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.本研究提出诊断分类型网络成瘾障碍（Diagnostic Classification Internet Gaming Disorder, DC-IGD），这是一款遵循《精神障碍诊断与统计手册第五版》（Diagnostic and Statistical Manual of Mental Disorders, 5th Edition, DSM-5）规范开发的新型评估方法，旨在提升网络成瘾障碍（Internet Gaming Disorder, IGD）的诊断精准度。本研究采用诊断分类模型（Diagnostic Classification Models, DCMs），在包含8662名中国大学生的大样本中对DC-IGD开展测试。研究借助诊断分类模型对IGD-27量表的项目参数进行了严谨校准，确保所有项目均被纳入分析。该方法使得DC-IGD具备了优异的心理测量学性能，这一结论通过经典测试理论（Classical Test Theory, CTT）与诊断分类模型分析得到验证，其信度与效度尤为显著。DC-IGD展现出出色的诊断性能：灵敏度为0.885，特异度为0.811，曲线下面积（Area Under the Curve, AUC）达0.901。该工具的九因子模型拟合效果良好，可提供精细化的诊断洞察，这对制定个性化干预方案至关重要。作为网络成瘾障碍心理测量工具领域的初步探索，DC-IGD在精准量化症状与提供精准诊断方面实现了重要进展。本研究为心理测量学研究作出了重要贡献，为网络成瘾障碍的认知与诊断开辟了全新路径。