To obtain accurate, valid, and rich information from the questionnaires for internet addiction, a diagnostic classification test for internet addiction (the DCT-IA) was developed using diagnostic classification models (DCMs), a cutting-edge psychometric theory, based on DSM-5. A calibration sample and a validation sample were recruited in this study to calibrate the item parameters of the DCT-IA and to examine the sensitivity and specificity. The DCT-IA had high reliability and validity based on both CTT and DCMs, and had a sensitivity of 0.935 and a specificity of 0.817 with AUC = 0.919. More important, different from traditional questionnaires, the DCT-IA can simultaneously provide general-level diagnostic information and the detailed symptom criteria-level information about the posterior probability of satisfying each symptom criterion in DMS-5 for each patient, which gives insight into tailoring individual-specific treatments for internet addiction.

No abstract

No abstract

No abstract

Internet addiction disorder has become one of the most popular forms of addiction in psychological and behavioral areas, and measuring it is growing increasingly important in practice. This study aimed to develop a computerized adaptive testing to measure and assess internet addiction (CAT-IA) efficiently. Four standardized scales were used to build the original item bank. A total of 59 polytomously scored items were finally chosen after excluding 42 items for failing the psychometric evaluation. For the final 59-item bank of CAT-IA, two simulation studies were conducted to investigate the psychometric properties, efficiency, reliability, concurrent validity, and predictive validity of CAT-IA under different stopping rules. The results showed that (1) the final 59 items met IRT assumptions, had high discrimination, showed good item-model fit, and were without DIF; and (2) the CAT-IA not only had high measurement accuracy in psychometric properties but also sufficient efficiency, reliability, concurrent validity, and predictive validity. The impact and limitations of CAT-IA were discussed, and several suggestions for future research were provided.

Most existing instruments for depression are developed based on classical test theory, factor analysis, or sometimes, item response theory, and focus on the accurate measurement of the severity of depressive disorder. Nevertheless, they tend to be less useful in supporting the decision based on ICD-10 or DSM-5 because of the lack of detailed information for symptoms. To gain rich and valid information at the symptom level, this article developed a depression test under the framework of cognitive diagnosis models (CDMs), referred to as CDMs-D. A total of 1,181 individuals were finally recruited and their responses were used to examine the psychometric properties of CDMs-D. After excluding poor items for statistical reasons (e.g., low discrimination, poor model-fit or having DIF), 56 items were included in the CDMs-D. The CDMs-D measures all ten symptom criteria for depression defined in ICD-10 and covers five domains of depression defined by Gibbons et al. (2012) . Comparing with the existing self-report measures (such as PHQ-9, SDS, CES-D and so on), a distinguishing feature of the CDMs-D is that it can provide both overall information about the severity of depressive disorder and the assessment information about specific symptoms, which could be useful for diagnostic and interventional purposes.

This article proposes a class of cognitive diagnosis models (CDMs) for polytomously scored items with different link functions. Many existing polytomous CDMs can be considered as special cases of the proposed class of polytomous CDMs. Simulation studies were carried out to investigate the feasibility of the proposed CDMs and the performance of several information criteria (Akaike’s information criterion [AIC], consistent Akaike’s information criterion [CAIC], and Bayesian information criterion [BIC]) in model selection. The results showed that the parameters of the proposed CDMs could be recovered adequately under varied conditions. In addition, CAIC and BIC had better performance in selecting the most appropriate model than AIC. Finally, a set of real data was analyzed to illustrate the application of the proposed CDMs.

Computer multistage adaptive test (MST) combines the advantages of paper and pencil-based test (P&P) and computer-adaptive test (CAT). As CAT, MST is adaptive based on modules; as P&P, MST can meet the need of test developers to manage test forms and keep test forms parallel. Cognitive diagnosis (CD) can accurately measure students’ knowledge states (KSs) and provide diagnostic information, which is conducive to student’s self-learning and teacher’s targeted teaching. Although MST and CD have a lot of advantages, many factors prevent MST from applying to CD. In this study, we first attempt to employ automated test assembly (ATA) to achieve the objectives of MST in the application of CD (called CD-MST) via heuristic algorithms. The mean correct response probability of all KSs for each item is used to describe the item difficulty of CD. The attribute reliability in CD is defined as the test quantitative target. A simulation study with the G-DINA model (generalized deterministic input noisy “and” gate model) was carried out to investigate the proposed CD-MST, and the results showed that the assembled panels of CD-MST satisfied the statistical and the non-statistical constraints.