Computers in Human Behavior

3. Method

The questionnaires used in the current study were bilingual (both English and Chinese were shown on the questionnaires). The English and Chinese versions of the Internet Addiction Test (IAT) were adopted from (Young, 1998a) and (Young, 2000). Participants were also asked to provide information on their gender, age, educational background, academic performance, weekly Internet usage, Internet experience, and the type of Internet activity in which they frequently engaged.

The participants in this study were undergraduates at eight universities in Hong Kong: the University of Hong Kong (HKU), the Chinese University of Hong Kong (CUHK), the Hong Kong University of Science and Technology (HKUST), the Hong Kong Polytechnic University (POLYU), the Hong Kong Baptist University (HKBU), the City University of Hong Kong (CITYU), Lingnan University (LU), and the Hong Kong Institute of Education (HKIED).

Over a 6-week data collection period, 480 paper-based questionnaires were evenly distributed to the eight universities. In each university, participants were recruited in campus libraries, canteens, computer centers and student hostels. The questionnaires were given to students who had agreed to participate in the survey; the students were given about 20 minutes to fill out the questionnaires by themselves; and the questionnaires were then collected from the students after they finished filling in the questionnaires.

A total of 410 usable questionnaires were returned, yielding an effective response rate of 87.5%. Among the respondents, 187 were males and 223 were females, with the number of participants rather evenly distributed among the eight universities. (The school shares ranged from 11.5% to 15.1%.) The sample included students majoring in diverse areas of study such as philosophy, arts, law, business administration, social sciences, mathematics, natural sciences, medicine, and computer science.

4. Analysis and results

The responses were subjected to factor analyses to examine the psychometric properties of the Internet Addiction Test scale. The original data set (n = 410) was randomly divided into two equal subsamples, one for exploratory factor analysis (EFA) and the other for confirmatory factor analysis (CFA). The EFA was conducted first to identify the underlying structure of the IAT scale. Then, CFA was performed to validate the results of the EFA.

Having completed the validation process, the next step in the analysis was to test whether Internet addiction scores correlated with academic performance, Internet usage, and Internet experience. To test if Internet addiction varied across genders and across different types of Internet activities, multivariate analysis of variance (MANOVA) was used.

4.1. Exploratory factor analysis

Data from the first subsample (n = 205) were submitted to EFA to investigate the dimensionality of the IAT scale. Principal components factor analysis with promax rotation was used. The promax rotation, an oblique rotation, was used because it is reasonable to assume that any extracted factors relevant to Internet addiction should be inter-correlated. Eigenvalues and Scree plots were used to determine the number of factors to be extracted.

Initially, the full set of IAT items, i.e., all 20 items presented in Appendix D, were subjected to factor analysis. Using the latent root criterion for retaining factors with Eigenvalues greater than 1.0 and the Scree plot, a four-factor structure was identified, with the extracted factors explaining 59.3% of the total variance. However, only one item loaded on Factor 4. That item was “How often do you check your e-mail before something else that you need to do?” While other items are measuring something quite general about the use of the Internet, the item “How often do you check your e-mail before something else that you need to do?” is measuring the usage of a particular kind of application. That may be the reason why it is loaded on its own factor. Since people may use the Internet for different purposes like instant messaging and online games, combined with the fact that email becomes an important means of communication, the item may not be a very good indicator of Internet addiction nowadays. Therefore, this item was discarded and the remaining 19 items were submitted to another principal component factor analysis.

The second factor analysis resulted in three factors. However, “How often do you find yourself anticipating when you will go online again?” (Q11) did not load highly on any of these three factors and consequently was removed. The deletion of the item was based on the empirical indicator of factor loadings. As IAT is still at the early stage of dimensionality assessment, empirically based item inclusion and exclusion were used in order to enhance its reliability and validity (Smith & McCarthy, 1995).

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