Measurement of internet addiction: an item response analysis approach

Two widely used scales of Internet addiction (IA), the Internet Addiction Test (IAT) and the Chen Internet Addiction Scale (CIAS), were compared and a new scale of IA was assembled from their items with improved reliability in terms of classification consistency. A total of 467 Chinese college students participated in the study. Items were calibrated using the Muraki's Generalized Partial Credit Model. Most items had higher item information on medium levels of addiction, but much lower item information on the two ends of the latent trait continuum. The average item information of the CIAS was significantly larger compared with IAT on most of the latent trait levels. A new scale assembled using the cutoff points of IAT had a larger classification consistency than the original IAT. It was shown that the classification consistency of the IA measurement could be improved by selecting items to optimize test information around cutoff points. Implications for test and item development of IA were discussed.