Normative Values for the Beck Anxiety Inventory, Fear Questionnaire, Penn State Worry Questionnaire, and Social Phobia and Anxiety Inventory

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1 Psychological Assessment, Vol., No.,- Copyright by the American Psychological Association, Inc. -//S. Normative Values for the Beck Anxiety Inventory, Fear Questionnaire, Penn State Worry Questionnaire, and Social Phobia and Anxiety Inventory Martha M. Gillis, David A. F. Haaga, and Gary T. Ford The American University Community norms are reported for the Beck Anxiety Inventory (BAI; A. T. Beck, N. Epstein, G. Brown, & R. A. Steer, ), Fear Questionnaire (FQ; I. M. Marks & A. Mathews, ), Penn State Worry Questionnaire (PSWQ; T. J. Meyer, M. L. Miller, R. L. Metzger, & T. D. Borkovec, ), and Social Phobia and Anxiety Inventory (AI; S. M. Turner, D. C. Beidel, C. V. Dancu, & M. A. Stanley, ). The demographic profile of the samples closely matched the U.S. national census. On the AI, women scored higher than men on the Agoraphobia subscale, and the lowest income group scored higher than higher income participants on the Difference and Social Phobia subscales. Participants under years of age exceeded those aged - on the BAI, the PSWQ, and FQ Social Phobia, Blood/Injury, and Total Phobia scores. scores are provided for all measures, as well as discussion of their usefulness for assessing clinical significance of therapy outcomes. Evaluations of the efficacy of treatments for psychological disorders have traditionally asked whether a specific treatment led to improvements in client functioning or which of several comparison treatments led to greatest improvement. Answering such questions via statistical significance testing alone fails to address the magnitude or meaningfulness of improvement. A statistically significant result could represent only modest benefits from therapy if within-group variability is small or sample size is large. Therefore, treatment research methodologists have developed criteria for measuring the extent to which a treatment produces practically meaningful, or clinically significant (Jacobson, Follette, & Revenstorf, ), benefits. One influential procedure for estimating the practical meaning of treatment effects considers a patient to have achieved clinically significant change if she or he (a) improves to a statistically reliable degree and (b) obtains a posttreatment score that is statistically more likely to stem from the functional population's distribution than from that of the pertinent dysfunctional population (Jacobson & Truax, ). One complication involved in applying this method is the difficulty of defining and identifying distinct functional and dysfunctional population distributions. A currently common method of developing dysfunctional norms is to accept the pretreatment scores of one's own patient sample as representative Martha M. Gillis and David A. F. Haaga, Department of Psychology, The American University; Gary T. Ford, Department of Marketing, The American University. This article is based on Martha M. Gillis's master's thesis. We thank Dianne L. Chambless, who also served as a committee member. Correspondence concerning this article should be addressed to David A. F. Haaga, Department of Psychology, The American University, Asbury Building, Washington, DC -. Electronic mail may be sent via Internet to Dhaaga@american.edu. of the dysfunctional population, which means that the dysfunctional "norms," and therefore the cutoff for clinical significance, are different in every study. The functional population is also not easy to define. Some studies screen for this group by excluding anyone who has received treatment for the condition being studied (e.g., Trull & Hillerbrand, ). However, having received treatment may be imperfectly correlated with being dysfunctional. The National Comorbidity Survey (NCS; Kessler et al., ) estimated, for example, that only % of those with at least one lifetime psychiatric diagnosis had received professional treatment and % of those with no lifetime diagnosis had received such treatment (Kessler et al., ). Hollon and Flick () recommended bypassing the complexities involved in defining separate functional and dysfunctional populations by basing norms on unscreened, demographically representative samples. "Normal" posttreatment scores would then be referenced solely to this distribution. This is the approach adopted in the present study. This study was designed to provide normative information for four outcome measures used in studies of the treatment of generalized anxiety disorder, social phobia, agoraphobia, and other anxiety disorders. The measures selected were the Beck Anxiety Inventory (BAI; Beck, Epstein, Brown, & Steer, ), the Fear Questionnaire (FQ; Marks & Mathews, ), the Penn State Worry Questionnaire (PSWQ; Meyer, Miller, Metzger, & Borkovec, ), and the Social Phobia and Anxiety Inventory (AI; Turner, Beidel, Dancu, & Stanley, ). These instruments are all self-report measures with evidence of high reliability and validity, and, with the exception of the FQ, no established adult community norms. For the FQ, two extensive normative studies have already been published (Mizes & Crawford, ; Trull & Hillerbrand, ), with markedly discrepant results. For example, Trull and Hillerbrand () reported a mean FQ Total Phobia score

2 NORMATIVE VALUES FOR ANXIETY MEASURES of. for women, compared to Mizes and Crawford's () score of.. The studies differed in several ways, and it is not certain why Trull and Hillerbrand () obtained higher FQ norms. They conducted their study in a larger urban area, collected data by a telephone survey, and excluded participants with a history of treatment for phobia, whereas Mizes and Crawford () studied a smaller community with mailed questionnaires and no exclusion rules. Neither study used a demographically representative, unselected sample. For example, the Mizes and Crawford () sample was % Caucasian. In addition to providing total-sample norms, we conducted subgroup comparisons because the Epidemiological Catchment Area (ECA) project (e.g., Boyd et al., ; Regier, Narrow, & Rae, ) and the NCS (Kessler et al., ) indicated that there may be significant age, sex, race, and socioeconomic differences in the prevalence of anxiety disorders. In summary, the present study was designed to provide the first adult community norms for the BAI, AI, and PSWQ, as well as additional normative data, from an unscreened and demographically representative group, for the FQ. Participants Method Two nonprobability quota samples were selected. The samples matched the demographic profile of U.S. adults between the ages of and (U.S. Bureau of the Census, ) across four variables: sex, race, income, and age. The AI alone was administered to one sample (N - ). The second sample (N = ) completed in random order the BAI, the PSWQ, and the FQ. The questionnaires were divided into two groups to keep administration time manageable, based on the results of a pilot study. The target national census quotas were as follows: (a) % men and % women; (b) % Caucasian (% Hispanic and % non-hispanic), % Black, and % other; (c) % from each of the following annual household income ranges: -$,, $,-,, $,-,, $,-,, and over $,; and (d) % age -, % age -, and % age -. Our samples of respondents to each questionnaire matched these quotas closely (e.g., in each case the sample was either % or % Caucasian and either % or % Black) and closely resembled U.S. Census data on several nonquota demographic variables (education level, marital status, and employment). Slight demographic differences from one questionnaire to another and from the target quotas resulted from missing or inaccurate data (e.g., two numbers circled on one item or responses during oral screening differing from those provided on the demographic questionnaire). Procedure Potential participants were approached at two shopping malls in the suburbs outside Washington, DC, by representatives of two professional marketing firms and asked if they were willing to answer some questions. Screening questions on the quota variables were asked first. If the potential participant was under or more than years old, or fit a demographic category for which our quota had already been reached, she or he was thanked and the interview was terminated. Remaining potential participants were asked to take min to fill out some questionnaires and received a letter explaining the purpose of the study and its voluntary nature. Originally, only mall intercepts were planned, but it proved extremely difficult to locate both Hispanics and lower income (below $,) participants at those locations. As a result, potential participants were approached at a Catholic church, a trailer park, and a homeless shelter, again in suburbs outside Washington, DC. Most of the participants (n = ) were offered the incentive of receiving a free District of Columbia lottery ticket in return for agreeing to complete the questionnaires. Many of the Hispanic participants recruited through the church did not read English (AI n = of ; BAI, PSWQ, and FQ, n = of ), so the questionnaires were orally translated into Spanish for them, but they wrote their own responses. A $ donation was made to the church for each questionnaire this group completed. Data analyses were primarily descriptive. Means, standard deviations, and percentile scores for all measures, including subscales of the FQ and AI, were computed for the complete samples and separately for three sets of subgroups defined by sex, race, and income. Subgroup scores were compared using / tests. The income analyses contrasted those in the lowest % of income with those in the upper %; participant selection had been based on quintile quotas to enable the matching of sample profiles to published census data (U.S. Bureau of the Census, ). ECA data (Regier etal., ) suggested that low socioeconomic status is associated with high prevalence of anxiety disorders and that the difference between the lowest quartile and all other groups is especially noticeable. Similarly, age analyses contrasted participants age - with those age -. One-month prevalence data of any anxiety disorder was lower in the age brackets and above than among those - (Regier etal., ). Measures Fear Questionnaire. The FQ (Marks & Mathews, ) includes three five-item subscales (for agoraphobia, social phobia, and blood/ injury phobia) measuring phobic avoidance. Scores can range from to on each. The Total Phobia score (possible range of to ) is the sum of scores on these three subscales. The FQ and its subscales have high retest reliability (Marks & Mathews, ). Arrindell and Buikhuisen () reported that, in general, the FQ is unaffected by social desirability response bias. Beck Anxiety Inventory. The BAI (Beck etal., ) consists of items describing anxiety symptoms. Respondents are asked to rate how much each symptom bothered them during the previous week. Scores can range from to. The BAI was developed to provide improved discrimination between depression and anxiety, and it has shown better ability to make that discrimination than other widely used anxiety measures (Beck et al., ). The BAI has shown high short-term retest reliability and internal consistency (Beck et al., ), as well as evidence of concurrent, convergent, and discriminant validity (Beck & Steer, ;Fydrich,Dowdall,&Chambless, ). Penn State Worry Questionnaire. The PSWQ (Meyer et al., ) is a -item measure of concerns associated with generalized anxiety disorder. Scores can range from to. Several studies in both clinical and nonclinical samples have reported high internal consistency, shortterm retest reliability, and convergent and criterion-related validity (Brown, Antony, & Barlow, ; Davey, ). Social Phobia and Anxiety Inventory. The AI (Turner et al., All references to the results of Trull and Hillerbrand () and Mizes and Crawford () in this article are based on their adult community samples, not their student samples. Mean scores from nonclinical groups have been reported for the PSWQ (e.g., Brown, Antony, & Barlow, ), the AI (Turner, Beidel, Long, Turner, & Townsley, ), and the BAI (e.g., Burgess & Haaga, ), but in each case the samples have been small (N < ), unrepresentative (e.g., only university students), or both. A complete table showing the demographic profile of respondents to each measure in comparison with the U.S. Census data can be obtained from David A. F. Haaga.

3 GILLIS, HAA, AND FORD ) contains items related to somatic symptoms, cognitions, anxiety, and escape or avoidance behaviors associated with social phobia. The AI has two subscales, one for agoraphobia (possible scores range from to ) and one for social phobia (scores ranging from to ), and a Total or Difference score obtained by subtracting the Agoraphobia subscale score from the Social Phobia subscale score. Through this procedure, the Agoraphobia subscale score serves "as a suppressor variable" allowing "finer differentiation between these two conditions" (Turner et al.,, p. ). However, Herbert, Bellack, and Hope () caution that, in participants with symptoms of both agoraphobia and social phobia, using the Difference score may produce false negatives. The AI has discriminated the socially anxious from other anxiety groups diagnosed by clinical interviews (Beidel, Turner, Stanley, & Dancu, ; Turner et al., ). It has high short-term retest reliability (Turner et al., ) and good convergent validity with a number of other measures of social anxiety (Clark et al., ; Herbert et al., ). Results Inclusion or exclusion of the church-recruited Hispanic participants did not significantly alter results despite the differences in assessment and sampling procedures. The results reported therefore include their data. Fear Questionnaire The overall sample (n = usable responses) obtained an FQ Total Phobia mean of. (SD =., range -). There were no significant sex, race, or income differences on the FQ and no significant age differences on the Agoraphobia subscale. Younger participants scored significantly higher (M =., SD =.) than did participants or older (M =., SD =.), t( ) =., p <., on the Social Phobia subscale. Similarly, younger participants scored significantly higher on the Blood/ Injury Phobia subscale (M= \.\,SD =. vs. M=., SD =., respectively), t() =., p <., and on Total Phobia (M =., SD =. vs. M =., SD =., respectively), i() =.,p <.. scores for the overall FQ sample, and for age-defined subgroups on the variables showing significant age differences, are presented in Table. Table Scores on the Fear Questionnaire for the Complete Sample and for Age-Defined Subgroups th th th th th th th th th th th Overall BI TOT Age - BI TOT Age - BI TOT Note. = Agoraphobia subscale; = Social Phobia subscale; BI = Blood/Injury Phobia subscale; TOT = Total Phobia score. Table Scores for the BAI and the PSWQfor the Complete Sample and for Age-Defined Subgroups th th th th th th th th th th th All BAI - - All PSWQ - - Note. BAI = Beck Anxiety Inventory; PSWQ = Penn State Worry Questionnaire. Beck Anxiety Inventory For the overall BAI sample (n = ), the range of scores was - (M =., SD =.). There were no significant sex, race, or income differences on the BAI. However, younger participants (age -) scored higher (M =., SD =.) than did the older subgroup (M =., SD =.), /() =., p <.. scores for the BAI for the complete sample and for age-defined subgroups appear in Table. Penn State Worry Questionnaire For the PSWQ, the range of total scores obtained from the undivided sample (n = ) was - (M=., SD =.). As in the case of the BAI, there were no significant sex, race, or income effects, but younger participants (M =., SD=.) significantly exceeded those age and above (M=., SD =.), t() =.,p <., in PSWQ scores. scores are presented for the whole sample and for age subgroups in Table. Social Phobia and Anxiety Inventory On the AI, Social Phobia subscale scores ranged from to, and Agoraphobia subscale scores ranged from to. In contrast to the other anxiety measures, there were no significant age differences on the AI, but there were several significant subgroup differences relating to other demographic variables. Women (M = l.l,sd=.) scored significantly higher than did men (M =., SD =.) on the Agoraphobia subscale, t() =., p <., but sex differences were nonsignificant on the Social Phobia subscale and the Difference score, the main uses of the AI. Those in the lowest income quintile (M =., SD =.) scored significantly higher than did others (M =., SD =.) on the Social Phobia subscale, r() =., p <.. Likewise, Difference scores were significantly higher for the lowest-income participants (M =., SD =.) than for those in the highest % of the income distribution (M =., SD =.), f() =.,p <..

4 NORMATIVE VALUES FOR ANXIETY MEASURES Table Scores on the AIfor the Complete Sample, for Blacks, and for Caucasians th th th th th th th th th th th Overall Blacks Caucasians Note. AI = Social Phobia and Anxiety Inventory; = Social Phobia subscale; = Agoraphobia subscale; = Difference score ( minus ). Caucasians (M=., SD =.) scored significantly higher than did Blacks (M =., SD =.) on the Social Phobia subscale, i() =., p <., and on the Difference score (Caucasian M=., SD =.; Black M=., SD =.), f() =., p <.. The race difference is in the opposite direction to what would be expected on the basis of EGA data. In the AI sample, however, mean income of Blacks exceeded that of Caucasians by $,. The NCS (Kessler et al., ) found that anxiety disorders were significantly associated with low income but not with race. Therefore, the race differences we obtained may well have been artifacts of the income differences on the AI. scores for the AI are presented for the entire sample and for Blacks and Caucasians separately in Table ; scores for the lowest income quintile and the upper % of the income distribution are shown separately in Table. Table Scores on the AIfor Income-Defined Subgroups th th th th th th th th th th th Lowest % income Upper % income Note. AI = Social Phobia and Anxiety Inventory; = Social Phobia subscale; = Agoraphobia subscale; = Difference score ( minus ). Discussion This study provides normative data for the BAI, PSWQ, FQ, and AI. The results can facilitate assessment of the clinical significance of treatment effects. Technical details on the computation of clinical significance may be found elsewhere (Jacobson & Truax, ; see also Hsu,, and Speer,, for suggested alternative means of evaluating the statistical reliability of change; Jacobson & Revenstorf,, on consideration of multiple outcome measures; and Nietzel & Trull,, for an approach based on group average data rather than individual patients). In this discussion we concern ourselves with the prior questions of whether the norms from this study may be depended on, and if so, which ones. Are the Present Norms Dependable? Strengths of this study include adequate sample size and a close match with the U.S. census on demographic variables. Accordingly, we believe that treatment researchers working with adult (nonelderly) samples could reasonably evaluate the clinical significance of their findings in terms of, for example, the proportion of patients reaching the median score from our sample by the end of treatment. However, several limitations of the study should be noted. First, we were unable to execute the original sampling plan mall intercepts only because of the paucity of Hispanic and lower income participants recruited in this manner. The church-recruited Hispanic participants, for instance, differed from other participants in recruitment method, incentive for participation, and in some cases, the procedure for administering measures (i.e., oral administration of Spanish translations of the scales). More generally, matching the U.S. demographic profile for nonelderly adults does not ensure a representative sample in the way that random sampling (and a % response rate among those selected) would. For instance, only one metropolitan area was sampled, from the Northeast, which showed the highest prevalence of anxiety disorders among U.S. regions in the NCS (Kessler et al., ). Tending to bias the results in the opposite direction, most participants were recruited at sites likely to be avoided by agoraphobics, and the screening interview was a situation that the more socially phobic potential participants might avoid. One way to evaluate the dependability of our data is to compare them with past findings. We obtained a sex difference only on the AI Agoraphobia subscale, whereas prior research suggests a higher prevalence of anxiety disorders among women than men (e.g., Kessler et al., ), as well as higher anxiety symptom scores for women in both clinical (e.g., Hewitt & Norton, ) and nonclinical (e.g., De Beurs, Van Dyck, Van Balkom, Lange, & Koele, ; Trull & Hillerbrand, ) samples. The finding here is not unique, however, as Mizes and Crawford () also found no significant differences between men and women on the FQ in their nonstudent community sample. Moreover, we found age differences on several mea- We are using the term Black rather than African American to take into account the presence of a few non-american participants.

5 GILLIS, HAA, AND FORD sures, with participants aged - years being more anxious than those -. These results are consistent with the -month prevalence rates for any anxiety disorder from the EGA (Regier et al., ) and are broadly consistent with the inverse relation of age with -month prevalence of anxiety disorders in the NCS (Kessler et al., ), though the NCS used different age categories and a younger overall age range (-) than our study. The two earlier normative studies of the FQ also provide points of comparison. Our FQ Total Phobia mean of. falls between that of Trull and Hillerbrand (;.) and that of Mizes and Crawford (;.). Trends were similar on subscale scores. As discussed earlier, the studies differ in location, recruitment method, screening procedures, and sample demographics, so it is difficult to explain the score differences conclusively. One possibility is that there is nothing substantive to explain; our results are within the % confidence intervals for both of the earlier studies. The differences may be the result of normal sampling error in a population with high variance. This possibility underscores the need for replication of our results, which are novel for the BAI, PSWQ, and AI. Which Norm or Norms Should Be Used? Provided that one concludes that the present results can be used to interpret treatment outcome results, the next question is whether a treated patient should be compared to our overall sample or to a subgroup when significant subgroup differences exist. It is sometimes stated without qualification that individuals should be compared to their own demographic subgroup when subgroup scores differ (e.g., Mizes & Crawford, ), but we believe that this issue merits further debate. One complication in using subgroup norms as reference points is how to decide which, or which combination, of the multiple subgroups to which a person belongs (one's race, socioeconomic status, sex, age, extraversion level, etc.) is relevant (Hayes & Haas, ). Even if there were just one known subgroup difference to consider, it is not necessarily the case that treatment effects are best judged by referring to subgroup norms. For example, the lowest income group exceeded others in social phobia symptoms as measured by the AI. However, taking into consideration the possibility that social anxiety might hamper job searching or job performance, it seems plausible that a low-income patient (and his or her therapist) might prefer to peg improvement criteria to the norms of the total population rather than the more lenient standard of the norms of the lowest income group. Such decisions may rest on the individual circumstances of patients and the goals they set in collaboration with their therapists. References Arrindell, W. A., & Buikhuisen, M. (). Dissimulation and the sex difference in self-assessed fears: A brief note. Behaviour Research and Therapy,, -. Beck, A. T., Epstein, N., Brown, G., & Steer, R. A. (). An inventory for measuring clinical anxiety: Psychometric properties. Journal of Consulting and Clinical Psychology,, -. Beck, A. T., & Steer, R. A. (). Relationship between the Beck Anxiety Inventory and the Hamilton Anxiety Rating Scale with anxious outpatients. Journal of Anxiety Disorders,, -. Beidel, D. C, Turner, S. M., Stanley, M. A., & Dancu, C. V. (). The Social Phobia and Anxiety Inventory: Concurrent and external validity. Behavior Therapy,, -. Boyd, J. H., Rae, D. S., Thompson, J. W., Burns, B. J., Bourdon, K.., Locke, B. Z., & Regier, D. A. (). Phobia: Prevalence and risk factors. 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Jacobson, N. S., & Revenstorf, D. (). Statistics for assessing the clinical significance of psychotherapy techniques: Issues, problems, and new developments. Behavioral Assessment,, -. Jacobson, N. S., & Truax, P. (). Clinical significance: A statistical approach to defining meaningful change in psychotherapy research. Journal of Consulting and Clinical Psychology,, -. Kessler, R. C., McGonagle, K. A., Zhao, S., Nelson, C. B., Hughes, M., Eshleman, S., Wittchen, H.-U., & Kendler, K. S. (). Lifetime and -month prevalence of DSM-III-R psychiatric disorders in the United States: Results from the National Comorbidity Survey. Archives of General Psychiatry,, -. Marks, I. M., & Mathews, A. M. (). Brief standard self-rating for phobic patients. Behaviour Research and Therapy,, -. Meyer, T. J., Miller, M. L., Metzger, R. L., & Borkovec, T. D. (). Development and validation of the Penn State Worry Questionnaire. Behaviour Research and Therapy,, -. Mizes, J. S., & Crawford, J. (). Normative values on the Marks and Mathews fear questionnaire: A comparison as a function of age and

6 NORMATIVE VALUES FOR ANXIETY MEASURES sex. Journal of Psychopathology and Behavioral Assessment,, -. Nietzel, M. T, & Trull, T, J. (). Meta-analytic approaches to social comparisons: A method for measuring clinical significance. Behavioral Assessment,, -. Regier, D. A., Narrow, W. E., & Rae, D. S. (). The epidemiology of anxiety disorders: The Epidemiologic Catchment Area EGA experience. Journal of Psychiatric Research,, -. Speer, D. C. (). Clinically significant change: Jacobson and Truax () revisited. Journal of Consulting and Clinical Psychology,, -. Trull, T. J., & Hillerbrand, E. (). Psychometric properties and factor structure of the Fear Questionnaire Phobia Subscale items in two normative samples. Journal of Psychopathology and Behavioral Assessment,, -. Turner, S. M., Beidel, D. C., Dancu, C. V., & Stanley, M. A. (). An empirically derived inventory to measure social fears and anxiety: The Social Phobia and Anxiety Inventory. Psychological Assessment: A Journal of Consulting and Clinical Psychology,, -. Turner, S. M., Beidel, D. C, Long, P. J., Turner, M. W., & Townsley, R. M. (). A composite measure to determine the functional status of treated social phobics: The Social Phobia Endstate Functioning Index. Behavior Therapy,, -. U.S. Bureau of the Census. (). Statistical abstract of the United States : The national data book (th ed.). Washington, DC: Author. Received August, Revision received January, Accepted February, AMERICAN PSYCHOLOGICAL ASSOCIATION SUBSCRIPTION CLAIMS INFORMATION Today's Date:. We provide this form to assist members, institutions, and nonmember individuals with any subscription problems. With the appropriate information we can begin a resolution. If you use the services of an agent, please do NOT duplicate claims through them and directly to us. PLEASE PRINT CLEARLY AND IN INK IF POSSDLE. HUNT FULL NAME OR KEY NAME OF INSTITUTION MEMBER OR CUSTOMER NUMBER (MAYBEFOUNDON ANY PAST ISSUE LABEL) DATE YOUR ORDER WAS MAILED (OR PHONED) CITY YOUR NAME AND PHONE NUMBER STATE/COUNTRY.PREPAID _CHECK CHARGE CHECK/CARD CLEARED DATE:_ (If possible, send a copy, front and back, of your cancelled check to help us in our research of your claim.) ISSUES: MISSING DAMED TITLE VOLUME OR YEAR NUMBER OR MONTH Thank you. Once a claim is received and resolved, delivery of replacement issues routinely takes - weeks. ^ (TO BE FILLED OUT BY APA STAFF) DATE RECEIVED:. ACTION TAKEN: _ STAFF NAME: DATE OF ACTION: _ INV. NO. & DATE: LABEL NO. & DATE: Send this form to APA Subscription Claims, First Street, NE, Washington, DC - PLEASE DO NOT REMOVE. A PHOTOCOPY MAY BE USED.