Assessing the Diagnostic Precision of the Internet Gaming Disorder Scale (IGD-27) Using Diagnostic Classification Models: An Exploratory Study

In this study, we present the Diagnostic Classification Internet Gaming Disorder (DC-IGD), a novel assessment method developed to improve diagnostic precision for Internet Gaming Disorder (IGD) in line with DSM-5 guidelines. Utilizing Diagnostic Classification Models (DCMs), the DC-IGD was tested on a large sample of 8,662 Chinese college students. The study meticulously calibrated the item parameters of the IGD-27 scale using DCMs, ensuring comprehensive item inclusion. This approach has endowed the DC-IGD with strong psychometric qualities, confirmed through both Classical Test Theory (CTT) and DCM analysis, highlighted by its reliability and validity. The DC-IGD demonstrated high diagnostic performance, with a sensitivity of 0.885, specificity of 0.811, and an Area Under the Curve (AUC) of 0.901. The tool's nine-factor model showed a good fit and provided detailed diagnostic insights, crucial for tailored intervention plans. As an initial exploration in the realm of psychometric tools for IGD, the DC-IGD marks an important advancement in accurately measuring symptoms and offering precise diagnostics. This investigation contributes significantly to psychometric research, paving a new path for understanding and diagnosing Internet Gaming Disorder.