Internet Addiction Test (IAT): Which is the Best Factorial Solution?

Background The Internet Addiction Test (IAT) by Kimberly Young is one of the most utilized diagnostic instruments for Internet addiction. Although many studies have documented psychometric properties of the IAT, consensus on the optimal overall structure of the instrument has yet to emerge since previous analyses yielded markedly different factor analytic results. Objective The objective of this study was to evaluate the psychometric properties of the Italian version of the IAT, specifically testing the factor structure stability across cultures. Methods In order to determine the dimensional structure underlying the questionnaire, both exploratory and confirmatory factor analyses were performed. The reliability of the questionnaire was computed by the Cronbach alpha coefficient. Results Data analyses were conducted on a sample of 485 college students (32.3%, 157/485 males and 67.7%, 328/485 females) with a mean age of 24.05 years (SD 7.3, range 17-47). Results showed 176/485 (36.3%) participants with IAT score from 40 to 69, revealing excessive Internet use, and 11/485 (1.9%) participants with IAT score from 70 to 100, suggesting significant problems because of Internet use. The IAT Italian version showed good psychometric properties, in terms of internal consistency and factorial validity. Alpha values were satisfactory for both the one-factor solution (Cronbach alpha=.91), and the two-factor solution (Cronbach alpha=.88 and Cronbach alpha=.79). The one-factor solution comprised 20 items, explaining 36.18% of the variance. The two-factor solution, accounting for 42.15% of the variance, showed 11 items loading on Factor 1 (Emotional and Cognitive Preoccupation with the Internet) and 7 items on Factor 2 (Loss of Control and Interference with Daily Life). Goodness-of-fit indexes (NNFI: Non-Normed Fit Index; CFI: Comparative Fit Index; RMSEA: Root Mean Square Error of Approximation; SRMR: Standardized Root Mean Square Residual) from confirmatory factor analyses conducted on a random half subsample of participants (n=243) were satisfactory in both factorial solutions: two-factor model (χ2 132= 354.17, P<.001, χ2/df=2.68, NNFI=.99, CFI=.99, RMSEA=.02 [90% CI 0.000-0.038], and SRMR=.07), and one-factor model (χ2 169=483.79, P<.001, χ2/df=2.86, NNFI=.98, CFI=.99, RMSEA=.02 [90% CI 0.000-0.039], and SRMR=.07). Conclusions Our study was aimed at determining the most parsimonious and veridical representation of the structure of Internet addiction as measured by the IAT. Based on our findings, support was provided for both single and two-factor models, with slightly strong support for the bidimensionality of the instrument. Given the inconsistency of the factor analytic literature of the IAT, researchers should exercise caution when using the instrument, dividing the scale into factors or subscales. Additional research examining the cross-cultural stability of factor solutions is still needed.

[1]  Roberta Messina,et al.  Group Dynamics in On-Line and Face-to-Face Interactions: An Experimental Study on Learning Methods , 2011 .

[2]  Huimin Zhang,et al.  Mental Health, Personality, and Parental Rearing Styles of Adolescents with Internet Addiction Disorder , 2010, Cyberpsychology Behav. Soc. Netw..

[3]  C. Holden.  'Behavioral' Addictions: Do They Exist? , 2001, Science.

[4]  B P O'Connor,et al.  SPSS and SAS programs for determining the number of components using parallel analysis and Velicer’s MAP test , 2000, Behavior research methods, instruments, & computers : a journal of the Psychonomic Society, Inc.

[5]  T. Field,et al.  The relationship of Internet use to depression and social isolation among adolescents. , 2000, Adolescence.

[6]  Christos C. Frangos,et al.  A Meta-analysis of the Reliabilty of Young's Internet Addiction Test , 2012 .

[7]  H. Akaike.  A new look at the statistical model identification , 1974 .

[8]  Robert F. DeVellis,et al.  Scale Development: Theory and Applications. , 1992 .

[9]  J. H. Steiger.  Structural Model Evaluation and Modification: An Interval Estimation Approach. , 1990, Multivariate behavioral research.

[10]  A. Gori,et al.  The Impact of Event Scale – Revised: psychometric properties of the Italian version in a sample of flood victims , 2013, Neuropsychiatric disease and treatment.

[11]  Kyunghee Kim,et al.  Internet addiction in Korean adolescents and its relation to depression and suicidal ideation: a questionnaire survey. , 2006, International journal of nursing studies.

[12]  Domenico De Berardis,et al.  Alexithymia and Its Relationships with Dissociative Experiences and Internet Addiction in a Nonclinical Sample , 2009, Cyberpsychology Behav. Soc. Netw..

[13]  P. Faraci,et al.  Factorial Structure and Psychometric Properties of the Sensation Seeking Scale – Form V (SSS-V) in a Sample of Italian Adolescents , 2013 .

[14]  Giuseppe Craparo,et al.  Internet addiction, dissociation, and alexithymia , 2011 .

[15]  Martial Van der Linden,et al.  French Validation of the Internet Addiction Test , 2008, Cyberpsychology Behav. Soc. Netw..

[16]  Mary McMurran,et al.  The Psychometric Properties of the Internet Addiction Test , 2004, Cyberpsychology Behav. Soc. Netw..

[17]  P. Bentler,et al.  Cutoff criteria for fit indexes in covariance structure analysis : Conventional criteria versus new alternatives , 1999 .

[18]  Mark D. Griffiths,et al.  Does Internet and Computer "Addiction" Exist? Some Case Study Evidence , 2000, Cyberpsychology Behav. Soc. Netw..

[19]  H. Akaike.  Factor analysis and AIC , 1987 .

[20]  Jonathan J. Kandell,et al.  Internet Addiction on Campus: The Vulnerability of College Students , 1998, Cyberpsychology Behav. Soc. Netw..

[21]  M. Griffiths.  Internet Addiction - Time to be Taken Seriously? , 2000 .

[22]  Kimberly Young,et al.  Internet Addiction: The Emergence of a New Clinical Disorder , 1998, Cyberpsychology Behav. Soc. Netw..

[23]  K. Young,et al.  Psychology of Computer Use: XL. Addictive Use of the Internet: A Case That Breaks the Stereotype , 1996, Psychological reports.

[24]  Steven J. Kass,et al.  A Factor Analytic Study of the Boredom Proneness Scale , 1990 .

[25]  Malcolm R. Parks.  Making Friends in Cyberspace , 1996, J. Comput. Mediat. Commun..

[26]  J. Gackenbach.  Psychology and the Internet : intrapersonal, interpersonal, and transpersonal implications , 1998 .

[27]  C. Ko,et al.  Proposed Diagnostic Criteria of Internet Addiction for Adolescents , 2005, The Journal of nervous and mental disease.

[28]  Carolyn A. Watters,et al.  Examining the structure of the Internet Addiction Test in adolescents: A bifactor approach , 2013, Comput. Hum. Behav..

[29]  Nazir S. Hawi.  Arabic Validation of the Internet Addiction Test , 2013, Cyberpsychology Behav. Soc. Netw..

[30]  R. Cattell.  The Scree Test For The Number Of Factors. , 1966, Multivariate behavioral research.

[31]  Karl G. Jöreskog,et al.  Lisrel 8: Structural Equation Modeling With the Simplis Command Language , 1993 .

[32]  R. Tao,et al.  Proposed diagnostic criteria for internet addiction. , 2010, Addiction.

[33]  M. Griffiths.  Internet addiction: Does it really exist? , 1998 .

[34]  P. Bentler,et al.  Comparative fit indexes in structural models. , 1990, Psychological bulletin.

[35]  Antonella D'Amico,et al.  Internet Addiction Disorder: An Italian Study , 2007, Cyberpsychology Behav. Soc. Netw..

[36]  Megan A. Moreno,et al.  Assessing the psychometric properties of the Internet Addiction Test (IAT) in US college students , 2012, Psychiatry Research.

[37]  Li Lei,et al.  Adolescents' Paternal Attachment and Internet Use , 2007, Cyberpsychology Behav. Soc. Netw..

[38]  M. Giannini,et al.  ASSESSING SOCIAL ANXIETY DISORDER: PSYCHOMETRIC PROPERTIES OF THE ITALIAN SOCIAL PHOBIA INVENTORY (I-SPIN) , 2013 .

[39]  J. Horn.  A rationale and test for the number of factors in factor analysis , 1965, Psychometrika.

[40]  W. Velicer,et al.  Comparison of five rules for determining the number of components to retain. , 1986 .

[41]  P. Faraci,et al.  Fear of flying assessment: A contribution to the Italian validation of two self-report measures , 2011 .

[42]  Peter M. Bentler,et al.  EQS : structural equations program manual , 1989 .

[43]  Zsolt Demetrovics,et al.  The three-factor model of Internet addiction: The development of the Problematic Internet Use Questionnaire , 2008, Behavior research methods.

[44]  B. Price.  A First Course in Factor Analysis , 1993 .

[45]  P. Bentler,et al.  Significance Tests and Goodness of Fit in the Analysis of Covariance Structures , 1980 .

[46]  Howard B. Lee,et al.  A First Course in Factor Analysis 2nd Ed , 1973 .

[47]  Vesile Oktan.  The predictive relationship between emotion management skills and Internet addiction , 2011 .

[48]  Xianggui Qu,et al.  Multivariate Data Analysis , 2007, Technometrics.

[49]  Antonia Barke,et al.  The German Version of the Internet Addiction Test: A Validation Study , 2012, Cyberpsychology Behav. Soc. Netw..

[50]  Adriano Schimmenti,et al.  Attachment disorganization and dissociation in virtual worlds: A study on problematic Internet use among players of online role playing games. , 2012 .