were then asked to respond to questions concerning their perceptions of looming threats. Rumination refers to thoughts and behaviors that draw one’s attention to feelings of depression and their implications. It was assessed by the 10-item version of Ruminative Response Style subscale (RRS; Treynor, Gonzalez, & Nolen-Hoeksema, 2003) of the Response Style Questionnaire (Nolen-Hoeksema & Morrow, 1991). The 10-item version was used here as it was less contam­ inated with depressive symptoms than the original RRS. The BFNE (a = .90), ASI-3 (a = .89), LMSQ (a = .75), and RRS

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(a = .76) were available in the exploratory sample whereas only the BFNE (a = .94) was available in the confirmatory sample. Personality and affectivity measures. Neuroticism and ex­ traversion were assessed using the NEO Personality InventoryRevised (NEO-PI-R; Costa & McCrae, 1992) in the exploratory sample and the Big Five Inventory (BFI; John, Donahue, & Kentle, 1991) in the confirmatory sample. In the exploratory sample, informants completed the NEO-PI-R so as to reduce common method variance due to a common data source (i.e., self-report). Participants themselves did not complete the NEO-PI-R. Reliabili­ ties were good for the neuroticism and extraversion domain scales (as greater than .88) and satisfactory for the facet-level scales (average a = .71). Reliabilities of the BFI neuroticism and extra­ version scales were .82 and .86, respectively. Carver and White’s (1994) 20-item BIS/BAS scales were used to measure BIS/BAS sensitivities as they have demonstrated good reliability and con­ struct validity. Scores were obtained for one BIS scale (a = .77), one BAS scale (a = .88), and three BAS-related subscales— reward responsiveness (a = .83), drive (a = .88), and fun seeking (a = .83). Several broad and specific dimensions of affectivity were obtained from the 60-item Positive and Negative Affect Schedule-Expanded version (PANAS-X; Watson & Clark, 1994). Respondents were asked to describe their affective experiences in general. The PANAS-X allows two broad affectivity dimensions and 11 lower-level specific affect variables to be scored. Of interest here were the two broad affectivity dimensions (i.e., neg­ ative and positive affectivity) and four specific affect variables (i.e., fear, guilt, sad, and self-assurance) expected to be related to IU. According to Watson and Clark, these scales had demonstrated excellent reliabilities (as greater than .80) and convergent and discriminant validities. Here, all PANAS-X scales exhibited ex­ cellent reliabilities (all as greater than .88). The BIS/BAS and PANAS-X scales were available only for the confirmatory sample.

Analytic Strategy Exploratory factor analyses (EFA) were first conducted on the exploratory sample to assess the factor structure of the IUS (using all 27 items) as no prior studies had reported such information using an Asian sample. The current EFA results were then com­ pared with those reported in Sexton and Dugas (2009). Among the previous EFA studies on the IUS, only the Sexton and Dugas analysis was regarded as adhering to best practice guidelines (Birrell et al., 2011). In particular, the sample size of that EFA was large (« = 1,230) and the number of factors to be retained was determined by multiple methods including the minimum average partial test (Velicer, 1976) and parallel analysis (Horn, 1965), widely regarded as superior to the eigenvalue-greater-than-one (Kaiser, 1970) or the scree test (Cattell, 1966) criteria (Zwick & Velicer, 1986). Furthermore, prin­ cipal axis factoring with promax rotation was used so as to uncover underlying correlated latent constructs (Fabrigar, Wegener, MacCallum, & Strahan, 1999). The purpose of this comparison was to (a) determine if a similar factor structure would emerge in the present sample, and (b) to guide current efforts in improving the IUS, if necessary. Following the exploratory phase, confirmatory factor analyses were performed to further evaluate the factor structures of the full-length and short versions of IUS (i.e., IUS-27 and IUS-12). After selecting the optimal model, construct validity of IUS scores

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was examined via a nomological network approach where rela­ tions with theoretically relevant constructs (e.g., psychopathological symptoms and vulnerabilities, personality, and affectivity) were evaluated. In particular, the discriminant validity between prospective and inhibitory IU scores in the context of this nomo­ logical network was investigated. This construct validation process was done through a series of correlation and regression analyses.

Results Exploratory Factor Analysis Parallel analysis (Horn, 1965) was used to determine the optimal number of factors to be retained. An initial check on IUS item characteristics revealed that item skewness ranged between —.34 and 1.20 and kurtosis ranged between -1 .0 6 to .48. Three items (19, 23, and 24) had skewness and three items (16, 18, and 22) had kurtosis slightly greater than ±1.00. Given that skewness and kurtosis were within acceptable limits, parallel analysis using Pearson (rather than polychoric) correlations among the IUS items was conducted. Garrido, Abad, and Ponsoda (2013) conducted simulations and found that parallel analysis using Pearson corre­ lations worked as well as similar analysis using polychoric corre­ lations, when variable skewness was between 0 and ±1.00. In addition, Garrido et al. (2013) have argued that parallel analysis is not suitable in the context of common factor analysis due to problems in determining which communality estimates should be used. Instead, parallel analysis should be conducted with principal component extraction to ensure that the number of factors to be retained could be accurately determined. Hence, in the current study, parallel analysis using principal component extraction was first conducted to determine the number of factors to be retained and then followed by a principal axis factor analysis specifying the suggested factor structure. Using O’Connor’s (2000) SPSS syntax, parallel analysis on 100 randomly generated data (with 27 variables and 565 cases as input specifications) yielded the following first three eigenvalues in the 95th percentile: 1.48, 1.40, and 1.35. The corresponding three eigenvalues produced by a principal component analysis on actual data were 10.68, 1.78, and 1.24. This suggested that a two-factor solution was most appropriate (which also converged with the scree test). Consequently, a principal axis factor analysis with promax (oblique) rotation was performed with the specification of extracting two factors. Promax rotation was used because the two factors underlying the IU construct were expected to be highly

correlated. Table 1 presents the factor loadings and communality estimates of the two-factor solution, which accounted for 42.01% of the variance. The correlation between the factors was .75. As seen in Table 1, items loading on the first factor were the ones typically identified as assessing prospective IU whereas items loading on the second factor assessed inhibitory IU. Although the current factor structure was very similar to the one reported in Sexton and Dugas (2009), there were five items that had different factor placements. Specifically, items 3, 9, 23, 4, and 25 loaded on prospective IU in the present study but they loaded on inhibitory IU in Sexton and Dugas (i.e., “Uncertainty has negative behavioral and self-referent implications,” using their label). In addition, unlike the Sexton and Dugas’s results, items 2, 23, and 24 in the current analysis failed to load on their respective factors, using a cut-off of .40. Given these discrepancies, it was clear that factor structure of the original IUS-27 was unstable and deletion of problematic items would be necessary. To that end, exploratory factor analytic results from both studies were compared to guide the process of item deletion. In total, nine potentially problematic items were identified with justifications for deletion presented in Table 2. Deleting the nine problematic items resulted in an 18-item version of IUS (henceforth referred to as IUS-18); with nine items in each of the factors. This 18-item version was subjected to a principal axis factor analysis and the two-factor solution explained 44.49% of the variance. Factor loadings ranged between .46 and .83, and the two factors correlated at r = .70. Inspection of item content suggested that the retained items were able to capture the core essence of prospective and inhibitory IU, respectively.

Confirmatory Factor Analysis Preliminary analyses on the confirmatory sample indicated that the individual IUS items did not deviate drastically from normal­ ity; skewness was between —.25 to 1.05 and kurtosis was be­ tween —1.15 to .19. Confirmatory factor analyses were conducted to further evaluate the factor structures of the different versions of IUS (i.e., IUS-12, IUS-18, and IUS-27). No extreme outliers were detected using the Mahalanobis distance. However, in all models, Mardia’s (1970) normalized estimate of multivariate kurtosis was significant, indicating non-normal data. As such, bootstrapping was done to assess the stability of parameter estimates when the assumption of multivariate normality was untenable (Byrne, 2010). Using the AMOS 21 program (Arbuckle, 2010), model parameters were evaluated using the bootstrap procedure (with a

Table 2 Justification fo r Item Deletion Item no. 9 25 3, 4, 23, 24 26, 27 2 Note.

Reasons for item deletion Item loaded on Prospective IU in current data but loaded on Inhibitory IU in S&D (2009). For both data sets, there is tendency for item to cross-load. Item loaded on Prospective IU in current data but loaded on Inhibitory IU in S&D (2009). Item exhibited relatively weak factor loadings in current and S&D (2009) data sets. Item exhibited evidence of cross-loading tendencies in S&D (2009). Relatively weak factor loadings in current and S&D (2009) data sets. Weak loading in current data set. Content ambiguity; being “disorganized” can be interpreted as the person being messy and untidy, which has nothing to do with paralysis due to uncertainty.

S&D (2009) = Sexton and Dugas (2009).

INTOLERANCE OF UNCERTAINTY

1,000 sampling rate) based on maximum likelihood estimation. In all models, the parameters obtained from bootstrapping were al­ most identical to those obtained from maximum likelihood esti­ mation. It appeared that data non-normality did not adversely impact the general results given the large sample size here (Lei & Lomax, 2005). Several fit indices were used to assess model fit: the TuckerLewis index (TLI), the comparative fit index (CFI), the standard­ ized root-mean-square residual (SRMR), the root mean square error of approximation (RMSEA), and the expected crossvalidation index (ECVI). For good model fit, the values of TLI and CFI should ideally be greater than .95 (Hu & Bentler, 1999), though values between .90 and .95 are acceptable (Bentler, 1990). SRMR should be less than .08 and RMSEA should be less than .06 with its 90% confidence interval not exceeding .10 (Browne & Cudeck, 1993; Hu & Bentler, 1999). For comparison among non­ nested models, the one with the lowest ECVI should be preferred (Hu & Bentler, 1999). The two-factor model as represented in the different versions of IUS was specified. In particular, the two-factor IUS-27 model was specified according to Sexton and Dugas (2009) and the two-factor IUS-12 model was specified according to Carleton, Norton et al. (2007). The two-factor IUS-18 model was estimated based on the results from the exploratory factor analyses. In all models, the latent factors were allowed to correlate. As the correlation between the factors was expected to be high, one-factor models of the respective versions were also evaluated. Table 3 presents the goodness-of-fit indices of the various models. Several observations are noteworthy. First, relative to the two-factor models, the unitary factor models yielded less fit to the data, as indicated by the various fit indices. Furthermore, chisquare difference tests comparing the one- and two-factor models converged onto the same conclusion: for IUS-27, Ax2(l) = 436.15, p < .05; for IUS-18, AX2(1) = 750.98, p < .05; and for IUS-12, AX2(1) = 134.80, p < .05. Second, among the two-factor models, IUS-12 yielded the best model fit, followed by IUS-18, and lastly by IUS-27. Correspondingly, the ECVI value was the lowest for IUS-12 (indicating good fit), followed by IUS-18, and lastly by IUS-27. However, the fit of the two-factor IUS-18 model could be improved further, as indicated by modification indices. Specifically, the error terms of items (a) 10 and 21, and (b) 16 and 17, could be allowed to correlate and this model yielded good model fit, X2(132) = 710.918, TLI = .931, CFI = .940, SRMR = .043, RMSEA = .070, 90%CI = .065 to .075, and ECVI = .880, 90%CI = .791 to .977. The error terms between items 10 and 21 likely correlated because both items assessed planning ahead to

611

reduce uncertainty. The theoretical reason for the covariance be­ tween the error terms of items 16 and 17 was less clear though it was speculated that seeing oneself as “stuck” and not knowing where one is going (relative to others) may be particularly dis­ tressing in the context of a Singaporean undergraduate student culture that emphasized competition. Therefore, the IUS-18 and IUS-12 had fit indices that were very similar. The third observation was that modification indices associated with the two-factor IUS-27 model were inspected for localized areas of data misfit. Thirteen modification indices greater than 50.0 were identified (range = 51.81 to 89.41). Several items previously identified in the exploratory factor analysis as problem­ atic (items 3, 9, and 26) were implicated here. For example, modification indices suggested that parameters between item 3’s error term and both prospective and inhibitory IU should be relaxed (the same applies to items 9 and 26). However, this could not be done without a substantive rationale. Hence, there were corroborating evidences from the exploratory and confirmatory factor analyses that several IUS-27 items were suboptimal and should be removed. Measurement invariance of the two-factor IUS model across gender was also evaluated. The IUS-18 model (without the corre­ lated errors) was used as it contained more items for evaluation than the IUS-12 model. As presented in Table 4, models with increasingly restrictive equality conditions were tested and chisquare difference tests indicated that the structural model (M3) should be preferred. Therefore, the IUS-18 structural model ap­ plies equally well to men and women. Men reported higher pro­ spective IU than women in both samples (exploratory: ALMEN = 23.97, SD = 7.31; Mwomen = 22.51, SD = 7.01; t(563) = 2.16, p = .03; confirmatory: = 25.06, SD = 7.54; Mwomen = 23.62, SD = 7.41; f(896) = 2.59, p = .01) but no gender differ­ ences were found for inhibitory and total IU. Overall, the confirmatoiy factor analyses suggested that the two-factor IUS-12 model was the best fitting model, and the two-factor IUS-18 model emerged as the second best model. The IUS-12 factor loadings ranged between .64 and .80, with a correlation of .90 between the two factors. The IUS-18 factor loadings ranged between .50 and .86, with a correlation of .81 between the two factors. Although one might select the two-factor IUS-12 model and disregard the rest, it was deemed that a decision made at this point might be premature. One compelling reason was that factor analyses were not designed to evaluate aspects of construct valid­ ity such as convergent and discriminant validities; this could only be achieved via other methods (e.g., nomological networks of constructs). Another reason was that two items in IUS-12 inhibi-

Table 3 Goodness-of-Fit Indices fo r CFA Models (n = 898) Model IUS-27; IUS-27; IUS-18; IUS-18; IUS-12; IUS-12; Note.

two-factor one-factor two-factor one-factor two-factor one-factor

X2

df

TLI

CFI

SRMR

RMSEA [90% Cl]

ECVI [90% Cl]

2310.352 2746.500 823.777 1574.759 349.489 484.290

323 324 134 135 53 54

.859 .829 .919 .832 .935 .908

.870 .842 .929 .852 .948 .925

.053 .055 .041 .063 .038 .045

.083 [.080; .086] .091 [.088; .094] .076 [.071;.081] .109 [.104; .114] .079 [.071; .087] .094 [.087; .102]

2.698 [2.532; 2.872] 3.182 [3.000; 3.373] 1.001 [.904; 1.106] 1.836 [1.698; 1.982] .445 [.384; .516] .593 [.519; .676]

The two-factor IUS-27 model was based on Sexton and Dugas (2009). The two-factor IUS-12 model was based on Carleton, Norton et al. (2007).

HONG AND LEE

612

Table 4 IUS-18 Measurement Invariance Across Gender Using Multiple-Group CFA on the Confirmatory Sample Model

x2

df

TLI

CFI

SRMR

RMSEA [90% Cl]

Comparison

M 1: Configural M2: Measurement M3: Structural

947.633 957.873 957.993

268 284 285

.920 .926 .926

.930 .931 .931

.052 .053 .053

.053 [.050; .057] .051 [.048; .055] .051 [.048; .055]

Ml vs M2 M2 vs M3

A X \d f )





10.24(16) 0.12(1)

Note. Configural model = parameters unconstrained across groups. Measurement model = factor loadings constrained to be equal. Structural model = factor loadings and covariance constrained to be equal. Sample sizes for men and women were 248 and 650, respectively.

tory (9 and 25) were identified in these analyses as potentially problematic (i.e., tendency for cross-loading). This suggested that the current set of five items representing inhibitory IU in IUS-12 might not be optimal. Accordingly, the subsequent analytic strat­ egy undertaken was to systematically compare the validities of IUS-12 and IUS-18 test scores in their relative abilities to account for meaningful variance in relevant criteria.

Psychometric Properties and Validity Evidence Table 5 displays the descriptive statistics and correlated item-total correlations for the IUS-18 in both samples. Internal consistency reliabilities of the various IUS versions (for exploratory and confir­ matory samples) were as follows: IUS-27 total (as = .94, .96), IUS-27 prospective (as = .88, .92), IUS-27 inhibitory (as = .90, .93), IUS-18 total (as = .91, .94), IUS-18 prospective (as = .86, .91), IUS-18 inhibitory (as = .87, .91), IUS-12 total (as = .88, .92), IUS-12 prospective (as = .82, .88), and IUS-12 inhibitory (as = .82, .87). IUS-18 scales correlated strongly with the corresponding IUS-27 scales across the two samples (IUS total, rs = .98 and .98; prospective IU, rs = .98 and .98; inhibitory IU, rs = .96 and .97). The IUS-12 scales also correlated with the corresponding IUS-27

scales (IUS total, rs = .95 and .96; prospective IU, rs = .94 and .96; inhibitory IU, rs = .93 and .94), though not as strongly as those between IUS-27 and IUS-18. The IUS-18 and IUS-12 scales were also correlated (IUS total, rs = .96 and .97; prospective IU, rs = .98 and .98; inhibitory IU, rs = .90 and .92). In general, the IUS-18 total and subscale scores exhibited excellent internal con­ sistency reliabilities and had strong associations with the other IUS versions. Bivariate correlations between prospective/inhibitory IU (for both IUS-18 and IUS-12 versions) and (a) psychopathology and (b) personality variables are presented in Tables 6 and 7, respec­ tively. The sample size and the specific variables available differed across the two samples. As seen in Table 6, IUS total scale and subscales were correlated with the various psychopathology mea­ sures. The effect sizes were predominantly medium to large (Co­ hen, 1988). To examine the relative abilities of IUS-18 versus IUS-12 in explaining more variance in the various criteria, the difference between the correlations associated with the two ver­ sions were formally tested using the Meng, Rosenthal, and Rubin (1992) procedure, with the p value set at .01. Out of the 48 pairs of dependent correlations, 24 were found to be significantly dif-

Table 5 Descriptive Statistics o f the IUS-18 Items fo r Both Samples Confirmatory sample (n = 898)

Exploratory sample (n = 565) Item

M

SD

Skew

Kurtosis

CITC

CITC-S

M

SD

Skew

Kurtosis

CITC

CITC-S

5 6 7 8 10 11 18 19 21 Prospective 1 12 13 14 15 16 17 20 22 Inhibitory Total

2.16 2.70 2.32 2.98 2.52 2.63 2.92 1.84 2.82 22.90 3.25 2.19 1.87 2.09 2.34 2.52 2.25 2.04 2.44 20.98 43.88

1.19 1.14 1.14 1.16 1.16 1.22 1.23 0.94 1.14 7.11 1.14 1.06 1.02 1.02 1.07 1.30 1.09 1.04 1.21 7.05 12.75

0.72 0.18 0.51 -0.02 0.30 0.30 0.08 1.03 0.06 0.33 -0.34 0.61 0.97 0.64 0.40 0.36 0.52 0.85 0.33 0.45 0.36

-0.62 -0.93 -0.67 -0.96 -0.86 -0.97 -1.02 0.48 -0.84 -0.60 -0.85 -0.43 0.06 -0.53 -0.83 -1.06 -0.67 0.04 -1.01 -0.59 -0.59

.60 .67 .69 .63 .37 .53 .57 .60 .41

.63 .64 .70 .66 .46 .55 .60 .60 .47

0.58 0.13 0.42 -0.10 0.06 0.22 -0.05 0.73 -0.06 0.22 -0.25 0.51 0.65 0.52 0.33 0.18 0.37 0.66 0.36 0.40 0.31

-0.70 -0.91 -0.62 -0.84 -0.86 -0.90 -0.86 -0.07 -0.75 -0.62 -0.84 -0.52 -0.45 -0.56 -0.73 -1.15 -0.74 -0.39 -0.89 -0.48 -0.52

.71 .76 .77 .70 .65 .63 .64 .69 .61

.38 .68 .65 .71 .75 .53 .69 .55 .64

1.15 1.11 1.11 1.08 1.10 1.13 1.13 0.98 1.06 7.47 1.12 1.07 1.09 1.02 1.06 1.30 1.07 1.06 1.19 7.59 13.97

.66 .76 .74 .67 .58 .63 .61 .72 .57

.33 .62 .62 .65 .73 .46 .71 .55 .60

2.28 2.78 2.52 3.07 2.77 2.65 3.00 2.04 2.89 24.02 3.10 2.32 2.16 2.19 2.51 2.70 2.43 2.18 2.49 22.09 46.11

.44 .73 .71 .73 .78 .53 .75 .70 .66

.49 .73 .73 .76 .79 .59 .73 .70 .70

Note.

CITC = corrected item-total correlation by total IUS-18 scale; CITC-S = corrected item-total correlation by the respective subscales.

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613

Table 6 Bivariate Correlations Between IUS and Psychopathology Measures Prospective Psychopathology measures Exploratory sample (n = 335) PSWQa BAI BDI-II IDAS social anxiety IDAS panic IDAS traumatic intrusions IDAS general depression BFNE ASI-3 LMSQ RRS Confirmatory sample (n = 898) IDAS social anxiety IDAS panic IDAS traumatic intrusions IDAS general depression BFNE

Inhibitory

Total

IUS-18

IUS-12

IUS-18

IUS-12

IUS-18

IUS-12

52., 35a • 41a ■37a ■19a • 23a • 44a .41a •4 4 . •27a •28a

.47 .30 .37 .33 .17 .19 .40 .38 .40 .25 .27

■46a ■43a ■50a ■57b ■35b •34b ■55b •52b •53b •39b ,34a

.47 .47 .53 .53 .38 .39 .58 .47 .54 .36 .33

.55 .44 .51 .53 .31 .32 .56 .52 .54 .38 .35

.51 .41 .48 .46 .29 .30 .52 .46 .51 .33 .32

40,, 27a .28a • 39a • 49a

.38 .26 .27 .37 .48

52„ ■39b •31 a • 50b • 60b

.48 .39 .33 .47 .55

.50 .36 .31 .48 .59

.45 .33 .31 .44 .54

.









Note. PSWQ = Penn State Worry Questionnaire; BAI = Beck Anxiety Inventory; BDI-II = Beck Depression Inventory-II; IDAS = Inventory of Depression and Anxiety Symptoms; BFNE = Brief Fear of Negative Evaluation scale; ASI-3 = Anxiety Sensitivity Index-3; LMSQ = Looming Maladaptive Style Questionnaire; RRS = Ruminative Response Style scale. All correlations are significant at p < .01. Coefficients in boldface imply that the correlations between (a) the criterion and IUS-18 versus (b) the criterion and IUS-12 are significantly different, based on Meng, Rosenthal, and Rubin’s (1992) formula for comparing dependent correlations, with a p-value set at .01. For coefficients under IUS-18 Prospective and Inhibitory, different subscripts represent correlations that are significantly different based on Meng et al.’s formula. Sample size for PSWQ is 565.

ferent (coefficients in boldface), and all of these cases had the IUS-18 coefficient stronger than the corresponding IUS-12 coef­ ficient. Out of a total of 60 pairs of dependent correlations in Table 7, 29 were significantly different (coefficients in boldface). The IUS-18 coefficient was stronger than the IUS-12 coefficient in 26 pairs whereas the reverse was true in two pairs (IUS total and BAS; IUS total and BAS drive). The remaining pair (IUS inhibi­ tory and BAS drive) had different directional signs which made interpretation difficult. Overall, although IUS-12 performed very well in terms of explaining substantive variance in relevant crite­ ria, IUS-18 performed even better. Considering that IUS-18 test scores exhibited stronger conver­ gent validity to criteria compared to IUS-12 scores, subsequent analyses were conducted using the former. In Tables 6 and 7, tests of difference between correlated correlations (Meng et al., 1992) were performed between prospective and inhibitory IU. The goal of this set of analyses was to determine if the two IUS-18 subscales exhibited discriminant validity vis-h-vis the various criteria. As shown in Table 6, in comparison to prospective IU, inhibitory IU exhibited stronger associations with BFNE, ASI-3, LMSQ, and all IDAS scales (with the exception of IDAS traumatic intrusions in the confirmatory sample). In general, inhibitory IU had stronger relations with psychopathological risk factors (i.e., fear of negative evaluation, anxiety sensitivity, and looming cognitive style) and symptoms (i.e., social anxiety, panic, and depression). Consistent with past research, worry was more associated with prospective IU (r = .52) than was with inhibitory IU (r = .46), but that difference was not significant at p < .01 (albeit it was at p < .05). Discriminant validity between prospective and inhibitory IU scores was also evident in their relations with personality and

affectivity variables (see Table 7). For the exploratory sample, neuroticism, and four of its facets (anxiety, depression, selfconsciousness, and vulnerability), were more strongly associated with inhibitory than with prospective IU. Assertiveness, one of the facets underlying extraversion, was negatively correlated with inhibitory but not with prospective IU. For the confirmatory sam­ ple, inhibitory (more than prospective) IU correlated positively with neuroticism but negatively with extraversion. Inhibitory IU also had stronger associations with all affectivity variables (i.e., PANAS-X scales) than prospective IU. A notable finding was that, relative to inhibitory IU, prospective IU showed stronger positive associations with the approach-oriented behavior activation system and two of its subscales (i.e., BAS reward responsiveness and drive). To further examine the unique contributions of prospective versus inhibitory IU on psychopathology symptom and vulnera­ bility variables, a series of hierarchical regression analyses were conducted, as presented in Table 8. Gender and age were entered in Step 1; followed by neuroticism in Step 2; and both prospective and inhibitory IU were entered in the last step. As neuroticism has strong links with a wide variety of psychopathology variables (Hong, 2013; Hong & Paunonen, 2011), it was important to evaluate if IU predicted those outcome criteria above and beyond neuroticism (McEvoy & Mahoney, 2011). In general, gender and age did not predict psychopathology variables, except for BDI-II and RRS in the exploratory sample where younger participants reported higher levels of BDI-II depression and rumination. As expected, neuroticism accounted for significant amounts of vari­ ance in almost all criteria (except for IDAS panic and LSMQ in the exploratory sample). It is important to note that neuroticism scores

HONG AND LEE

614 Table 7

Bivariate Correlations Between IUS and Personality Measures Inhibitory

Prospective Personality measures Exploratory sample (n = 335) NEO neuroticism N1 anxiety N3 depression N4 self-consciousness N6 vulnerability NEO extraversion E3 assertiveness Confirmatory sample (n = 898) BFI neuroticism BFI extraversion BIS BAS BAS reward responsiveness BAS drive BAS fun seeking PANAS-X negative affectivity PANAS-X positive affectivity PANAS-X fear PANAS-X guilt PANAS-X sad PANAS-X self assurance

Total

IUS-18

IUS-12

IUS-18

IUS-12

IUS-18

IUS-12

•13. ■13. ,07a •08a ■07a - .0 7 . -•0 3 .

.10 .11 .05 .07 .04 -.0 7 -.0 1

•23% .29% .19% .22% •20% - . 17% - • 26 %

.20* .25* .15* .16* .16* -.1 0 - . 19*

.20 * .24 * . 15* . 17* . 15*

. 16* . 19*

- . 17*

.10 .12 .10 -.0 9 -.1 0

•43 * a -.2 1 * . .45 *., •09*a ■14*. .17% -.09% .33* - .0 8 . .34 *. .27*. .33% - . 08 .

.41 * -.20* .44 * .10* .14* .18* -.0 8 .32* -.0 6 .32 * .26* .32* -.0 6

•50% - . 31 % .48 *.

.46 * - . 25 * .42 *

.50 * - . 28 * .50 *

.45 * - . 24 * .46 *

—.02b •04b —.02b -•0 6 . .43% - . 18% ■45% •40% ■40% - • 20 %

.02 .05 .06 - .0 6 .45* - . 11* .45* .38* .38* - . 11 *

.04 .09* .08 -.0 8 .41* - . 14* .43’ .36 * .39 * - . 15*

.07 .11* . 14* -.0 7 .40* - . 09 * .40 * .33 * .37 * - . 09 *

-.1 3

Note. BFI = Big Five Inventory; BIS = Behavior Inhibition System; BAS = Behavior Activation System; PANAS-X = Positive and Negative Affect Schedule-Expanded Form. NEO-PI-R neuroticism and extraversion facet scales (informant-rated) that are not correlated with IUS are omitted. Coefficients in boldface imply that the correlations between (a) the criterion and IUS-18 versus (b) the criterion and IUS-12 are significantly different, based on Meng et al.’s (1992) formula for comparing dependent correlations, with a p-value set at .01. For coefficients under IUS-18 Prospective and Inhibitory, different subscripts represent correlations that are significantly different based on Meng et al.’s formula. > < . 01 .

in the exploratory samples were obtained from informants. Hence, the amounts of variance accounted for were smaller than selfreported neuroticism in the confirmatory sample. Most important, in Step 3, IU provided significant incremental validity above and beyond neuroticism in predicting psychopathology. In almost all cases, inhibitory IU emerged as the stronger predictor of the criterion compared to prospective IU. The only exception was observed in the prediction of PSWQ, where prospective IU was the stronger predictor.

Discussion Using exploratory and confirmatory factor analyses in two large sam­ ples, support was found for a two-factor model underlying the IUS. In the process of evaluating the original IUS-27, it became clear that several items were potentially problematic and thus were removed. The modified scale, IUS-18, seemed promising in mea­ suring prospective and inhibitory IU, exhibited excellent internal consistency reliability, and was invariant across gender. As the IUS-12 currently enjoys a prominent status in the literature as a brief and psychometrically valid variant of the IUS, it was imper­ ative to systematically compare the relative merits of IUS-18 and IUS-12 in terms of their factorial and construct validity. The present confirmatory factor analyses suggested that the two-factor IUS-12 model provided a slightly better fit to the data than the two-factor IUS-18 model. The convergent validity of the IUS-18 to theoretically relevant constructs, however, was stronger than that of the IUS-12. This suggests that, in using the IUS-12, associations

with other variables might be slightly attenuated, possibly due to inadequate content representation. An extensive nomological net­ work analysis provided compelling evidence of discriminant va­ lidity between scores from the prospective and inhibitory IU subscales, which further clarified the nature of these two dimen­ sions of IU. When the best practice recommendations for EFA were fol­ lowed, the present study and Sexton and Dugas’ (2009) study converged onto a two-factor model underlying the IUS. However, in spite of the convergence, several IUS items were considered to be problematic in this study. Upon close inspection of Sexton and Dugas’ results, these same items were also not the most optimal ones, relative to other items. Hence, item removal was done after considering the findings from both studies; which resulted in an 18-item version. The IUS-18 did not fit the data as well in the confirmatory factor analysis as the IUS-12, but the former’s va­ lidity with other relevant constructs appeared to be stronger. This suggests that criticisms leveled at the IUS-12 had some merit; that is, content representation of the IUS might have been compro­ mised. Carleton (2012) has argued that some items from the original IUS are specific to generalized anxiety disorder whereas the IUS-12 assesses the core essence of IU that is purportedly transdiagnostic. The implication is that the IUS-12 should predict a wide range of anxiety and mood symptoms better than the IUS-27, perhaps with the exception of worry and generalized anxiety disorder. This was not observed in the current data, at least when the validity coefficients of IUS-18 was compared with those

INTOLERANCE OF UNCERTAINTY

615

Table 8 Hierarchical Regression Analyses Predicting Psychopathology Criteria Step 1 Criterion Exploratory sample (n = 335) PSWQ BAI BDI-II IDAS social anxiety IDAS panic IDAS traumatic intrusions IDAS general depression BFNE ASI-3 LMSQ RRS Confirmatory sample (n = 898) IDAS social anxiety IDAS panic IDAS traumatic intrusions IDAS general depression BFNE

Step 2

Step 3

AR2

Gender

Age

AR2

Neur“

AR2

Pro

Inh

sr (Pro)

sr (Inh)b

.01 .01 .03 .02 .00 .01 .01 .01 .02 .02 .03*

.01 .00 .11 -.0 1 .03 .07 .02 .00 .15 -.0 7 .07

-.0 9 -.0 1 -.18* -.1 5 -.0 5 -.1 0 - .1 0 - .0 9 -.0 5 -.1 1 -.20*

.04* .03* .05* .02* .02 .04* .06* .05* .02* .02 .02*

.21* .19* .23* .16* .13 .19* .24* .23* .15* .14 .14*

.30* .17* .22* .29* .11* .09* .27* .24* .28* .14* .10*

.36* .17* .19* .08 -.0 2 .05 .20* .18* .18* .08 .13

.27* .30* .35* .50* .36* .28* .40* .38* .42* .33* .23*

.29 .14 .15 .07 -.0 2 .04 .16 .15 .15 .07 .11

.21 .24 .27 .40 .28 .22 .32 .30 .33 .26 .18

.00 .00 .01 .01 .01

-.01 -.0 3 -.0 3 -.0 5 -.0 6

- .0 6 -.0 5 -.0 5 -.0 5 -.0 3

.24* .16* .13* .36* .26*

.50* .40* .36* .61* .52*

.10* .05* .03* .05* .16*

.01 -.0 5 .09 -.0 1 .10*

.36* .28* .12* .27* .38*

.01 -.0 3 .06 -.0 0 .07

.24 .19 .08 .18 .25

Note. Neur = Neuroticism; Pro = Prospective IU; Inh = Inhibitory IU; PSWQ = Penn State Worry Questionnaire; BAI = Beck Anxiety Inventory; BDI-II = Beck Depression Inventory-II; IDAS = Inventory of Depression and Anxiety Symptoms; BFNE = Brief Fear of Negative Evaluation scale; ASI-3 = Anxiety Sensitivity Index-3; LMSQ = Looming Maladaptive Style Questionnaire; RRS = Ruminative Response Style scale. Standardized coefficients at each step of the regression are presented. a Neuroticism was assessed by informant-report NEO-PI-R for exploratory sample and by self-report BFI for confirmatory sample. b Semipartial correlations of Prospective and Inhibitory IU. * p < .01.

of IUS-12. IUS-18’s validity coefficients with a wide spectrum of psychopathology symptoms and vulnerability factors were either similar to or stronger than the corresponding IUS-12 coefficients (see Table 6). Hence, the IUS-18 ensures adequate content validity and at the same time demonstrates strong associations with a broad array of psychopathology criteria. In sum, the IUS-18 appears to be a promising abbreviated form of the original IUS. Previous research suggests that prospective IU is more corre­ lated with worry and symptoms of obsessive-compulsive disorder whereas inhibitory IU is more associated with symptoms of social anxiety, panic disorder and agoraphobia, posttraumatic stress dis­ order, and depression (Berenbaum et al., 2008; Fetzner et al., 2013; Helsen et ah, 2013; McEvoy & Mahoney, 2011, 2012; Sexton & Dugas, 2009). The current findings are consistent with this general picture. (Obsessive-compulsive disorder was not assessed in this research and thus its links to the IU facets could not be deter­ mined.) In addition, inhibitory IU was more predictive of the various symptoms (except for worry) than prospective IU, even when the influence of neuroticism was controlled for. In general, the current results add to an emerging body of literature that prospective IU, with its emphasis on anticipatory anxiety over uncertainty, is most implicated in nonphobic anxiety disorders (e.g., generalized anxiety disorder) whereas inhibitory IU, with its focus on avoidance behaviors under uncertainty, is most associated with phobic anxiety disorders (e.g., social anxiety, panic disorder and agoraphobia, and posttraumatic stress disorder) and depression (McEvoy & Mahoney, 2011). Relations of prospective and inhibitory IU with other vulnera­ bility factors, such as for fear of negative evaluation (Whiting et al., 2014) and anxiety sensitivity (Fetzner et al., 2013), were

examined only recently. In both of those studies, inhibitory IU was more strongly associated with the respective vulnerability factor than was prospective IU. Beyond replicating those results, the present findings also show, for the first time, that inhibitory IU was more predictive of the looming cognitive style and rumination when neuroticism was controlled for. As proposed by Carleton, Sharpe et al. (2007), the fundamental fear of the unknown and its potential negative consequences is shared among IU, fear of neg­ ative evaluation (e.g., “how would other people perceive me?”), and anxiety sensitivity (e.g., “what do these bodily sensations mean?”) and thus these variables should be correlated. More specifically, emerging evidence suggests that it is the inhibitory component of the IU that is more strongly associated with these phobic-related vulnerabilities (cf. McEvoy & Mahoney, 2011). The tendency to see a looming threat escalating in intensity is more associated with inhibitory, rather than prospective, IU. This sug­ gests that a looming cognitive style elicits paralysis over the uncertain threats more than an active strategy to increase predict­ ability. Rumination and worry share a common element of nega­ tive repetitive thinking (Watkins, 2008) that increases people’s vulnerability to perseverative and avoidant behavior that impedes effective coping (Hong, 2007; Lyubomirsky & Nolen-Hoeksema, 1995; Robichaud & Dugas, 2005). This is in line with the current data suggesting that the feeling of being “stuck” and unable to resolve problems as amplified by inhibitory IU is similar to the sense of perseveration brought about by passively ruminating on one’s failures and inadequacies. Differential relations between the two IU facets and personality/ affectivity variables were also found. In contrast to McEvoy and Mahoney (2011, 2012), inhibitory IU was more strongly associ-

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ated with neuroticism and extraversion (negatively) than was pro­ spective IU (see also Berenbaum et al., 2008). A notable strength in the current data was the use of NEO-PI-R (in the exploratory sample), which allowed for more fine-grained analysis at the facet level of the personality domains. Inhibitory IU was positively associated with the neuroticism facets of anxiety, depression, self-consciousness, and vulnerability, and negatively associated with the extra version facet of assertiveness. These results clarify the specific personality facet-level traits responsible for the rela­ tions found at the higher-order domain level. For example, the lack of assertiveness was the only extraversion facet linked to inhibi­ tory IU, suggesting that it is this particular facet driving the relation between extraversion and inhibitory IU. These results were also consistent with the abovementioned findings associated with psychopathology symptoms and vulnerabilities (e.g., low assertiveness was associated with inhibitory IU, which itself was linked to social anxiety). Similarly to neuroticism and extraver­ sion, inhibitory IU’s relations with negative and positive affectivity were also stronger than those of prospective IU. Moreover, specific affect variables like fear, guilt, sadness, and self-assurance were all more strongly linked to inhibitory than prospective IU. This study was the first to examine the relations between IU and BIS/BAS sensitivities. Contrary to the expectation that inhibitory IU would be more associated with BIS sensitivity, results indicated that both IU facets were equally associated with BIS sensitivity. Interestingly, prospective IU had a small but significant associa­ tion with BAS sensitivity, in particular with BAS reward respon­ siveness and drive. This novel finding provides some preliminary support for Birrell et al.’s (2011) hypothesis that prospective IU might represent an approach orientation toward reducing uncer­ tainty. As BAS sensitivity organizes behavior toward incentives or nonpunishment, it should correlate with prospective IU’s focus on adopting an active approach to increase predictability. Obtaining relevant information to reduce uncertainty can be rewarding as it helps in planning ahead for individuals high on prospective IU. Overall, delineating differential functions of the IU facets from the perspective of Gray’s (1991) reinforcement sensitivity theory ap­ pears promising, and future studies should examine if the present results could be replicated. The current research provides an emerging portrait of the IU facets (see also Birrell et ah, 2011). Prospective IU reflects a desire for predictability driven by a sense of uneasiness with uncertainty. This desire translates to approach-oriented actions such as infor­ mation seeking aimed at reducing uncertainty. Its strong link with worry suggests that it may facilitate positive beliefs about worry (e.g., worrying can reduce uncertainty; Dugas & Koerner, 2005). Prospective IU is related to self-reported neuroticism/negative affectivity and extraversion (negatively) but not to positive affectivity. Its associations with personality/affectivity dimensions in general are less robust than those of inhibitory IU. For instance, individuals high on prospective IU were not seen by informants to be emotionally unstable and vulnerable. Prospective IU’s small positive association with BAS sensitivity is consistent with its focus on increasing predictability (which may be seen as a reward­ ing experience for high prospective IU persons). Inhibitory IU, in contrast, represents paralysis and helplessness brought about by uncertainty and its toxicity can be seen from its strong associations with a wide-ranging spectrum of psychopathological symptoms and vulnerability factors. Individuals high on

inhibitory IU tend to be high on neuroticism/negative affectivity, along with a sensitivity toward punishment (i.e., BIS), but low on extraversion/positive affectivity. They “freeze up” in face of un­ certainty, engage in cognitive avoidance (Borkovec et al., 1998; Stapinski et al., 2010), and experience a myriad of negative emo­ tions such as fear and sadness. Over time, such cognitive perse­ veration and avoidance can impede coping with negative life events and lead to psychological maladaptation (Chen & Hong, 2010; Zlomke & Jeter, 2014). Taken together, it seems that inhib­ itory IU is the more maladaptive component of the two IU facets. The different personality and affectivity configurations of prospec­ tive versus inhibitory IU suggest that they might have different etiologic trajectories and further investigations might shed light on their development (e.g., what mechanisms might lead an individual to go from a prospective focus to a more detrimental inhibitory orientation).2 It is becoming increasingly clear that IU serves as a transdiag­ nostic vulnerability factor that predicts a broad range of psychopathological symptoms (Carleton, 2012). This makes IU a natural target for intervention that can potentially alleviate comorbid symptoms in anxiety and depression (Dugas & Ladouceur, 2000; Mahoney & McEvoy, 2012). The current findings and those of others (e.g., Helsen et al., 2013; McEvoy & Mahoney, 2011; Sexton & Dugas, 2009) suggest that assessing the specific IU facets can be informative from a clinical perspective. Individuals with generalized anxiety disorder or obsessive-compulsive disor­ der may have a strong focus on prospective IU and thus they may benefit most from interventions targeting at reducing perceived threat of uncertainty of future events (e.g., cognitive restructuring to limit exaggerated threats arising from uncertainty). Individuals with phobic-related disorders (i.e., social anxiety, anxiety sensi­ tivity) and depression may have elevated levels of inhibitory IU and hence may gain most from interventions that promote active engagement with uncertainty (e.g., seeking out information). For example, Chen, Liu, Rapee, and Pillay (2013) recently reported success in reducing worry, depression, cognitive avoidance, and intolerance of uncertainty using a behavioral activation treatment paradigm. Furthermore, clinicians should consider assessing basic personality dimensions as part of clinical assessment so as to obtain a holistic picture of patient functioning. As seen in this current research, the two IU facets have differential links with personality/affectivity dimensions and this can provide clinicians with information about clients’ personal strengths and weaknesses. For example, knowing a client possesses high inhibitory IU and neuroticism/negative affectivity might prompt the clinician to ex­ amine possible comorbid symptoms and their underlying pro­ cesses. Several limitations of this research warrant attention. First, the current results were derived from nonclinical samples of under­ graduate students and this limits the generalizability of results to clinical samples. The use of nonclinical samples here was some­ what justified given that (a) it was necessary to recruit a large number of participants for factor analysis, and (b) this was the first 2 Carleton et al. (2012) proposed conceptualizing the prospective facet as emphasizing the cognitive aspects of IU whereas the inhibitory facet as assessing the behavioral aspects of IU. This might be overly simplistic as both aspects of IU clearly contain cognitive, affective, and behavioral components.

INTOLERANCE OF UNCERTAINTY

study to examine the factorial validity of the IUS in a non-Western sample. Previous research on clinical samples in Western cultures (Dugas & Robichaud, 2007; Jacoby et al., 2013; McEvoy & Mahoney, 2011) has documented similar factor structures and psychometric properties of the IUS. Although the factorial and construct validities of the IUS are not expected to deviate too much, it is important for future research to examine these issues in a non-Western clinical sample. Second, the participants were pre­ dominantly women (73%), which might limit generalizability of findings to men. Although the factor structure of the IUS applied equally well across gender, men were higher on prospective IU than women. Third, previous research has documented strong links between IU and obsessive-compulsive disorder (Fergus, 2013; Gentes & Ruscio, 2011), and the current research is limited in that no measures of obsessive-compulsive disorder or related con­ structs (e.g., perfectionism) were available. Fourth, the crosssectional nature of the data precludes interpretations on causal relations. Fifth, almost all measures were self-reports, which raised the possibility of common method variance inflating the observed associations. Sixth, the temporal stability of the IUS was not examined in this study though previous research has reported test-retest reliability coefficients ranging between .74 and .78 over a 5-week period (Buhr & Dugas, 2002; Dugas, Freeston, & Ladouceur, 1997). In conclusion, current findings support the differentiation of IU, as measured by the IUS, into its constituent components—pro­ spective and inhibitory IU. Using a comprehensive nomological network, the construct validity of the IU components was exam­ ined, with observed relations largely conforming to theoretical expectations. This represents an important step toward establishing the construct validity of the IUS subscale scores and clarifying their nature. In the process, an 18-item version of the IUS was proposed as a psychometrically sound alternative to the full IUS-27 or the brief IUS-12 versions. The IUS-18 shows promise as the version that is shorter than the original IUS and yet retains adequate content validity. The IUS-18 would serve as a useful assessment tool in delineating the nature of prospective and inhib­ itory IU in clinical research and practice.

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Further clarifying prospective and inhibitory intolerance of uncertainty: Factorial and construct validity of test scores from the Intolerance of Uncertainty Scale.

The Intolerance to Uncertainty Scale (IUS) was developed to measure a dispositional tendency to react negatively to uncertain events, regardless of th...
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