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tures appear far from robust. In the case of the LSAS, some

authors have identified four factors (Safren et al., 1999;

Slavkin, Holt, Heimberg, Jaccard, & Liebowitz, 1990),

whereas others have found a 5-factor solution (Baker,

Heinrichs, Kim, & Hofmann, 2002).An additional problemis that not only the number, but the general content of the

factors differ across studies. Similar inconsistent findings

in factor solutions have been reported for other social anx-

iety/phobia measures, such as the SPIN (Antony, Coons,

McCabe, Ashbaugh, & Swinson, 2006; Connor et al., 2000;

Johnson, Inderbitzen-Nolan, & Anderson, 2006; Radom-

sky et al., 2006), the SPAI (Olivares, Garcia-Lopez, Hidal-

go, Turner, & Beidel, 1999; Osman, Barrios, Aukes, & Os-

man, 1995; Turner, Stanley, Beidel, & Bond, 1989), and the

SAD and FNE (Hofmann, DiBartolo, Holaway, & Heim-

berg, 2004; Olivares, Garca-Lpez, Hidalgo, 2004;

Turner, McCanna, & Beidel, 1987).

In addition to the aforementioned methodological prob-

lems with the nonobjective method of social anxiety scale

development is the fact that all of the above measures were

created exclusively for English speakers, primarily from

North America and Australia. The use of these instruments

with Spanish-speaking samples usually involves a some-

what simplistic direct translation of thequestionnaires from

English to Spanish (e.g., Olivares et al., 1999, 2004). Un-

fortunately, this procedure ignores cultural differences in

the expression of social anxiety and social norms (Hein-

richs et al., 2006). This is rather ironic when one considers

that social interaction stylesand norms are probably among

the most important defining features of a culture and areoften precisely the locus of differences across cultures.

Thus, it remains to be seen whether a questionnaire that

describes a variety of social situations is applicable across

cultures. To address the cultural and methodological limi-

tations of the existing literature, we conducted an extensive

series of studies in order to develop a new social anxiety

questionnaire, without directly relying on items from exist-

ing self-report instruments. In contrast to existing mea-

sures, we developed the instrument based on items gener-

ated by large and very diverse Spanish and Portuguese

speaking samples.

Study 1: Development of theInitial Scale

Method

Initial Item Selection

For 3 months per year over a period of 6 years, volunteer

students from the Department of Psychology at the Univer-

sity of Granada (Spain), along with their volunteer family

members, partners, and friends, were asked to keep a diary

of social situations that elicited some degree of anxiety,nervousness, uneasiness, fear, or stress. Several examples

were given to students, who in turn had to explain the task

to their significant others, who also kept such a diary. Dif-

ferent students took part each year and the situations only

had to be recorded if they directly affected the participants.

It should be noted that the University of Granada teachesstudents from all over Spain. Furthermore, the 3 months of

data collection included periods during the regular academ-

ic year as well as holidays (Christmas). Accordingly, a va-

riety of different situations from people varying greatly in

age, schooling, and geographical origin were generated by

these diaries.

More than 1,000 participants recorded situations over 6

years, generating a pool of more than 10,000 social situa-

tions. From these, two pairs of social anxiety experts se-

lected scenarios for initial analysis, excluding those situa-

tions that were redundant or were not social in nature (i.e.,

another person[s] played a role in the situation). This left

2,171 scenarios, which were then grouped together based

on substantive similarity, leaving a total of 512 social situ-

ations.

Scale Construction

The experts then paraphrased the 512 social situations into

items. Four additional situations that typically produce

great distress were also selected (stressful life events, such

as suffering an armed attack) and added to control re-

sponse biases. These 516 items formed theSocial Anxiety

Questionnaire for Adults (SAQ-A)(Cuestionario de An-siedad Social para Adultos; CASO-A), the initial version

of a new self-report instrument intended to assess social

anxiety. The items were randomly ranked and each item

could be answered on a seven-point Likert scale to indicate

the level of uneasiness, stress or nervousness in response

to each situation: 0 =not at all, 1 =very slight, 2 =slight,

3 = moderate, 4 =high, 5 =very high, and 6 =extremely

high. Instructions given to those completing the scale were

as follows:

There follows a series of social situations that may cause you

unease, stress or nervousness to a lesser or greater extent.

Please place an X on the number that best reflects your reac-

tion. If you have never experienced the situation described,

please imagine what your level of unease, stress, or nervous-

ness might be if you were in that situation, placing an X on

the corresponding number.

Several blank lines were included at the end of the answer

sheet for participants filling out the questionnaire to add

more social situations if they wanted to do so.

Participating Countries and Researchers

A large number of potential collaborators were contacted

via e-mail and asked to assist in conducting the study. Atotal of 106 research collaborators from 10 Latin American

96 V.E. Caballo et al.: Measuring Social Anxiety in 11 Countries

European Journal of Psychological Assessment2010; Vol. 26(2):95107 2010 Hogrefe & Huber Publishers


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countries and Spain agreed to participate in data collection.

Most worked at academic institutions, and some worked in

private clinical service centers. The distribution by country

of researchers (and research groups) was as follows: Ar-

gentina = 16 collaborators (6 groups of researchers); Brazil

= 7 collaborators (5 groups of researchers); Chile = 7 (3

groups of researchers); Colombia = 16 (8 groups of re-

searchers); Costa Rica = 1 (1 group of researchers); Spain

= 10 (8 groups of researchers); Mexico = 35 (22 groups of

researchers); Paraguay = 3 (1 group of researchers); Peru

= 8 (8 groups of researchers); Uruguay = 2 (1 group of

researchers); and Venezuela = 1 (1 group of researchers).

Procedure

The SAQ-A was sent to each collaborator with a request to

suggest changes in the wording of the items to be more

consistent with the specific language style of their culture.

The questionnaires were also completed by several stu-

dents in each country to evaluate whether the wording of

the items was correct. In order to derive the Portuguese

version, the SAQ-A wastranslated andbacktranslated from

Portuguese to Spanish until agreement was reached be-tween translators.Data wascollectedover thecourseof one

year and five months. Collaborators used a prepared data-

base in Excel to enter the data.

Participating Subjects

An initial pool of 13,397 participants completed the SAQ-

A (mean age = 25.43; SD = 10.13) (see Table 1 for the

distribution of participating subjects by country). Approx-

imately half (7,271) were women (mean age = 25.15;SD

= 10.05), and 6,126 were men (mean age = 25.76;SD =

10.22). The minimum age for subjects was 16 years. Withregard to age distribution, 5,420 (40.4%) subjects were

younger than 20 years old, 3,029 (22.7%) were between the

ages of 20 and 24, 1674 (12.49) were between 25 and 30,

2225 (16.61) were between 31 and 50, and 1,049 (7.83%)

were 51 years or older. The participants had different levels

of education (students, workers, etc.). Specifically, 17.6%

were university psychology students, 40.6% were univer-

sity students from other majors, 14% were workers with a

university degree, 13.1% were workers with no university

degree, 9.3% were high school students, and 3.7% could

not be included in any of the former categories (e.g., retired

or unemployed). No data were obtained for the remaining

1.7% of participants.Missing data were expected, given the size of the partic-

Table 1.Distribution of subjects by country in Study 1 (SAQ-A) and Study 2 (SAQ-AR)

Participant subjects by country in the first study with the SAQ-A Participant subjects in the second study with the SAQ-AR

Women Men All subjects Women Men All subjects

Country N Mean age(SD)

N Mean age(SD)

N Mean age(SD)

N Mean age(SD)

N Mean age(SD)

N Meanage(SD)

Argentina ,499 30.25

(10.89)

,378 29.82

(11.42)

,877 30.05

(11.11)

,329 23.38

(5.42)

,348 24.77

(8.53)

,677 24.09

(1.56)

Brazil ,702 26.07

(9.48)

,547 27.55

(10.79)

1,249 26.76

(10.12)

,405 31.04

(13.06)

,358 30.12

(11.39)

,763 30.61

(12.30)

Chile ,376 26.90

(10.86)

,308 27.91

(11.52)

, 684 27.36

(11.16)

,310 26.76

(11.65)

,297 26.53

(10.83)

,607 26.64

(11.25)

Colombia ,852 24,70

(9.60)

,774 25,47

(9.81)

1,626 25.21

(9.78)

,870 26,11

(11.98)

,857 27,80

(13.00)

1,727 26.96

(12.53)

Costa Rica ,205 23.23

(9.42)

,122 18.87

(5.82)

,327 21.58

(8.51)

,363 25.87

(9.10)

,186 25.35

(9.68)

,549 25.69

(9.29)

Spain ,905 22.80

(8.80)

,668 27.01

(12.00)

1,573 24.58

(10.48)

1,335 23.24

(8.66)

,907 26.21

(11.41)

2,242 24.44

(9.97)Mexico 2,377 25.14

(10.34)

1,954 25.29

(9.68)

4,331 25.22

(10.05)

1,258 25.25

(12.16)

1,128 25.55

(9.93)

2,386 25.39

(11.16)

Paraguay ,91 24.62

(8.03)

,77 21.91

(6.82)

,168 23.27

(7.57)

,100 22.48

(5.83)

,100 21.85

(5.85)

,200 22.16

(5.83)

Peru 1,002 23.08

(8.37)

,978 23.25

(8.00)

1,980 23.16

(8.18)

,529 21.27

(6.33)

,497 21.71

(6.84)

1,026 21.49

(6.58)

Uruguay ,101 32.39

(12.27)

,100 33.43

(10.93)

,201 32.92

(11.60)

,135 31.30

(12.78)

,114 34.29

(13.11)

,249 32.67

(12.99)

Venezuela ,195 27.53

(11.91)

,186 25.56

(9.73)

,381 26.52

(10.88)

,301 19.77

(4.12)

,299 20.53

(4.57)

600 20.15

(4.36)

All countries 7,271 25.15

(10.05)

6,126 25.75

(10.22)

13,397 25.43

(10.13)

5,935 24.79

(10.51)

5,091 25.81

(10.74)

11,026 25.65

(10.63)

V.E. Caballo et al.: Measuring Social Anxiety in 11 Countries 97

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ipant pool, but did not appear to affect validity of statistical

analyses. To confirm that there was no systematic data loss

pattern we testeddata with SPSS MVA (missingvalue anal-

ysis). None of the variables exceeded 5% of missing data,

so it was not necessary to uset-test to verify if there was asystematic relationship for missingness between the differ-

ent pairs of variables, nor was there a need to implement

multiple imputation to substitute missing data. We opted

for a listwise deletion of cases with missing data. Of the

13,397 subjects in the original sample, a total of 12,144

participants were retained for the different factor analyses.

Results

Factor Analysis of the Initial Version of theQuestionnaire (SAQ-A = 516 Items)

In order to reduce the number of items, we performed an

exploratory principal components factor analysis with vari-

max rotation, which optimizes complex structures by cap-

turing a small number of large loadings and a large number

of small loadings for each factor. Examination of the scree

plot suggested a 6-factor solution.Thehierarchicalanalysis

of oblique factors gave the same 6-factor solution (Statsoft,

2006). We then performed an oblique principal component

cluster analysis in order to group the items into nonover-

lapping clusters, so each cluster could be interpreted as uni-

dimensional. This procedure allowed us to substitute a

group of variables with a smaller one (n-m) with the min-

imum loss of information in order to maximize the ex-plained variance by thecomponents of thecluster. This pro-

cedure is iterative, at each step suppressing those variables

that have the highest ratio values. The smaller these values

are, the greater the evidence that the variable has a strong

relationship with the rest of the components of the cluster

and a weak relationship with the components of the other

clusters. The 512 variables were considered in the analysis

(forcing a solution of 6 clusters). The four control items

were not included in the analysis, but they did allow us to

estimate how many subjects might be filling the question-

naire at random because they were answerable in only one

direction of increasing distress. Given the large sample size

relative to the extremely small number of participants

flagged by the control items, no action was taken. After

successive analyses suppressing variables with the highest

(1 R2own)/(1 R2next)

1 ratio values, a solution of 12 items

per cluster was reached. The final distribution of the items

by cluster that were used in the subsequent analyses (ex-

ploratory and confirmatory factor analyses) is the same as

that found in Table 2.

Table 2.Item loadings for every factor and correlations item-total score for the SAQ-A

Factor loadings

Items and name of each factor F1 F2 F3 F4 F5 F6 Itemtotal

F1. Awkward Behavior in Embarrassing Situations

304. Making a mistake in front of other people .54 .02 .06 .05 .06 .23 .648

306. Wanting to start a conversation and not knowing how .54 .05 .13 .11 .03 .01 .659

307. Realizing that I am boring the person that I am talking to .68 . 05 .02 .17 .02 .08 .629

386. Not knowing how to continue a conversation after a topic has been exhausted .52 .00 .25 .07 .02 .03 .634

387. Speaking and it appearing like nobody is listening to me .79 .05 .04 .16 .02 .09 .592

388. Proposing an idea to a group of friends and not being taken seriously .71 .05 .05 .14 .03 .08 .600

389. Being alone at a party where I do not know anyone .58 .11 .08 .12 .11 .07 .654

417. Wanting to end a conversation, but not knowing how .52 .08 .14 .04 .10 .01 .665

420. Being at a friends house and not having anyone talking to me .69 .06 .08 .10 .01 .01 .609

456. Being told off or scolded by a superior or a person in authority .60 . 08 .20 .01 .18 .15 .621

470. Talking to a stranger who keeps prying into my personal life .66 . 12 .15 .04 .06 .02 .557

487. Being in the home of strangers and not knowing what to say or do .47 .09 .07 .09 .05 .04 .617

F2. Interactions with the Opposite Sex

230. Being phoned by a person I am very attracted to .29 .65 .4 .20 .12 .07 .570

247. Feeling watched by people of the opposite sex .10 .48 .13 .09 .02 .08 .658

289. Expressing to a person of the opposite sex that I love them .04 .74 .07 .04 .00 .03 .549



1 In the formula,R2ownrepresents the determination coefficient of each variable with its own cluster, and R2nextthe determination coefficient

of each variable with the nearest cluster. Naturally, we would want each component of the cluster to be strongly related with its own cluster(R2own 1) and less related with the nearest cluster (R

2next 0).


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Factor loadings

Items and name of each factor F1 F2 F3 F4 F5 F6 Item

total

316. Approaching someone I am attracted to but have never met .26 .45 .05 .02 .03 .03 .656

342. Maintaining a conversation with a person of the opposite sex whom I find attractive .01 .73 .09 .02 .01 .02 .640

343. Being openly stared at by someone .25 .50 .02 .03 .05 .05 .601

362. Asking someone attractive of the opposite sex for a date .20 .67 .06 .03 0.05 .02 .642

397. Being told by someone of the opposite sex that they like me .11 .72 . 01 .02 .01 .02 .636

421. Asking someone I find attractive to dance .19 .52 . 11 .03 .03 .03 .616

447. Being alone with someone I like very much .13 .74 .05 .01 .06 .03 .643

452. Being asked out by a person I am attracted to .02 .71 . 16 .06 .06 .02 .642

453. Talking about my personal feelings with someone of the opposite sex .05 .61 .17 .06 .05 .04 .611

F3. Interactions with Strangers

270. My friends bringing along people I do not know .06 .03 .56 .07 .09 .05 .569

275. Greeting each person at a social meeting when I dont know most of them .26 .05 .41 .07 .07 .11 .641

283. Attending a social event where I know only one person .18 .01 .43 .10 .03 .11 .630

332. Talking on the phone with someone I do not know very well .03 .03 .68 .07 .05 .04 .554

333. Greeting someone I do not know very well .03 .02 .76 .07 .00 .00 .563

418. Making new friends .04 .13 .58 .04 .00 .09 .542

441. Talking to a stranger .07 .09 .70 .02 .05 .02 .594

443. Being introduced to new people .07 .09 .78 .00 .02 .00 .567

449. Being asked to dance at a party .02 .33 .37 .06 .10 .00 .545

467. Maintaining a conversation with someone Ive just met .11 .20 .54 .03 0.04 .09 .667

501. Looking into the eyes of someone I have just met while we are talking .07 .22 .44 .01 .07 .03 .523

504. Asking a stranger a question .12 .04 .67 .00 .15 .01 .470

F4. Criticism and Embarrassment

14. Going to a party on my own when I dont know anyone .05 .14 .08 .56 .19 .08 .479

18. Asking for a favor from a stranger .04 .02 .09 .55 .00 .04 .456

20. Being told that I am doing something wrong .12 .08 .21 .50 .04 .15 .458

39. Sitting at a table with strangers at a wedding .00 .05 .20 .57 .10 .07 .521

44. Being criticized .05 .08 .12 .48 .11 .08 .455

52. Greeting someone and being ignored .12 .05 .17 .61 .02 0.10 .470

54. Expressing my opinion and not being understood .09 .05 .03 .51 .18 .07 .446

70. Being teased in public .07 .11 .04 .47 .05 .15 .488

73. Talking to someone who does not look at me .21 .08 .06 .55 .02 .17 .369

128. Asking for a favor that is denied .19 .02 .05 .48 .20 .00 .545

147. Entering or leaving in the middle of a social event .08 .02 .13 .40 .09 .12 .551

197. Asking a question in public and not getting an answer .31 .08 .08 .40 .13 .15 .582

F5. Assertive Expression of Annoyance, Disgust or Displeasure

160. While on a bus, asking someone not to step on me or push me .05 .01 .04 .20 .56 .11 .542

201. Asking someone to stop kicking the back of my chair .13 .02 .01 .19 .63 .5 .511

217. Expressing my annoyance to someone that is picking on me .10 .04 .10 .14 .64 .13 .524

222. Asking someone who is speaking loudly at the movies to lower their voice .08 .03 .03 .14 .63 .09 .549

260. Asking someone for an explanation .07 .14 .19 .02 .46 .05 .578

263. Contradicting my parents opinion .15 .01 .15 .06 .54 .15 .464

264. Arguing with my parents because I do not want to do a chore .26 .06 .09 .02 .52 .16 .472

285. Having to ask a neighbor to stop making noise .27 .03 .06 .01 .53 .01 .597




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Exploratory Factor Analysis

Inorder to testwhether the72 items of the abbreviated instru-

ment map onto the 6-factor structure of theoriginal scale, weconducted an exploratory factor analysis. Given the ordinal

nature of thedata, we first computed a polychoric correlation

matrixfrom thedirectscoresof the72 items. We then verified

that the items complied with the following conditions: (1)

there were no items with extreme distributions (skewness

from .36 to .41 with standard error of .023, kurtosis from

1.07 to .33 with SE= .05); (2) all the items within each

cluster separately had high corrected item-total correlations

(homogeneity index)(from .459 to .726); (3)all theproposed

factors had more than four items; (4) the sample was big

enough to thwart possible fluctuations of correlations; (5)

most of the elements of the anti-image correlation matrix

tendedtozero; and(6)theKMO(Kaiser-Meyer-Olkin)indexexceeded the recommended cut-off of .50 (.98 in the current

sample). Given that the data met these conditions, we pro-

ceeded to apply the ordinal analysis through the unweighted

least squares (ULS) method and promax rotation.

Results by Bartletts test with 2556df= 352275.768(p< .000) showed that the variables were positively corre-

lated, and that the data were adequate for an exploratory

factor analysis. Furthermore, the KMO index of .984

showed a high proportion of common variance explained

by factors. Both indices support the adequacy of factorial

analysis of data.

Matrixsampling adequacy (MSA) indices (ranging from

.951 to .994) confirm that the measure of sampling adequa-cy of the variables in all cases fits the structure of the rest

of the variables (in fact, they are above the value of .500

which is usually used as a threshold to discard a variable

from analysis). Finally, 60% of communalities were above

.50 (ranging from .35 to .70).In order to decide the optimal number of factors, a parallel

analysis (Velicer, Eaton, & Fava, 2000; Watkins, 2000) was

implemented using theMonte Carlo procedure with 200 rep-

lications to determine the number of eigenvalueswith values

above those that could be obtained from the same number of

subjects and variables (i.e., generating a group of random

valueswith normaldistribution,calculating thematrixofcor-

relations and subjecting it to principal components analysis

to calculate the mean eigenvalues). Results show that the 6-

factor solution is thebest fit to our data, given that the size of

randomly generated eigenvalues after factor 6 is higher than

the observed eigenvalues.

This exploratory factor analysis identified 6 factors witheigenvalues higher than 1.00 explaining 50.24% of the cu-

mulative variance. Item loadings are presented in Table 2.

The first factor (eigenvalue = 25.49) explained 35.42% of

the variance. The 12 items loading highly on this factor

describeAwkward Behaviors in Embarrassing Situations.

The second factor showed an eigenvalue of 3.22 and ex-

plained 4.47% of the total variance. The 12 high loading

items describe situations ofInteraction with the Opposite

Sex. Factor 3 showed an eigenvalue of 2.32 and explained

3.23% of the variance. The items of this factor refer to sit-

uations ofInteraction with Strangers. Factor 4, with an ei-

genvalue of 1.98, explained 2.76% of the variance. The

items refer to situations ofCriticism and Embarrassment.Factor 5, with an eigenvalue of 1.67, explained 2.33% of

Factor loadings

Items and name of each factor F1 F2 F3 F4 F5 F6 Item

total

299. Telling a taxi driver that he/she has taken an abnormally long route .17 .02 .06 .06 .55 .00 .548

411. Telling a family member that they are bothering me .32 .06 .05 .09 .46 .02 .596

482. Telling someone that their behavior bothers me and asking them to stop .13 .04 .06 .07 .56 .01 .549

513. Telling a colleague they have done something that bothers me .14 .05 .07 .08 .55 .01 .554

F6. Speaking/Performing in Public/ Talking with People in Authority

23. Being asked a question in class by the teacher or by a superior in a meeting .11 .02 .00 .26 .10 .65 .503

167. Talking to a famous person or celebrity .12 .16 .03 .13 .16 .45 .578

194. Having to speak in class, at work, or in a meeting .11 .07 .08 .12 .01 .77 .578

195. Being interviewed .06 .02 .04 .15 .09 .62 .576

208. Being summoned to speak to my superiors or a person in authority .09 .16 .03 .16 .21 .42 .603

249. Participating in a meeting with people in authority .11 .14 .10 .06 .11 .44 .647

269. Performing in public .29 .04 .03 .06 .08 .60 .577

327. Speaking in public .27 .02 .08 .10 .10 .68 .624

376. Asking questions in class, at a public event or in a crowded meeting .25 .03 .17 .10 .02 .57 .651

401. Starting and maintaining a conversation with people in authority .19 .14 .17 .15 .13 .39 .680

465. Taking the initiative in front of a group of strangers .46 .03 .12 .07 .01 .32 .681

476. Making a presentation to people who know more than I do .45 .13 .09 .13 .03 .39 .636

Note.Factor loadings of items grouped under each specific factor are marked in bold.




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the variance and is related to Assertive Expression of An-

noyance, Disgust or Displeasure.Factor 6, with an eigen-

value of 1.46, explained 2.03% of variance andwasdefined

asSpeaking/Performing in Public/Talking with People in

Authority. Interfactor correlations were moderate (range =.33 to .60) (see Table 6).

Confirmatory Factor Analysis

The results obtained through the exploratory factor analysis

were then subjected to confirmatory factor analysis from the

corresponding covariance and asymptotic variance-covari-

ance matrices of items. Given the type of initialdata (ordinal

variables and distributions that did not present multivariate

normality)therobust maximumlikelihood(RML)estimation

method was used. The models that we tested included: (1)

single factor, (2) 6 factors, and (3) 6 first-order factors and

one second-order factor. The reasons for including these

modelswere that some studies have found a singlehigher-or-

der factor explaining social anxiety (e.g., Mattick & Clarke,

1998; Osman et al., 1996) even with Spanish samples (Oli-

vares et al., 2004) while others have found from three to 6

factors (e.g.,Bakeretal., 2002;Connoret al., 2000;Davidson

et al., 1997; Safren et al., 1999). Given that a 6-factor struc-

ture was found in our analyses, the 1-factor, 6-factor, and

combined models were tested. Following the recommenda-

tions made by Bentler (1995), a comparison of robust and

nonrobust estimation factors suggested that neither the kur-

tosis nor the skewness of distributions affected the results.Multivariate kurtosis tests offered the following results: Sri-

vastavas test: b2p = 3.9672; N(b2p) = 106.583; p= .000.

Mardias test: b2p = 787.3477; N(b2p)= 254.7749;p= .000.

When the analyses were applied to the transformed scores,

theresults didnotdiffer significantly in the three models. The

statistical programs SAS v.9.1.3 (The SAS Institute, 2006),

PRELIS, v. 2.3 and LISREL, v. 8.8 (Scientific Software In-

ternational, 2006a, 2006b) were used to perform the various

analyses.

Given that the number of items (72) was very high for

conducting a confirmatory factor analysis, we decided to

use the parceling procedure (Bandalos, 2002; Coffman &

McCallum, 2005; Nasser-Abu Alhija & Wisenbaker, 2006;Sass & Smith, 2006). Each parcel was formed by the sum

of three items selected at random from every factor. Thus,

a total of 24 parcels were defined as indicators of the 6

latent variables. Before forming the parcels, the unidimen-

sionality of each factor was verified. Furthermore, the re-

liability estimates (Cronbach ) for every group of itemsof the hypothesized 6 factors were good, F1 = .92, F2 = .92,

F3 = .91, F4 = .86, F5 = .88, and F6 = .91.

The hypotheses tested can be summarized for the three

modelsas follows: (1) observed responses canbe explained

by 1, 6, or 6 first-order factors and 1 second-order factor;

(2) each of the indicators has a loading that is statistically

different from 0 (i.e.,tvalues higher than 2.58) in the hy-pothesized factor and zero loadings in the remaining fac-

tors, and (3) measurement errors associated with the indi-

cators are not correlated with each other. The results of the

contrast comparisons of the three models are summarized

in Table 3.

As can be seen in Table 3, Models 2 (6 correlated fac-tors) and 3 (6 first-order factors and one second-order fac-

tor) showed a good overall fit, suggesting that the restric-

tions we specified for the models were correct. However,

the fit of Model 2 was slightly better: the RMSEA index

was .063 in Model 2 and .066 in Model 3; indices SRMR

(.036 vs. .043), GFI (.91 vs. .89), NNFI and RFI (.99 vs.

.98) were also better for Model 2. Other indices comparing

the fit of Models 2 and 3, such as composite reliability and

average variance extracted (AVE) indicated a similar fit for

both models, although again slightly better for Model 2

than Model 3 (see Table 4). The average interitem correla-

Table 3.Fit indices of the three tested models

Model 1 Model 2 Model 3

#Absolute fit S-B 51629.98 12746.49 14706.52

p= .000 p= .000 p= .000

DF 252 237 246

GFI .70 .91 .89

SRMR .064 .036 .043

Relative fit NFI .95 .99 .99

NNFI .95 .99 .98

RFI .94 .99 .98

Noncentrality

based fit

CFI .95 .99 .99

RMSEA .12 .063 .066

RMSEA 90% (.12;.12) (.062; .064) (.065;.067)

PCLOSE .000 .000 .000

Note:RMSEA (root mean square error of approximation): Values less

or equal to .05 indicate close approximate fit; values between .05 and

.08 suggest reasonable error of approximation, and values higher or

equal to .10 suggest poor fit. SRMR (standardized root mean squareresidual): values less than .10 are generally considered favorable; the

smaller the SRMR, the better the model fit. GFI (goodness of fit in-

dex), CFI (comparative fit index), NNFI (nonnormed fit index, Tuck-

er-Lewis index), and RFI (relative fit index): values higher than .90indicate good fit. NFI (normed fit Index): values higher than .95 in-

dicate good fit (see Kline, 2005, for a review of all these indices).

Table 4.Composite reliability and average variance ex-

tracted of the three models

Model 1 Model 2 Model 3

Compos-

ite reli-

ability

AVE Composite

reliability

AVE Compos-

ite reli-

ability

AVE

Factor 1 .963 .522 .903 .699 .903 .699

Factor 2 .912 .721 .913 .724

Factor 3 .886 .660 .886 .660

Factor 4 .839 .567 .840 .568

Factor 5 .869 .624 .868 .622

Factor 6 .883 .654 .883 .654Note.AVE = Average variance extracted.




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tion was 0.486 for Factor 1, 0.487 for Factor 2, 0.436 for

Factor 3, 0.337 for Factor 4, 0.382 for Factor 5, and 0.442

for Factor 6. The total average interitem correlation was

0.337. Interfactor correlations were from moderate to rela-

tively high (range = .64 to .84) (see Table 6).In order to determine discriminant validity, the average

variance extracted (AVE) was compared with the coeffi-

cient of determination (R2) for each couple of latent vari-

ables. All the comparisons (10) carried out showed an AVE

greater thanR2. This can be considered as a clear evidence

of discriminant validity since each latent construct must

explain the measures composing it rather than other con-

structs measures.

Composite reliability of each of the latent variables

(construct reliability) was calculated through the formula:

where are the loadings and is the indicator of errorvariances. As Table 4 shows, the composite reliability for

latent variables in Model 2 was very similar to that of Mod-

el 3. These results were derived by calculating the average

variance extracted using the following formula:

In Models 2 and 3, the 6 factors showed an AVE greater

than 0.50, so we can therefore conclude that a high amount

of the indicator variance in both models is captured by theconstruct.

Study 2: Development of the FinalScale

Based on the analysis with the initial scale, we further ex-

amined the psychometric properties of the 72-item scale.

For this purpose, we constructed the Social Anxiety Ques-

tionnaire for Adults Revised (SAQ-AR) (Cuestionario de

Ansiedad Social para Adultos Revisado; CASO-AR),

which included the derived 72 randomly distributed items

on a 7-point (17) Likert rating scale. Administration in-

structions were the same as in the former version. The Pear-son correlation of the SAQ-A (516 items) with the SAQ-

AR (72 items) wasr= .98.

Participating Countries and Researchers

Thesame countries from Study 1 participated in this second

study. However, the number of participating researchers

and subjects differed slightly: The total group of research-

ers in this second study consisted of 103 collaborators from

the same 11 countries. The numbers of researchers (and

groups of research) per country were as follows: Argentina= 13 collaborators (3 groups of research); Brazil = 13 col-

laborators (5 groups of research); Chile = 6 (3 groups of

research); Colombia = 14 (8 groups of research); Costa Ri-

ca = 3 (2 group of research); Spain = 14 (8 groups of re-

search); Mexico = 24 (12 groups of research); Paraguay =

3 (1 group of research); Peru = 5 (5 groups of research);

Uruguay = 3 (1 group of research); and Venezuela = 5 (3

groups of research).

Procedure

The procedure was similar to the first study. Collaborators

from each country revised each item of the SAQ-AR to fit

the everyday language of their country and culture. There

was no option to add new items. No significant changes

were made to the 72 items composing the CASO-AR. Data

collection took place over a period of 1 year.

In order to calculate additional psychometric properties

of this new questionnaire, such as consistency, validity, and

reliability, we selected someself-report instruments usually

employed to assess social phobia/anxiety, such as the SPAI

Table 5.Correlations (Pearson) among the SAQ-AR and its 6 factors with other self-report measures of social anxietyQuestionnaires for assessing social phobia/anxiety

SAQ-AR and its factors SPAI

96 items

SPAI

Sp Ag

LSAS

Anxiety

LSAS

Avoidance

SPIN

F1. Awkward behavior in social embarrassing situations .64 .59 .59 .43 .59

F2. Interactions with the opposite sex .62 .58 .58 .45 .58

F3. Interactions with strangers .75 .75 .62 .44 .64

F4. Criticism and embarrassment .69 .64 .62 .51 .60

F5. Assertive expression of annoyance, disgust or displeasure .49 .44 .50 .39 .48

F6. Speaking/performing in public/ Talking with people in authority .62 .55 .55 .44 .56

Sum of factors score (SAQ-AR) .78 .74 .72 .56 .69

Note: All correlations significant atp< .0001. SPAI = Social Phobia and Anxiety Inventory; LSAS = Liebowitz Social Anxiety Scale; SPIN =

Social Phobia Inventory. SPAI 96 items = Sum of the score on the 96 items of the Social Phobia Subscale without averaging the items with foursubitems; SPAI SP-Ag = Typical scoring procedure of the questionnaire, Social Phobia subscale score Agoraphobia subscale score.




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(Turner, Beidel et al., 1989), the LSAS (Liebowitz, 1987),

and the SPIN (Connor et al., 2000).

Participating Subjects

A total of 11,026 subjects participated in the second study.

The mean age of the total sample was 25.65 years (SD=

10.63) and consisted of 5,935 women (mean age = 24.79;

SD= 10.51) and 5,091 men (mean age = 25.81; SD =

10.74). The minimum age for subjects was 16 years, but

there was no upper age limit. Table 1 shows the sex, age,

and number of subjects in the participating countries. The

participants had different levels of education (students,

workers, etc.). Specifically, 22% were psychology stu-

dents, 39.5% were university students with other majors,14.7% were workers with a university degree, 6.9% were

workers with no university degree, 4.9% were school stu-

dents, and 6.4% could not be included in any of the former

categories. No data were obtained for the remaining 5.6%

of participants. Missing data were addressed using listwise

deletion, as in the first study, so that the final number of

subjects for factor analysis was 10,118.

Instruments

As noted above, three self-report measures of social phobia

were used, together with the SAQ-AR, to obtainconcurrentvalidity ratings. The measures were:

a)Social Phobia and Anxiety Inventory (SPAI; Turner,Beidel et al., 1989), a 45-item self-report instrument de-

signed to measure social phobia. Each item is rated for

frequency on a 7-point scale ranging from 0 (never) to 6

(always). The inventory consists of 2 subscales: social

phobia (32 items) and agoraphobia (13 items). However,

18 items of the social phobia subscale have 4 subitems

each, 2 items have 5 subitems each, and 1 item has 3

subitems.

b)The Liebowitz Social Anxiety Scale (LSAS; Liebowitz,1987) is a 24-item self-report instrument that assesses

fear and avoidance of specific social situations. Respon-

dents are asked to rate fear on a 4-point scale ranging

from 0 (none) to 3 (severe) and avoidance on a 4-point

scale ranging from 0 (never) to 3 (usually).

c)The Social Phobia Inventory (SPIN;Connor et al., 2000)

is a 17-item questionnaire that assesses symptoms ofsocialphobia. Each item contains a symptom that is rated

by the respondent based on how much he or she was

bothered by the symptom during the prior week on a

5-point scale ranging from 0 (not at all) to 4 (extremely).

Results

Confirmatory Factor Analysis

The univariate and multivariate normality of indicators

were analyzed using the program PRELIS 2.3 (Scientific

Software International, 2006). As the data did not meet thecondition of multivariate normality (Skewness-z= 79.114,

p= .000; Kurtosis-z= 98.164,p= .000), confirmatory fac-

tor analysis was implemented on variance-covariance and

asymptotic covariance matrices through the robust maxi-

mum likelihood estimation method (RML). The same par-

celing procedure used in Study 1 was implemented in this

Study 2.

Goodness of fit was verified through different absolute,

relative, and noncentrality indices, such as GFI, SRMR,

NFI, NNFI, RFI, CFI, and RMSEA. Acceptable fit was de-

fined by the following criteria: GFI > .90; SRMR < .08;

NFI > .95; NNFI > .95; RFI > .95; CFI > .95; and RMSEA

( < .06 90% CI < .06). Multiple fit indices were used be-cause they provide us with varied information about model

fit, and, when used together, they provide us with a more

conservative and reliable evaluation of the solution.

The analysis of the SAQ-AR indicated that two models

should be tested: (1) Model 2, with 6 correlated factors, and

(2) Model 3, with 6 first-order factors and 1 second-order

factor. Consistent with theprevious analysesof theSAQ-A,

the 6-factor model (GFI = .94; SRMR = .038; NFI = .99;

NNFI = .99; RFI = .99; CFI = .99; RMSEA = .052) pre-

sented a better fit overall than the hierarchical model (GFI

= .88; SRMR = .072; NFI = .98; NNFI = .98; RFI = .98;

CFI = .98; RMSEA = .072).

All freely estimated unstandardized parameters (rangefrom .64 to .88) were statistically significant (pvalues

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