Assessing Computer Anxiety Validation

Assessing computer anxiety with the interactionmodel of anxiety: development and validation of

the computer anxiety trait subscale

Jean-Philippe Gaudron*,a, Emmanuelle Vignolib

aUFR de Psychologie, University of Toulouse le Mirail, 5 Allee Machado, 31058 Toulouse Cedex, FrancebUniversity of Provence (Aix-Marseille I), France

Abstract

This study was conducted to develop and validate a new computer anxiety scale. The scaleis based on an interaction model of anxiety that emphasizes the influential role of specific

situations e.g. computer interaction situations. The data supported the reliability and thevalidity of the scale. During a computer interaction, greater computer anxiety was associatedwith greater state anxiety. Interest of using the scale with the Endler Multidimensional Anxi-

ety scales are discussed. # 2002 Elsevier Science Ltd. All rights reserved.

Over the past three decades, a considerable body of literature has built up con-cerning person–machine rapport. Since the description of the effects of the negativeattitudes about computers on learning and skills (Loyd & Gressard, 1984; Massoud,1991), most of researches have been focused on a new form of anxiety called com-puter anxiety. The terms of computer anxiety (Raub, 1981), computerphobia (Jay,1981; Rosen, Sears, & Weil, 1987) or computer aversion (Meier, 1985) have beenproposed and used to describe the negative reactions of individuals who experiencebad feelings and agitations in the presence of, interacting with, or thinking aboutcomputers. One of the most best-known definitions of computer anxiety has beenproposed by Maurer (1983), who defined it as the fear and the apprehension felt byan individual when considering the implications of utilizing computer technology, orwhen actually using computer technology. A significant amount of research has beendone by authors who have created computer anxiety scales. Some research literature(Cambre & Cook, 1985; Lalomia & Sidowsky, 1993) and validity studies (Dukes,Discenza, & Couger, 1989; Harrison &Rainer, 1992;Meier & Lambert, 1991;Woodrow,1991) have been produced. They showed the high reliability estimate reported

Computers in Human Behavior 18 (2002) 315–325

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by authors and high correlations between these scales, supporting the validity of theconstruct. They have also pointed out the existence of numerous correlates (Maurer,1994). Computer anxiety has been associated with a lack of computer experience(Cohen & Waugh, 1989; Morrow, Prell, & Elroy, 1986), gender (Lankford, Bell, &Elias, 1994; Pope-Davis & Vispoel,1993), age (Dyck & Smither, 1994; Rosen et al.,1987), personnel characteristics and other anxieties (Kernan & Howard, 1990;Marcoulides, 1988). But, taken together, these reviews or studies indicated someconcerns that are noteworthy. One concern is that most of the scales have no explicitpsychological or psycho-sociological theory basis. With a few exceptions, none ofthe computer anxiety scales appear to be based on existing anxiety theory (Lalomia& Sidowsky, 1993). As Meier has suggested (1985), one consequence is that whilethese scales may be helpful in evaluating such global outcomes as increased societalacceptance of computers, they offer little specific direction for future research orintervention and can make interpretation of results problematic. Another concern isthe difficulty to clarify the relationship between computer anxiety and other anxi-eties such as math anxiety, test anxiety or trait anxiety. If many studies supportedthe claim that computer anxiety and other anxieties are not identical constructs, theyhave all verified the assumption that these anxieties are related constructs. And thiscan also make the interpretation of results difficult. The use of an instrument whichwould integrate several anxieties could overcome that serious problem. A modelexists yet: the interaction model of anxiety.The interaction model of personality (Endler, 1983; Endler & Magnusson, 1976)

proposes that behavior is a function of the interaction of persons and situations. Italso suggests that the assessment of an individual’s predisposition (such as traitanxiety) must be made with specific reference to the situational context. Endler(1983) and Endler and Edward (1985) have argued in favor of a distinction betweenstate anxiety and trait anxiety and have clarified their relationship in an interactionmodel of anxiety that emphasizes the influential role if specific situations. This modelproposes that trait anxiety is multidimensional and that trait anxiety and congruentsituation factors interact to determine appraisal of situational threat, resulting instate anxiety (Endler, Parker, Bagby, & Cox, 1991). According to this model, indi-viduals differ in anxiety proneness with respect to certain types of situations. Theidentification of the threatening situations is based on empirical works. Four stress-ful contexts have been proposed: social evaluation, physical danger, ambiguous, anddaily routine. The first three were found on the basis of factor-analyses of variety ofpotentially anxiety-provoking situations (Endler, Hunt, & Rosenstein, 1962). Thelast one was added as a baseline measure of the likelihood of being anxious in adaily routine situation (Endler & Osaka, 1975).The interaction model of anxiety proposes an explanation for differential changes

in state anxiety (Endler, Edwards, & Vitelli, 1991). There is a significantly greaterincrease in state anxiety level of individuals who have a high level of a specificdimension of trait anxiety when such individuals encounter a congruent situationalthreat. When trait anxiety and situational stress are not congruent, the interactionwith respect to state anxiety does not occur. This is called the differential hypothesis,as the model can predict differential increases in state anxiety. Numerous studies

316 J.-P. Gaudron, E. Vignoli / Computers in Human Behavior 18 (2002) 315–325

have been conducted in a variety of settings such as examination situations (Endler& Magnusson, 1977), competition situations (Flood & Endler, 1980), public speak-ing situations (Muller, Endler, & Parker, 1990), dental treatment (Ackerman &Endler, 1985), or rope-climbing tasks (Endler et al., 1991). They have providedempirical support for this differential hypothesis.The purpose of the present research was to develop and validate a measure of

computer anxiety based on the interaction model of anxiety. According to themodel, it was predicted that almost everyone’s state anxiety increases somewhat as afunction of threat, students who have high levels of computer trait anxiety will showa greater increase in state anxiety than those who have low levels of that particulardimension only when the dimension of trait anxiety (computer) and type of situa-tional stress (computerized interacting) are congruent.

1. Method

1.1. Subjects

Participants at this study were 151 volunteer undergraduate students attendingpsychology courses at Rouen University (France). There were 136 women and 15men. The mean age was 19.38 years.

1.2. Instruments

1.2.1. State anxiety scale (EMAS-S)The EMAS-S (Endler et al., 1991) is a self report measure of state anxiety. The

scale consists of 20 items, 10 that measure the autonomic-emotional component ofstate anxiety as ‘‘Breathing is irregular’’ or ‘‘Hands feel unsteady’’ and 10 thatmeasure the cognitive-worry component of state anxiety as ‘‘Distrust myself’’ or‘‘Feel uncertain’’. Each item is rated on a five-point Likert scale raging from 1 (notat all) to 5 (very much). Alpha reliability coefficient is 0.85 for the first component,0.89 for the second component, and 0.92 for total scale.

1.2.2. Trait anxiety scales (EMAS-T)The EMAS-T (Endler et al., 1991) are self-report measures of the predisposition to

experience anxiety in four different threatening situational contexts: social evalua-tion, physical danger, ambiguous, and daily routines. Fifteen response items such as‘‘Look forward to these situations’’ or ‘‘Perspire’’ or ‘‘Feel self-confident’’ followeach of four situation descriptions: (1) for social evaluations (EMAS-T1): ‘‘You arein situations where you are being evaluated by other people’’; (2) for physical danger(EMAS-T2): ‘‘You are in situations where you are about to or may encounter phy-sical danger’’; (3) for ambiguous (EMAS-T3): ‘‘You are in new or strange situa-tions’’; (4) for daily routine (EMAS-T4): ‘‘You are involved in your daily routine’’.The items are rated on a five-point Likert scale from 1 (not at all) to 5 (very much).Alpha reliability coefficient for each scale were 0.92, 0.93, 0.93, and 0.92, respectively.

J.-P. Gaudron, E. Vignoli / Computers in Human Behavior 18 (2002) 315–325 317

1.2.3. Computer anxiety trait scale (CATS)The CATS is built on the model of the EMAS-T described above, and presented

as a measure of another situational dimension of trait anxiety: the predisposition toexperience anxiety in a context of computer interactions (Appendix). This scale withthe same 15 response items was included in the booklet of the EMAS-T as a fifthsituation described as follow: ‘‘You are about or you interact with a computer.’’

1.2.4. Computer anxiety rating scale (CARS)The CARS (Heinssen, Glass, & Knight, 1986) is a 19-item five-point Likert scale

developed to assess the relative level of computer anxiety in individuals. Thescale has a test–retest reliability coefficient of 0.70 (4-week interval), and a internalconsistency alpha coefficient of 0.87. This scale has been used in numerous studies.

1.2.5. Computer experience questionnaire (CEQ)The CEQ (Gaudron, 1998) consisted of two subscales with six statements each.

Respondents have to identify twice which of the six statements most closely sum-marized the time they have spend with computers (first scale) and their level ofexperience and expertise (second scale).

1.2.6. The testsTwo tests were used in this study. The first was the pencil and paper D.48 Dom-

inoes Test, a french adaptation of the famous Anstey’s Dominoes Test. The secondwas the computerized version of the Standard Progressive Matrices (Raven, 1948),another well-known intelligent test.

1.3. Procedure

Subjects willingly completed first the CARS, the CEQ, the EMAS-S, the fourEMAS-T and the CATS at the end of a regularly scheduled class. Volunteers werethen asked to take two tests with a 1-month interval. The first test was administeredwith the traditional paper and pencil booklet. The administration of the second testwas with computers. Students had no prior knowledge of the second testing conditionto prevent computer anxious subjects from avoiding the experimentation. At the endof each test, students completed the state anxiety scale EMAS-S. At the close of thesecond session, the students were debriefed and results were individually commentedto those who wanted. One hundred and sixteen students completed the two tests.

2. Results

2.1. Computer trait scale characteristics

The computer anxiety trait scale (CATS) was found to have a mean of 34.17(n=151, S.D.=13.74). The CATS demonstrated high internal consistency (Cron-bach alpha=0.94). Means, standard deviations, and item–total correlations of the


CATS are presented in Table 1. All items correlated significantly with the correctedtotal score, ranging from 0.52 to 0.81.

2.2. Validity analyses

Pearson correlations were conducted to examine the relationship between thecomputer anxiety trait scale (CATS) and the other measures of computer anxiety andexperience. As expected, the CATS highly correlated with the Computer anxietyrating scale : r=0.76; P< 0.0005. The correlation between the CATS and the com-puter experience questionnaire was also significant (r=�0.65; P< 0.0005) indicatingthat higher computer anxiety was associated with less computer experience.A principal component factor analysis with oblimin rotation was calculated as

factors are supposed to be correlated. All factors with eigenvalues greater than onewere retained and interpreted. These three factors presented in Table 2 explained77.2% of the variance in the CATS scores. CATS items with loadings of 0.30 orgreater are listed, with eigenvalues, and percentages of variance explained by eachfactor.The loading patterns can be interpreted as follows. The first factor is feelings

(positive feelings and unpleasant feelings) about computers. The second factor dealswith the attractiveness of computers. The third factor is activation of the autonomicnervous system.In Endler’s interaction model of anxiety hypothesis there is a significantly greater

increase in the state anxiety level of individuals who have a high level of a specificdimension of trait anxiety when such individuals encounter a congruent situationalthreat. When trait anxiety and situational stress are not congruent, the interactionwith respect to state anxiety does not occur. Intercorrelations between the three

Table 1

Means, standard deviations, and item–total correlations of the CATS

CATS Item M S.D. Item-total r

1 2.83 1.43 0.66

2 2.00 1.16 0.71

3 1.40 0.79 0.59

4 2.55 1.29 0.81

5 2.05 1.22 0.74

6 2.89 1.45 0.64

7 1.32 0.75 0.52

8 2.77 1.36 0.70

9 2.06 1.18 0.79

10 2.78 1.44 0.72

11 1.56 0.99 0.66

12 2.56 1.38 0.71

13 2.08 1.24 0.73

14 3.23 1.34 0.77

15 2.07 1.23 0.78


anxiety scales EMAS-S, the four trait scales EMAS-T and the CATS are presentedin Table 3.Intercorrelations among the subscales measuring state anxiety after three different

situations show a similar and very moderate relationship between the non-stressfulsituation and the two testing situations and a high relationship between the twotesting situations.Intercorrelations among the five last subscales measuring trait anxiety in different

situations are low to moderate, ranging from 0.13 to 0.51. The highest relationship isbetween social evaluation and ambiguous anxiety. Among these trait anxiety sub-scales, social evaluation has significant correlation with the other four traitsubscales, and computer interaction has a significant correlation with physical dan-ger and daily routines.As expected, correlations between the CATS and the three state anxiety scales

show that state anxiety increases after computerized testing for those who have highcomputer anxiety trait level. Students who have high levels of computer trait anxietyshow a greater increase in state anxiety than those who have low levels only whenthe dimension of trait anxiety (computer) and type of situational stress (computer-ized interacting) are congruent. The correlations between the social evaluation sub-scale and state anxiety are greater when state anxiety is measured after testing, withno differences between the two testing conditions (pen and pencil vs. computer):testing is associated with evaluation. There are very moderate correlations betweenthe daily routines trait subscale and the two first state anxiety scales, (after the

Table 2

Oblimin-rotated factor loadings, eigenvalues, and percentages of total variance for the CATS (n=151)

CATS item Factor 1 Factor 2 Factor 3

14. Feel self-confident 0.94

12. Feel secure 0.82

13. Feel anxious 0.79

9. Feel tense 0.78

15. Feel nervous 0.72

4. Feel relaxed 0.71

8. Feel comfortable 0.71

2. Feel upset 0.68

5. Have an ‘‘uneasy feeling’’ 0.67

6. Look forward to these situations 0.98

1. Seek experiences likes this 0.93

10. Enjoy these situations 0.87

7. Get fluttering feeling in stomach 0.87

11. Heart beats faster 0.76

3. Perspire 0.74

Eigenvalues 8.42 1.93 1.09

% variance 56.16 12.89 7.25


non-stressful situation and after the first testing), but not between the state anxietyscale after computerized testing. It seems to indicate that the third and last situationis perceived as anxious by individuals who score high in daily routines trait anxiety.The correlations between ambiguous trait anxiety and the three state anxiety scalesincrease from a non-stressful context to computerized testing context, indicatingthat the first situation is perceived as a common situation, and the last situation isperceived as a new or a strange situation.

3. Discussion

The results of this study suggest that the computer anxiety tait sale (CATS) is areliable and valid measure of computer anxiety. High internal consistency wasfound. The CATS is correlated with another measure of computer anxiety, thecomputer anxiety rating scale (Heinssen et al., 1987). In addition, it is negativelycorrelated with computer experiences. As observed by several studies on differentpopulations (Maurer, 1994), computer anxiety is associated with a lack of comput-ing experience. Correlated factors extracted using principal components analysis andoblique rotation with the Oblimin method are comparable with the factors identifiedin the numerous comparisons and concurrent validities of computer anxiety scalestudies. The first factor is similar to those called computer anxiety (Dukes et al.,1989; Woodrow, 1991) or negative and positive feelings (Harrison & Rainer, 1992;Meier & Lambert, 1991). The second factor is related to computer liking orenthusiasm toward computer used. The third factor is activation of the autonomicnervous system. But very few scales get such items (Gaudron, 1998).

Table 3

Intercorrelations between EMAS-State, EMAS-Trait and CATS subscales

Subscales 1 2 3 4 5 6 7

EMAS-State

1. Non stressful

2. After pen and pencil test 0.25**

3. After computerized test 0.29** 0.63**

EMAS-Trait

4. Social evaluation 0.24** 0.41** 0.40**

5. Physical danger 0.27** 0.27** 0.33** 0.35**

6. Ambiguous situations �0.01 0.19* 0.37** 0.51** 0.13

7. Daily routines

CATS 0.27** 0.33** 0.08 0.34** 0.17 0.14

8. Computer interacts 0.04 0.37** 0.47** 0.31** 0.38** 0.13 0.20*

* P < 0.05.

** P< 0.01.


The high correlation between the CATS and the EMAS state anxiety subscalesparticularly after computerized testing is a major feature of all analyses conducted.It confirms the validity of the computer anxiety trait scale and the usefulness of theinteraction model of anxiety developed through Endler’s works. As expected, weobserved the greatest increase in state anxiety for individuals who score high incomputer trait anxiety when they are exposed to a congruent threat: using a compu-ter. When trait anxiety and situational stress are not congruent, the interaction withrespect to state anxiety does not occur or are moderate. From those validity analyses,the CATS should constitute another measure of computer anxiety. But this new scalehas many interests: first, it is based on a model of anxiety, the interaction model ofanxiety, and second, it can be use with the others EMAS trait scales within this model.If many studies supported the claim that computer anxiety and other anxieties

such as math anxiety, test anxiety or trait anxiety are not identical constructs, theyhave all verified the assumption that these anxieties are related constructs. So, thedifficulty to clarify the relationship between computer anxiety and other anxietiesmakes the interpretation of results difficult. The use of the CATS and the EMAStrait scales could overcome this problem. The results of the present studies give anexample of such use. Table 3 indicated that the use of tests as support of interactionswith computers has also provoked an increase in state anxiety for those who scorehigh in social evaluation trait. This is not surprising as testing is evaluation. Themodel predicted differential increases in state anxiety when people who have highlevels of social evaluation trait anxiety experience a situation in which they perceivethey are being evaluated, judged or appraised (Endler et al., 1991). Future researchshould use computer interaction activities that are not perceived as evaluations andthat do not require any previous computer experience or knowledge of computers.Finally, the moderate intercorrelations between the CATS and the EMAS trait

scale raise the question of the existence of a fifth threatening situation in this multi-dimensional anxiety model. First, the addition of a computer anxiety subscale doesnot seem to modify the intercorrelations among the four EMAS-T subscales. Theseintercorrelations are equivalent to those reported by Endler et al. (1991). Forinstance, the highest relationship they observed was between social evaluation andambiguous anxiety (0.43), and we have reported 0.51. In fact, in their study, allcorrelations among the trait scales were significant (P< .001) except between physi-cal danger and daily routine for women (0.03). These significant correlations rangedfrom �0.08 to 0.43 (participants to this study were 2009). Second, the correla-tions between the CATS and the other trait scales are equivalent to those observedbetween the four EMAS-T: some are low, others are moderate. But, as the foursituations used in this model are general situations, or task independent situations,and computer interaction situation is specific, or task dependent, can computeranxiety trait scale be considered as a fifth situational dimension of the Endler’smultidimensional anxiety scale? We need more data, more analyses to give anyanswer to that question.This study presented limitations. Some were concerning the nature of the sample.

Even an interest of the present study is the possibility to establish comparisons withEnglish speaking country studies (and particularly North-American studies), the


results of this research was limited by a possible effect of translation. We tried topresent the French items and the original items as close as possible. But mostauthors in the area of trans-cultural validity studies have shown that whatever thequality of translation, culture can provoke a differential in interpretation. So weneed further research on the American population to evaluate the structure factor ofthe CATS. Another limitation of the sample was that 86% of the sample werewomen. We could not explore the effect of gender. As Heinssen et al. suggested in1987, the question of sex differences in computer anxiety demands further investi-gation. It is still necessary because only a few authors have taken prior experienceinto account (Maurer, 1983). On a French population, we have observed that whentaking experience with computers into account, there is no difference in computeranxiety levels between men and women. Another sample limitation was that subjectswere all university freshmen. In future, researchers should attempt to replicate thesefindings with well-balanced gender sample, diverse age and educational groups. Alast limitation concerned the nature of these scales. As they are self-report scales,some subjects could respond according to their recognition about what the experi-menter wants to know.The findings of the present study have shown that the CATS is a reliable and valid

measure of computer anxiety. Based on an interaction model of anxiety, it should beused with the Endler multidimensional anxiety trait scales. Thus, altogether, thesefive scales should constitute a useful tool for investigating relationships betweencomputer anxiety and other anxieties in different situations. But their ultimate pro-mise will be fulfilled when they are subjected to cross validation and factor analysiswith other populations and when computer interaction is considered as a fifthsituation in the interaction model of anxiety.

Acknowledgements

This research was supported by the INETOP—Paris (Institut National d’Etude duTravail et de l’Orientation Professionnelle).

Appendix. The Computer Anxiety Trait Scale (CATS)

You are in situations where you use or you are about to use a computer.

1. Seek experiences like this2. Feel upset3. Perspire4. Feel relaxed5. Have an ‘‘uneasy feeling’’6. Look forward to these situations7. Get fluttering feeling in stomach8. Feel comfortable


9. Feel tense10. Enjoy these situations11. Heart beats faster12. Feel secure13. Feel anxious14. Feel self-confident15. Feel nervous

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Assessing Computer Anxiety Validation