Personality and Individual Differences 53 (2012) 224–230

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Personality and Individual Differences

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Beliefs over control and meta-worry interact with the effect of intoleranceof uncertainty on worry

Giovanni M. Ruggiero a, Lexine Stapinski c, Gabriele Caselli d, Francesca Fiore b, Marcello Gallucci e,Sandra Sassaroli b,⇑, Ronald M. Rapee c

a Psicoterapia Cognitiva e Ricerca, Post-Graduate Cognitive Psychotherapy School, Foro Buonaparte 57, 20121 Milano, Italyb Studi Cognitivi, Post-Graduate Cognitive Psychotherapy School, Foro Buonaparte 57, 20121 Milano, Italyc Centre for Emotional Health 720 C3A, Macquarie University, Sydney, NSW 2109, Australiad London South Bank University, 103 Borough Road, City of London SE1 0AA, UKe Department of Psychology, University of Milano-Bicocca, Piazza dell’Ateneo Nuovo 1, 20126 Milan, Italy

a r t i c l e i n f o

Article history:Received 9 October 2011Received in revised form 15 March 2012Accepted 19 March 2012Available online 14 April 2012

Keywords:ControlIntolerance of uncertaintyWorryMeta-worryGeneralized anxiety disorder

0191-8869/$ - see front matter � 2012 Elsevier Ltd. Ahttp://dx.doi.org/10.1016/j.paid.2012.03.016

⇑ Corresponding author. Tel.: +39 02 6570350; fax:E-mail addresses: grupporicerca@studicognitivi.ne

pinski@mq.edu.au (L. Stapinski), casellig@lsbu.ac.ukstudicognitivi.net (F. Fiore), marcello.gallucci@unimibca@studicognitivi.net (S. Sassaroli), Ron.Rapee@psy.m

a b s t r a c t

Cognitive theory conceptualizes worry as influenced by metacognitive beliefs about worry, intolerance ofuncertainty, and perceptions of control over events and reactions. This study tests the hypothesis that theeffect of intolerance of uncertainty would interact with meta-cognitive beliefs on worry and perceivedcontrol. One hundred eighteen individuals with generalized anxiety disorder and 54 controls completedthe Meta-Cognition Questionnaire, the Intolerance of Uncertainty Scale, the Anxiety Control Scale, andthe Penn State Worry Questionnaire. Models were tested measuring interactive effects in multiple regres-sion linear analysis. The interaction model was confirmed. The effect of intolerance of uncertainty onworry was increased by its interaction with metacognitive and control beliefs. The finding emphasizesthe significant role of metacognitive and control beliefs in the cognitive process that leads to the devel-opment of worry.

� 2012 Elsevier Ltd. All rights reserved.

1. Introduction

1.1. Cognitive models of generalized anxiety disorder and worry

In their exhaustive review Behar, DiMarco, Hekler, and Staples(2009) concluded that current cognitive models for understandinggeneralized anxiety disorder (GAD) fall into five types: (1) the cog-nitive avoidance model of Borkovec (1994); (2) the intolerance ofuncertainty model (Dugas, Buhr, & Ladouceur, 2004); (3) the meta-cognitive model of Wells (2004); (4) the emotion dysregulationmodel (Mennin, 2004); and (5) the acceptance-based model ofRoemer and Orsillo (2005). In addition to these five, we couldadd at least two other theoretical models which have been appliedto anxiety disorders in general and that can be consequently linkedto GAD: the mood-as-input hypothesis (Davey, 2006) and the anx-iety control model (Rapee, Craske, Brown, & Barlow, 1996).

The existence of rival theoretical models suggests the explora-tion of possible interactions between the variables focused on byeach model. This work aims to explore reciprocal influences and

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+39 02 36554665.t (G.M. Ruggiero), lexine.sta(G. Caselli), grupporicerca@.it (M. Gallucci), grupporicerq.edu.au (R.M. Rapee).

interactions between complementary aspects of some of the men-tioned models. In order to explore clinically meaningful interac-tions, the current study focuses on components of three of thesemodels: intolerance of uncertainty, metacognitive beliefs and anx-iety control. We selected these models since all of them focus onmechanisms that relate to discrete cognitive constructs measur-able using psychometrically sound self-report instruments tappingthe central construct, given that factor analyses have shown thatthese questionnaires load onto a single dimension (Antony, Orsillo,& Roemer, 2001; Freeston, Rhéaume, Letarte, Dugas, & Ladouceur,1994; Wells & Cartwright-Hatton, 2004; Zebb & Moore, 1999).On the other hand we did not consider complex information pro-cessing mechanisms not measurable using single variables, likeemotion dysregulation, acceptance, avoidance, and mood-as-input.

1.2. The dependent variable: worry

Worry is a thought activity characterized by a predominance ofanxious predictions about possible future negative events(Borkovec, 1994). Worry is a good indicator of the severity ofGAD for several reasons. Worry is present in other anxiety disor-ders, although generally less so than in GAD. In addition, worryis described as a core criterion of GAD in the DSM. The specialrelationship between worry as a symptom and GAD as a DSM

G.M. Ruggiero et al. / Personality and Individual Differences 53 (2012) 224–230 225

diagnosis is further shown by the fact that the intensity of worry isable to distinguish patients with GAD from healthy controls(Brown, Antony, & Barlow, 1992; Paulesu et al., 2009) and is alsoable to distinguish between subjects meeting all, some, or noneof the DSM criteria for GAD (Meyer, Miller, Metzger, & Borkovec,1990). Therefore, measures of worry can be seen as a useful proxyfor the presence and severity of GAD (Norton, Sexton, Walker, &Norton, 2005).

1.3. Intolerance of uncertainty

Individuals scoring high on this construct tend to evaluate anyuncertain or ambiguous situation as dangerous, stressful andupsetting. For these individuals, any potential risk of a negativeoutcome is perceived as threatening. Given the degree of uncer-tainty present in everyday life, intolerance for uncertainty isthought to contribute to the chronic worry and anxiety observedin GAD (Dugas et al., 2004).

The empirical studies supporting the significant role of intoler-ance for uncertainty have shown that this construct is one of themore powerful predictors of worry in GAD, while other factors,such as positive beliefs about worry or cognitive avoidance, arecommon across anxiety disorders (Dugas et al., 2007). In conclu-sion, it seems that intolerance of uncertainty is a cognitive factorthat is closely linked to the arousal of anxiety states in GAD.

1.4. Negative beliefs about worry

Wells’ metacognitive model of GAD (2004) describes five meta-cognitive beliefs that include different domains of beliefs aboutanxious states, worry, and perceived threats and danger. Of thesefive dimensions, two are conceptually related to worry and statis-tically correlated with proneness to develop worry and GAD: posi-tive beliefs about worry and negative beliefs about worryconcerning uncontrollability and danger (from now on: negativebeliefs about worry). Positive beliefs about worry would encourageindividuals with GAD to be involved in the execution of prolongedworry sequences about possible danger-related questions. Wells(2004) calls this process Type 1 worrying. On the other hand neg-ative beliefs about worry are related to a negative appraisal of wor-ry, and the fear that continuous worrying is an uncontrollable andpotentially harmful mental condition. Wells calls this processmeta-worry and reports that it is more specific for GAD than Type1 worry. Due to this specificity, we focused on negative beliefsabout worry in our set of predictors of worry.

1.5. Anxiety control

Another cognitive factor that has been related more broadly to arange of anxiety disorders is the perception of low control overexternal threats and internal emotional reactions. The perceptionof control over a threat (a construct and variable called ‘anxietycontrol’) involves both the perception of being able to both masterthe threatening event itself (control of events) and also being ableto control and master emotional reactions of fear (control of reac-tions) in a way that enhances the sense of personal competenceand self-efficacy (Rapee et al., 1996; Shapiro & Astin, 1998, pp.23). Given that one of the key diagnostic criteria for GAD is thatworry is uncontrollable (American Psychiatric Association, 2000),low perceived control is a construct that is relevant to GAD. Con-sidered within a control framework, the chronic worry and behav-ioural avoidance associated with GAD can be conceptualised asreflecting persistent (and futile) efforts to gain control over futurethreat.

1.6. Interactions between predictors

As described above, the scientific literature suggests that intol-erance of uncertainty, and negative beliefs on worry are powerfulpredictors of worry, while anxiety control is significantly linkedto anxiety disorders and anxiety states, states which include worryand GAD. However, little is known about their possible interactiveeffects on worry. A significant interaction between AC, intoleranceof uncertainty, and negative beliefs on worry would mean thatthese variables have a reciprocally reinforcing effect on the sever-ity of worry and that their combined effect on worry is higher thanthe sum (Baron & Kenny, 1986).

This work seeks to test the hypothesis that these interactive ef-fects exist and significantly influence the severity of worry. In par-ticular, negative beliefs on worry and the reactions subscale ofanxiety control are cognitive beliefs which influence the severityof worry via the appraisal of other internal mental states. This sug-gests that their mechanism of action may be intrinsically interac-tive, in that they exacerbate the severity of worry and GADinitiated via other cognitive processes.

2. Methods

2.1. Participants

Two groups of participants were recruited to the study. Theclinical group were 119 participants meeting diagnostic criteriafor GAD (Diagnostic and Statistical Manual of Mental Disorders,4th ed., text rev.; American Psychiatric Association, 2000). Addi-tional criteria for inclusion to the study were a minimum age of18 years, and adequate written language abilities. This study in-cluded a mixed sample combining 90 Italian individuals and 29Australian with GAD (Italian sample: 64 females and 26 males,mean age 34.15 ± 9.74 years; Australian sample: 25 females and4 males, mean age 37.79 ± 13.08 years; t = 1.18, p = 0.23). The Ital-ian sample was recruited from a population that was undergoingthe initial assessment phase for cognitive therapy, and diagnosiswas made using the Italian version of SCID-I (First, Spitzer, Gibbon,& Williams, 1997; Mazzi, Morosini, De Girolamo, Bussetti, & Guar-aldi, 2000). Diagnostic interviews were conducted by psychologiststrained in cognitive therapy (4 years training, according to the cri-teria of the Italian Ministero dell’Istruzione, dell’Università e dellaRicerca and the Società Italiana di Terapia Comportamentale e Cog-nitiva). The Australian sample was participants seeking treatmentfor GAD at the Macquarie University Centre for Emotional Health.For these participants, diagnosis was based on the Anxiety Disor-ders Interview Schedule for DSM–IV (ADIS-IV; DiNardo, Brown, &Barlow, 1994). All diagnostic interviews were conducted by gradu-ate students who had been trained by clinical psychologists expe-rienced in the assessment and treatment of anxiety disorders.

Nineteen Italian individuals and thirty-five Australian individu-als without GAD were recruited as control participants (Italian sam-ple: 14 females and 5 males, mean age 37.79 ± 7.96 years;Australian sample: 21 females and 14 males, mean age 37.65 ±15.73 years; t = 0.34, p = 0.97). The Structured Clinical Interview(Italian sample) and Anxiety Disorders Interview Schedule forDSM–IV (Australian sample) were used to verify that the controlparticipants did not meet criteria for GAD or any other clinicaldisorder.

Six of the Italian controls were students in the Studi Cognitivipost-graduate program at the Cognitive Psychotherapy School ofMilano, Milan, Italy. Seven Italian controls were recruited from ablacksmith firm in Bollate (an industrial town near Milano, Italy)and six Italian controls were expert therapists in private practiceworking in Milano. The Italian control group were not remuneratedfor their participation. The Australian control participants were

Table 1Descriptive statistics and t-test.

Variables Groups N Mean Std. deviation t Sig. (2-tailed)

ACQ GAD group 118 69.25 18.49 7.32 .000***

Control group 54 109.23 17.15

IUS GAD group 118 81.80 20.31 14.40 .000***

Control group 54 40.59 7.90

NBWCD-MQ GAD group 118 16.69 3.75 12.19 .000***

Control group 54 9.28 3.57

PWSQ GAD group 118 62.94 8.05 20.67 .000***

Control group 54 33.85 9.60

*** p-value less than the significance level a .001.

Table 2Mediation/moderation model of intolerance of uncertainty, metacognition andanxiety control on worry.

Dependent variable: worry Betaa F p. pg2b

ACQ �0.257 14.345 <.001 .080IUS 0.308 25.832 <.001 .135NBWCD-MQ 0.340 31.299 <.001 .159ACQ – IUS 0.150 4.750 <.05 (.031) .028ACQ – NBWCD-MQ �0.017 .056 .814 .001IUS – NBWCD-MQ �0.146 5.037 <.05 (.026) .030

R squared = .774 (Adjusted R squared = .765).a Standardized coefficients were computed by standardizing all variables

involved in the analysis.b Partial eta-squared.

226 G.M. Ruggiero et al. / Personality and Individual Differences 53 (2012) 224–230

recruited through advertisements in the local community, andwere paid a small sum as reimbursement for their time and travel.

It is also true that gender disparities are present in both the Ital-ian and Australian samples. However, we stress that this disparityis consistent with the gender ratio of current diagnostic rates ofGAD (Kessler, Keller, & Wittchen, 2001).

2.2. Instruments

The Anxiety Control Questionnaire (ACQ; Rapee et al., 1996) as-sesses the perception of control over emotional reactions andexternal threats. The questionnaire attributes lower scores to indi-viduals with lower perceptions of control and consists of 30 itemsthat provide a total score based on two subscales: perceived con-trol over external events (16 items) and perceived control over per-sonal reactions (14 items). Participants respond on a 6-point LikertScale. We restricted our analysis to the ACQ total score because thepsychometric properties of the total score are better than those ofthe two subscales (Antony et al., 2001; Zebb & Moore, 1999). Thetotal score has demonstrated strong internal consistency and hightest-retest reliability as well as the ability to discriminate betweenanxious and non-anxious individuals (Rapee et al., 1996). For theItalian sample, an Italian translation of the scale was developedby G. M. R. The Italian version of the ACQ was then back-translatedinto English by a native English speaker who is not familiar withthe questionnaire. The original authors of ACQ compared the origi-nal version and the back-translated version of ACQ and did not findmeaningful differences (Ron Rapee, 11 November 2009, e-mailcommunication).

The Intolerance of Uncertainty Scale (IUS) (Freeston et al., 1994)assesses the degree to which an individual has difficulty toleratinguncertainty. The IUS is a 27-item measure using a 5-point Likert-type scale indicating how characteristic each statement is of them.For the Italian sample, an Italian translation of the IUS was devel-oped by G. M. R. The Italian version of the IUS was then back-trans-lated into English by a native English speaker who is not familiarwith the questionnaire. Michel Dugas, one of the original authorsof IUS, compared the original version and the back-translated ver-sion of IUS and did not find meaningful differences (Dugas, 6December 2004, e-mail communication).

The 30 item Metacognition Questionnaire (MQ-30) (Wells &Cartwright-Hatton, 2004), which is a reduction from the original64-item version (Cartwright-Hatton & Wells, 1997) is a self-reportinstrument which measures the five metacognitive beliefs theo-rized by Wells. Items are scored on a 4-point Likert scale. The sub-scale ‘‘negative beliefs on worry, controllability and danger’’(NBWCD-MQ) corresponds to the construct ‘negative beliefs onworry’ which in turn is by far the more specific metacognitive fac-tor of worry and GAD (Wells & Cartwright-Hatton, 2004). In addi-tion, NBWCD-MQ has the highest correlation with worry, beinghigher than .6, while the other four scales of the MQ-30 showed

a correlation lower than .4 (Wells & Cartwright-Hatton, 2004).For the Italian sample, we used the official Italian translation ofthe MQ published by Wells in (2000) (pp. 327–333) and the rele-vant 30 items were selected from these.

The Penn State Worry Questionnaire (PSWQ), developed byMeyer et al. (1990), is a 16-item self reported questionnaire basedon what has been theorized about worry by Borkovec and his col-laborators. The PSWQ measures the intensity and excessiveness ofworry. For the Italian sample, we used the official Italian version ofthe PSWQ published by Morani, Pricci, and Sanavio (1999).

2.3. Procedures

Self-report measures were administered to both clinical andcontrol participants. Statistical analyses incorporated data fromboth groups in the same analysis. This permitted us to test interac-tive effects at each level of the entire range of worry, not only athigh and low values but also reflecting values for clinical or non-clinical populations. A potential problem of combining the twogroups is that residuals of the scores of the dependent variableswill not be normally distributed, which is a necessary conditionfor regression analyses (Cohen, Cohen, West, & Aiken, 2003, pp.137–141). However, Kolmogorov-Smirnov test, Shapiro-Wilk test,and Normal Q–Q plot gave converging evidence that the distribu-tion of residuals approximates normality.

In order to explicate mechanisms underlying relationshipsbetween anxiety control, intolerance of uncertainty and negativebeliefs on worry, we used a moderated regression analysis – thatis, regression with interactions analysis (Baron & Kenny, 1986).

3. Results

3.1. Preliminary analyses

The independent variables explored in this study were corre-lated with each other (ACQ – NBWCD-MQ, r = �.77; IUS – ACQ,

Fig. 1. Effect of IUS on worries at different representative levels of ACQ.

Fig. 2. Effect of IUS on worries at different representative levels of NBWCD-MQ.

G.M. Ruggiero et al. / Personality and Individual Differences 53 (2012) 224–230 227

Intolerance of Uncertainty Worry

Negative beliefs on worry

Anxiety Control

Fig. 3. Confirmed moderation model.

228 G.M. Ruggiero et al. / Personality and Individual Differences 53 (2012) 224–230

r = �.77; IUS – NBWCD-MQ, r = .68; all p’s < .001). This may result,particularly for small sample sizes, in multicollinearity, which canproduce computational lack of precision in moderated regression(Aiken & West, 1991). To quantify multicollinearity we measuredthe variance inflation factor (VIF). There is no formal cut off valuefor VIF for determining the impact of multicollinearity on regres-sion results. Values of VIF >10 are often regarded as indicatingproblematic multicollinearity, although some authors suggestedthat even values above >2.5 may be a cause for concern (Fox,1991). The VIF was 1.52 for beliefs about controllability and dan-ger, 1.65 for intolerance of uncertainty and 1.19 for anxiety control.The highest multicollinearity index was 2.41, much lower than areasonable cut off of 15, indicating that the impact of the observedcollinearity should be weak in our analyses.

3.2. Descriptive statistics and t-tests

As expected, the GAD group had lower average scores than thecontrol group on ACQ and higher average scores on IUS, NBWCD-MQ and PSWQ (Table 1).

These results suggest that participants with GAD had a greatertendency to worry about possible danger; higher negative meta-beliefs; a greater intolerance of uncertainty and less perceptionof control over their internal feelings and external events.

3.3. Moderated regression analyses

A regression analysis with worry as dependent variable andACQ, IUS, NBWCD-MQ, and their two-way interactions was esti-mated. A model with the three-way interaction was estimated aswell showing the absence of such an effect (F(1164) = .272,p. = .603).

The independent variables were centered to their means to al-low interpretation of the linear effects as average (main) effects(Aiken & West, 1991). Table 2 reports results of the regressionanalysis. The significant main effects of ACQ, IUS, and NBWCD-MQ indicate that AC, intolerance of uncertainty, and negative be-liefs on worry are, for the average participant, good predictors ofworry. The significant interaction effect between ACQ and IUSand between IUS and NBWCD-MQ demonstrates that the effectof IUS on worry depends on the intensity of both ACQ andNBWCD-MQ. In particular, simple slope analysis (Aiken & West,1991) showed that, keeping NBWCD-MQ constant at its mean,the effect of IUS on worries (cf. Fig. 1) is particularly strong for high

levels of ACQ, although patients with high levels of ACQ tend tohave an average lower level of worries (as testified by its main ef-fect). As the ACQ level decreases, the strength of the effect of IUS onworry decreases as well. As regards the moderating effect ofNBWCD-MQ (Fig. 2), a very similar pattern was found. In this case,however, the effect of IUS was stronger for low levels of NBWCD-MQ.

Figures 1 and 2 show how the relation between intolerance ofuncertainty and worry changes at different levels of anxiety con-trol and negative beliefs on worry respectively. Both figures clearlyillustrate how at a low level of perceived control (AC) and at a highlevel of meta-worry (negative beliefs on worry) even a low level ofintolerance of uncertainty is correlated to a level of worry higherthan the average level of worry (represented by the 0.00 z-scoreline).

Figure 3 graphically represents the supported model in whichthe predictive effect of intolerance of uncertainty is moderatedby anxiety control and negative beliefs on worry. In addition, anx-iety control and negative beliefs on worry are also direct predictorsof worry.

4. Discussion

4.1. Summing up the results

The results support the relevance of intolerance of uncertaintyand negative beliefs on worry for understanding cognitive mecha-nisms underlying worry and GAD and suggest that anxiety controlis an equally relevant factor and should not be overlooked in cog-nitive models of this disorder. In addition, results support an origi-nal model of interaction in which negative beliefs on worry andanxiety control interact with and strengthen the effect of intoler-ance of uncertainty on worry.

4.2. The role of intolerance of uncertainty

From a clinical viewpoint, these results could be interpretedassuming that intolerance of uncertainty is the initial belief of acognitive process, while negative beliefs on worry and anxiety con-trol represent a secondary meta-appraisal process that increasesthe effect of intolerance of uncertainty on worry. Of course, thehypothesis that intolerance of uncertainty is the trigger of the pro-cess is grounded only on clinical reasoning. Interaction regressionanalysis can only tell that two variables interact with each other

G.M. Ruggiero et al. / Personality and Individual Differences 53 (2012) 224–230 229

but is not able to determine which variable moderates the effect ofthe other. Actually, only a longitudinal study could definitely con-firm or reject this hypothesis. Another possible strategy could en-tail an experimental design that permits one to directly manageand manipulate the psychological variables.

Intolerance of uncertainty is defined as uncertainty of externalevents and unpredictability of the world that generates anxietyand worry (Freeston et al., 1994). Given that this construct relatesto cognitive evaluations of external events and situations preced-ing the rise of anxiety, from a psychological viewpoint intoleranceof uncertainty seems to us the best candidate for the place of initialtrigger of the cognitive process.

4.3. The role of anxiety control and negative beliefs on worry

According to cognitive theory, anxiety control and negative be-liefs on worry may exacerbate (and hence moderate) the degree ofanxiety related to intolerance of uncertainty. In fact, negative be-liefs on worry is by definition an appraisal which implies the eval-uation of a pre-existing worry. Anxiety control also features asignificant portion of appraisal over a pre-existing state of anxiety,given that this variable includes not only the perception of beingable to control external events but also of being able to controland master emotional reactions to these events.

It is noteworthy that there is no significant interaction betweenanxiety control and negative beliefs on worry. This is further con-firmed by the non-significant result of the three-way interactionmodel. Therefore, while both the variables seem to contribute tothe process underlying anxiety, it seems that they do not influenceeach other during the process.

A further analysis implemented using only the subscale of anx-iety control focused on control over internal reactions in place ofthe whole scale provided a non significant result. This result sug-gests that the corresponding cognitive beliefs (i.e., the appraisalof worry as negative and dangerous, and the perception of havinginsufficient control over external events and internal reactions) donot influence each other in terms of capacity of generating GADand worry.

4.4. Toward a more comprehensive model?

In conclusion, this paper suggests that three models of GAD (i.e.,the intolerance of uncertainty model, the metacognitive model andthe anxiety control model) may be better combined into a morecomprehensive model. In the integrated model, worry and GADare related to intolerance of uncertainty, while negative beliefsabout worry and low perceived control over either external eventsand internal states further increase the degree of worry directly,and indirectly via an exacerbation of worry associated with intol-erance of uncertainty.

4.5. Clinical implications

From a clinical viewpoint, this work stresses the importance oftargeting anxiety control and negative beliefs on worry in the cog-nitive treatment of GAD. Particularly important, in our opinion, isthe finding that emphasizes the significant role of anxiety controlin the process, given that in our opinion scientific and clinical liter-ature has overlooked this factor in the recent past. Anxiety controlseems to play a psychopathological role not reducible to intoler-ance of uncertainty or negative beliefs on worry and thereforethe treatment of anxiety control should be an added tool in the bat-tery of available interventions for cognitive therapists.

The therapist should develop a strategy that challenges the be-lief that the degree of control exerted by the patient is insufficient.This belief depends on a dichotomous conceptualisation, according

to which the only real and acceptable control is absolute controlover events and internal reactions. The therapist should aim toencourage the patient to judge also a partial degree of control asbeing sufficient and to think that he or she is able to achieve andtolerate a smaller and realistically achievable degree of control.

4.6. Limitations

The major limitations of the study regard the use of interactionsin general. In fact, interactions are notoriously difficult to replicate.Additional replications are needed.

The second limitation regards recruitment. Clinical and the con-trol samples were combined across two different sites, Italian andAustralian and participants from each country were screened usingdifferent structured interviews. On the other hand, the two instru-ments were both well known and validated instruments aimed toassess the same variable.

Another possible limitation is the psychology specialization of asubset of the Italian control group. We attempted to balance itusing another subset of controls enrolled in a population of peopleworking in a blacksmith firm. Anyway, the significant differencebetween average scores of GAD group and control group supportsthe reliable use of the non pathological groups as a control sample.

Another limitation concerns demographic differences betweenthe Italian and Australian samples that admittedly imply educa-tional differences between the two samples.

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