Using exploratory factor analysis, we identified a two-factor model for the IAT; the factors, “dependent use” and “excessive use,” together accounted for 91% of the total variance. The high degree of variance explained, strong inter-item correlation and theoretical consistency of our model suggests that the IAT is a valid instrument for assessing Internet addiction in adolescent to young adult college students.
Previous analyses have found between one- and six-factor solutions for the IAT. In spite of these differences, factor arrangements from prior analyses show striking resemblances to our results (). The items clustered within the first factor of the two-factor solution by Korkeila et al. (2010)
and the first and third factors in the three-factor solutions by both Chang and Law (2010) and Widyanto et al. (2010), show broad overlap with our first factor. Similarly, our second factor, “excessive use,” is consistent with the second factors of all three previous analyses. Although there is variation in factor loadings for a small subset of questions between the models, we believe these discrepancies are most likely due to differences inherent to the samples used for analysis such as age or differences in cultural norms surrounding Internet use, rather than differing representations of the overall construct.
Comparison of current factor solution with previous analyses
Thus, our results suggest that Internet addiction symptoms, as measured by the IAT, cluster into two distinct constructs which we have named, “dependent use” and “excessive use.” The first component, “dependent use,” encompassed the majority of previously established addiction symptoms, such as preoccupation and withdrawal, along with social impairment. The second component, “excessive use” grouped other forms of functional impairment with symptoms of overuse and loss of control. While the intrinsic nature of the two factors suggests that some degree of connection is to be expected, their moderate linear correlation supports the representation of these constructs as unique, distinguishable components.
Our findings also suggest several areas for refinement of the IAT. First, several IAT items produced lower factor loadings. Cultural and technological changes in Internet use since the development of the IAT, and characteristics specific to this population, may account for why these items are less precise in assessing Internet addiction symptoms. In particular, while the item, “do you form new relationships with fellow online users,” (factor loading 0.40) may have represented a problematic behavior in the earlier context of this instrument’s development, the recent rise of social networking sites, blogs, chat rooms and other internet social utilities may have normalized this behavior. Similarly, given the substantial integration of e-mail as a communication utility and increases in Internet availability through wireless connections and mobile devices, “do you check your e-mail before something else that you need to do” (factor loading 0.49) may no longer represent an abnormal behavior. Additionally, the items, “do you prefer the internet to intimacy with your partner” (factor loading 0.44) and “does your job performance suffer because of the internet,” (factor loading 0.44) may refer to lifestyle characteristics uncommon among young, full-time students, and thus may not be appropriate for assessing a disorder in this population. Although these items did produce acceptable factor loadings, re-working or removing them may enhance the performance of the IAT in this population.
A second area of refinement may be the instrument’s scoring system. Currently, in calculating the overall IAT score, equal weight is given to each item. However, several items such as “do you snap, yell or act annoyed when bothered online” and “do you feel depressed, moody, or nervous when offline” showed a reduced range of responses and lower mean scores, despite strong factor loadings (0.83 and 0.85, respectively). Thus, while these items appear to identifying key symptoms of Internet addiction, their contribution to the overall score may be diminished. Applying a weighted scoring scheme to the IAT may be useful in improving the instruments precision. Further, although IAT scores could range between 0 and 100, the limited range of overall scores suggests that narrowing the response scale may enhance the feasibility of categorizing non-problematic, problematic and severely problematic behavior.
There are several potential limitations of our study. First, because we identified potential participants through searching a social networking site database, there is a possible sampling bias in our study. However, the near-universal use of this site within our target population, our strong response rate and the comparable demographic breakdown between our sample and reference populations suggest that our methods are reliable. Second, because we focused on college students, generalizing results to other adolescent or young adult populations may not be warranted. Given that college students are a key population in which intense Internet use is common and potentially consequential, this was our population of choice for this analysis. Our findings should be interpreted with some caution, as results may have been affected by characteristics specific to a campus environment. In particular, participants’ reporting of excessive use symptoms may have been augmented in the context of high standards of Internet use found among students. If this were the case, it would suggest that when developing approaches for screening, diagnosing and treating Internet addiction, careful consideration should be taken to integrate clinical guidelines with the technology use norms relevant to the sample of interest.
In spite of these limitations, there are several ways our findings can inform future research on both the IAT and Internet addiction. First, while these findings support the IAT as a valid assessment of Internet addiction in this population, confirmatory analysis of our two-factor solution is necessary to corroborate these results. The effect of removing or reworking low-loading items on overall scale performance should also be assessed. Second, the concurrent and predictive validity of the overall score cut-offs, item-weighting scheme and sub-scale scores should be evaluate using clinical assessments. These will be challenging tasks, given the paucity of gold-standard measures for Internet dependency and excessive use available at present. Third, additional work is needed to confirm whether Internet addiction symptoms cluster similarly in other relevant populations such as individuals with existing psychiatric comorbidities or those reporting more severe addiction symptoms.
Our results also shed light on the potential significance of the dimensionality of Internet addiction. While increased reporting of both symptom categories was present among those scoring higher on the IAT, the more substantial rise in excessive use symptoms suggests that changes in this subscale may be a better indicator of increasingly addictive behavior. Alternatively, given the modest prevalence of PIU noted in this sample (12%), we may not have fully captured the extent of dependent use symptoms effecting problematic users. Thus, while determining the absolute number of symptoms present has thus far been used to determine the severity of an individual’s problematic use, further investigation into the influence of symptom dimensions may help to refine this practice. Given the growing exposure to Internet use within our society, continuing to develop effective means for identifying both those at risk for and currently suffering from problematic behavior is of the upmost importance.