Objective To obtain more precise and rich information from the measurements for schizotypal personality disorder (SPD), a cutting‐edge psychometric theory called diagnostic classification models (DCMs) was first employed in the present study to develop a diagnostic classification version of the Schizotypal Personality Questionnaire (DC‐SPQ) based on the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders. Methods Under the framework of DCMs, 980 college students were recruited to calibrate item parameters of the Schizotypal Personality Questionnaire. Items that fit the psychometric characteristic would be selected to compose the DC‐SPQ, prior to an analysis of its indexes. Results Results showed that the DC‐SPQ had high reliability and validity in both the classical test theory and DCMs, in addition to showing a sensitivity of 0.921 and a specificity of 0.841 with area under receiver operating characteristic curve = 0.936. Meanwhile, the four‐factor model proposed adequately fits with the data. More importantly, the DC‐SPQ provides not only the general‐level information similar to traditional questionnaires but also the symptom‐level information with the posterior probability, which provides an insight into delivering the individual‐specific intervention that is tailor made to schizotypal personality disorder. Conclusions This study demonstrates that the DC‐SPQ is very valuable for psychometric detection in that it can clarify the symptom being measured and provide more reasonable estimates.

Objective To detect the individual’s severity of alcohol use disorder (AUD) in an effective and accurate manner, this study aimed to build an item bank for AUD screening and derive the computerized adaptive testing (CAT) version of AUD (CAT-AUD). Methods The initial CAT-AUD item bank was selected from the Chinese version of the questionnaires related to AUD according to the DSM-5 criteria. Then 915 valid Chinese samples, covering the healthy individuals and the AUD high-risk individuals, completed the initial CAT-AUD item bank. By testing the unidimensionality, test fit, item fit, discrimination parameter and differential item functioning of the initial item bank, the final CAT-AUD item bank confirming to the requirements of the item response theory (IRT) were obtained. Subsequently, the CAT-AUD simulation study based on the real data of the final item bank conducted to detect characteristics, reliability, validity, and predictive utility (sensitivity and specificity) of CAT-AUD. Results The CAT-AUD item bank meeting the IRT psychometric measurement requirements could be well geared into the graded response model. The Pearson’s correlation between the estimated theta via CAT-AUD and the estimated theta via the full-length item bank reached 0.95, and the criterion-related validity was 0.63. CAT-AUD can provide highly reliable test results for subjects whose theta above −0.8 with an average of 16 items. Besides, the predictive utility of CAT-AUD was better than AUDIT and AUDIT-C. Conclusion In brief, the CAT-AUD developed in this study can effectively screen the AUD high-risk group and accurately measure the AUD severity of individuals.

To efficiently obtain information about both the general abilities and detailed cognitive profiles of examinees from a single model that uses a single-calibration process, higher-order cognitive diagnostic computerized adaptive testing (CD-CAT) that employ higher-order cognitive diagnostic models have been developed. However, the current item selection methods used in higher-order CD-CAT adaptively select items according to only the attribute profiles, which might lead to low precision regarding general abilities; hence, an appropriate method was proposed for this CAT system in this study. Under the framework of the higher-order models, the responses were affected by attribute profiles, which were governed by general abilities. It is reasonable to hold that the item responses were affected by a combination of general abilities and attribute profiles. Based on the logic of Shannon entropy and the generalized deterministic, inputs, noisy “and” gate (G-DINA) model discrimination index (GDI), two new item selection methods were proposed for higher-order CD-CAT by considering the above combination in this study. The simulation results demonstrated that the new methods achieved more accurate estimations of both general abilities and cognitive profiles than the existing methods and maintained distinct advantages in terms of item pool usage.