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Published on 22.04.15 in Vol 2, No 2 (2015): April-June

This paper is in the following e-collection/theme issue:

    Original Paper

    Factor Structure of the Internet Addiction Test in Online Gamers and Poker Players

    1Geneva University Hospitals, Geneva, Switzerland

    2Laboratory for Experimental Psychopathology, Psychological Sciences Research Institute, Catholic University of Louvain, Louvain-la-Neuve, Belgium

    3Sherbrooke University, Montreal, QC, Canada

    Corresponding Author:

    Yasser Khazaal, MD

    Geneva University Hospitals

    Grand Pré 70C

    Geneva, 1206

    Switzerland

    Phone: 41 223725550

    Fax:41 223202840

    Email:


    ABSTRACT

    Background: The Internet Addiction Test (IAT) is the most widely used questionnaire to screen for problematic Internet use. Nevertheless, its factorial structure is still debated, which complicates comparisons among existing studies. Most previous studies were performed with students or community samples despite the probability of there being more problematic Internet use among users of specific applications, such as online gaming or gambling.

    Objective: To assess the factorial structure of a modified version of the IAT that addresses specific applications, such as video games and online poker.

    Methods: Two adult samples—one sample of Internet gamers (n=920) and one sample of online poker players (n=214)—were recruited and completed an online version of the modified IAT. Both samples were split into two subsamples. Two principal component analyses (PCAs) followed by two confirmatory factor analyses (CFAs) were run separately.

    Results: The results of principal component analysis indicated that a one-factor model fit the data well across both samples. In consideration of the weakness of some IAT items, a 17-item modified version of the IAT was proposed.

    Conclusions: This study assessed, for the first time, the factorial structure of a modified version of an Internet-administered IAT on a sample of Internet gamers and a sample of online poker players. The scale seems appropriate for the assessment of such online behaviors. Further studies on the modified 17-item IAT version are needed.

    JMIR Mental Health 2015;2(2):e12

    doi:10.2196/mental.3805

    KEYWORDS



    Introduction

    As the main medium of modern life, the Internet is used in a wide range of human activities. This expansion has numerous benefits, including its use for social, psychological, and medical purposes, as shown by a wide range of studies on eHealth [1-11]. In parallel, however, serious concerns have been raised related to problematic or excessive uncontrolled Internet use [12-15].

    In recent years, several studies proposed incorporating Internet addiction as a new diagnosis into the Diagnostic and Statistical Manual of Mental Disorders (DSM) [16,17]. However, it was recently decided that empirical research is still too scarce to allow its inclusion as a new psychiatric disorder in the fifth edition (DSM-5). The validity of the Internet addiction construct is challenged by both theoretical and empirical concerns. For some researchers, Internet addiction is an umbrella construct that encompasses a variety of different dysfunctional behaviors related to involvement in different online activities that do not necessarily coexist (eg, video games, cybersex, social networks, online gambling). Internet addiction thus seems to pertain to specific domains of use (ie, gaming, cybersex, social networks, or gambling) rather than to a general pattern of Internet use [12,18]. From this perspective, Internet gaming disorder (IGD) was proposed as a new condition and included in Section III of the DSM-5. The goal of Section III is to foster research on the conditions included therein [19].

    With this potential new diagnosis, an important challenge is to develop assessment tools that are able to capture the specificity of this phenomenon, not only in terms of presence or absence of a given diagnostic, but also in terms of gradient severity. Since the initial research on Internet addiction, several psychometric measures have been developed [20-22]. One of the most translated tool is the Internet Addiction Test (IAT) [23]. The IAT is considered to be one of the instruments providing the most relevant clinical information about Internet gaming addiction [22,24]. The IAT has been moderately to highly correlated with a number of other measures of excessive Internet use, such as the Compulsive Internet Use Scale [25-28], the Generalized Problematic Internet Use Scale [29], the Revised Chen Internet Addiction Scale [30], and the Korean Internet Addiction Scale [31].

    Despite the large diffusion of the IAT for research purposes, there is wide disagreement related first, to its factor structure [29] and second, to possible problematic items. In regard to the factor structure (see Multimedia Appendix 1), one to six factors were reported across studies published in English [27-45]. Furthermore, when similar numbers of factors were found, differences were observed in the distribution of the items on the factors. In addition, when more than one factor was extracted, a number of studies reported high correlations between factors [30,41,43], or a low contribution of some factors to the percentage of explained variance [27,35,42-45].

    The issues related to the factorial structure of the IAT were possibly complicated by specific item-related concerns [28]. This led some authors to modify or discard items [27,28,30,31,34,41,45]. The most repeatedly reported concerns were the following:

    1. Item 4—“How often do you form new relationships with fellow online users?”—has problematic loadings in a number of studies [27,28,36]. The recent rise of social networking, which widely disseminated this phenomenon, was suggested as a plausible explanation.

    2. Item 6—on consequences on school work—and Item 8—related to job performance—ask about similar fields. However, the answer may differ depending on the participant’s understanding and on the specific status of the participant. Unsurprisingly, covariance was repeatedly found between these items [29,30,32,33]. Some authors removed Item 8 [28,34,44,45] or Item 6 [45] after analysis.

    3. A similar type of overlap was shown between Item 3—“How often do you prefer the excitement of the Internet to intimacy with your partner?”—and Item 19—“How often do you choose to spend more time online over going out with others?” This lead some authors to discard one of the items [28] or to modify Item 3 [27] because of the influence of lifestyle (ie, having a partner or not) on the answer options.

    4. In contrast with the other IAT components, Item 7—“How often do you check your email before something else that you need to do?”—is not related to the Internet in general, but to a specific use (ie, emails). Concerns were reported in a number of studies [27-29,31,35,41,44], in part because of the wide dissemination of email, and in part because of changes in this type of communication—the automatic notification of its reception, thus, no need to “check”, and its progressive replacement by social media. The item was deleted by some authors [28,30,41] and modified by others [31].

    5. As a result of permanent Internet access (ie, without a specific need to log in), a rewording of Item 14 was proposed, as follows: “How often do you lose sleep due to being online late at night?” [28].

    As shown, some of these IAT items involved specific patterns of life, such as being employed or being in a relationship. The “not applicable” answer option was probably included for this reason. It was, however, considered to be problematic by some authors [28] and was not systematically used.

    In addition to conflicting results on the structure and certain items of the IAT, the psychometric characteristics were mostly assessed with students or community samples (Multimedia Appendix 1).

    To our knowledge, no previous studies have assessed the psychometric characteristics of the instrument specifically for users of a given Internet application such as Internet games or gambling sites, despite the wide use of the scale, with or without modification, in studies related to these specific patterns of use [28,46,47].

    In the context of increasing interest in possible Internet addiction-related disorders—with common involvement of gaming and gambling—and the emergence of the DSM-5 concept of the Internet gaming disorder, the use of a modified IAT for assessment of online gaming and online gambling may be worthwhile. Textbox 1 shows a modified version of the IAT for Internet game use.

    Moreover, the lack of published studies on the psychometrical properties of the IAT on samples of gamers or gamblers appears to be an important weakness. This is of particular importance considering the increasing resemblance between gambling and gaming [48] and the possible links between related online activities (ie, massively multiplayer online role-playing games, online poker) and patterns of problematic Internet use [10,49-53].

    The main goal of this study, therefore, was to investigate the factorial structure of the French version of the IAT modified for Internet gaming—or gambling—when used online, using samples of Internet gamers and Internet gamblers.

    Table 1 shows the correspondence of the IAT items in Textbox 1 with the DSM-5 gaming and gambling criteria.


    Textbox 1. The Internet Addiction Test modified for Internet game use.
    View this box
    Table 1. Internet Addiction Test item correspondence with the Diagnostic and Statistical Manual of Mental Disorders, fifth edition, gaming and gambling disorders criteria.
    View this table

    Methods

    Participants

    Two samples were used in this study: a French-speaking sample of World of Warcraft (WoW) players and a French-speaking sample of Internet poker players. These two samples completed the same modified version of the IAT. The ethical committee of the Department of Psychology of the University of Geneva—for the WoW sample—and the ethical committee of the Geneva University Hospitals—for the poker players sample—approved the study.

    World of Warcraft Players Sample

    The WoW sample was taken from a larger study on the relationships between players’ self-reported motives to play and their in-game behaviors [46]. To participate in this study, an individual had to be a French-speaking WoW player and at least 18 years old. Participants were recruited through advertisements posted in dedicated French-language forums: a guilds forum, an official Blizzard WoW forum, and more general online and video games forums. Some participants also joined the study after hearing about it through the local press or television interviews. All participants gave their online consent prior to starting the online survey. The sample included 920 subjects who completed the French-language translation of the IAT [32] modified for online gaming. The mean age of IAT completers was 26.0 years (SD 7.8) and 807 of the 920 subjects (87.7%) were men.

    Poker Players Sample

    Overview

    The poker players sample was taken from a study on online gambling. Inclusion criteria included playing online poker, speaking French, and being at least 18 years old. Participants were recruited through advertisements posted in dedicated French-language forums on online gambling or poker. All participants gave their online consent. The sample of poker players included 442 participants, of whom 214 (48.4%) completed the IAT. The mean age of IAT completers was 31.9 years (SD 9.5) and 425 of the 442 participants (96.2%) were men.

    Measurements

    All participants—WoW gamers and poker players—completed the same modified IAT. The scale is a 20-item auto-questionnaire [23] rated on a 5-point Likert scale from 1 (rarely) to 5 (always) with a maximum total score of 100. The rating also includes a “not applicable” option that has a rating of 0. In this study, we used the validated French version [32], which was adapted by replacing words directly related to the Internet with words describing the specific activity (eg, “How often do you find that you stay online longer than you intended?” was replaced with “How often do you find that you stay in-game longer than you intended?”).

    Data Analyses

    Because no clear factor structure has emerged in the literature and because different studies that found the same number of factors were inconsistent regarding factor loadings, we decided to assess the factor structure underlying this questionnaire from scratch (ie, without imposing a specific model or number of factors). In order to achieve this goal, both samples were randomly split into two subsamples of half of the size of the original ones (ie, 107 subjects for the poker sample and 460 for the WoW sample). Two principal component analyses (PCAs) were first performed on the first subsamples separately. With the discrete nature of the IAT items, PCA is preferred over factor analysis since PCA does not assume any particular multivariate model, which is not the case for factor analysis [54]. Moreover, it is known that when the same number of factors or components are extracted, both techniques yield highly similar results [55]. The number of components to extract was determined by the scree test [56] and by Velicer’s minimum average partial (MAP) test done on the correlation matrix [57]. The reliability of the questionnaire was assessed by using the Cronbach alpha coefficient [58], which is a measure of internal consistency.

    In a second step, two confirmatory factor analyses (CFAs) were conducted to validate the structure that emerged from the PCA. The CFAs were run on the second subsamples. For the same reasons that PCA was preferred, the unweighted least-square method was chosen as the procedure for estimation. Four preestablished criteria were selected as indicators of the goodness of fit to the data: (1) goodness-of-fit index >.90 [59], (2) adjusted goodness-of-fit index >.80 [59], (3) normed-fit index >.90 [60], and (4) root-mean-square error <.08. The use and cutoff of the goodness-of-fit index and the adjusted goodness-of-fit index were recommended by Cole [61], the normed-fit index by Bentler and Bonnet [60], and the root-mean-square error by Hu and Bentler [62].

    The PCA was done with R 3.1.0, using psych and bootstrap packages, and the CFA was done with AMOS 21.0.0 [63].


    Results

    Principal Component Analysis

    The MAP test and the scree test clearly suggested in both subsamples that one component be extracted. In order to evaluate the stability of the PCA, a bootstrap technique [64] was performed first with the MAP test, which confirmed the one-factor solution—among the 1000 bootstrap samples, 60.10% (601) and 84.60% (846) suggested retaining one factor in the poker and in the WoW subsamples, respectively. The bootstrap was also applied to factor loadings of the PCA. Items 4 and 6 had a very low loading on the factor, confirmed by the confidence intervals based on the bootstrap in both subsamples, suggesting that these questions may not be well-suited for the questionnaire (Table 2).

    The percentage explained variance (95% CI) was 41.6 (31.6-51.1) for poker players and 36.1 (32.6-39.8) for WoW players. The reliability, as reported by Cronbach alpha (95% CI), was .92 (.88-.95) for poker players and .90 (.88-.92) for WoW players.

    Table 2. The 20-item Internet Addiction Test results from principal component analysis.
    View this table

    Reliability

    Cronbach alpha was above .90 in both subsamples, which was found to be excellent. It is worth noting that when Item 4 or Item 6 were removed, Cronbach alpha increased from .92 to .93 for poker players and from .90 to .91 for WoW players.

    Confirmatory Factor Analysis

    According to the cutoff defined above, all four goodness-of-fit indices were considered excellent in both subsamples (Table 3).

    Shorter Version of the Internet Addiction Test: 17-Item Questionnaire

    Because some items had low loadings and some questions had more missing values than occurred in the rest of the questionnaire, we performed additional investigations. In particular, Question 4—“How often do you form new relationships with fellow game users?”—seemed somewhat outdated and thus no longer relevant. Moreover, it had a low loading and decreased Cronbach alpha. Therefore, we decided to remove it.

    Since Question 6—“How often do your grades or school work suffer because of the amount of time you spend in-game?”—is more suitable for school-aged persons, whereas Question 8—“How often does your job performance or productivity suffer because of the game?”—is more adapted to adults, we decided to merge the two questions into one. This new question addresses the consequences for the participant’s principal occupation, either school or work, preventing the participant from omitting the answer because it is not applicable. For the same reasons, we also merged Question 3—“How often do you prefer the excitement of the game to intimacy with your partner?”—and Question 19—“How often do you choose to spend more time in-game over going out with others?”

    These modifications led to a 17-item questionnaire. Despite the fact that this version had not been tested on new subjects, we performed the same analyses as we did for the original questionnaire—randomly split both samples into two subsamples, running MAP, PCA, and CFA—by using the data at hand. For the merged questions, we decided to create two new items as follows: use the maximum mark of the IAT Item 3 and Item 19, as well as of Item 6 and Item 8, for each participant when both questions have been answered, or use the mark of only a single answered question.

    In accordance with these modifications, the WoW sample size of IAT completers increased from 920 to 942 subjects, and the French-speaking poker sample size increased from 214 to 232. As expected, the results with this new method of coding the questionnaire led to the same conclusions regarding the number of components to extract—the bootstrapped MAP test suggested retaining only one factor in 74.2% and 95.6% of the poker and the WoW subsamples, respectively—regarding the factorial solution with the benefit of avoiding low loadings (Table 4), and regarding Cronbach alpha. Moreover, every fit index from the CFA was the same or slightly better (Table 3).

    The percentage explained variance (95% CI) was 48.4 (37.5-58.1) for poker players and 37.3 (33.4-41.0) for WoW players. The reliability, as reported by Cronbach alpha (95% CI), was .93 (.90-.96) for poker players and .89 (.87-.91) for WoW players.

    Table 3. Results from unweighted least-square confirmatory factor analysis.
    View this table
    Table 4. The 17-item Internet Addiction Test results from principal component analysis.
    View this table

    Discussion

    Principal Findings

    This study is the first to assess, to our knowledge, the psychometric characteristics, and specifically the factorial structure, of the IAT in online samples of WoW gamers and poker players. The main finding is that the one-factor model of the IAT has good psychometric properties and fits the data well in these samples.

    In consideration to both the important discrepancies in the factorial solutions found in the previous studies on the IAT and the inconsistencies in the items included in a given factor in studies with a similar number of factors (Multimedia Appendix 1), the study at hand used an exploratory approach (ie, PCA) rather than an assessment of multiple competitive CFA models. 

    Although heterogeneous results were found regarding the one-factor solution in previously reported studies, it was considered the best factor solution, or as possibly an acceptable factor solution (Multimedia Appendix 1) [32-34,37-39,44,45].

    As suggested elsewhere [39], the instability of the IAT factorial structure is possibly linked to its multiple strongly correlated facets. The variability of the samples studied, as well as some specific item-related aspects, may have contributed to the phenomenon. This consideration led some authors to propose modifications of the IAT [27,28,30,31,34,39].

    The modified 17-item IAT scale proposed here (Table 4) also has a one-factor structure. The modification and combination of items related to a specific consequence on lifestyle (eg, students, workers) may reduce the sample variability of the proposed tool. The changes related to Item 6—impact on school—and Item 8—impact on occupation—are concordant with other findings [29,30,32,33], which previously led some authors to remove one of the two items [28,34,44,45]. Removing Item 6 or 8, however, may cause variable results, depending on the population studied (ie, students or workers). The modification proposed here may allow the answer to the combined item to depend less on the population studied. Similarly, observations about Item 3 and Item 19 of the IAT led some authors to discard or modify one of these items [28]. Again, the 17-item IAT may reduce sample-related changes in the scale characteristics.

    The problematic loading of Item 4 was also reported in some studies [27,28,31,36]. In consideration of the emergence and wide dissemination of online social networks, this item appears to be less pertinent for the assessment of Internet addiction. In contrast with the results of previous studies, however, those of this study did not indicate specific problems related to Item 7—“How often do you check your email before something else that you need to do?” [27,28,35,41,44]—or Item 14—“How often do you lose sleep due to being online late at night?” [28]. Because of the concerns related to Item 7, some authors chose to delete it [28,30,41], whereas others proposed replacing “emails” with “Internet” [31]. In relation to this, the modification proposed by Lee and colleagues [31] seems relevant for further studies. The word “Internet” could be replaced with the specific Internet usage studied (ie, game). An interesting rewording of Item 14 was also proposed elsewhere [28] as follows: “How often do you lose sleep due to being online late at night?” The development of the Internet has led to possible permanent access without a requirement to log in. The lack of a specific problem with Items 7 and 14 in this study was possibly due to the age of the sample participants, for whom checking email or logging in were more familiar concepts than they were to younger people. The 17-item IAT could be adapted to include the modification proposed by Lee et al [31] for Item 7 and to that proposed by Pawlikowski et al [28] for Item 14 (Table 4).

    The 17-item IAT scale (Textbox 2) and the 20-item IAT (Textbox 1, Table 1) offer interesting coverage of the main items of the DSM-5 criteria for IGD, with the exception of tolerance and time frame, which were not covered by these tools. As shown in Textbox 2, it is easy to add a time frame using a question such as “During the last year, how often…?”

    As there is an absence of questions related to tolerance in the original IAT, it is then also the case for the proposed data-driven 17-item IAT. A further study may add questions specifically related to tolerance in order to assess the full range of symptoms proposed by the DSM-5 criteria for IGD.

    The relatively good coverage of the 17-item IAT of the DSM-5 criteria for IGD is a possible advantage in comparison to other shorter forms of the IAT that lead, for example, to withdrawal of the escape-related item. Unsurprisingly, the 17-item IAT scale and the original IAT do not cover items related to the financial conflict-related items of the DSM-5 criteria for gambling disorder.

    One of the strengths of this paper is related to the design of a 17-item IAT scale through a data-driven approach, rather than a priori choices.

    Despite the possible lack of items linked to specific Internet use, it appears from this study that the original IAT and the 17-item IAT are interesting assessment tools for disorders related to excessive Internet use. In particular, the results assessed in this study add to the validity of IAT use with specific rewording, such as replacing “Internet” with “game.”


    Textbox 2. Proposed new 17-item Internet Addiction Test gaming questionnaire. a: Possible modification to: “How often do you stay in-game before something else that you need to do?“ b: Possible modification to: “How often do you lose sleep due to being in-game late at night?“
    View this box

    Limitations

    The main limitations of this study include the representativeness of self-selected samples [65], the lack of direct assessment with the modified 17-item IAT version, and the lack of concomitant diagnostic interviews.

    Conclusions

    Further studies may test the 17-item IAT among various samples in parallel with other psychopathological and Internet-related behavior assessments, including the use of the DSM-5 proposed criteria and other assessment tools, with possibly different or complementary coverage of the concept of Internet addiction [20,66-70]. The use of the original IAT, and possibly the 17-item IAT, may stimulate further research on IGD and Internet addiction. Due to the wide variety of Internet activities, such as Internet gambling and Internet gaming, it should be necessary to specify the assessed behavior (eg, WoW, other game, gambling) for each use of the 17-item IAT. Research in these fields seems to be of particular interest, as suggested by the recent inclusion of IGD in the DSM-5, Section III. The relevance of the proposed criteria in the DSM-5 needs to be confirmed, however, as some criteria, such as deception and tolerance, continue to be debated [17].

    Conflicts of Interest

    None declared.

    Multimedia Appendix 1

    Summary of previous studies on the Internet Addiction Test factorial structure.

    PDF File (Adobe PDF File), 111KB

    References

    1. Rodda S, Lubman DI, Dowling NA, Bough A, Jackson AC. Web-based counseling for problem gambling: exploring motivations and recommendations. J Med Internet Res 2013;15(5):e99 [FREE Full text] [CrossRef] [Medline]
    2. Powell J, Hamborg T, Stallard N, Burls A, McSorley J, Bennett K, et al. Effectiveness of a web-based cognitive-behavioral tool to improve mental well-being in the general population: randomized controlled trial. J Med Internet Res 2013;15(1):e2 [FREE Full text] [CrossRef] [Medline]
    3. Bolier L, Haverman M, Kramer J, Westerhof GJ, Riper H, Walburg JA, et al. An Internet-based intervention to promote mental fitness for mildly depressed adults: randomized controlled trial. J Med Internet Res 2013;15(9):e200 [FREE Full text] [CrossRef] [Medline]
    4. Harris IM, Roberts LM. Exploring the use and effects of deliberate self-harm websites: an Internet-based study. J Med Internet Res 2013;15(12):e285 [FREE Full text] [CrossRef] [Medline]
    5. van Gaalen JL, Beerthuizen T, van der Meer V, van Reisen P, Redelijkheid GW, Snoeck-Stroband JB, et al. Long-term outcomes of internet-based self-management support in adults with asthma: randomized controlled trial. J Med Internet Res 2013;15(9):e188 [FREE Full text] [CrossRef] [Medline]
    6. Choi NG, Dinitto DM. Internet use among older adults: association with health needs, psychological capital, and social capital. J Med Internet Res 2013 May;15(5):e97 [FREE Full text] [CrossRef] [Medline]
    7. Ramo DE, Prochaska JJ. Broad reach and targeted recruitment using Facebook for an online survey of young adult substance use. J Med Internet Res 2012;14(1):e28 [FREE Full text] [CrossRef] [Medline]
    8. Schaub M, Sullivan R, Stark L. Snow control - an RCT protocol for a web-based self-help therapy to reduce cocaine consumption in problematic cocaine users. BMC Psychiatry 2011;11:153 [FREE Full text] [CrossRef] [Medline]
    9. Morel V, Chatton A, Cochand S, Zullino D, Khazaal Y. Quality of web-based information on bipolar disorder. J Affect Disord 2008 Oct;110(3):265-269. [CrossRef] [Medline]
    10. Zanetta Dauriat F, Zermatten, Billieux J, Thorens G, Bondolfi G, Zullino D, et al. Motivations to play specifically predict excessive involvement in massively multiplayer online role-playing games: evidence from an online survey. Eur Addict Res 2011;17(4):185-189. [CrossRef] [Medline]
    11. Khazaal Y, Chatton A, Cochand S, Coquard O, Fernandez S, Khan R, et al. Brief DISCERN, six questions for the evaluation of evidence-based content of health-related websites. Patient Educ Couns 2009 Oct;77(1):33-37. [CrossRef] [Medline]
    12. Spada MM. An overview of problematic internet use. Addict Behav 2014 Jan;39(1):3-6. [CrossRef] [Medline]
    13. Koo C, Wati Y, Lee CC, Oh HY. Internet-addicted kids and South Korean government efforts: boot-camp case. Cyberpsychol Behav Soc Netw 2011 Jun;14(6):391-394. [CrossRef] [Medline]
    14. Aboujaoude E. Problematic Internet use: an overview. World Psychiatry 2010 Jun;9(2):85-90 [FREE Full text] [Medline]
    15. Kuss DJ, Griffiths MD, Karila L, Billieux J. Internet addiction: a systematic review of epidemiological research for the last decade. Curr Pharm Des 2014;20(25):4026-4052. [Medline]
    16. Block JJ. Issues for DSM-V: internet addiction. Am J Psychiatry 2008 Mar;165(3):306-307. [CrossRef] [Medline]
    17. Tao R, Huang X, Wang J, Zhang H, Zhang Y, Li M. Proposed diagnostic criteria for internet addiction. Addiction 2010 Mar;105(3):556-564. [CrossRef] [Medline]
    18. Cho H, Kwon M, Choi JH, Lee SC, Choi SW, Choi SW, et al. Development of the Internet addiction scale based on the Internet Gaming Disorder criteria suggested in DSM-5. Addict Behav 2014 Sep;39(9):1361-1366. [CrossRef] [Medline]
    19. Petry NM, Rehbein F, Gentile DA, Lemmens JS, Rumpf HJ, Mößle T, et al. An international consensus for assessing internet gaming disorder using the new DSM-5 approach. Addiction 2014 Sep;109(9):1399-1406. [CrossRef] [Medline]
    20. Demetrovics Z, Urbán R, Nagygyörgy K, Farkas J, Griffiths MD, Pápay O, et al. The development of the Problematic Online Gaming Questionnaire (POGQ). PLoS One 2012;7(5):e36417 [FREE Full text] [CrossRef] [Medline]
    21. King DL, Delfabbro PH, Griffiths MD. Trajectories of problem video gaming among adult regular gamers: an 18-month longitudinal study. Cyberpsychol Behav Soc Netw 2013 Jan;16(1):72-76. [CrossRef] [Medline]
    22. Lortie CL, Guitton MJ. Internet addiction assessment tools: dimensional structure and methodological status. Addiction 2013 Jul;108(7):1207-1216. [CrossRef] [Medline]
    23. Young KS. Caught in the Net: How to Recognize the Signs of Internet Addiction--and a Winning Strategy for Recovery. New York, NY: John Wiley & Sons; Feb 1998.
    24. King DL, Haagsma MC, Delfabbro PH, Gradisar M, Griffiths MD. Toward a consensus definition of pathological video-gaming: a systematic review of psychometric assessment tools. Clin Psychol Rev 2013 Apr;33(3):331-342. [CrossRef] [Medline]
    25. Meerkerk GJ, Van Den Eijnden RJ, Vermulst AA, Garretsen HF. The Compulsive Internet Use Scale (CIUS): some psychometric properties. Cyberpsychol Behav 2009 Feb;12(1):1-6. [CrossRef] [Medline]
    26. Guertler D, Rumpf HJ, Bischof A, Kastirke N, Petersen KU, John U, et al. Assessment of problematic internet use by the Compulsive Internet Use Scale and the Internet Addiction Test: a sample of problematic and pathological gamblers. Eur Addict Res 2014;20(2):75-81 [FREE Full text] [CrossRef] [Medline]
    27. Chong Guan N, Isa SM, Hashim AH, Pillai SK, Harbajan Singh MK. Validity of the Malay version of the internet addiction test: a study on a group of medical students in Malaysia. Asia Pac J Public Health 2015 Mar;27(2):NP2210-NP2219. [CrossRef] [Medline]
    28. Pawlikowski M, Altstotter-Gleich C, Brand M. Validation and psychometric properties of a short version of Young's Internet Addiction Test. Comput Human Behav 2013 May;29(3):1212-1223. [CrossRef]
    29. Barke A, Nyenhuis N, Kröner-Herwig B. The German version of the internet addiction test: a validation study. Cyberpsychol Behav Soc Netw 2012 Oct;15(10):534-542. [CrossRef] [Medline]
    30. Lai CM, Mak KK, Watanabe H, Ang RP, Pang JS, Ho RC. Psychometric properties of the internet addiction test in Chinese adolescents. J Pediatr Psychol 2013 Aug;38(7):794-807 [FREE Full text] [CrossRef] [Medline]
    31. Lee K, Lee HK, Gyeong H, Yu B, Song YM, Kim D. Reliability and validity of the Korean version of the internet addiction test among college students. J Korean Med Sci 2013 May;28(5):763-768 [FREE Full text] [CrossRef] [Medline]
    32. Khazaal Y, Billieux J, Thorens G, Khan R, Louati Y, Scarlatti E, et al. French validation of the internet addiction test. Cyberpsychol Behav 2008 Dec;11(6):703-706. [CrossRef] [Medline]
    33. Hawi NS. Arabic validation of the Internet addiction test. Cyberpsychol Behav Soc Netw 2013 Mar;16(3):200-204. [CrossRef] [Medline]
    34. Panayides P, Walker MJ. Evaluation of the psychometric properties of the Internet Addiction Test (IAT) in a sample of Cypriot high school students: the Rasch Measurement Perspective. EJOP 2012 Aug 29;8(3):327-351. [CrossRef]
    35. Tsimtsiou Z, Haidich AB, Kokkali S, Dardavesis T, Young KS, Arvanitidou M. Greek version of the Internet Addiction Test: a validation study. Psychiatr Q 2014 Jun;85(2):187-195. [CrossRef] [Medline]
    36. Jelenchick LA, Becker T, Moreno MA. Assessing the psychometric properties of the Internet Addiction Test (IAT) in US college students. Psychiatry Res 2012 Apr 30;196(2-3):296-301 [FREE Full text] [CrossRef] [Medline]
    37. Pontes HM, Patrão IM, Griffiths MD. Portuguese validation of the Internet Addiction Test: An empirical study. J Behav Addict 2014 Jun;3(2):107-114 [FREE Full text] [CrossRef] [Medline]
    38. Korkeila J, Kaarlas S, Jääskeläinen M, Vahlberg T, Taiminen T. Attached to the web--harmful use of the Internet and its correlates. Eur Psychiatry 2010 May;25(4):236-241. [CrossRef] [Medline]
    39. Teo T, Kam C. Validity of the Internet Addiction Test for Adolescents and Older Children (IAT-A): tests of measurement invariance and latent mean differences. J Psychoeduc Assess 2014 May 07;32(7):624-637. [CrossRef]
    40. Ferraro G, Caci B, D'Amico A, Di Blasi M. Internet addiction disorder: an Italian study. Cyberpsychol Behav 2007 Apr;10(2):170-175. [CrossRef] [Medline]
    41. Chang MK, Law SPM. Factor structure for Young's Internet Addiction Test: A confirmatory study. Comput Human Behav 2008;24(6):2597-2619.
    42. Widyanto L, Griffiths MD, Brunsden V. A psychometric comparison of the Internet Addiction Test, the Internet-Related Problem Scale, and self-diagnosis. Cyberpsychol Behav Soc Netw 2011 Mar;14(3):141-149. [CrossRef] [Medline]
    43. Widyanto L, McMurran M. The psychometric properties of the internet addiction test. Cyberpsychol Behav 2004 Aug;7(4):443-450. [CrossRef] [Medline]
    44. Faraci P, Craparo G, Messina R, Severino S. Internet Addiction Test (IAT): which is the best factorial solution? J Med Internet Res 2013;15(10):e225 [FREE Full text] [CrossRef] [Medline]
    45. Watters CA, Keefer KV, Kloosterman PH, Summerfeldt LJ, Parker JDA. Examining the structure of the Internet Addiction Test in adolescents: A bifactor approach. Comput Human Behav 2013;29(6):2294-2302.
    46. Billieux J, Van der Linden M, Achab S, Khazaal Y, Paraskevopoulos L, Zullino D, et al. Why do you play World of Warcraft? An in-depth exploration of self-reported motivations to play online and in-game behaviours in the virtual world of Azeroth. Comput Human Behav 2013 Jan;29(1):103-109.
    47. Brand M, Laier C, Pawlikowski M, Schächtle U, Schöler T, Altstötter-Gleich C. Watching pornographic pictures on the Internet: role of sexual arousal ratings and psychological-psychiatric symptoms for using Internet sex sites excessively. Cyberpsychol Behav Soc Netw 2011 Jun;14(6):371-377. [CrossRef] [Medline]
    48. King D, Delfabbro P, Griffiths M. The convergence of gambling and digital media: implications for gambling in young people. J Gambl Stud 2010 Jun;26(2):175-187. [CrossRef] [Medline]
    49. Dufour M, Gendron A, Brunelle N, Leclerc D, Cousineau MM. Adolescent technology use: Profiles of distinct groups and associated risky behaviors. J Addict Res Ther (in press) 2015 (forthcoming).
    50. Billieux J, Chanal J, Khazaal Y, Rochat L, Gay P, Zullino D, et al. Psychological predictors of problematic involvement in massively multiplayer online role-playing games: illustration in a sample of male cybercafé players. Psychopathology 2011;44(3):165-171. [CrossRef] [Medline]
    51. Yau YH, Pilver CE, Steinberg MA, Rugle LJ, Hoff RA, Krishnan-Sarin S, et al. Relationships between problematic internet use and problem-gambling severity: findings from a high-school survey. Addict Behav 2014 Jan;39(1):13-21 [FREE Full text] [CrossRef] [Medline]
    52. Starcevic V. Is Internet addiction a useful concept? Aust N Z J Psychiatry 2013 Jan;47(1):16-19. [CrossRef] [Medline]
    53. Durkee T, Kaess M, Carli V, Parzer P, Wasserman C, Floderus B, et al. Prevalence of pathological internet use among adolescents in Europe: demographic and social factors. Addiction 2012 Dec;107(12):2210-2222. [CrossRef] [Medline]
    54. Schneeweiss H, Mathes H. Factor analysis and principal components. J Multivar Anal 1995 Oct;55(1):105-124. [CrossRef]
    55. Velicer WF, Jackson DN. Component analysis versus common factor analysis: some issues in selecting an appropriate procedure. Multivariate Behav Res 1990 Jan;25(1):1-28. [CrossRef]
    56. Cattell RB. The scree test for the number of factors. Multivariate Behav Res 1966 Apr;1(2):245-276. [CrossRef]
    57. Velicer WF. Determining the number of components from the matrix of partial correlations. Psychometrika 1976;41:321-327.
    58. Cronbach LJ, Meehl PE. Construct validity in psychological tests. Psychol Bull 1955 Jul;52(4):281-302. [CrossRef]
    59. Joreskog KG, Sorbom D. LISREL 8: User's Reference Guide. Skokie, IL: Scientific Software International; 1996.
    60. Bentler PM, Bonett DG. Significance tests and goodness of fit in the analysis of covariance structures. Psychol Bull 1980;88(3):588-606. [CrossRef]
    61. Cole DA. Utility of confirmatory factor analysis in test validation research. J Consult Clin Psychol 1987 Aug;55(4):584-594. [Medline]
    62. Hu LT, Bentler P. Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct Equ Modeling 1999;6:1-55. [CrossRef]
    63. Arbuckle JL. IBM Corporation. Crawfordville, FL: Amos Development Corporation; 2012. IBM SPSS Amos 21: User's Guide   URL: ftp:/​/public.​dhe.ibm.com/​software/​analytics/​spss/​documentation/​amos/​21.0/​en/​Manuals/​IBM_SPSS_Amos_Users_Guide.​pdf [accessed 2015-04-16] [WebCite Cache]
    64. Efron B. The Jackknife, the Bootstrap, and Other Resampling Plans. Philadelphia, PA: Society for Industrial and Applied Mathematics; 1987.
    65. Khazaal Y, van Singer M, Chatton A, Achab S, Zullino D, Rothen S, et al. Does self-selection affect samples' representativeness in online surveys? An investigation in online video game research. J Med Internet Res 2014;16(7):e164 [FREE Full text] [CrossRef] [Medline]
    66. Demetrovics Z, Szeredi B, Rózsa S. The three-factor model of Internet addiction: the development of the Problematic Internet Use Questionnaire. Behav Res Methods 2008 May;40(2):563-574. [Medline]
    67. Caplan SE. Theory and measurement of generalized problematic Internet use: A two-step approach. Comput Human Behav 2010;26(5):1089-1097.
    68. Meerkerk GJ, Van Den Eijnden RJ, Vermulst AA, Garretsen HF. The Compulsive Internet Use Scale (CIUS): some psychometric properties. Cyberpsychol Behav 2009 Feb;12(1):1-6. [CrossRef] [Medline]
    69. Lemmens JS, Valkenburg PM, Peter J. Development and validation of a game addiction scale for adolescents. Media Psychol 2009 Mar 05;12(1):77-95. [CrossRef]
    70. Mak KK, Lai CM, Ko CH, Chou C, Kim DI, Watanabe H, et al. Psychometric properties of the Revised Chen Internet Addiction Scale (CIAS-R) in Chinese adolescents. J Abnorm Child Psychol 2014 Oct;42(7):1237-1245. [CrossRef] [Medline]


    Abbreviations

    CFA: confirmatory factor analysis
    DSM: Diagnostic and Statistical Manual of Mental Disorders
    DSM-5: Diagnostic and Statistical Manual of Mental Disorders, fifth edition
    IAT: Internet Addiction Test
    IAT17: 17-item Internet Addiction Test
    IAT20: 20-item Internet Addiction Test
    IGD: Internet gaming disorder
    MAP: minimum average partial
    N/A: not applicable
    PCA: principal component analysis
    WoW: World of Warcraft


    Edited by G Eysenbach; submitted 25.08.14; peer-reviewed by Z Demetrovics, G Craparo, G Floros; comments to author 10.01.15; revised version received 24.02.15; accepted 06.03.15; published 22.04.15

    ©Yasser Khazaal, Sophia Achab, Joel Billieux, Gabriel Thorens, Daniele Zullino, Magali Dufour, Stéphane Rothen. Originally published in JMIR Mental Health (http://mental.jmir.org), 22.04.2015.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Mental Health, is properly cited. The complete bibliographic information, a link to the original publication on http://mental.jmir.org/, as well as this copyright and license information must be included.