Three widely-used self-report anxiety scales, including the Self-Rating Anxiety Scale (SAS), the Beck Anxiety Inventory (BAI), and the State Anxiety Inventory (S-AI), were used to simultaneously compare the psychometric properties via an item response theory (IRT) model with Chinese university students as the sample. Although these scales were probably to measure the same underlying construct, namely, anxiety, their psychometric properties were different. Results showed that the BAI’s measurement error was fewer than that of the other scales, with their anxiety severity ranging approximately from the 0.8 standard deviations below the mean to 3 standard deviations above the mean, while the S-AI’s measurement error was fewer than that of the other degrees of anxiety. The S-AI provided more information than the other scales when the student’s scale was less than approximately 0.8 standard deviations below the mean of anxiety severity. In general, the BAI showed better, for it provided more information than the other scales at the broadest range of anxiety severity. The SAS provided less information than the other scales at all anxiety severity range. In conclusion, BAI shows good psychometric quality. Finally, the three instruments were combined on a common scale by using IRT model and a conversion table was provided so as to achieve the transformation of each scale score.

This study was carried out to examine the factor structure and psychometric properties of the Social Interaction Anxiety Scale (SIAS) and Social Phobia Scale (SPS) in a sample of 1,001 Chinese college students (male: 34%; female: 66%). Confirmatory factor analysis (CFA) indicated that the two-factor of shorten version SIAS-6/SPS-6 fitted the data well. In addition, the item response theory (IRT) method confirmed the construct and items for the 12-item of SIAS-6/SPS-6 with satisfactory discrimination, threshold parameters, and test information curve. It is concluded that the factor structure and the psychometric properties of the SIAS-6/SPS-6 support the use of the scales for assessment in Chinese college sample.

Different from the item response models that postulate a single underlying proficiency, cognitive diagnostic assessments (CDAs) can provide fine-grained diagnostic information about students’ knowledge state to aid classroom instructions. In CDAs, a Q-matrix that associates each item in a test with the cognitive skills is required to infer students’ knowledge states. In practice, the Q-matrix is typically performed by domain experts, which is certainly affected by the subjective tendency of experts and, to a large extent, may consist of some misspecifications. In addition, if the number of items increases, the expert-based Q-matrix specification will be time-consuming and costly. To address this concern, this paper proposed several approaches based on the likelihood ratio test to estimate Q-matrix with partial known Q-matrix and the response data, which can be used with a wide class of cognitive diagnosis models (CDMs). The feasibility and effectiveness of the proposed methods were evaluated by simulated data generated under various conditions and an example to real data. Results show that new methods can estimate Q-matrix correctly and outperforms the existing method in most conditions.

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.

With the increasing prevalence of depression, creating a simple and precise tool for measuring depression is becoming more important. This study developed a computer adaptive testing for depression (CAT-Depression) from a Chinese sample. The depression item bank was constructed from a sample of 1,135 participants with or without depression using the Graded Response Model (GRM; Samejima, 1969). The final depression item bank with strict unidimensionality comprised 68 items, which had local independence, good item-fit, high discrimination, no differential item functioning (DIF), and each item measured at least one symptom of diagnostic criteria for depression in ICD-10. In addition, the mean IRT discrimination of the item bank reached 1.784, which clearly showed that the item bank of CAT-Depression was high-quality. Moreover, a simulation CAT study with real response data was conducted to investigate the characteristics, marginal reliability, criterion-related validity, and predictive utility (sensitivity and specificity) of CAT-Depression. The results revealed that the proposed CAT-Depression had acceptable and reasonable marginal reliability, criterion-related validity, and sensitivity and specificity.