Fearing the unknown: A short version of the Intolerance of Uncertainty Scale

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Abstract

Intolerance of uncertainty is the tendency of an individual to consider the possibility of a negative event occurring unacceptable, irrespective of the probability of occurrence. It is a key component of worry, state anxiety, and related anxiety pathologies. The 27-item Intolerance of Uncertainty Scale (IUS) was developed to measure intolerance of uncertainty. Previous psychometric analyses of the IUS have suggested both four- and five-factor models. High inter-item correlations, factor instability, and previous theoretical research support the development of a reduced measure. The present study used two undergraduate samples and evaluated a psychometrically stable 12-item two-factor version of the IUS. The reduced measure (IUS-12) retained exemplary internal consistency, while correlating extremely well with the original IUS and related measures of anxiety and worry. The IUS-12 also demonstrated a stable two-factor structure, representing both anxious and avoidance components of intolerance of uncertainty. Directions for future research and potential applications for assessment are discussed.

Introduction

Fear is a protective response to a current, identifiable threat (e.g., being attacked by a harmful animal) and is typically accompanied by a fight or flight response having a strong physiological reaction (e.g., blood pressure increase, muscle tension, startle response) (Barlow, 2002; McNeil & Vowles, 2004). In contrast, anxiety is a response to a potential threat that may or may not occur at some point in the future (e.g., a harmful animal might attack, somewhere, sometime) and is typically accompanied by an attenuated version of the physiological reaction to fear (Asmundson, Norton, & Vlaeyen, 2004; Barlow, 2002; McNeil & Vowles, 2004). Worrying can be interpreted as ruminating about the possibility of a negative occurrence (e.g., another attack), thereby maintaining a heightened level of anxiety (Dugas, Gosselin, & Ladoucer, 2001). These ruminations are validated and supported because the possibility of re-exposure to the fearful stimulus is not zero (e.g., the harmful animal, or similar ones, continue to exist in the same environment as the worrier) (Dugas et al., 2001). Therefore, how tolerant a person is of uncertainty surrounding the possibility of re-exposure will impact how often they worry (Laugesen, Dugas, & Bukowski, 2003) and therein their anxiety (Greco and Roger, 2001, Greco and Roger, 2003).

Intolerance of uncertainty has been identified as a discriminating individual difference characteristic involved in excessive worry (Laugesen et al., 2003), state anxiety (Greco & Roger, 2001), and to have strong positive associations with anxiety pathologies such as Generalized Anxiety Disorder (GAD), Obsessive Compulsive Disorder (OCD), and Panic Disorder (Dugas, Gagnon, Ladouceur, & Freeston, 1998; Dugas et al., 2001; Tolin, Abramowitz, Brigidi, & Foa, 2003). It is intolerance of the notion that negative events may occur and there is no definitive way of predicting such events. Indeed, people who are intolerant of uncertainty are likely to interpret all ambiguous information as threatening (Heydayati, Dugas, Buhr, & Francis, 2003), contributing to significant somatic stress reactions (e.g., increased heart rate and blood pressure; Greco and Roger, 2001, Greco and Roger, 2003). Furthermore, high intolerance of uncertainty may impair problem-solving skills, leading to inaction and avoidance of ambiguous situations (Dugas, Freeston, & Ladoucer, 1997). The Intolerance of Uncertainty Scale (IUS; Freeston, Rhéaume, Letarte, Dugas, & Ladouceur, 1994) is one of several scales developed to measure intolerance of uncertainty, which is conceptually linked to ambiguity (Greco & Roger, 2001). Popular alterative measures are either longer than the IUS (e.g., Uncertainty Response Scale, Greco & Roger, 2001) or suffer from poor internal reliability and convergent validity (e.g., Intolerance of Ambiguity Scale, Budner, 1962; Tolerance of Ambiguity Scale, Kirton, 1981).

The original IUS was developed (in French) to assess reactions to ambiguous situations, uncertainty, and future events (Freeston et al., 1994). The items were derived from a larger list of statements that had irrelevant or redundant items removed using rational relatedness followed by discriminant and correlational validations. Assuming the possibility that intolerance of uncertainty might be a multidimensional construct, an exploratory factor analysis was performed with an unspecified rotation and Cattell's (1966) scree test to determine the number of factors to retain. The final scale consisted of 27 items representing five different factors; however, one item of the 27 was hyperplanar (i.e., did not load significantly on any factor) and four items cross-loaded (i.e., loaded onto more than one factor). The five factors were the Unacceptability and Avoidance of Uncertainty (nine items), Negative Social Evaluation Caused by Uncertainty (nine items), Uncertainty-Related Frustration (three items), Uncertainty Causes Stress (four items), and Uncertainty Preventing Action (two items; Freeston et al., 1994).1 Each item was rated on a Likert scale from 1 (not at all characteristic of me) to 5 (entirely characteristic of me).

Using an undergraduate university sample, Freeston et al. (1994) demonstrated the IUS has excellent internal consistency (α = .91) and good test–retest reliability (r = .74) at five weeks. Convergent validity was indicated via correlations with measures of, or associated with, worry. Specifically, the total IUS score correlated moderately (Westgard, 1999) with French versions of the Beck Anxiety Inventory (r = .57; Beck, Epstein, Brown, & Steer, 1988), the Beck Depression Inventory (r = .52; Beck, Ward, Mendelson, Mock, & Erbaugh, 1961), the Worry Domains Questionnaire (Tallis, Eysenck, & Mathews, 1992), and the Penn State Worry Questionnaire (r = .63, Meyer, Miller, Metzger, & Borkovec, 1990). Further, the IUS accounted for additional variance in the Worry Domains Questionnaire and Penn State Worry Questionnaire over and above variance shared with anxiety and depression.

While Freeston et al. (1994) found support for a five-factor model, Buhr and Dugas (2002) found stronger support for a four-factor structure in a back-translated English version of the IUS.2 A principal components analysis was performed with an undergraduate sample using Promax (oblique) rotation, the Kaiser rule (eigenvalues > 1; Kaiser, 1961), and Cattell's (1966) scree test. Results suggested a four-factor solution was best, accounting for 56.8% of the variance and having excellent internal consistency (α = .94); however, six items continued to cross-load on multiple factors. The four factors were Uncertainty Leading to Inability to Act (10 items), Uncertainty Being Stressful and Upsetting (12 items), Unexpected Events are Negative and Should be Avoided (7 items), and Uncertainty Being Unfair (5 items).

Norton (2005) performed a subsequent assessment of the IUS factor structure, evaluating reliability and validity across various racial groups in an undergraduate sample. Again, principle factor analysis with Promax (oblique) rotation was used to delineate the factor structure. Factor retention was based on the Kaiser rule (Kaiser, 1961), Cattell's scree (1966) test, and the interpretability of the factor structure. This resulted in extremely divergent five- and six-factor solutions having extensive multi-vocal item loadings and poor interpretability. Nevertheless, the internal consistency within each racial group was very high (α > .93). Based on these findings, and because of the high alpha coefficients reported with the IUS to date (Buhr & Dugas, 2002; Freeston et al., 1994), Norton suggested item-removal might improve factor structure of the IUS without substantively impacting scale reliability.

The earlier psychometric analyses of the IUS are limited in several ways. First, all relied on eigenvalues >1 and Cattel's scree test, both of which can be too liberal in their derivations of number of factors to retain (Tabachnick & Fidell, 2001). Second, despite high item-total correlations and alpha coefficients, a unitary factor structure has yet to be evaluated. Third, as suggested by Norton (2005), the continued instability of the factor structure and lack of simple structure, in spite of large sample sizes, suggests redundancy and unrelatedness within the items. The intent of this study was two-fold. First, to perform confirmatory factor analyses (CFA) comparing unitary, four-, and five-factor structures. Second, to evaluate Norton's (2005) suggestion that a shortened IUS might be a more stable and efficient measure for evaluating intolerance of uncertainty.

Section snippets

Participants

This study required two independent data sets for evaluation. The initial CFAs were performed on data collected at the University of Regina. Participants included 254 university students [61 men ages 19–37 (M = 23.3; S.D. = 4.2) and 193 women ages 18–50 (M = 23.0; S.D. = 5.8)] from the faculties of Psychology and Kinesiology and Health Studies who volunteered following a brief presentation during one of their classes. All of the participants from the sample had either some university/technical school

Original IUS summary scores

Using the data from the Regina sample, scores on the full IUS ranged from 27 to 119 (M = 50.28, S.D. = 17.22). No significant differences based on sex were observed for the total score (women: M = 49.99, S.D. = 17.16; men, M = 51.19, S.D. = 17.51), t(252) = 0.474, P = .636, r2 < .01. There is as of yet no reason to assume substantive sex-based differences on the IUS; therefore, sex was not considered as a covariate. In line with Norton's (2005) findings and supporting item reduction, the internal consistency for

Discussion

This study had two main goals. The first goal was to perform the first CFA of the English version of the IUS, comparing the unitary, four-, and five-factor models. The results of the CFA indicated that none of these models appropriately fit the data. Furthermore, the comparison did not result in any one model being superior. This pattern of findings did, however, lend support to the notion of revising the measure as recommended by Norton (2005), which was the second goal of this study. Using

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