characteristic factor structures of the japanese version of the state-trait anxiety inventory:...

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Characteristic FactorStructures of the JapaneseVersion of the State-TraitAnxiety Inventory: Coexistenceof Positive-Negative and State-Trait Factor StructuresTakashi Suzuki , Kohji Tsukamoto & Kazuhiko AbePublished online: 10 Jun 2010.

To cite this article: Takashi Suzuki , Kohji Tsukamoto & Kazuhiko Abe (2000)Characteristic Factor Structures of the Japanese Version of the State-Trait AnxietyInventory: Coexistence of Positive-Negative and State-Trait Factor Structures, Journalof Personality Assessment, 74:3, 447-458, DOI: 10.1207/S15327752JPA7403_8

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Characteristic Factor Structures of theJapanese Version of the State–TraitAnxiety Inventory: Coexistence ofPositive–Negative and State–Trait

Factor Structures

Takashi Suzuki, Kohji Tsukamoto, and Kazuhiko AbeDepartment of Psychiatry

University of Occupational and Environmental Health

Previous factor studies of the State–Trait Anxiety Inventory (STAI; Spielberger,Gorsuch, & Lushene, 1970) have reported certain typical factors that are state–trait(S–T) 2-factor solutions and positively–negatively (P–N) worded item 2-factor solu-tions in addition to 4-factor solutions (positively and negatively worded state factors,positively and negatively worded trait factors). We explored the possibility that thesefactor structures are included in a factor space. Responses to the Japanese version ofthe STAI in a sample of 848 male workers were factor analyzed. The first-order fac-tors obtained from principal-component analysis were almost equal to the previous 4factors, except for a minor factor, and their second-order factors were the P–N factors.However, the S–T factors were also obtained from the same first-order factors by theoblique Procrustes rotation. Moreover, coexistence of these two 2-factor structureswas determined in the same factor space by the orthogonal Procrustes rotation.

The State–Trait Anxiety Inventory (STAI) was developed by Spielberger et al.(1970) to provide an operational measure of state anxiety and trait anxiety (Vagg,Spielberger, & O’Hearn, 1980); later, the original STAI Form X (STAI–X) was re-vised to Form Y (STAI–Y; Spielberger, Vagg, Barker, Donham, & Westberry,1980). Each scale consists of 20 items, approximately half of which are positivelyworded (e.g., “I feel regretful” about anxiety that is present) and the other half nega-tively worded (e.g., “I feel pleasant” about anxiety that is absent). In this article, weterm these itemspositiveandnegative.Previous factor-analytic studies in which all40 STAI items were factored together have reported typical four-factor solutionscomposedofapairofstate factors that includedapositive itemandacomplementary

JOURNAL OF PERSONALITY ASSESSMENT,74(3), 447–458Copyright © 2000, Lawrence Erlbaum Associates, Inc.

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negative item(stateanxiety-presentandanxiety-absent factors)andasimilarpair oftrait factors (trait anxiety-present and anxiety-absent factors; Bernstein & Eveland,1982; Shek, 1991; Spielberger et al., 1980; Vagg et al., 1980). Some studies have re-ported a fairly clear-cut distinction between state–trait (S–T) anxiety factors ob-tained from a rotation of two principal components (Oei, Evans, & Crook, 1990;Vagg et al., 1980). Gaudry and Poole (1975) administered the STAI for Children(Spielberger, Edwards, Lushene, Monturi, & Platzek, 1972) and defined the S–Tsecond-order factors, although the Trait scale did not contain reversed items. Otherfactor solutions have included some of the previously mentioned factor types(Barker, Barker, & Wadsworth, 1977; Brown & Duren, 1988; Gaudry, Vagg, &Spielberger, 1975; Kendall, Finch, Auerbach, Hooke, & Mikulka, 1976; Spiel-berger et al., 1980). However, a solution with positive and negative (P–N) factors,the former of which have salient loadings of all positive (anxiety-present) items andthe latter of all negative (anxiety-absent) items, is found in Kabacoff, Segal, Hersen,and Van Hasselt’s (1997) report. Such a solution is relatively rare, although factorsolutions that includeeitherapositiveoranegative factorareoccasionallyobserved(Barker et al., 1977; Schotte, Maes, Cluydts, & Cosyns, 1996; Spielberger et al.,1980). Spielberger et al. (1980), however, mentioned that the anxiety-present andanxiety-absent factors have been identified in several studies.

On the basis of the information reported earlier, it seems that previously re-ported factors vary between the S–T factor structure and the P–N factor structure.In the first section of this article, we examine both an S–T factor structure and aP–N factor structure in a whole-factor space, obtaining one by second-order factoranalysis and the other by the Procrustes factor rotation. In the next section, we ex-amine a 4-factor solution consisting of the S–T and P–N factors.

EXPERIMENT 1: S–T FACTORS AND P–NFACTORS IN A FACTOR SPACE

Method

Participants and materials. The participants were 881 industrial workers(848 men and 33 women) who completed the Japanese version of the STAI–X,which was translated and developed by members of the Department of Psychologyat the University of Kwansei Gakuin. The reliability and validity of the scale werereported to be sufficient (Koga, 1980). The arrangement of anxiety-present andanxiety-absent items is identical to the original version. There are 7 trait anxi-ety-absent items, 13 trait anxiety-present items, 10 state anxiety-absent items, and10 state anxiety-present items in the Japanese version of the STAI–X. The re-sponses were anonymously collected. We excluded the 33 female participants fromthe data analysis and analyzed only the 848 male participants (M age = 41.3 years,SD= 7.9), to obtain clearer results.

448 SUZUKI, TSUKAMOTO, ABE

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Procedure. We conducted the principal-components analysis and the pro-max rotation (Hendrickson & White, 1964) to obtain higher order factors. Sec-ond-order factors were rotated according to the Hendrickson and White (1966)method, in which projections (O = AF, orthogonal factor matrixO) of the first-or-der factors (primary factor pattern matrixA) on the second-order factors (orthogo-nal matrixF) were rotated by the promax method. As Hendrickson and White(1966) pointed out, this solution provides an adequately simple structure becausethe rotation is carried out on a larger number of variables.

We also carried out an oblique Procrustes rotation to determine whether thefactor space consisting of the primarily obtained factors also included anotherhypothetical factor structure. The Procrustes method provides procedures fortransforming an initial factor matrix to be as similar as possible to a given targetmatrix in the least-squares sense. In this study, the target matrix for the Procrus-tes rotation consisted of two hypothetical vectors and three arbitrary dummyvectors, which were added to equalize column numbers. The obtained referencestructure factors (Harman, 1967) corresponding to the hypothetical vectors weresubjected to consideration because each factor loading vector in a refer-ence-structure system was independently computed (Browne, 1967) and not af-fected by the other vectors (viz. arbitrary vectors). However, reference-patternfactors and primary factors cannot be determined because they are not independ-ent of the arbitrary target vectors. Therefore, these rotational results only suggestthe possibility of the hypothetical factors.

For computation, we used the SAS system (SAS Institute, 1988) for the promaxrotation and the oblique Procrustes rotation.

Results

In the first-order solution, the first seven eigenvalues over 1.0 were 9.9, 4.4, 3.1,1.6, 1.4, 1.1, and 1.1, but the scree test indicated three factors. The three-factor solu-tion was composed of a negative factor and positive state and trait factors. How-ever, because we assumed a four-factor simple structure as a typical factor solution,four components needed to be retained. A four-factor rotation satisfied this simplefactor structure: the positive state, negative state, positive trait, and negative traitfactors. However, loadings of Item 19 (“I feel joyful”) and Item 20 (“I feel pleas-ant”) of the negative state items were low on the negative state but salient rather onthe negative trait factor. A rotation of five factors separated these two items into afactor, whereas the same four-factor structure was preserved and clearer. The screetest also revealed a small drop between Eigenvalues 5 and 6. We used this five-fac-tor solution for the following analyses.

In the second-order analysis, the first two components had eigenvalues greaterthan 1. The rotated factors showed a clear simple structure except for a few items

CHARACTERISTIC FACTOR STRUCTURES OF THE STAI 449

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(see Table 1), one of which had salient loadings of the positive state and trait itemsand the other of which had salient loadings of the negative items in the state andtrait items (P–N factors). It also is possible that the same P–N factors were ob-tained from the first two principal components by the varimax rotation.

We carried out the oblique Procrustes rotation to ascertain the existence of theS–T factor structure in addition to the P–N factor structure in the same factorspace. The first five principal components were rotated to a target matrix that wascomposed of two 1,0-pattern vectors representing the S–T factors and threedummy vectors. The state vector consisted of 1 weights on the negative state items,–1 weights on the positive state items, and 0 weights on the trait items. The traitvector was composed in the same way. After rotation, the corresponding factors inthe reference structure were well fitted to the intended target vectors (Table 1).These S–T factors were also well replicated using the first three principal compo-nents, but the first two components failed to replicate the S–T factors.

Discussion

In the first-order factor analysis, we assumed the four-factor solution to be reason-able, but the scree test indicated three factors. In Experiment 2, we refer to this dis-crepancy in relation to coexistent dimensions of the S–T and P–N factor patterns.


TABLE 1P–N and S–T Factors Obtained From the Five Principal Components of STAI–X

P–N Second-Order Factorsa S–T Factorsb

Variable Negative Factor Positive Factor State Factor Trait Factor

State anxiety symptom: Negatively worded items (anxiety absent)ST1 .51 –.27 .40 .10ST2 .51 –.31 .43 .08ST5 .43 –.19 .47 –.12ST8 .54 –.18 .48 –.07ST10 .67 –.11 .46 .03ST11 .68 –.07 .40 .13ST15 .52 –.22 .52 –.08ST16 .75 –.03 .40 .12ST19 .81 .42 .33 .02ST20 .82 .42 .28 .08

State anxiety symptom: Positively worded items (anxiety present)ST3 –.03 .43 –.53 .15ST4 –.06 .47 –.45 –.03ST6 –.05 .46 –.53 .08ST7 –.12 .47 –.58 .05

(Continued)

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Using the Chinese version of the STAI (C–STAI), Shek (1991) demonstrateda model with S–T second-order factors and five first-order factors that showed agoodness of fit of .883 with LISREL confirmatory factor analysis (Jöreskog &Sörbom, 1986). Shek labeled the five anxiety factorsTrait Anxiety Present, TraitAnxiety Absent, State Anxiety Present, Calmness,andHappiness.A combinationof the last two factors were equivalent to the State Anxiety Absent factor. TheHappiness factor, which consisted of Items 16, 19, and 20, was similar to ourfifth factor. However, on the basis of parsimonious grounds, Shek suggested that


TABLE 1 (Continued)

P–N Second-Order Factorsa S–T Factorsb

Variable Negative Factor Positive Factor State Factor Trait Factor

ST9 –.17 .49 –.61 .04ST12 .10 .66 –.41 –.03ST13 –.04 .66 –.48 –.09ST14 .07 .55 –.39 .00ST17 .08 .63 –.30 –.14ST18 .18 .59 –.26 –.05

Trait anxiety symptom: Negatively worded items (anxiety absent)TR21 .65 .06 .12 .35TR26 .64 –.13 .11 .43TR27 .47 –.14 –.00 .42TR30 .66 .00 .03 .45TR33 .64 –.11 .13 .41TR36 .63 –.05 .01 .50TR39 .35 –.18 –.01 .36

Trait anxiety symptom: Positively worded items (anxiety present)TR22 –.21 .18 .04 –.40TR23 –.09 .50 –.04 –.44TR24 .02 .43 .02 –.38TR25 –.10 .40 .03 –.44TR28 .00 .48 .11 –.52TR29 –.10 .51 .03 –.52TR31 –.16 .43 –.08 –.42TR32 –.17 .43 .02 –.52TR34 –.19 .18 .04 –.38TR35 –.24 .45 –.01 –.54TR37 –.17 .44 –.01 –.52TR38 –.14 .43 .01 –.49TR40 –.08 .51 –.06 –.44

Note. P–N = positive–negative; S–T = state–trait; STAI–X = State–Trait Anxiety Inventory FormX; ST1–ST20 and TR21–TR40 = state and trait anxiety items and original numbers. The original itemswere grouped according to positive and negative wording.

aFrom five principal components: primary pattern (promax rotation).bIn five-principal-componentspace: reference structure (Proctrustes rotation).

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the C–STAI comprised four dimensions: the State Anxiety Present and Absentfactors and the Trait Anxiety Present and Absent factors. We also do not believethat the fifth factor spoils this four-factor structure as the basic structure of theSTAI.

Vagg et al. (1980) reported State and Trait Anxiety-Present factors and Stateand Trait Anxiety-Absent factors that are almost the same as our first-order fac-tors, and in a rotation of the first two principal components, they recognizedclearly defined State and Trait Anxiety factors. This alternative solution suggeststhe possibility of four oblique first-order factors and two second-order factors. Wealso found this hierarchical property in this study, although we obtained differentsecond-order factors.

We obtained the P–N factors as second-order factors. Such dimensions may becharacteristics of the STAI–X; however, Kabacoff et al. (1997) obtained the P–Nfactors from the STAI–Y (Dutch STAI; van der Ploeg, Defares, & Spielberger,1980). Mook, Kleijn, and van der Ploeg (1991) obtained the P–N factors on pooleditems of the Trait scale of the STAI–Y and the Zung Depression Scale (DutchTrait-adapted version; Mook, Kleijn, & van der Ploeg, 1989; Mook, van der Ploeg,& Kleijn, 1990), which also consists of positively and negatively worded items.

Recent studies (e.g., Watson & Tellegen, 1985) concerning self-rated moodscales have indicated that positive and negative affect are basic dimensions of moodthatemergeas the first twovarimax factorsor the first twosecond-order factors.Oursecond-order factors may be interpreted as positive and negative affectivity (Mook,Kleijn, & van der Ploeg, 1991).

Generally, second-order factors are determined according to dominant relationsbetween first-order factors. Whether the positive state factor correlates more withthe positive trait factor or the negative state factor determines the formation of ei-ther the state factor or the positive factor. The same formation process also likelyyields either the trait or negative factor. However, both S–T and P–N factor struc-tures have been recognized in this study and in previous factor studies, suggestingthat dominant correlations among the four first-order factors—the positive state,negative state, positive trait, and negative trait factors—fluctuate case by case sothat second-order factors must alternate between the S–T and P–N structures.When the first two components are rotated to a simple structure, the same factorformation process seems to act on the item correlation matrix because the four sim-ply structured first-order factors represent coherent item groups and correlatepairwise. Then, either an S–T or P–N factor structure is selected.

In this study, the structures of both the S–T and the P–N factors were found inthe first five principal-components’ space or at least in the first three components.This suggests that the STAI includes not just one but both structures, so thedimensionality of the STAI may not consist of a simple structure. It seems that theS–T dimensions and the P–N dimensions coexist in the same factor space and com-petitively influencesimplystructured factorsolutionsof theSTAI. InExperiment2,we ascertained whether such two-factor structures coexist in this factor space.


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EXPERIMENT 2: S–T AND P–NFOUR-FACTOR SOLUTION

Method

We carried out the generalized solution of the orthogonal Procrustes problem bySchönemann (1966) to obtain the intended factors. An ideal pattern of the hypothet-ical factor structure was represented by a target matrix constructed with 1, –1, or 0weights. In a target vector to the state factor, the vector elements corresponding tothe negatively worded state (N–S) items were assigned a weight of 1, and elementscorresponding to the positively worded state (P–S) items were assigned a weight of–1, whereas vector elements corresponding to the positively and negatively wordedtrait items (P–T and N–T) were assigned a weight of 0. A target vector to the traitfactor was constructed in the same manner, with weights to the P–T and N–T itemsalso oppositely assigned, and the state items were assigned a weight of 0.

In a target vector to the negatively worded factor, weights to the N–S and N–Titems were assigned as 1, and those to the P–S and P–T items were assigned as 0.Similarly, in the positively worded vector, the P–S and P–T item weights were as-signed as 1, and the N–S and N–T item weights were assigned as 0.

All the items in the target matrix had a two-factor complexity, and the target ma-trix had 40 rows and four columns, but it was not full-column rank. This does notmean, however, that the ideal factor structure is necessarily a four-column matrixwith rank3.Nevertheless,a factormatrixof rank3composedof the first threeprinci-pal components and a 0 vector can be rotated to the intended four-factor structure bythe Schönemann (1966) method (Suzuki, Tsukamoto, Abe, & Iwata, 1996). How-ever, because such a reduced-rank factor structure is not concordant with the usualfactor analysis model, the first four principal components were rotated. When rankof the target matrix is also reduced, the orthogonal Procrustes solution is not unique(Schönemann, 1966). To prevent rank reduction of the target matrix, we appropri-ately used two kinds of weights of 1 and .9 (see Table 2). A reason for the orthogonalrotation was that this target matrix induced an unfeasibly highly correlated factorstructure by oblique Procrustes rotation.

A preliminary analysis revealed that loadings of two N–S items, Items 19 and 20(State Anxiety Absent items), were low on the state factor; then we added a fifth tar-get vector, which assigned weights of 1 to these items and weights of 0 to the otheritems, to theoriginal fourvectors.Theirweightsonthestate factor targetvectorwerechanged to 0, and those on the negative factor target vector remained the same.

Results

We rotated the five principal components to the target matrix. The Procrustes rota-tion yielded almost satisfactory results. Table 2 shows factor loadings and the 1 or.9 weight assignments in the target matrix. In the first factor, intended as a state fac-


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TABLE 2Orthogonal Procrustes Rotation of STAI–X

Factor

Variable State Trait Negative Positive ST#19/20

State anxiety symptom: Negatively worded items (anxiety absent)ST1 .43a .18b .57a –.11b –.18b

ST2 .46a .16b .56a –.14b –.18b

ST5 .38a –.07b .58a –.13b –.26b

ST8 .40a –.01b .67a –.12b –.23b

ST10 .34a .09b .71a –.13b –.08b

ST11 .21a .15b .65a –.21b .08b

ST15 .45a –.01b .64a –.13b –.21b

ST16 .27a .17b .73a –.10b .04b

ST19 –.02b .04b .57a .00b .62a

ST20 –.07b .09b .58a –.01b .59a

State anxiety symptom: Positively worded items (anxiety present)ST3 –.46a .13b –.01b .39a –.07b

ST4 –.40a –.04b .05b .44a –.19b

ST6 –.48a .05b .06b .42a –.22b

ST7 –.46a .04b .01b .50a –.26b

ST9 –.49a .02b –.06b .52a –.23b

ST12 –.62a –.11b .15b .30a .04b

ST13 –.66a –.17b .06b .35a –.06b

ST14 –.59a –.09b .16b .23a –.06b

ST17 –.53a –.21b .15b .29a .03b

ST18 –.52a –.13b .21b .21a .10b

Trait anxiety symptom: Negatively worded items (anxiety absent)TR21 .13b .42a .42c .04b .35b

TR26 .31b .54a .48c .09b .11b

TR27 .21b .51a .36c .09b .01b

TR30 .25b .57a .43c .20b .26b

TR33 .30b .52a .47c .06b .16b

TR36 .22b .61a .41c .13b .24b

TR39 .24b .45a .29c .11b –.08b

Trait anxiety symptom: Positively worded items (anxiety present)TR22 .05b –.36a –.07b .26a –.16b

TR23 –.15b –.41a –.04b .38a .09b

TR24 –.13b –.37a .02b .27a .11b

TR25 .08b –.34a –.10b .48a .14b

TR28 .02b –.46a .04b .42a .13b

TR29 .05b –.43a –.11b .55a .20b

TR31 .10b –.29a –.16b .65a .08b

TR32 .04b –.44a –.12b .49a .06b

TR34 .23b –.26a –.16b .46a –.01b

(Continued)

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tor, 16 of the 20 state items loaded over .3 on this factor, but 2 trait items that wereassigned weights of 0 also loaded at .3. In the next factor, intended as a trait factor,18 loadings in the 20 trait items were over .3, and there were no salient loadings ofstate items. The third factor, which was intended to be a negative factor, was in al-most complete agreement with the assignment, and the next, positive factor had 18loadings over .3 in the 23 positive items and no excessive salient loadings of thenegative items. The fifth factor had 2 salient loadings according to the 2 intendeditems but one .3 loading to a 0-weight item.

The Cattell’s salient variable similarity indexes (Levine, 1977) between the tar-get weights and the rotated factor loadings were .74, .82, .74, and .81 for the state,trait, negative, and positive factors, respectively, at .1 salient hyperplane criterionand were .91, .95, .97, and .88 at .3 criterion.

Discussion

The obtained orthogonal Procrustes solution revealed good concordance with thehypothetical target matrix, which represented the S–T and P–N factors. As men-tioned in Experiment 1, these data also yielded the simply structured four-factorpattern of P–S, N–S, P–T, and N–T. With regard to this solution, we would like toexplain that a correlation matrix expected to correspond to these hypothetical fac-tors has the possibility of yielding such a four-factor structure.

The relation between the correlation matrix and the target matrix is analogi-cally represented by the factor model equationR* = FF´ whereR* is a correla-tion matrix with communalities on the diagonal,F is an orthogonal factorloading matrix, andF´ is its transposed factor matrix. For simplification, it is as-


TABLE 2 (Continued)

Factor

Variable State Trait Negative Positive ST#19/20

TR35 –.07b –.51a –.16b .41a .03b

TR37 .14b –.38a –.14b .65a .07b

TR38 .10b –.38a –.12b .57a .09b

TR40 –.01b –.35a –.06b .60a .10b

Cattell’s salient variable similarity indexHyperplane criterion at .1 .74 .82 .74 .82 .17Hyperplane criterion at .3 .91 .95 .97 .88 .80

Note. STAI–X = State–Trait Anxiety Inventory Form X; ST#19/20 = the hypothetical factor withtwo salient loadings of the ST19 and ST20 items; ST1–ST20 and TR21–TR40 = state and trait anxietyitems and original item numbers. The original items were grouped according to positive and negativewording. State, trait, positive, negative four-factor structure, and ST#19/20 factor are hypothesized.

a1 or –1 assignments in the target matrix.b0 assignments in the target matrix.c.9 assignments in thetarget matrix.

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sumed that the STAI items are rearranged into P–S, N–S, P–T, and N–T itemgroups, as in Tables 1 and 2. When a pattern matrix of correlation is recon-structed by a product of the target matrix and its transposed matrix, four diago-nal blocks corresponding to the previous groups contain more coherent variables(Figure 1). Therefore, if such a correlation matrix is factor analyzed, the simplystructured four factors probably appear. Also, if two components are rotated bythe varimax method, the state and trait factors or the positive and negative fac-tors may be obtained according to dominant relations among the P–S, N–S, P–T,and N–T item groups. Second-order factors are also determined by dominantcorrelations among the first-order factors. However, if oblique factors are hy-pothesized, these relations will be more complex.

Although we have hypothesized the four-factor target matrix as an ideal pat-tern, the variables scatter within a three-dimensional space for its reduced rank.According to our hypothetical factor structure, the variables must be included in analmost three-dimensional space. It may be relevant to the present dimensionalitythat the scree test suggests three factors.

GENERAL DISCUSSION

In Experiment 1, in addition to the five-component solution, we separately showedthe existence of the P–N factor structure and the S–T factor structure in the first twoand three principal components, respectively. In Experiment 2, although weshowed the coexistence of these two 2-factor structures, the hypothetical factor pat-tern conforming to the ideal factor model approximated to a four-column matrix ofrank 3. Both facts indicate a possibility that these two 2-factor structures were in-cluded in the three-factor space; however, the reduced-rank factor pattern is not


FIGURE 1 Correlation matrix expected from hypothetical factor matrix with orthogonality byfactor modelR = FF´. The factor matrix of positive, negative, state, and trait factors on the left re-constructs the correlation matrix including positive state, positive trait, negative state, and negativetrait factors on the right. + and – = positive and negative factor loadings or correlations, respec-tively; Fsand Ft = state and trait factors, respectively; Fp and Fn = positively and negatively wordeditem factors, respectively; S(+) and T(+) = positively worded items on the State and Trait scales,respectively; S(–) and T(–) = negatively worded items on the State and Trait scales, respectively.

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congruent with the factor analysis model. Furthermore, it was difficult to obtain areasonable oblique factor pattern coexisting of the P–N and S–T factors by theoblique Procrustes rotation. Therefore, it remains to be determined whether thesetwo 2-factor structures must be obtained alternately or simultaneously. Neverthe-less, in either experiment, this double-factor structure may represent a property ofthe STAI or, more generally, the balancing of self-report inventories including pos-itively and negatively worded items.

We showed that in a male sample the Japanese version of the STAI included thethree types of factor structures: the positive and negative state and trait four-factorstructure, the S–T two-factor structure, and the P–N two-factor structure. Al-though the four-factor solution is not a primary concern, further studies of balanc-ing self-report scales, including the STAI–Y, are needed to test the existence ofthese two 2-factor structures in a factor space, and the dimensionality of the STAI(40 items) should be more elucidated.

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Takashi Suzuki11-12 Yamatedai-Nishi 2-chomeTakarazuka-shiHyogo 665–0886Japan

Received November 15, 1998Revised October 25, 1999


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characteristic factor structures of the japanese version of the state-trait anxiety inventory:...

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