computer attitude in psychiatric inpatients

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Computers in

Computers in Human Behavior 24 (2008) 1741–1752

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Human Behavior

Computer attitude in psychiatric inpatients

Bernhard Weber *, Barbara Schneider, Stefan Hornung,Tilman Wetterling, Jurgen Fritze

Clinic for Psychiatry, Psychosomatics and Psychotherapy, Johann Wolfgang Goethe University,

Heinrich-Hoffmann-Strasse 10, D-60528 Frankfurt/Main, Germany

Available online 4 September 2007

Abstract

Negative computer attitude has been shown to be a possible co-variable in computerized exam-inations of psychiatric patients, affecting patient–computer interaction as well as reliability andvalidity of assessments.

It remains still uncertain if the psychological construct of computer attitude can be dependablymeasured in acute psychiatric inpatients or whether it is impeded by the effects of mental illness.For that reason a German translation of the Groningen Computer Attitude Scale (GCAS) was eval-uated in 160 acute psychiatric inpatients under naturalistic conditions.

General test criteria (internal structure, item analysis, internal consistency, split half reliability) toa large extent corresponded to those formerly found in healthy subjects and psychiatric outpatients.The mean GCAS score was calculated as 56.2 ± 10.8 points and a significantly better computer atti-tude was found in male, better educated and younger patients. Some diverging correlation patternswere found in diagnostic subgroups, indicating a possible minor impact of mental disorder on com-puter attitude.

Overall, the GCAS was found to be a suitable instrument for measuring computer attitude inacute psychiatric inpatients. It should be used in identifying patients with a negative attitude to com-puters in order to ensure reliability and validity of computerized assessment.� 2007 Elsevier Ltd. All rights reserved.

Keywords: Computer attitude; Computer anxiety; Psychological assessment; Mental health; Psychiatry

0747-5632/$ - see front matter � 2007 Elsevier Ltd. All rights reserved.

doi:10.1016/j.chb.2007.07.006

* Corresponding author. Tel.: +49 69 6301 5347; fax: +49 69 6301 7087.E-mail address: [email protected] (B. Weber).

mailto:[email protected]

1742 B. Weber et al. / Computers in Human Behavior 24 (2008) 1741–1752

1. Introduction

Psychiatric patients are increasingly confronted to computerized therapy (Cavanagh &Shapiro, 2004; Kaltenthaler et al., 2002; Proudfoot, 2004) and assessment (Butcher, Perry,and Hahn, 2004; Das, 2002; Emmelkamp, 2005; Freudenmann & Spitzer, 2001; McGuireet al., 2000; Weber, Fritze, Schneider, Simminger, & Maurer, 1998). Computerization hasimportant advantages: it is less time consuming and thus potentially cost-effective, itassures standardization and reliability (Canfield, 1991), it enables a precise time registra-tion, it facilitates quality monitoring and statistical analysis, and some publications evenindicate more openness by increased reporting of even deviant behaviors in computerizedinterviews (Jarlais et al., 1999; Lucas, Mullin, Luna, & McInroy, 1977; Mensch, Hewett, &Erulkar, 2003; Simoes & Bastos, 2004; Turner et al., 1998).

A number of publications have dealt with the problem of computer attitude (Levine &Donitsa-Schmidt, 1998; Pancer, George, & Gebotys, 1992; Smith, Caputi, & Rawstorne,2000) in health care personnel (e. g. al-Hajjaj & Bamgboye, 1992; Knight, O’Malley, &Fletcher, 1987; Lacey, 1993; Lu, Xiao, Sears, & Jacko, 2005). Up to now, only few pub-lications have addressed the computer attitude of patients (Reis & Wrestler, 1994; Rosen-man, Levings, & Korten, 1997; Salzer & Burks, 2003; Samoilovich, Riccitelli, Schiel, &Siedi, 1992; Spinhoven, Labbe, & Rombouts, 1993; Weber, Fritze, Schneider, Kuehner,and Maurer, 2002; Weber et al., 1998).

As shown by some studies in the last years even healthy subjects may be handicappedby computer stress, computer anxiety or computerphobia (Ballance & Rogers, 1991; Bec-kers, Rikers, & Schmidt, 2006; Chang, 2005; Hudiburg, Ahrens, & Jones, 1994; Rosen,Sears, & Weil, 1987a; Tseng, Tiplady, Macleod, & Wright, 1998; Weil, Rosen, & Wugalter,1990) and even for them measurement of computer attitude becomes more and moreimportant (Rosen, Sears, & Weil, 1987b). Just psychiatric patients might be assumed tobe particularly susceptible to those phenomena. This assumption is supported by recentfindings indicating that – in spite of a good general acceptance and feasibility of comput-erized examinations (Weber et al., 2003) – computer attitude might be a relevant co-var-iable (Spinhoven et al., 1993; Weber et al., 1998) even causing a bias in computerizedassessment of psychiatric patients under certain conditions (Weber et al., 2002).

Computerized assessment is not only a simple support by a machine – as often sup-posed in daily practice – but a particular form of processing with regimentation and dis-ciplining by the computer (Jager & Krieger, 1994). Human–computer interaction isregarded as an increasingly important issue (Carroll, 2001; French & Beaumont, 1987;Sutcliffe, 2001), which has still been widely ignored in terms of patient–computer interac-tion. Anxiety, overstressing, opposition against and low familiarity with computers mightbe the mediators leading to invalid results (Beckers et al., 2006; Chang, 2005; Klinck,2002). From an ethical point of view unnecessary burden caused by ‘techno-stress’ (Weil& Rosen, 1998) should be avoided in psychiatric patients (Schulenberg & Yutrzenka,2004).

During the last decades, several instruments have been developed in order to measurecomputer attitude (e.g. Gordon, Killey, Shevlin, McIlroy, & Tierney, 2003; Kay, 1993;Loyd & Gressard, 1984; Pancer et al., 1992; Popovich, Hyde, Zakrajsek, & Blumer,1987; Rainer & Miller, 1996; Richter, Naumann, & Groeben, 2000; Rosen et al.,1987b). Spinhoven et al. (1993) presented data on computer attitude in psychiatric outpa-tients, collected by using the Groningen Computer Attitude Scale (GCAS) (Bouman,

B. Weber et al. / Computers in Human Behavior 24 (2008) 1741–1752 1743

Wolters, & Wolters-Hoff, 1989). They found computer attitude as well as educational leveland previous experience with computers to be related to a successful patient–computerinteraction. The authors concluded that computerized assessment is not feasible for allpsychiatric outpatients.

With regard to acute and severely ill psychiatric inpatients the question arises whetherthe psychological construct of ‘computer attitude’ is dependably measurable at all or pos-sibly just covered by effects of mental illness. The aim of the present study to evaluate theapplicableness of a German translation of the GCAS in acute psychiatric inpatients undernaturalistic conditions, and to get information about computer attitude in these patients.

2. Methods and patients

For this purpose 160 psychiatric inpatients completed a German translation of theGroningen Computer Attitude Scale (GCAS) (Bouman et al., 1989) following their admis-sion to an open psychiatric ward of the Clinic for Psychiatry, Psychosomatics and Psycho-therapy at the Johann Wolfgang Goethe University Frankfurt/Main, Germany. Patientswith severe cognitive disorders, organic brain disease, lack of German language skills orlack of readiness to cooperate were excluded. Few patients (less than 10%) refused the par-ticipation. The age of the examined patients was m = 45.6 ± SD = 14.9 (range = 18–82)years. Eighty patients were female (age: 46.6 ± 14.4, 20–77 years) and 80 were male(age: 44.6 ± 15.5, 18–82 years). DSM IV diagnoses (American Psychiatric Association,1994) are shown in Table 1. For statistical analysis, patients were divided in three diagnos-tic subgroups: psychotic, mood and other disorders. Age differed significantly betweendiagnostic subgroups (Kruskal–Wallis ANOVA); higher age in mood disorders comparedto psychotic disorders [Mann–Whitney U test: Z = 5.3, p < 0.000001] and to other

Table 1Diagnoses and diagnostic subgroups of the 160 psychiatric inpatients

Diagnoses (DSM IV) N

Mood disordersMajor depressive disorder 58Bipolar disorder 11Dysthymic disorder 4

Psychotic disordersSchizophrenia 35Schizoaffective disorder 11Brief psychotic disorder 2Substance-induced psychotic disorder 1

Other disordersSubstance related disorder 24Personality disorder 9Posttraumatic stress disorder 1Adjustment disorder with depressed mood 1Obsessive–compulsive disorder 1Generalized anxiety disorder 1Social phobia 1

Total 160

Table 2Mean values and standard deviations of GCAS total scores and age and gender distribution for all patients anddiagnostic subgroups

GCAS score (computer attitude) Age (years) Female/male (N)

All patients (N = 160) 56.2 ± 10.8 (21–78) 45.6 ± 14.9 (18–82) 80/80

Psychotic disorders (N = 49) 55.7 ± 10.0 (35–73) 37.7 ± 10.1a (20–58) 18/31b

Mood disorders (N = 73) 56.0 ± 10.6 (35–76) 52.6 ± 15.1a (22–82) 47/26b

Other disorders (N = 38) 57.3 ±12.5 (21–78) 42.4 ± 13.9a (18–73) 15/23b

a Difference significant (mood – psychotic disorders: Z = 5.3, p < 0.000001; mood – other disorders: Z = 3.2,p = 0.001).

b Difference significant (v2(2) = 11.2; p = 0.004).


disorders [Z = 3.2, p = 0.001] as did gender distribution (v2(2) = 11.2; p = 0.004) (formean values of subgroups, see Table 2). However, no significant differences in educationwere found (for distribution of the educational levels in the total sample, see Table 5).

The GCAS originally had been developed by Bouman et al. (1989) in order to investi-gate the influence of computer attitude on computerized psychological testing. Computerattitude was comprehended as interest in or attractiveness of working with a computer.The scale is well adapted to European conditions of computer experience and consistsof 16 statements (Table 3) with five possible answers varying between ‘totally agree’ and‘totally disagree’. The scores of negatively formulated items are calculated inversely tothose presented positively, so higher total scores exclusively represent a better computerattitude.

Items originally had been selected from the ‘Attitudes Towards Computers Scale’(ATCS) (Rosen et al., 1987b), the ‘Attitudes Towards Computers Usage Scale’ (ATCUS)(Popovich et al., 1987) and the ‘Computer Attitude Scale’ (CAS) (Loyd & Gressard, 1984).The theoretical minimum of the scale is 16 and the maximum 80 points. Because neutralanswers score 3 points for each item, a score higher than 48 can be considered to representa positive attitude on average (Bouman et al., 1989). Thanks to the proximity of the twolanguages Dutch and German the transformation of the items could be done without re-translation.

Reliability and internal structure of the GCAS were analyzed by Cronbach’s alpha, cal-culation of split half reliability and a principle factor analysis (centroid method, varimaxrotation). The Spearman Rank Order Correlation was used for analysis of relationsbetween age, educational level and GCAS scores; the Mann–Whitney U test for genderrelated differences. The Kruskal–Wallis ANOVA and the Mann–Whitney U test were usedfor analysis of differences between diagnostic subgroups. Additionally (forward stepwise)multiple regression analyses for GCAS scores were performed. All statistic calculationswere carried out with STATISTICA for Windows (version 7.1., StatSoft, Inc., 2005,www.statsoft.com).

3. Results

The internal structure of the scale was analyzed by a principle factor analysis (centroidmethod, varimax rotation) of standardized item scores. Three factors with eigenvalueshigher than 1 were extracted: factor I (eigenvalue = 4.1) explained 26.0% of total variance,

http://www.statsoft.com

Table 3Mean GCAS single item scores of the total sample and diagnostic subgroups and results of a principle factor analysis (centroid method) with varimax rotation ofGCAS items

Means, standard deviations and factorloadings of GCAS items

Psychoticdisorders

Mooddisorders

Otherdisorders

Totalsample

Factor Iloading

Factor IIloading

FactorIIIloading

m SD m SD m SD m SD

1 I would like to be informed on the latesttechnological developments

3.4 1.2 3.4 1.4 3.6 1.4 3.4 1.3 0.41 0.47 0.12

2a I will never understand to handlecomputers

4.0 1.2 3.7 1.4 4.1 1.4 3.9 1.3 0.64 �0.20 0.29

3a Working with a computer takes up far tomuch time

3.7 1.4 4.2 1.3 3.8 1.3 3.9 1.4 0.40 �0.19 �0.07

4 Computers are a blessing for mankind 3.3 1.3 3.8 1.2 3.2 1.2 3.5 1.2 0.29 0.26 �0.37

5 I know quite well how to use a computer 2.4 1.5 2.2 1.4 2.5 1.4 2.3 1.4 0.44 0.27 0.286a Computers do not interest me at all 3.6 1.2 3.7 1.5 3.7 1.5 3.7 1.4 0.67 0.21 0.097a I would rather NOT learn to handle a

computer4.0 1.1 3.9 1.4 4.1 1.4 4.0 1.3 0.81 0.14 0.15

8 I think I have a way of using computers 2.9 1.3 2.8 1.4 3.3 1.4 3.0 1.4 0.50 0.21 0.419a I find it annoying that you cannot see

what’s going on with computers3.5 1.4 3.7 1.4 3.7 1.4 3.7 1.4 0.24 �0.62 �0.21

10a It is my opinion that using computers atschool produces a negative effect onchildren’s language skills

3.6 1.3 3.6 1.3 3.5 1.3 3.6 1.3 0.38 �0.09 �0.20

11a I feel that I am in control of a computerand that the computer is not in control ofme

3.7 1.3 3.6 1.4 3.8 1.4 3.7 1.4 0.46 �0.35 0.39

12 Children should become familiar withcomputers at an early age

3.8 1.0 4.1 1.1 3.9 1.1 4.0 1.1 0.33 0.17 �0.33

13a Computers make more serious mistakesthan people

4.0 1.0 3.9 1.2 4.0 1.2 4.0 1.1 0.43 �0.48 �0.28

14 I would like to be a computer expert 2.7 1.5 2.5 1.6 2.7 1.6 2.6 1.5 0.41 0.32 �0.2015a I do not want to have anything to do with

computers4.0 1.2 3.8 1.4 4.0 1.4 3.9 1.4 0.74 0.17 �0.15

16a People will eventually become slaves ofmachinery

3.2 1.4 3.3 1.4 3.3 1.4 3.2 1.4 0.61 �0.28 0.08

Factor loadings: clusters of loadings are marked.a Item values are mirrored before computing the average item score and the total score of CGAS.

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factor II (ev = 1.5) 9.4% and factor III (ev = 1.0) 6.5% (Table 3). Item–total correlations,sample characteristics, score distribution and general test criteria are compared to otherGCAS studies in Table 4. Cronbach’s alpha (internal consistency) was found to bea = 0.81 and the split half reliability was calculated as r = 0.82.

In the total sample the mean score of the Groningen Computer Attitude Scale wascalculated as 56.2 ± 10.8 [21–78] and GCAS scores showed a normal distribution

Table 4Sample characteristics, score distribution and general test criteria of studies using the GCAS

Healthy subjects(Bouman et al., 1989)

Psychiatric inpatients(present study)

Psychiatric outpatients(Spinhoven et al., 1993)

N 148 160 157Age 32.1 ± 14.8 45.6 ± 14.9 �36 ± 11

[18–82] [18–70]a

Gender(% f/m)

46.6/53.4 50/50 �60/40a

GCAS scoresMean 59.0 56.2 60.2SD 10.5 10.8 11.7Min. 29 21Max. 79 78

Frequency distribution in intervalsScore 16–27 0.0% 0.6%Score 28–37 3.3% 4.4%Score 38–47 9.3% 15.0%Score 48–57 27.5% 34.4%Score 58–67 36.9% 29.3%Score 68–80 22.8% 16.3%

Item–total correlationsItem 1 0.36 0.46Item 2 0.53 0.65Item 3 0.31 0.48Item 4 0.23 0.32Item 5 0.40 0.48Item 6 0.60 0.69Item 7 0.71 0.78Item 8 0.46 0.53Item 9 0.11 0.24Item 10 0.34 0.42Item 11 0.36 0.51Item 12 0.31 0.30Item 13 0.32 0.41Item 14 0.30 0.47Item 15 0.65 0.76Item 16 0.52 0.60

Internal consistency and split half reliabilitya 0.87 0.81Split half

reliability0.82

a Personal communication.

Table 5Distribution of educational levels in all patients (N = 160)

Educational level %

Primary school 3.1General secondary school 32.7Lower technical or vocational training 20.1Higher secondary school 9.4Grammar school 15.7Higher technical or vocational training 6.9University 11.9


(Kolmogorov–Smirnov test). A positive attitude to computers (GCAS score > 48) wasfound in 123 (76.9%) patients whereas 37 (23.1%) showed a negative attitude.

Men showed significantly higher GCAS total scores (representing a better computerattitude) compared to women (Z = 2.4, p = 0.02) and in particular significantly higherscores in GCAS item 1 (‘I would like to be informed on the latest technological develop-ments’; Z = 2.9, p = 0.004), item 6 (‘Computers do not interest me at all’; Z = 2.4,p = 0.02), item 11 (‘I feel that I am in control of a computer and that the computer isnot in control of me’; Z = 2.8, p = 0.004) and item 14 (‘I would like to be a computerexpert’; Z = 3.1, p = 0.002). (Please keep in mind: higher scores represent a better com-puter attitude!).

GCAS total scores (R = 0.34, p = 0.00001) and most single item scores (except items 1,4, 10 and 12–14) were found to be significantly correlated to the educational level. Further-more, GCAS total scores (R =�0.22, p = 0.002) as well as many single item scores (item 2,4–8, 11, 12 and 15–16) were found to be significantly negatively correlated to age.

Significant differences of GCAS scores between diagnostic subgroups (Kruskal–WallisANOVA) were found only for GCAS item 4 (‘Computers are a blessing for mankind’)with higher scores in mood disorders compared to psychotic disorders (Mann–WhitneyU test: Z = 2.0, p = 0.04) and to ‘‘other’’ disorders (Mann–Whitney U test: Z = 2.2,p = 0.03). GCAS total and single item scores of the diagnostic subgroups are shown inTable 2.

A (forward stepwise) multiple regression analysis with GCAS total scores as dependentvariable and age, gender (dichotomized), diagnostic subgroups (dichotomized) and educa-tional level as independent variables showed 12% of the GCAS variance to be explained byeducation (b = 0.30, p = 0.00007), further 6% by male gender (b = 0.25, p = 0.0006), 4%by age (b =�0.27, p = 0.0006) and finally 2% by psychotic disorder (b =�0.16, p = 0.047).

4. Discussion

In correspondence to the original Dutch scale, the criteria for a one-dimensional atti-tude scale may be regarded as fulfilled (Krauth, 1995, p. 90). This is due to the relativelyhigh item homogeneity with high loadings mainly on factor I. The results for internal con-sistency and split half reliability support the scale not only to be useful for statisticalbetween-group differentiation but also for single case assessment of computer attitude(Lienert & Raatz, 1994).

Despite the diagnostic heterogeneity of the sample of psychiatric patients investigatedhere, the general test criteria (internal structure, item analysis, internal consistency, split


half reliability; see Tables 3 and 4) to a large extent correspond to those found by Boumanet al. (1989) in their sample of healthy subjects.

Furthermore, GCAS total scores did not differ significantly between diagnostic sub-groups. This corresponds to the results of Spinhoven et al. (1993), who did not find acorrelation between psychopathology (measured by SCL-90) and computer attitude in lessseverely ill psychiatric outpatients.

Item-by-item analysis showed a single statistically significant difference standing outbetween diagnostic subgroups: GCAS item 4 (‘Computers are a blessing for mankind’)scored significantly higher in the subgroup of mood disorders. Looking at the remainingsingle item scores of this diagnostic subgroup there could be identified a general trend tohigher scores on items reflecting general opinions (items 3, 4, 12,13,16) and lower scores onitems representing personal statements containing ‘I’, ‘my’ or ‘me’ (items 1, 2, 5–11, 14, 15), respectively (see Table 3). Comparing the average scores of these two itemgroups, a significant difference (Wilcoxon matched pairs test: Z = 4.8, p = 0.000001) couldbe confirmed which was, however, not replicated in the two other diagnostic subgroups.

This distinctive feature in the subgroup of mood (depressive) disorders might reflect aminor impact of mental illness, indeed. It might be explained by a tendency to answerquestions in a way supposed to be socially expected. This tendency might be restrictedto the context of general opinions and change to the opposite when patients are personallyaddressed by other items which are suitable to activate subjective computer experience(Smith, Caputi, Crittenden, Jayasuriya, & Rawstorne, 1999), feelings of personal insuffi-ciency and related depressive dysfunctional cognitions (American Psychiatric Association,1994; Beck, 1979) followed by the consequence of lower scoring.

This possible effect of depressive disorders should be taken as an opportunity to focusfurther research on a possible link between depressive dysfunctional cognitions, computerattitude and patient–computer interaction.

Astonishingly, even on the level of item-to-item analysis no indication of an impact ofparanoid symptoms could be found in the subgroup of psychotic disorders: Item 9 (‘I findit annoying that you cannot see what’s going on with computers’) or particularly item 11(‘I feel that I am in control of a computer and that the computer is not in control of me’)might be suspected to be vulnerable for such an influence. But psychotic patients showedordinary scores on these items, not differing from those of the other diagnostic subgroups.

The significant correlations between GCAS scores and educational level, age and gen-der, indicating a more positive computer attitude in male, better educated and youngerpatients, are in accordance with and in the range of the findings of several former com-puter attitude studies (Bouman et al., 1989; Czaja & Sharit, 1998; Popovich et al., 1987;Spinhoven et al., 1993; Whitley, 1997) and again confirm the construct validity of theGCAS.

In conclusion – despite of some possible minor specific impact of mood disorders on thescale – the German translation of the GCAS might be considered to be a suitable tool forthe assessment of computer attitude in acute psychiatric inpatients.

With regard to the GCAS total scores, a positive computer attitude (76.9% of allpatients (Bouman et al., 1989)) was found to predominate even in the severely ill psychi-atric inpatients. Compared to the results of Bouman et al. (1989) with a mean GCAS scoreof 59.0 in 148 healthy subjects, and of Spinhoven et al. (1993) who found a mean score of60.2 in 157 primarily neurotic outpatients, the mean GCAS score of 56.2 in the presentstudy seems to indicate a more negative attitude to computers in severely ill psychiatric


inpatients. The comparison of GCAS total score distribution between healthy subjects(Bouman et al., 1989) and psychiatric inpatients (present study) shows a linear shift tolower scores in the patients’ group (Table 4). On the other hand a healthy subgroup inthe sample of Bouman et al. with higher age and less computer experience showed a meanGCAS total score of 53.4 ± 11.8. In addition, the refusal rate of more than 50% in thestudy of Spinhoven et al. might be assumed to have caused a selection of patients witha better computer attitude, which puts into perspective the relatively low GCAS scoresfound in the present study.

It seems likely that the differences between the studies quoted are not specific for severepsychiatric illness, but result from differences in age, gender and educational level distribu-tion. Compared to the sample investigated in the present study, the healthy subjects in thestudy of Bouman et al. were younger and better educated, the psychiatric outpatients inthe investigation of Spinhoven et al. were at least younger (see Table 4).

Unfortunately, experience with computers had not been recorded in the whole sample.Anyhow, remarkably low scores in those GCAS items reflecting satisfying computer expe-rience in the past (items 5 and 8, see Table 3) indicate a possible lack of positive experience,which might additionally affect computer attitude (Bouman et al., 1989; Garland & Noyes,2004; Spinhoven et al., 1993).

In the total sample, the educational level was found to be at least the best predictor ofcomputer attitude. This might indicate computer attitude to be a function of learning pro-cesses. Thus, computer attitude might improve by habituation and/or positive computerexperience (Bouman et al., 1989; Jay & Willis, 1992; Spinhoven et al., 1993).

Overall rates of explained GCAS variance were relatively low in the multiple regressionmodels with age, gender, diagnostic subgroup and educational level as independent vari-ables. This is in accordance to the results of the former GCAS studies (Bouman et al.,1989; Spinhoven et al., 1993) but, nevertheless, it indicates the involvement of otherunknown factors in the development of computer attitude.

In conclusion, further research on computer attitude, acceptance of computerizedassessment (Morrison, ROSS, Gopalakrishnan, & CASEY, 1995; Samoilovich et al.,1992) and patient–computer interaction is needed in order to ensure reliability and validityof computerized examinations. Instruments like the GCAS are recommended to be used toidentify patients with a negative computer attitude or computer anxiety (Tseng et al.,1998). This is important not only in order to cover the possible impact of computer atti-tude on the acceptance of computerized assessment (Coffin & MacIntyre, 1999; Weberet al., 2003) including its ethical implications, but also – and even more important – onthe results of computerized assessment (Weber et al., 2002).

Acknowledgement

Part of this work was supported by grant We2263/1-1 from Deutsche Forschungsgeme-inschaft (DFG).

References

al-Hajjaj, M. S., & Bamgboye, E. A. (1992). Attitudes and opinions of medical staff towards computers [see

comments]. Computers in Biology and Medicine, 22(4), 221–226.


American Psychiatric Association (1994). Diagnostic and Statistical Manual of Mental Disorders: DSM IV.Washington, DC: American Psychiatric Association.

Ballance, C. T., & Rogers, S. U. (1991). Psychology of computer use: XXIV. Computer-related stress among

technical college students. Psychological Reports, 69(2), 539–542.Beck, A. T. (1979). Cognitive therapy of depression. New York: The Guilford Press.Beckers, J. J., Rikers, R. M. J. P., & Schmidt, H. G. (2006). The influence of computer anxiety on experienced

computer users while performing complex computer tasks. Computers in Human Behavior, 22(3), 456–466.Bouman, T. K., Wolters, F. J. M., & Wolters-Hoff, G. H. (1989). Een schaal om attitudes tegenover computers te

meten. Nederlands Tijdschrift voor de Psychologie, 44, 288–292.Butcher, J. N., Perry, J., & Hahn, J. (2004). Computers in clinical assessment: Historical developments, present

status, and future challenges. Journal of Clinical Psychology, 60(3), 331–345.Canfield, M. L. (1991). Computerized rating of the psychotherapeutic process. Psychotherapy, 28, 304–316.Carroll, J. M. (2001). Human–computer interaction, the past and the present. In J. M. Carroll (Ed.), Human–

computer interaction in the new millennium (pp. XXVII–XXXVII). New York, Boston, San Francico: ACMPress and Addison-Wesley.

Cavanagh, K., & Shapiro, D. A. (2004). Computer treatment for common mental health problems. Journal of

Clinical Psychology, 60(3), 239–251.Chang, S. E. (2005). Computer anxiety and perception of task complexity in learning programming-related skills.

Computers in Human Behavior, 21(5), 713–728.Coffin, R. J., & MacIntyre, P. D. (1999). Motivational influences on computer-related affective states. Computers

in Human Behavior, 15, 549–569.Czaja, S. J., & Sharit, J. (1998). Age differences in attitudes toward computers. Journals of Gerontology Series B-

Psychological Sciences and Social Sciences, 53(5), 329–340.Das, A. K. (2002). Computers in psychiatry: A review of past programs and an analysis of historical trends.

Psychiatric Quarterly, 73(4), 351–365.Emmelkamp, P. M. (2005). Technological innovations in clinical assessment and psychotherapy. Psychotherapy

and Psychosomatics, 74(6), 336–343.French, C. C., & Beaumont, J. G. (1987). The reaction of psychiatric patients to computerized assessment. British

Journal of Clinical Psychiatry, 26(Pt 4), 267–278.Freudenmann, R. W., & Spitzer, M. (2001). [Computer-assisted patient survey as the basis for modern quality

assurance in psychiatry. Results of pilot studies] Computergestutzte Patientenbefragungen als Grundlage fur

eine moderne Qualitatssicherung in der Psychiatrie. Ergebnisse der Pilotstudien. Nervenarzt, 72(1), 40–51.Garland, K. J., & Noyes, J. M. (2004). Computer experience: A poor predictor of computer attitudes. Computers

in Human Behavior, 20(6), 823–840.Gordon, M., Killey, M., Shevlin, M., McIlroy, D., & Tierney, K. (2003). The factor structure of the Computer

Anxiety Rating Scale and the Computer Thoughts Survey. Computers in Human Behavior, 19(3), 291–298.Hudiburg, R. A., Ahrens, P. K., & Jones, T. M. (1994). Psychology of computer use. 31. Relating computer users

stress, daily hassles, somatic complaints, and anxiety. Psychological Reports, 75(3), 1183–1186.Jarlais, D. C., Paone, D., Milliken, J., Turner, C. F., Miller, H., Gribble, J., et al. (1999). Audio-computer

interviewing to measure risk behaviour for HIV among injecting drug users: A quasi-randomised trial. Lancet,

353(9165), 1657–1661.Jay, G. M., & Willis, S. L. (1992). Influence of direct computer experience on older adults’ attitudes toward

computers. Journal of Gerontology, 47(4), 250–257.Jager, R. S., & Krieger, W. (1994). Future prospects of computer-based assessment, exemplified on the basis of

treatment-oriented assessment. Diagnostica, 40(3), 217–243.Kaltenthaler, E., Shackley, P., Stevens, K., Beverley, C., Parry, G., & Chilcott, J. (2002). A systematic review and

economic evaluation of computerised cognitive behaviour therapy for depression and anxiety. Health

Technology Assessment, 6(22), 1–89.Kay, R. H. (1993). An exploration of theoretical and practical foundations for assessing attitudes toward

computers: The Computer Attitude Measure (CAM). Computers in Human Behavior, 9(4), 371–386.Klinck, D. (2002). Computergestutzte Diagnostik. Gottingen, Bern, Toronto, Seattle: Hogrefe.Knight, B. P., O’Malley, M. S., & Fletcher, S. W. (1987). Physician acceptance of a computerized health

maintenance prompting program. American Journal of Preventive Medicine, 3(1), 19–24.Krauth, J. (1995). Testkonstruktion und Testtheorie. Weinheim: Psychologie Verlags Union.Lacey, D. G. (1993). Nurses’ attitudes towards computerization: A review of the literature. Journal of Nursing

Management, 1(5), 239–243.


Levine, T., & Donitsa-Schmidt, S. (1998). Computer use, confidence, attitudes, and knowledge: A causal analysis.Computers in Human Behavior, 14(1), 125–146.

Lienert, G. A., & Raatz, U. (1994). Testaufbau und Testanalyse. Weinheim: Psychologie Verlags Union.Loyd, B. H., & Gressard, C. P. (1984). Reliability and factorial validity of computer attitude scales. Educational

and Psychological Measurement, 44, 501–505.Lu, Y. C., Xiao, Y., Sears, A., & Jacko, J. A. (2005). A review and a framework of handheld computer adoption

in healthcare. International Journal of Medical Informatics, 74(5), 409–422.Lucas, R. W., Mullin, P. J., Luna, C. B., & McInroy, D. C. (1977). Psychiatrists and a computer as interrogators

of patients with alcohol-related illnesses: A comparison. British Journal of Psychiatry, 131, 160–167.McGuire, M., Bakst, K., Fairbanks, L., McGuire, M., Sachinvala, N., von Scotti, H., et al. (2000). Cognitive,

mood, and functional evaluations using touchscreen technology. Journal of Nervous and Mental Disease,

188(12), 813–817.Mensch, B. S., Hewett, P. C., & Erulkar, A. S. (2003). The reporting of sensitive behavior by adolescents: A

methodological experiment in Kenya. Demography, 40(2), 247–268.Morrison, G. R., ROSS, S. M., Gopalakrishnan, M., & CASEY, J. (1995). The effects of feedback and incentives

on achievement in computer based instruction. Contemporary Educational Psychology, 20(1), 32–50.Pancer, S. M., George, M., & Gebotys, R. J. (1992). Understanding and predicting attitudes towards computers.

Computers in Human Behavior, 8(2–3), 211–222.Popovich, P. M., Hyde, K. R., Zakrajsek, T., & Blumer, C. (1987). The development of the attitudes toward

computer usage scale. Educational and Psychological Measurement, 47, 261–270.Proudfoot, J. G. (2004). Computer-based treatment for anxiety and depression: Is it feasible? Is it effective?

Neuroscience and Biobehavioral Reviews, 28(3), 353–363.Rainer, R. K., & Miller, M. D. (1996). An assessment of the psychometric properties of the computer attitude

scale. Computers in Human Behavior, 12(1), 93–105.Reis, J., & Wrestler, F. (1994). Consumer attitudes towards computer-assisted self-care of the common cold.

Patient Education and Counseling, 23(1), 55–62.Richter, T., Naumann, J., & Groeben, N. (2000). Attitudes toward the computer: Construct validation of an

instrument with scales differentiated by content. Computers in Human Behavior, 16(5), 473–491.Rosen, L. D., Sears, D. C., & Weil, M. M. (1987a). Computerphobia. Behavior Research Methods Instrumentation

and Computers, 19, 167–179.Rosen, L. D., Sears, D. C., & Weil, M. M. (1987b). Computerphobia measurement. A manual for administration

and scoring of three instruments: Computer Anxiety Rating Scale (CARS), Attitudes Towards Computers

Scale (ATCS), Computer Thoughts Survey (CTS). Carson, CA: California State University DominguezHills.

Rosenman, S. J., Levings, C. T., & Korten, A. E. (1997). Clinical utility and patient acceptance

of the computerized Composite International Diagnostic Interview. Psychiatric Services, 48(6),815–820.

Salzer, M. S., & Burks, V. (2003). A mediational study of computer attitudes, experience, and training interests

among people with severe mental illnesses. Computers in Human Behavior, 19(5), 511–521.Samoilovich, S., Riccitelli, C., Schiel, A., & Siedi, A. (1992). Attitude of schizophrenics to computer videogames.

Psychopathology, 25(3), 117–119.Schulenberg, S. E., & Yutrzenka, B. A. (2004). Ethical issues in the use of computerized assessment. Computers in

Human Behavior, 20(4), 477–490.Simoes, A. M., & Bastos, F. I. (2004). [Audio Computer-Assisted Interview: A new technology in the assessment

of sexually transmitted diseases, HIV, and drug use] Audio Computer-Assisted Interview: uma nova

tecnologia em avaliacao de comportamento de risco em doencas sexualmente transmissiveis, HIV e uso de

drogas. DEP – 20041013. Cad Saude Publica, 20(5), 1169–1181.Smith, B., Caputi, P., Crittenden, N., Jayasuriya, R., & Rawstorne, P. (1999). A review of the construct of

computer experience. Computers in Human Behavior, 15(3), 227–242.Smith, B., Caputi, P., & Rawstorne, P. (2000). Differentiating computer experience and attitudes toward

computers: An empirical investigation. Computers in Human Behavior, 16(1), 59–81.Spinhoven, P., Labbe, M. R., & Rombouts, R. (1993). Feasibility of computerized psychological testing with

psychiatric outpatients. Journal of Clinical Psychology, 49(3), 440–447.Sutcliffe, A. (2001). On the effective use and reuse of HCI knowledge. In J. M. Carroll (Ed.), Human–computer

interaction in the new millennium (pp. 3–29). New York, Boston, San Francico: ACM Press and Addison-Wesley.


Tseng, H. M., Tiplady, B., Macleod, H. A., & Wright, P. (1998). Computer anxiety: A comparison of pen-based

personal digital assistants, conventional computer and paper assessment of mood and performance. British

Journal of Psychology, 89(Pt 4), 599–610.Turner, C. F., Ku, L., Rogers, S. M., Lindberg, L. D., Pleck, J. H., & Sonenstein, F. L. (1998). Adolescent sexual

behavior, drug use, and violence: Increased reporting with computer survey technology [see comments].Science, 280(5365), 867–873.

Weber, B., Fritze, J., Schneider, B., Kuehner, T., & Maurer, K. (2002). Bias in computerized neuropsychological

assessment of depressive disorders caused by computer attitude. Acta Psychiatrica Scandinavica, 105(2),126–130.

Weber, B., Fritze, J., Schneider, B., Simminger, D., & Maurer, K. (1998). Computerized self-assessment in

psychiatric in-patients: Acceptability, feasibility and influence of computer attitude. Acta Psychiatrica

Scandinavica, 98(2), 140–145.Weber, B., Schneider, B., Fritze, J., Gille, B., Hornung, S., Kuehner, T., et al. (2003). Acceptance of

computerized compared to paper-and-pencil assessment in psychiatric inpatients. Computers in Human

Behavior, 19(1), 81–93.Weil, M. M., & Rosen, L. D. (1998). TechnoStress: Coping with technology. New York, Chichester, Weinheim,

Brisbane, Singapore, Toronto: John Wiley & Sons.Weil, M. M., Rosen, L. D., & Wugalter, S. (1990). The etiology of computerphobia. Computers in Human

Behavior, 6(4), 361–379.Whitley, B. E. (1997). Gender differences in computer-related attitudes and behavior: A meta-analysis. Computers

in Human Behavior, 13(1), 1–22.

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