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Exploring the psychometric properties of the English versionof the Internet Addiction Test in the Pakistani population: across-sectional surveyAhmed Waqas, Faisal Farooq, Anum Bhatti, Saamia Javed, Mahrukh Elahi Ghumman, Mohsin Raza, Spogmai Khan, Waqas Ahmad

Introduction: Despite growing concerns over pathological use of the Internet, studiesbased on validated psychometric instruments are still lacking in Pakistan. The presentstudy aimed to examine the psychometric properties of Young’s Internet Addiction Test(IAT) in a sample of the Pakistani population. We examined the validity, internalconsistency, readability and floor and ceiling effects of IAT scores.Methods: This cross-sectional study was conducted at CMH Lahore Medical College and Institute of Dentistry,Lahore, Pakistan from 1 March 2015 to 30 May 2015. A total of 522 medical and dentalstudents completed the questionnaire, which consisted of three sections: (a) demographicsand percentage grades in annual examinations, (b) a categorical question to record theestimated number of hours spent on the Internet per day, and (c) the English version ofthe IAT. All data were analyzed in SPSS v. 20. Principal axis factor analysis was used tovalidate the factor structure of the IAT in our study sample. An alpha coefficient > .7 wassought in the reliability analysis. Histograms and the values of skewness and kurtosis wereanalyzed for floor and ceiling effects. In addition, readability of the IAT was assessed as theFlesch Reading Ease score and Flesch-Kincaid Grade level function. Results: A total of 522medical and dental students participated in the survey. Most respondents were femalemedical students enrolled in preclinical years of their degree program. Median age (min-max) of the respondents was 20 years (17-25 years). A single-factor model for IAT scoreexplained 33.71% of the variance, with a high alpha coefficient of .893. In addition, the IAThad good face and convergent validity and no floor and ceiling effects, and was judgedeasy to read by participants. Conclusion: The English version of the IAT showed goodpsychometric properties in a sample of Pakistani university students. A single-factor modelfor assessing internet addiction showed good reliability and was found suitable with ourstudy sample.

PeerJ PrePrints | https://dx.doi.org/10.7287/peerj.preprints.1531v1 | CC-BY 4.0 Open Access | rec: 24 Nov 2015, publ: 24 Nov 2015

1 Exploring the psychometric properties of the English version of the Internet Addiction Test

2 in the Pakistani population: a cross-sectional survey

3

4 Ahmed Waqas1, Faisal Farooq1, Anum Bhatti2, Saamia Tahir Javed1, Mahrukh Elahi Ghumman1,

5 Mohsin Raza1, Spogmai Khan1, Waqas Ahmad1

6

7 Affiliations: 1Undergraduate students, CMH Lahore Medical College and Institute of Dentistry,

8 Lahore Cantt, Pakistan

9 2Undergraduate student, Portland State University, Oregon, USA

10

11 Running title: Psychometric properties of Internet Addiction Test in the Pakistani population

12

13 Corresponding author:

14 Ahmed Waqas

15 Email: [email protected]

16 Contact #: +92-03434936117

17 Address: CMH Lahore Medical College and Institute of Dentistry, Lahore Cantt, Pakistan

18

19 Abstract word count: 319

20 Manuscript word count: 2569 (excluding tables and references)

21 Funding: none

22 Conflicts of interest: none

23


mailto:[email protected]

24 Abstract

25 Introduction:

26 Despite growing concerns over pathological use of the Internet, studies based on validated

27 psychometric instruments are still lacking in Pakistan. The present study aimed to examine the

28 psychometric properties of Young’s Internet Addiction Test (IAT) in a sample of the Pakistani

29 population. We examined the validity, internal consistency, readability and floor and ceiling

30 effects of IAT scores.

31 Methods:

32 This cross-sectional study was conducted at CMH Lahore Medical College and Institute of

33 Dentistry, Lahore, Pakistan from 1 March 2015 to 30 May 2015. A total of 522 medical and

34 dental students completed the questionnaire, which consisted of three sections: (a) demographics

35 and percentage grades in annual examinations, (b) a categorical question to record the estimated

36 number of hours spent on the Internet per day, and (c) the English version of the IAT. All data

37 were analyzed in SPSS v. 20. Principal axis factor analysis was used to validate the factor

38 structure of the IAT in our study sample. An alpha coefficient > .7 was sought in the reliability

39 analysis. Histograms and the values of skewness and kurtosis were analyzed for floor and ceiling

40 effects. In addition, readability of the IAT was assessed as the Flesch Reading Ease score and

41 Flesch-Kincaid Grade level function.

42 Results:

43 A total of 522 medical and dental students participated in the survey. Most respondents were

44 female medical students enrolled in preclinical years of their degree program. Median age (min-

45 max) of the respondents was 20 years (17-25 years). A single-factor model for IAT score

46 explained 33.71% of the variance, with a high alpha coefficient of .893. In addition, the IAT had


47 good face and convergent validity and no floor and ceiling effects, and was judged easy to read

48 by participants.

49 Conclusion:

50 The English version of the IAT showed good psychometric properties in a sample of Pakistani

51 university students. A single-factor model for assessing internet addiction showed good

52 reliability and was found suitable with our study sample.

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70

71

72 Introduction

73 There has been tremendous growth in the number of Internet users in Pakistan, from 0.1% of the

74 total population in 2000 to 10.9% (over 20 million) in 2013 1. This sudden rise in Internet usage

75 has attracted much attention from mental health experts interested in exploring the potential

76 harms associated with its excessive or pathological use.

77 The problematic, uncontrollable and impulsive use of the Internet has been

78 conceptualized as a behavioral addiction comparable to pathological gambling 2. The etiology of

79 these behavioral addictions is rooted in biological processes such as conditioned learning and the

80 brain’s reward system. Hence, certain behaviors elicit short-term rewards, promote continuous

81 behavior and diminish control over behavior 3. Such behaviors are being recognized as a

82 compulsive-impulsive spectrum disorder 2 entailing at least three domains: gaming, pornography

83 and emailing/text messaging 4. According to Shapira et al., the diagnostic criteria for Internet

84 addiction include (a) maladaptive and excessive use of the Internet for longer times than planned,

85 (b) significant impairment in social, occupational and other domains of functioning, and (c)

86 excessive use that cannot be accounted for by any axis I disorders 5.

87 Previous studies have increasingly delineated the harmful effects of problematic use of

88 the Internet in different populations. Such use has been associated with significant psychosocial

89 impairment leading to low work performance, relationship problems, loneliness, self-destructive

90 behaviors and several psychiatric disorders such as depression, anxiety, social phobias and

91 Attention Deficit Hyperactivity Disorder5–11. Recently, a number of cross-sectional and

92 longitudinal studies have explored the association of Internet use with axis II disorders. An


93 interesting study by Floros and colleagues reported a high comorbidity of axis II disorders

94 including personality disorders and use of impaired ego defenses in a clinical sample of people

95 with Internet addiction disorder (IAD) 12. However, in several longitudinal studies, the evidence

96 for the direction of a causal relationship between IAD and axis I and II disorders is not yet clear

97 10–12, and more evidence is required.

98 Because of the potential harms of Internet addiction, internet gaming disorder has been

99 incorporated into section III of the Diagnostic and Statistical Manual of Mental Disorders fifth

100 edition (DSM-5) 13. A new diagnostic category of behavioral addictions including Internet

101 addiction is also being introduced in the International Classification of Diseases (ICD-11) 14.

102 At present, there are no estimates of the prevalence of IAD in the Pakistani population.

103 However, according to Jadoon et al., 44% of Pakistani medical students are regular Internet users

104 15. Despite growing concerns over pathological use of the Internet, studies based on validated

105 psychometric instruments are still lacking in Pakistan. Therefore, the paucity of validated tools to

106 study IAD in the Pakistani population warranted the present study. This study aimed to examine,

107 in a sample of the Pakistani population, the psychometric properties of the Internet Addiction

108 Test (IAT) devised by Young 9. We studied the validity, internal consistency, readability and

109 floor and ceiling effects of IAT scores.

110 The IAT continues to be one of the most extensively used validation tools to study IAD

111 in different populations, and in both clinical and nonclinical settings. This test was selected for

112 analysis because it has shown excellent psychometric properties in many languages, cultures,

113 ethnicities and countries including China 16, Germany 17, France 18, Sweden, Lebanon 19, Greece

114 20 and Bangladesh 21. However, validations in different settings have led to heterogeneous factor


115 structures of the IAT, ranging from a one-factor structure to as many as six factors 16–21,

116 sometimes giving rise to different constructs.

117

118 Methods

119 Study design

120 This cross-sectional study was conducted at CMH Lahore Medical College (CMH LMC) and

121 Institute of Dentistry, Lahore, Pakistan from 1 March, 2015 to 30 May, 2015. Ethical approval

122 was sought from and granted by the Ethical Review Committee of CMH LMC. A total of 550

123 questionnaires were distributed among medical and dental students enrolled in all years of the

124 medical or dental degree program. Respondents were selected with a random sampling approach

125 using computer software. All respondents read and signed a consent form, and were ensured

126 anonymity and that only group findings would be reported.

127 The questionnaire consisted of three sections: (a) demographics of the respondents, (b) a

128 categorical question to document the estimated number of hours spent on the Internet per day,

129 and (c) the IAT developed by Young 2,22. This instrument has shown excellent psychometric

130 properties 16–21 in a variety of settings. It consists of 20 items that investigate the respondent’s

131 potentially problematic use of the Internet and disruption in psychosocial functioning 22.

132 Responses are recorded on a 6-point Likert scale of frequencies, ranging from “does not apply”

133 (0) to “always” (5). For purposes of analysis, a global score is obtained by adding the scores for

134 the responses to each item.

135

136 Pilot survey


137 Before starting the survey, a pilot study was conducted at CMH LMC in a sample of 20 medical

138 students selected by convenience sampling. We received positive comments from the

139 participants that the English version of the IAT was easily comprehensible. Therefore, we did not

140 feel the need to translate it into Urdu (the official language of Pakistan).

141

142 Sample size calculation

143 Sample size calculations are generally based on expected effect sizes and variability in the study

144 sample. Both of these were unknown before we started the study; so we relied on “rules of

145 thumb” to calculate the sample size for factor analysis. Comrey and Lee suggested samples of

146 500 or more for factor analysis studies 23. According to their rating scale, a sample size of 100 is

147 considered poor, 200 fair, 300 good, 500 very good, and 1000 or more as excellent. Therefore,

148 we aimed for a sample size of at least 500 respondents as recommended by Comrey and Lee 23.

149

150 Data analysis

151 All data were analyzed in SPSS v. 21 (IBM Chicago, IL, USA). Frequencies and were calculated

152 for demographic variables, and descriptive statistics were obtained for total scores on the IAT.

153 Assumptions of normality and floor and ceiling effects of IAT scores were verified by plotting

154 histograms and Q-Q plots. The percentages of individuals with the lowest and highest possible

155 IAT scores were recorded to examine floor and ceiling effects, and values >20% were considered

156 significant.

157 Exploratory factors with principal axis factoring and quartimax rotation were studied to

158 analyze the factor structure of the IAT. The suitability of principal axis factoring for our

159 purposes was determined with the following criteria: correlation coefficient >0.3 for all


160 variables, Kaiser-Meyer-Olkin (KMO) value greater than 0.6, and a statistically significant

161 Barlett test of sphericity (P < 0.05). The maximum number of components to be retained was

162 determined with a Cattell’s scree plot, eigenvalues >1, an interpretability criterion, the amount of

163 variance explained, and reliability analysis. Factor loading values > 0.3 were sought. Internal

164 consistency of the IAT was evaluated with Cronbach’s alpha reliability analysis, and an alpha

165 coefficient of > .70 was considered acceptable 24. Item total correlations were analyzed with

166 Pearson’s product moment correlation coefficient, and values between 0.2 to 0.8 were sought 24.

167 Convergent validity of the IAT was evaluated by analyzing the association between the number

168 of hours spent on the Internet per day and the IAT score. Participants were asked, “How many

169 hours do you estimate you spend on the Internet each day?”. Responses were recorded on the

170 following scale; (a) <30 min to 1 hour, (b) >1 hour to 3 hours, (c) >3 to 6 hours or more. This

171 association was analyzed with one-way analysis of variance (ANOVA). A similar study that

172 analyzed the convergent validity of the IAT in a Greek population sample used the same method

173 to determine convergent validity 20.

174 Readability of the questionnaire was recorded as the Flesch Reading Ease score and

175 Flesch-Kincaid Grade level function 25. Associations between gender, residence, year of study,

176 degree program and respondents’ IAT scores were analyzed with independent sample t-tests.

177

178 Results

179 Of the 550 questionnaires distributed, 522 (94.90%) were returned. Most of the respondents were

180 female medical students enrolled in preclinical years of their degree program. Median age (min-

181 max) of the respondents was 20 years (17-25 years). Most students were average users of the


182 Internet (from >1 to 3 hours daily). Detailed results for demographic characteristics and Internet

183 use are shown in Table 1.

184 The independent sample t-test showed that male students scored significantly higher on

185 the IAT than females. According to one-way ANOVA and post-hoc least significant difference

186 (LSD) tests, IAT scores were positively associated with hours spent using the Internet. Detailed

187 results are given in Tables 1 and 2.

188 The IAT demonstrated easy readability, with a Flesch reading ease value of 71.1 (rated as

189 fairly easy) and a Flesch-Kincaid level of 7.1. Respondents indicated that items 1, 2, 12, 14, 16

190 and 17 were the easiest to understand, whereas items 3, 4, 19, 20 were the hardest.

191

192 Construct validity

193 The overall KMO value for sample adequacy was 0.920, which is classified as meritorious which

194 according to Kaiser’s criteria (1974). The Bartlett test of sphericity was significant (P < 0.001).

195 Therefore, the data in the present study were suitable for exploratory factor analysis. Inspection

196 of the correlation matrix showed that all variables had at least one correlation coefficient greater

197 than 0.3.

198 Initially, principal axis factor analysis with the quartimax method extracted three

199 components/factors with an eigenvalue >1 and a high degree of cross-loading of statements on

200 different factors. Most of the variance in IAT scores was explained by the first factor (33.71%),

201 while the second factor was associated with a modest variance of 7.68%, and the third with a

202 variance of 5.79%. However, the lack of theoretical underpinning for this three-factor structure,

203 visualization of the Cattell’s scree plot (Figure 1) together with the modest variance explained by

204 second and third factors favored a unidimensional model of the IAT. Factor loadings of


205 the rotated three-component solution for the 20-item IAT based on eigenvalues greater than 1 are

206 presented in Table 3. Factor loadings for the unrotated one-factor structure of the test are given

207 in Table 4.

208

209 Normality and floor and ceiling effects

210 Mean IAT score was 43.8. Inspection of skewness (−0.030, std. error = 0.107), kurtosis (−0.486,

211 std. error = 0.213) and the histogram (Figure 2) revealed that IAT scores did not deviate

212 significantly from normality, and no floor or ceiling effects were found in IAT scores for the

213 present sample.

214

215 Reliability analysis

216 The IAT consists of 20 items. Cronbach’s alpha value for the one-factor structure of the IAT was

217 .893, which indicates excellent reliability of this tool in the present study sample. Item total

218 statistics for the IAT are detailed in Table 5. All items had corrected item correlations greater

219 than 0.3, thus exhibiting the same construct. To further analyze internal consistency, item total

220 correlations adjusted for overlap were calculated for each item; these values ranged from 0.32 to

221 0.60, which reflect substantial and moderate correlation.

222

223 Discussion

224 The English version of the IAT was judged easy to read, and had good face, content and

225 convergent validity and high internal reliability in our sample of Pakistani medical and dental

226 students. In addition, our analysis detected no floor or ceiling effects in IAT scores.


227 Initial exploratory factor analysis revealed a three-factor structure based on Kaiser’s

228 criterion of eigenvalues >1. However, the factor structure of the IAT was not very clear cut, with

229 a few items cross-loading on different factors. The three-factor structure for the IAT lacks any

230 theoretical underpinning, therefore the Cattell’s scree plot together with the modest variance

231 explained by the second and third factors favor a unidimensional factor structure for the IAT.

232 These results are in consonance with the factor structures of the Arabic 19, French 26 and

233 Portuguese 27 versions of this test. However, studies of the psychometric properties of the IAT in

234 Germany 17, Korea 28, USA 29, Bangladesh 21, Malaysia 30, Italy 31, Greece 20 and China 16 have

235 reported heterogeneous factor structures for the IAT. Factor models ranging from two to six

236 components have been proposed in different studies with different factor loadings and constructs,

237 albeit with very high internal consistency for this instrument. These discrepancies may be due to

238 the use of different factor analysis techniques, and/or to variations in demographics, culture and

239 age groups of the respondents. As noted, sample sizes ranged from as low as 151 postgraduate

240 and undergraduate medical students in Greece 20 to as high as 1882 respondents in a validation

241 study of the IAT in Germany 17. The respondents in most studies were college and university

242 students; however, sampling strategies differed in some studies such as the German study 17, in

243 which respondents completed an online questionnaire as well as an offline sample, a study of

244 students in the USA who were contacted through Facebook29, and research in China16 and

245 Lebanon19, which involved adolescent male and female students. As noted in the literature, most

246 studies used principal components analysis, although others used exploratory factor analysis and

247 confirmatory factor analysis to validate the factor structure of the IAT in their respective

248 population samples. Regardless of the number of factors that have been extracted to date, all

249 studies reported high internal consistency with alpha values ranging from 0.89 21 to 0.93 26. Table


250 6 summarizes the factor models that have been proposed in different studies, highlighting the

251 population characteristics, methods and results of the different factor analysis techniques.

252 Our sample included medical and dental students from Pakistan, which is a

253 predominantly Muslim country. Therefore, we expected findings similar to those reported in

254 other Muslim populations. For example, as reported in an Arab population, our analysis revealed

255 that item 4 had a low inter-item correlation – a finding that highlights the cultural restrictions

256 regarding premarital relationships in Muslim cultures 19. Similarly, item 1 had the lowest inter-

257 item correlation (0.323), indicating that the number of hours spent online is not a strong

258 determinant of the IAT score, and other factors such as impairment in psychosocial life might

259 play a greater role than has been recognized thus far. A validation study of the IAT in a Bengali

260 population identified four sub-scales, i.e. ‘Neglect of duty’, ‘Online dependence’, ‘Virtual

261 fantasies’, and ‘Privacy and self- defense’21.

262 Male students scored higher on the IAT than their female counterparts, probably because

263 males are more likely to engage in cyber-sexual behavior, online gaming and gambling –

264 behaviors that are likely to affect their psychosocial health 32. Similar trends in the scores for

265 male and female respondents were observed in the Bengali population 21.

266

267 Limitations

268 This study evaluated the psychometric properties of the English version of the IAT in a sample

269 of Pakistani medical and dental students. The test demonstrated fairly easy readability and

270 comprehensibility in our study sample. But because most of the general population is not

271 proficient in the English language, further studies are advised to test the psychometric properties

272 of the translated Urdu version of the IAT. Our sample comprised randomly selected respondents


273 from a single medical school; therefore, future studies should include a more diverse study

274 sample. Moreover, comparator instruments were not included in the present study to establish the

275 criterion validity of the IAT; therefore further efforts to address this issue are needed.

276

277 Conclusion

278 The English version of the Internet Addiction Test demonstrated good psychometric properties in

279 a sample of the Pakistani population. However, future studies are encouraged to assess the

280 psychometric properties of the Urdu version of this instrument in the general Pakistani

281 population.

282

283 Acknowledgment

284 The authors thank K. Shashok (AuthorAID in the Eastern Mediterranean) for improving the use

285 of English in the manuscript.

286

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Table 1(on next page)

Demographic characteristics of respondents and mean Internet Addiction Test scores (n= 522)


1 Table 1. Demographic characteristics of respondents and mean Internet Addiction Test scores (n

2 = 522)

Variable Frequency

(n)

Median (min-

max)

Mean IAT

score (SD)

Statistical

value

Gender Male 194 (37.2%) - 48.96 (15.9) t value =

5.41

Female 328 (62.8%) - 40.77 (17.2)

Median age (min-max) 20 (17-25) 43.82 (4-95) -

Study year Preclinical 296 (56.7%) - 43.28 (16.7) t value =.8

Clinical 226 (43.3%) - 44.52 (17.8)

Degree MBBS 414 (79.3%) - 45.02 (17.4) t value = 3.1

BDS 108 (20.7%) - 39.19 (15.35)

Residence* Off-campus 267 (51.1%) - 44.27 (16.6) t value =.54

On-campus 253 (48.5%) - 43.46 (17.5)

Hours spent surfing

Internet per day*

<30 min – 1 hour 142 (27.2%) - 37.07 (17.3) F value=

25.691

>1 hour – 3 hours 220 (42.1%) - 43.40 (15.4)

>3 – 6 or more

hours

157 (30.1%) - 50.67 (16.9)

Level of addiction Average user 320 (61.3%) 20-49 Χ2 = 2881

Frequent problems 193 (37%) 50-79

Significant

problems

6 (1.1%) 80-100


3 *Missing values 2 or more, 1 denotes P < 0.001

4



Post-hoc least significant difference test for hours spent surfing the Internet.(Dependent variable: Internet Addiction Test scores)


1 Table 2. Post-hoc least significant difference test for hours spent surfing the Internet. (Dependent

2 variable: Internet Addiction Test scores)

3

4

95% Confidence interval(I) hours spent (J) hours spent Mean

difference (I-J)

Std. error Sig.

Lower bound Upper bound

>1 – 3 hours -6.32803* 1.76834 0.000 -9.8021 -2.8540<30 min – 1 hour

>3 – 6 hours or more -13.59694* 1.90243 0.000 -17.3344 -9.8595

<30 min – 1 hour 6.32803* 1.76834 0.000 2.8540 9.8021>1 – 3 hours

>3 – 6 hours or more -7.26891* 1.71624 0.000 -10.6406 -3.8972

<30 min – 1 hour 13.59694* 1.90243 0.000 9.8595 17.3344>3 – 6 hours or more

>1 –3 hours 7.26891* 1.71624 0.000 3.8972 10.6406

*Mean difference significant at the 0.05 level.



Factor matrix for Internet Addiction Test scores in a sample of Pakistani medical anddental school students (n = 522)


Table 3. Factor matrix for Internet Addiction Test scores in a sample of Pakistani medical and

dental school students (n = 522)

Factor

1 2 3

Item 1: How often do you find that you stay on-line longer than you

intended?0.349 0.461

Item 2: How often do you neglect household chores to spend more time

on-line?0.498 0.348

Item 3: How often do you prefer the excitement of the Internet to

intimacy with your partner?0.437

Item 4: How often do you form new relationships with fellow on-line

users?0.416 −0.366

Item 5: How often do others in your life complain to you about the

amount of time you spend on-line?0.549

Item 6: How often do your grades or school work suffers because of the

amount of time you spend on-line?0.609

Item 7: How often do you check your email before something else that

you need to do?0.418

Item 8: How often does your job performance or productivity suffer

because of the Internet?0.542 0.393

Item 9: How often do you become defensive or secretive when anyone

asks you what you do on-line?0.574


Item 10 : How often do you block out disturbing thoughts about your life

with soothing thoughts of the Internet?0.583

Item 11 : How often do you find yourself anticipating when you will go

on-line again?0.563

Item 12: How often do you fear that life without the Internet would be

boring, empty, and joyless?0.483

Item 13: How often do you snap, yell, or act annoyed if someone bothers

you while you are on-line?0.641

Item 14: How often do you lose sleep due to late-night log-ins? 0.605

Item 15: How often do you feel preoccupied with the Internet when off-

line, or fantasize about being on-line?0.637

Item 16: How often do you find yourself saying “just a few more minutes”

when on-line?0.602

Item 17: How often do you try to cut down the amount of time you spend

on-line and fail?0.565

Item 18: How often do you try to hide how long you’ve been on-line? 0.628

Item 19: How often do you choose to spend more time on-line over going

out with others?0.611

Item 20: How often do you feel depressed, moody or nervous when you

are off-line, which goes away once you are back on-line?0.640


1



Unrotated factor solution for Internet Addiction Test scores (n = 522)


1 Table 4. Unrotated factor solution for Internet Addiction Test scores (n = 522)

Factor

Item 1 0.338

Item 2 0.487

Item 3 0.431

Item 4 0.405

Item 5 0.549

Item 6 0.601

Item 7 0.418

Item 8 0.530

Item 9 0.576

Item 10 0.586

Item11 0.565

Item 12 0.484

Item 13 0.641

Item 14 0.605

Item 15 0.634

Item 16 0.596

Item 17 0.565

Item 18 0.629

Item 19 0.606

Item 20 0.637

Extraction method: Principal axis factor analysis


2



Item total statistics for statements in the Internet Addiction Test (n = 522)


1 Table 5. Item total statistics for statements in the Internet Addiction Test (n = 522)

Item Scale mean if item

deleted

Scale variance if

item deleted

Corrected item

total correlation

Squared multiple

correlation

Cronbach’s alpha if

item deleted

Item 1 39.41 270.579 0.323 0.272 .894

Item 2 39.77 266.092 0.471 0.344 .889

Item 3 41.08 266.434 0.406 0.286 .891

Item 4 41.06 270.416 0.375 0.291 .892

Item 5 40.33 262.256 0.522 0.311 .888

Item 6 40.39 262.554 0.573 0.425 .886

Item 7 40.36 268.442 0.401 0.215 .891

Item 8 40.54 264.702 0.501 0.386 .888

Item 9 40.57 262.794 0.547 0.370 .887

Item 10 40.20 259.364 0.556 0.371 .887

Item 11 40.46 262.782 0.531 0.346 .887

Item 12 39.66 263.897 0.457 0.278 .890

Item 13 40.46 260.269 0.598 0.426 .885

Item 14 40.08 257.881 0.575 0.398 .886

Item 15 40.63 261.108 0.589 0.418 .886

Item 16 39.62 259.275 0.562 0.399 .886

Item 17 40.12 262.735 0.531 0.353 .887

Item 18 40.45 259.193 0.586 0.418 .886

Item 19 40.67 261.338 0.564 0.425 .886

Item 20 40.68 259.375 0.593 0.468 .885


2



Comparison of psychometric properties of different versions of the Internet AddictionTest


1 Table 6. Comparison of psychometric properties of different versions of the Internet Addiction

2 Test

Version Characteristics of

study sample

Factor analysis method Criteria for

number of

factors

Names of factors, % variance

and reliability

Arabic19 817 intermediate

and secondary

school students,

mean age 15 (2.12)

years

PCA with oblimin

rotation

Parallel analysis and

Velicer’s minimum

average partial (MAP)

test

Confirmatory factor

analysis

Two

components

with eigenvalue

>1 but only one

of these

retained

One factor, 40.64%, α= .921

Malay30 162 undergraduate

medical students,

mean age 19 (0.19)

years

Principal component

analysis with varimax

rotation method

Five factors

with eigenvalue

>1

Lack of control, Neglect of duty,

Social relationship disruption,

Problematic use, email primacy,

63.84%, Factor-wise alpha

values: .55 – .89, For all items:

.91

Banglades

h21

177 internet users,

mean age 22.33

(2.01) years

Principal component

(PC) with varimax

rotation

Four based on

Cartell scree

plot

Neglect of duty, Online

dependence, Virtual fantasies,

and Privacy and


self-defense

55.68%, Cronbach’s alpha = .89

for the IAT, and .60 – .84 for the

factors

Italian31 485 college

students, mean age

24.05 (SD 7.3)

years

Principal axis factoring

with oblique rotation

(promax criterion)

Parallel

analysis, scree

plot, eigenvalue

>1

Emotional and cognitive

preoccupation with the Internet

and

Loss of control and interference

with daily life,

One factor model = 36.18%

Two factor model = 42.15%,

Alpha values for one-factor

solution (Cronbach’s alpha =

.91), and the two-factor

solution

(Cronbach’s alpha = .88 and

Cronbach’s alpha = .79)

Chinese16 844 Hong Kong

Chinese

adolescents

(37.7% boys),

mean age 15.9

(standard deviation

Confirmatory factor

analysis results

indicated 18-item

second-order

three-factor model

- Withdrawal

and social problems, time

management and performance,

and reality substitute. A total of

80.5%, 94.7% and 95.9% of the

total variances


3.5) years of the second-order factor were

accounted for.

Cronbach’s alpha = .93).

Factor wise = .87, .86 and

.70

Korean28 279 college

students at a

national university

Principal components


rotation

Eigenvalue >1 Excessive use, Dependence,

Withdrawal, and Avoidance of

reality, 58.91%, α= .91

Portugese

27

593 Portuguese

students, average

age 19.9 (SD =

2.7) years

Confirmatory factor

analysis, with robust

maximum-likelihood

estimates (MLR)

- One factor, α = .90

Greek20 151 postgraduate

and undergraduate

medical students

Exploratory factor


rotation

Visual

examination of

a scree plot and

eigenvalues >1

‘Psychological/Emotional

conflict, Time management and

Neglect work, 55.3%, α= .91

USA29 215 Undergraduate

students selected

through Facebook

Exploratory factor


rotation

Scree plot and

eigenvalues >1

Dependent use and Excessive

use, 91%, α= .90 – .93

German17 Online (ON)

sample (n= 1041,

age 24.2 – 7.2

EFA with varimax

rotation

Horn’s parallel

analysis

Emotional and cognitive

preoccupation with the Internet

and Loss of control and


years) and Offline

[OF] sample, n =

841, age: 23.5 –3.0

years

interference with daily life,

46.7% (ON) and 42.0% (OF), α

= .91 (ON) and α = .89 (OF)

French26 246 adults, age:

mean 24.11,

standard deviation

9, range 18–54

years)

Exploratory factor

analysis, confirmatory

factor analysis

Velicer’s

minimum

average partial

(MAP) test

One factor, 45%, α = .93

3


1Cattell's scree plot for Internet Addiction Test scores in a sample of Pakistani medicaland dental students


2Histogram of the distribution of Internet Addiction Test scores in a sample of Pakistanimedical and dental students


exploring the psychometric properties of the english ... · pdf file5 mohsin raza1, spogmai...

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