Gerald Hurowitz, M.D.·Published 8 May 2008·27 min read
Key points
A number of diagnostic scales have been developed in recent years to assess Internet addiction.
Over the years, researchers have devised different kinds of measurements to operationalize the concept of Internet addiction and related concepts.
The questionnaires used in the current study were bilingual (both English and Chinese were shown on the questionnaires).
The responses were subjected to factor analyses to examine the psychometric properties of the Internet Addiction Test scale.
A number of diagnostic scales have been developed in recent years to assess Internet addiction. To better understand the structure, validity, and reliability of such assessment instruments, Young's Internet Addiction Test (IAT) was evaluated using a confirmatory approach.
Data collected through a survey of 410 Hong Kong university undergraduates was subjected to exploratory factor analysis and data from a hold-out sample was analyzed using confirmatory factor analysis in order to assess the psychometric properties and factor structure of the IAT scale. Three dimensions, namely, "Withdrawal and Social Problems", "Time Management and Performance", and "Reality Substitute" were extracted.
These dimensions were then correlated with a number of criterion variables, including academic performance, online activities, gender, and Internet usage. The results show that academic performance was negatively correlated with the Internet addiction scores. The degree of Internet addiction was also found to vary across different types of online activity, with people engaged in cyberrelationships and online gambling having higher Internet addiction scores.
Keywords: Internet addiction; Young's Internet Addiction Test; Confirmatory factor analysis
The 21st century promises to be a digital era when technologies, especially the Internet will have a profound influence on daily life. The Internet has already changed our life enormously, and the benefits brought about by such a powerful tool are obvious to all. Nevertheless, many studies have suggested that people may use the Internet addictively and that this can exert harmful effects on individuals, altering their social behavior, habits and abilities in a negative way ([Chen et al., 2004], [Stanton, 2002] and [Young, 2004]).
Researchers studying the problems related to Internet use have adopted different terminologies such as Internet addiction, Internet addiction disorder, Internet dependence, problematic Internet use, or pathological Internet use to describe the negative effects of excessive Internet use on personal lives ([Chen et al., 2004],[Chou and Hsiao, 2000], [Davis, 2001], [Goldberg, 1995], [Griffiths, 1998], [Kandell, 1998], [Morahan-Martin and Schumacher, 2000], [Scherer, 1997], [Shaffer, 1996] and [Young, 1998a]). Young (1996) has linked excessive Internet use to DSM-IV criteria (American Psychiatric Association, 1994) and considered it a behavioral addiction similar to pathological gambling. She characterizes Internet addiction as an impulse-control disorder that mainly involves psychological dependence on the Internet (Young, 2004). Although there is no standard definition to date, the phenomenon is commonly conceptualized as the compulsive behavior and cognitions associated with Internet use which results in marked distress in daily life ([Caplan, 2002], [Shapira et al., 2000] and [Young, 1996]).
Increased interest in Internet addiction has prompted the development of instruments like the Internet Addiction Test (Young, 1998a), the Pathological Internet Use scale (Morahan-Martin & Schumacher, 2000), and the Generalized Problematic Internet Use Scale (Caplan, 2002) for assessing Internet use behavior. To better understand the phenomenon, it is crucial to establish the validity and reliability of such instruments. This study attempted to evaluate one of the instruments: Young's (1998a) Internet Addiction Test (IAT).
Written and revised by
Gerald Hurowitz, M.D.
Our doctors write and review articles for accuracy and keep them up to date.
health.am provides general information, not medical advice. Always consult a qualified health professional about your own health.
Young and her associates have done a lot of work on defining Internet addiction (Yellowlees & Marks, 2007), and the IAT is one of the early diagnostic scales that has been developed. Although the IAT was developed ten years ago, it is still employed in recent studies to investigate important phenomena such as the relationship between different kinds of addictions (Pallanti, Bernardi, & Quercioli, 2006), psychiatric comorbidity ([Ha et al., 2006] and [Yang et al., 2005]), and other correlates with Internet addiction ([Ferraro et al., 2007] and [Li and Chung, 2006]).
The IAT has demonstrated strong internal reliability across studies (e.g., [Widyanto and McMurran, 2004], [Yang, 2001], [Yang et al., 2005] and [Young, 1998a]). While the overall reliability of the IAT scale as measured by Cronbach's alpha has been good in these studies, there is a paucity of research that assesses the factor structure of the IAT, and thus analyzes Internet addiction as a multi-dimensional construct. Dimensionality assessment is important because the definition of instrument structure is a prerequisite to subsequent instrument refinement and failure to identify the dimensionality of the scale may lead to inaccurate specifications of theories (Smith & McCarthy, 1995). Moreover, the subscales can provide a greater level of detail than just utilizing Internet addiction as one overall concept. For instance, people may suffer different severity of negative impacts in different aspects of Internet addiction. Knowing this helps us to focus treatments on the area that requires special attention. Consequently, the factor scores can provide information beyond that obtained from the global score of the entire scale (Floyd & Widaman, 1995).
Despite its importance, at the time of our study, only one study with a small sample size of 86 participants could be found to investigate the dimensionality of the IAT (Widyanto & McMurran, 2004). Hence, the present study aimed to examine, refine and validate the dimensionality of the IAT instrument using a confirmatory approach.
{pagebreak}
2. Prior research
Over the years, researchers have devised different kinds of measurements to operationalize the concept of Internet addiction and related concepts. Some of these measurements were built upon the ideas of mental disorder, with items adapted from the diagnostic criteria of DSM-IV (American Psychiatric Association, 1994), whereas others were based on certain theoretical perspectives like the cognitive-behavioral model (Davis, 2001). Besides, there are others which were developed from case studies, experts' opinions or published literature on the symptoms of Internet addiction.
Most assessment tools developed in the early stages were based on the behavioral criteria for substance abuse or substance dependence in the DSM-IV. They were usually designed as a set of diagnostic criteria or checklists to describe the phenomenon of Internet addiction and distinguish people having Internet-related addictive behaviors (Chou, Condron, & Belland, 2005). Some of these examples include Goldberg's (1995) Internet addiction disorder diagnostic criteria (IADDC), Brenner's (1997) Internet-Related Addictive Behavior Inventory (IRABI), and Scherer's (1997) Clinical Symptoms of Internet Dependency (CSID).
The notion of impulse-control disorder and pathological gambling is also a foundation upon which the measurements were built. For example, borrowing from the DSM-IV criteria for pathological gambling, Young (1998b) developed the Diagnostic Questionnaire (YDQ) to survey the world-wide prevalence of Internet addiction and distinguish Internet addicts from other Internet users. In a later study, Young (1998a) expanded her YDQ and constructed a Likert scale assessment called the Internet Addiction Test (IAT). The IAT scale comprises 20 items which assess the severity of any negative consequences arising from excessive Internet use. These items cover an individual's Internet use habits, his/her thoughts about the Internet as well as the related problems of Internet use. For each item, a graded response (1 = "not at all" to 5 = "always") can be selected and the higher summed item scores represent higher levels of Internet addiction.
Problematic Internet Use Questionnaire (PIUQ) was developed on the basis similar to that of the IAT. According to Thatcher and Goolam (2005b), the measurement items of PIUQ were derived from the pathological gambling questionnaire (Lesieur & Blume, 1987), Young's (1996) criteria for Internet addiction as well as published literature on the symptoms of Internet addiction. Validating the PIUQ on a sample of online respondents, Thatcher and Goolam (2005b) suggested that this instrument could measure Internet addiction from three dimensions, namely, online preoccupation, adverse effects, and social interactions.
Some researchers attempted to design the measurements rooted in theoretical perspectives other than the DSM-IV criteria. For example, based on the cognitive-behavioral model (Davis, 2001) derived from psychopathology, Davis, Flett, and Besser (2002) constructed the Online Cognitive Scale (OCS) to measure pathological Internet use. They performed confirmatory factor analysis on a sample of university students and identified four dimensions (diminished impulse control; loneliness/depression; social comfort; distraction) for the OCS. Also based on the cognitive-behavioral approach, Caplan (2002) developed the Generalized Problematic Internet Use Scale (GPIUS) and discovered seven dimensions (mood alteration; social benefits; negative outcomes; compulsive use; excessive time online; withdrawal; social control) for this instrument.
Moreover, there are some measurements that were developed from case studies, experts' opinions or published literature on Internet addiction, such as Griffiths's (1998) Addiction Components Criteria, and the Pathological Use Scale (PUS) devised by Morahan-Martin and Schumacher (2000). Instruments along this vein also include those developed from a factor-analytic approach. For example, using the published literature on the common diagnostic criteria for Internet addiction, Lin and Tsai (2002) designed the Internet addiction scale for Taiwanese high school students (IAST) and have found four dimensions (tolerance; compulsive use and withdrawal; family, school and health problems; interpersonal and financial problems) for this instrument. Besides, based on the items derived from experts' opinions, Ceyhan, Ceyhan, and Gu^rcan (2007) generated the Problematic Internet Usage Scale (PIUS) to assess problematic Internet use for university students. From their results of exploratory factor analysis, they identified three dimensions (negative consequences; social benefit/social comfort; excessive use) for the PIUS.
Although the above-mentioned instruments were developed from different theoretical perspectives and operationalization procedures, our analysis of the literature has revealed certain similarities among the identified dimensions of Internet addiction. Referring to the instruments that measure Internet addiction as a multi-faceted construct – OCS (Davis et al., 2002); GPIUS (Caplan, 2002); IAST (Lin & Tsai, 2002); PIUQ (Thatcher & Goolam, 2005b); PIUS (Ceyhan et al., 2007), the following dimensions are found similar across these instruments:
• Compulsive Internet use and excessive time spent online: extent of compulsive Internet use and failure to control the amount of time spent on the Internet.
• Withdrawal symptoms: feelings of difficulties, depression or moodiness when being restricted from Internet use.
• Using the Internet for social comfort: using the Internet to seek social comfort and disposition toward using online social interaction to replace real-life interpersonal activities.
• Negative consequences related to Internet use: the negative outcomes such as social, academic, or work-related problems resulting from Internet use.
The foregoing discussion indicates that recent instrument developments have explored the multi-dimensional nature of Internet addiction and related constructs. Although the dimensionality of IAT has not been assessed when it was developed, Widyanto and McMurran's (2004) study has extracted six factors – salience, excess use, neglecting work, anticipation, lack of self-control, and neglecting social life – from the 20-item IAT and found that these factors had moderate to good internal consistency (Cronbach's alphas ranged from .54 to.82). While this study has provided some insights into the structure of IAT, one limitation of the study was the small sample size it utilized, as only 86 participants were recruited through the Internet to fill out the Web-based questionnaire. As mentioned, knowing the dimensions of a measurement instrument is important; the current study, thus, attempted to further investigate the factorial structure of IAT and examine how the dimensions may correlate with a number of criterion variables.
Some criterion variables that have been widely studied include the amount of time people spend online, their experience of using the Internet, the negative impacts of Internet addiction on their performances, and the differences in the severity of addictive behavior between genders, or among people involved in different types of Internet activities.
Internet usage has been regarded as an important indicator of Internet addiction, with many studies demonstrating a correlation between the amount of time spent online and the risk of having addictive behaviors ([Brenner, 1997], [Chou and Hsiao, 2000], [LaRose et al., 2003], [Leung, 2004], [Liang, 2003], [Lin and Tsai, 2002], [Morahan-Martin and Schumacher, 2000], [Suhail and Bargees, 2006], [Thatcher and Goolam, 2005a], [Wang, 2001] and [Young, 1998b]). Yet, researchers have argued that the amount of time spent online may not be a sufficient condition to determine Internet addiction as people can use the Internet for different purposes (Hansen, 2002). For example, the extent of addictive behavior could be quite different for people using the Internet for work purposes and for those using it for personal entertainment (Widyanto & McMurran, 2004).
Prior studies have also investigated whether individuals' addictive behaviors will fade out when they become more experienced in using the Internet. However, the findings are still inconclusive. While some researchers have found that beginners are more likely to get addicted to the Internet than experienced users ([Kraut et al., 1998], [Widyanto and McMurran, 2004] and [Young, 1998b]), others have shown that there is no difference between these two groups in terms of the severity of addictive behaviors ([Leung, 2004] and [Thatcher and Goolam, 2005a]).
Moreover, Internet addiction can interfere with one's academic performance and daily life routines ([Chou and Hsiao, 2000], [Scherer, 1997] and [Yoo et al., 2004]). Prior studies have shown that students were vulnerable to Internet addiction and they might use the Internet excessively and ignore their schoolwork ([Chou, 2001], [Nalwa and Anand, 2003] and [Tsai and Lin, 2003]). In Young's (1998b) study, she found that the student respondents encountered work or school-related problems because they had spent too much time on the Internet.
Gender difference is another area that has interested the researchers. Although many studies have investigated this issue, researchers cannot reach an agreement on which gender represents a high-risk group of having Internet-related addictive behaviors. While some studies have shown that Internet addicts tended to comprise females ([Leung, 2004] and [Young, 1998b]), other findings have indicated that males were more inclined to develop Internet addiction than females ([Chou and Hsiao, 2000], [Liang, 2003] and [Scherer, 1997]).
In addition, previous studies have demonstrated that people might not be addicted to the Internet itself but to particular Internet activities. Research findings showed that interactive functions of the Internet were related to the negative impacts of excessive Internet use and people involved in online interactive applications tended to exhibit addictive behaviors ([Davis et al., 2002], [Leung, 2004] and [Li and Chung, 2006]). For instance, Young (1998b) noticed that Internet addicts were attracted to the social support functions of the Internet. Moreover, Thatcher and Goolam (2005a) found that people belonging to the high-risk group of Internet addiction were inclined to play online interactive games and use Internet communication tools. They also found that online gambling was one of the favorite activities for the high-risk group.
In the next section, the methods employed in the current study to investigate the factorial structure of the IAT, and the correlations between the identified IAT dimensions and the above-discussed criterion variables are described.
{pagebreak}
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).
{pagebreak} 4.4.2. Gender and Internet activity
Using the total IAT score and the scores of the three IAT factors as dependent variables, a 2 x 7 multivariate analysis of variance (MANOVA) was performed to test for significant score differences between genders and in terms of seven types of Internet activity.
4.3. Interpretation of the factors
On the basis of this validated factor model, the analysis proceeded to examine the content of the 18 IAT items, which then resulted in assigning the labels "Withdrawal and Social Problems", "Time Management and Performance", and "Reality Substitute" to the three factors respectively.
Factor 1, "Withdrawal and Social Problems", captures one's degree of moodiness or difficulties when constrained to be away from the Internet (e.g., "Q20: How often do you feel depressed, moody or nervous when you are offline, which goes away once you are back online?"). This factor also includes items focusing on interpersonal problems due to Internet use (e.g., "Q13: How often do you snap, yell, or act annoyed if someone bothers you while you are online?").
Factor 2, "Time Management and Performance" involves the degree of compulsive Internet use and one's failure to control or reduce the amount of time spent online (e.g., "Q17: How often do you try to cut down the amount of time you spend online and fail?"). It also covers academic (or, by extension, work) performance problems (e.g., "Q6: How often do your grades or school work suffer because of the amount of time you spend online?").
Factor 3, "Reality Substitute" mainly describes the extent to which an individual regards the Internet environment as another reality and over-depends on it for relieving real-life problems (e.g., "Q12: How often do you fear that life without the Internet would be boring, empty, and joyless?").
The aggregated item scores from the refined IAT instrument represent a person's overall Internet addiction level while the summed item score for each factor reflects the severity of addictive behavior in the corresponding area. Both the total IAT score and factor scores were then further analyzed.
4.4. Internet addiction and other criterion variables
The scores of the three extracted factors and the total IAT score were used to analyze the relationship between the dimensions of Internet addiction and the criterion variables previously discussed.
{pagebreak}
5. Discussion and conclusions
In order to understand Internet addiction from a multi-dimensional perspective and to explore the factor structure of its measurement instrument, the current study sought to explore the factor structure of Young's Internet Addiction Test (IAT) using a confirmatory approach. The results from factor analyses show that the IAT can be characterized as exploring three dimensions of Internet behavior: Withdrawal and Social Problems, Time Management and Performance Effects, and Reality Substitution.
Widyanto and McMurran (2004) have previously proposed a six-factor structure for the IAT. Their six factors were salience, excess use, neglecting work, anticipation, lack of self-control, and neglecting social life. The results of this study suggest that a three-factor structure is a satisfactory representation of the IAT instrument. Comparing the two factor structures, the "Withdrawal and Social Problems" dimension proposed here contains items that loaded on Widyanto and McMurran's (2004) "salience" and "neglecting social life" factors; the "Time Management and Performance" dimension resembles their "lack of self-control" and "neglecting work" factors; while the "Reality Substitute" factor does not have any correspondence. This difference in factor structure may be caused by sampling different groups of people in the respective studies. Widyanto and McMurran (2004) recruited a sample of 86 participants with diverse backgrounds and unknown nationalities through the Internet, whereas our sample consisted of 410 Chinese university students. Thus, these two samples have different demographic and cultural backgrounds. But this is just a speculation as we have got only two samples to compare. For any assessment instruments, it is important to test whether the underlying dimensions are invariant across different samples because this allows the comparison among groups (Byrne, 1989). In order to test for the stability of IAT dimensions, more studies should be conducted on people from different groups and cultures so that comparisons can be made.
Compared with the six factors identified by Widyanto and McMurran (2004), our results suggest that the symptoms for Internet addiction tend to cluster together more strongly in our sample. This information can be useful in understanding the interplay amongst various problem areas when dealing with Internet addiction. Our first factor, "Withdrawal and Social Problems", actually comprises two blocks of items. The "Withdrawal" block is related to the DSM-IV's set of substance dependence criteria, while the "Social Problems" block is related to its substance abuse set (American Psychiatric Association, 1994). These two building blocks have usually been developed as separate dimensions in other measurements of Internet addiction ([Cheng et al., 2003] and [Griffiths, 1998]). However, the results of the current study suggest that the two blocks of items load on a single factor, demonstrating the strong interplay between withdrawal symptoms and an individual's interpersonal problems. This might be explained by Davis's (2001) cognitive-behavioral model of pathological internet use. He found that people suffering from Internet addiction exhibit certain withdrawal symptoms (e.g., defensiveness, diminished impulse control) which can distress their interpersonal relationships. Although people notice that their Internet use behaviors are socially undesirable, they fail to control them, and the frustrations encountered in their offline social life can in turn lead to further withdrawal symptoms. As a result, "Withdrawal" and "Social Problems" can reinforce each other and maintain a vicious cycle. Despite the fact that these two blocks are related to different criteria sets in the DSM-IV, some studies have shown that the abuse and dependence criteria measure similar latent constructs (e.g., [Fulkerson et al., 1999], [Harrison et al., 1998] and [Lewinsohn et al., 1996]). This further supports the contention that "Withdrawal" and "Social Problems" are not easily separable due to their duality, and that it is reasonable to treat them as a single factor.
"Reality Substitute" is a particularly interesting dimension. This dimension reflects the phenomenon of some people using the Internet environment as a substitute and becoming addicted. This dimension is especially specific to Internet addiction because of the unique nature of the Internet. Many significant activities conducted in the real world – shopping, gambling, studying, social interaction, etc. – can be accomplished through the Internet where some less desirable aspects of offline interaction, such as the awkwardness of meeting new people, can be avoided. This might explain how people can become addicted to the Internet in a way that encourages them to, in some respects, live in a virtual world. Although "Reality Substitute" provides another direction for defining the diagnostic criteria for Internet addiction, its construct reliability is relatively low. To enhance the validity and diagnostic utility of this dimension, future research needs to identify a set of representative items for this dimension and validate them empirically.
In addition to measurement validation, this study has investigated how the dimensions of the modified IAT instrument relate to several other variables. It was found that Internet experience was not related to any Internet addiction dimension, while Internet usage was related only with the "Reality Substitute" dimension, and then only weakly. This result is consistent with the suggestion that it is not appropriate to use solely the amount of time spent online as the criterion for identifying Internet addicts, because people may use the Internet for different purposes (Hansen, 2002). As the functions of the Internet and Web are enhanced continuously, people are spending more and more time online to perform productive tasks. Thus, in the long run, it may be necessary to review the criteria used to judge the extent of Internet addiction.
Prior studies have shown that the Internet can distract students from their work ([Chou, 2001], [Chou and Hsiao, 2000], [Hur, 2006], [Scherer, 1997], [Tsai and Lin, 2003] and [Young, 1998b]). In keeping with these findings, the results of the present study also show a significant negative relationship between academic performance and the three dimensions of Internet addiction. This suggests that disrupted academic performance is one of the obvious problems related to Internet addictive behavior.
The results show that people who frequently participate in cyberrelationships and online gambling have relatively higher Internet addiction scores in general. They display more withdrawal symptoms and experience greater social problems, as compared with those who prefer other kinds of Internet activity. These results seem to provide some support for the view that Internet addicts are actually dependent on rewards associated with the Internet use that could also exist offline (Yellowlees & Marks, 2007).
Addictive behaviors are especially serious for people involved in cyberrelationships. This group tends to substitute the real world with the online environment and thinks that life without the Internet is empty and joyless. Consistent with the results of previous studies ([Amichai-Hamburger and Ben-Artzi, 2003], [Caplan, 2002], [Li and Chung, 2006] and [Lin and Tsai, 2002]), these results indicate that the social support offered by a cyberrelationship can lead to more severe addictive behavior. The anonymity of online communication helps ensure that people who seek social contact from the Internet are not necessarily subject to any social consequences in real-life: if an individual offends someone on the Internet, he/she can simply change online identities and start another relationship. Although this clearly might help people fulfill interpersonal needs, heavy reliance on it can make them fail in offline social encounters. For example, people who get used to the virtual context may find it difficult to get along with others without the anonymity of online social interactions, because they can no longer change their identities when they face an unsatisfactory relationship. Feeling frustrated in real-life social contacts, they might prefer to turn back to cyberrelationships and treat them as a substitute.
Gambling is another activity which plays an important role in the development of Internet addiction. This raises the possibility of escalated addiction due to the interaction between pathological gambling and Internet addiction. Gambling in itself is a challenging yet rewarding activity, as it provides people a sense of mastery by requiring them to master a changing bet outcome. The Internet offers gambling opportunities without time or geographical restrictions, and this may result in higher levels of both gambling and Internet addiction. With the growing number of online casinos, people are now facing ample gambling opportunities. Thus, future research should pay attention to the interplay between pathological gambling and the Internet as this may help to inform the proper and early treatment.
Results of this study should be interpreted in the context of its limitations. First, the data in our study were collected from Chinese students and thus the results may not be generalized to the Internet users from other groups and cultures. However, since students represent one of the groups that are vulnerable to both substance and non-substance addictions (Pallanti et al., 2006), they may be at high risk for developing problematic Internet use ([Pallanti et al., 2006] and [Yellowlees and Marks, 2007]). Along with the fact that the IAT has been used in the research on Internet addiction targeting students (e.g., [Pallanti et al., 2006] and [Yang et al., 2005]), it is practical to understand the psychometric properties of the IAT for this group. Second, we recognize that the data used in this study were cross-sectional, with the level of Internet addiction being measured at one point rather than as it was emerging. The development of addictive behavior is an ongoing process whose proper delineation requires a time dimension. Third, this study focused on the relationship between Internet addiction and the criterion variables without addressing the possibility that these variables might influence one another as the addictive behavior develops. Finally, collecting the questionnaires directly from the students might affect the participants' disclosure of some sensitive information. Thus, a social desirability scale may be added to the questionnaire in the future studies to check this aspect of the response.
Despite these limitations, the implications drawn from the results extend the understanding of Internet-related addictive behavior and provide a good basis for future research. More studies on the structure of Internet addiction can enhance our understanding of the phenomenon and the characteristics of the measurement instruments. Future studies of a similar nature can be conducted using different groups of people so that the validity and the reliability of IAT can be evaluated. Moreover, the reality-substitute effect of the Internet is an area worth of further investigation.
References
American Psychiatric Association, 1994 American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders (DSM-IV). (4th ed.) Washington, DC: American Psychiatric Association.
Amichai-Hamburger and Ben-Artzi, 2003 Y. Amichai-Hamburger and E. Ben-Artzi, Loneliness and Internet use, Computers in Human Behavior 19 (1) (2003), pp. 71–80.
Anderson and Gerbing, 1988 J.C. Anderson and D.W. Gerbing, Structural equation modeling in practice: A review and recommended two-step approach, Psychological Bulletin 103 (3) (1988), pp. 411–423.
Bollen, 1989 K.A. Bollen, Structural equations with latent variables, Wiley, New York (1989).
Brenner, 1997 V. Brenner, Psychology of computer use 47: Parameters of Internet use, abuse and addiction: The first 90 days of the Internet usage survey, Psychological Reports 80 (3) (1997), pp. 879–882.
Byrne, 1989 B.M. Byrne, Multigroup comparisons and the assumption of equivalent construct validity across group: Methodological and substantive issues, Multivariate Behavioral Research 24 (1989), pp. 503–523.
Caplan, 2002 S.E. Caplan, Problematic Internet use and psychosocial well-being: Development of a theory-based cognitive-behavioral measurement instrument, Computers in Human Behavior 18 (5) (2002), pp. 553–575.
Ceyhan et al., 2007 E. Ceyhan, A.A. Ceyhan and A. Gu^rcan, The validity and reliability of the Problematic Internet Usage Scale, Educational Science: Theory and Practice 7 (1) (2007), pp. 411–416.
Chang et al., 2004 C.J. Chang, G. Torkzadeh and G. Dhillon, Re-examining the measurement models of success for Internet commerce, Information and Management 41 (5) (2004), pp. 577–584.
Chen et al., 2004 K. Chen, M. Tarn and B.T. Han, Internet dependency: Its impact on online behavioral patterns in e-commerce, Human Systems Management 23 (2004), pp. 49–58.
Cheng et al., 2003 S.H. Cheng, L.J. Weng, Y.J. Su, H.M. Wu and P.F. Yang, Development of a Chinese Internet addiction scale and it's psychometric study, Chinese Journal of Psychology 45 (3) (2003), pp. 279–294.
Chou, 2001 C. Chou, Internet heavy use and addiction among Taiwanese college students: An online interview study, Cyberpsychology and Behavior 4 (5) (2001), pp. 573–585.
Chou and Hsiao, 2000 C. Chou and M.C. Hsiao, Internet addiction, usage, gratification, and pleasure experience: The Taiwan college students' case, Computers and Education 35 (1) (2000), pp. 65–80.
Chou et al., 2005 C. Chou, L. Condron and J.C. Belland, A review of the research on Internet addiction, Educational Psychology Review 17 (4) (2005), pp. 363–388.
Davis, 2001 R.A. Davis, A cognitive-behavioral model of pathological Internet use, Computers in Human Behavior 17 (2) (2001), pp. 187–195.
Davis et al., 2002 R.A. Davis, G.L. Flett and A. Besser, Validation of a new scale for measuring problematic Internet use: Implications for pre-employment screening, Cyberpsychology and Behavior 5 (4) (2002), pp. 331–345.
Ferraro et al., 2007 G. Ferraro, B. Caci, A. D'Amico and M. Di Blasi, Internet addiction disorder: An Italian study, Cyberpsychology and Behavior 10 (2) (2007), pp. 170–175.
Floyd and Widaman, 1995 F.J. Floyd and K.F. Widaman, Factor analysis in the development and refinement of clinical assessment instruments, Psychological Assessment 7 (3) (1995), pp. 286–299.
Fulkerson et al., 1999 J.A. Fulkerson, P.A. Harrison and T.J. Beebe, DSM-IV substance abuse and dependence: Are there really two dimensions of substance use disorders in adolescents?, Addiction 94 (4) (1999), pp. 495–506.
Goldberg, 1995 Goldberg, I. (1995). Internet Addiction Disorder (IAD) – Diagnostic criteria. Retrieved March 30, 2007 from
Griffiths, 1998 M. Griffiths, Internet addiction: Does it really exist?. In: J. Gackenbach, Editor, Psychology and the Internet: Intrapersonal, interpersonal, and transpersonal implications, Academic Press, San Diego, CA (1998), pp. 61–75.
Griffiths, 2000 M. Griffiths, Does Internet and computer "Addiction" exist? Some case study evidence, Cyberpsychology and Behavior 3 (2) (2000), pp. 211–218.
Ha et al., 2006 J.H. Ha, H.J. Yoo, I.H. Cho, B. Chin, D. Shin and J.H. Kim, Psychiatric comorbidity assessed in Korean children and adolescents who screen positive for Internet addiction, Journal of Clinical Psychiatry 67 (5) (2006), pp. 821–826.
Hair et al., 2006 J.R.J. Hair, R. Anderson, R. Tatham, W. Black and B. Babin, Multivariate data analysis (6th ed.), Prentice Hall, New Jersey (2006).
Hansen, 2002 S. Hansen, Excessive Internet usage or "Internet addiction? The implications of diagnostic categories for student users, Journal of Computer Assisted Learning 18 (2002), pp. 235–236.
Harrison et al., 1998 P.A. Harrison, J.A. Fulkerson and T.J. Beebe, DSM-IV substance use disorder criteria for adolescents: A critical examination based on a statewide school survey, American Journal of Psychiatry 155 (4) (1998), pp. 486–492.
Man Kit Chang and Sally Pui Man Law
Department of Finance and Decision Sciences, School of Business Hong Kong Baptist University, Kowloon Tong, Kowloon, Hong Kong
doi:10.1016/j.chb.2008.03.001 Factor structure for Young's Internet Addiction Available online 14 April 2008
News
News
News
