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Maximum deviation global discrimination index (MDGDI) is a new item selection method for cognitive diagnostic computerized adaptive testing that allows for attribute coverage balance. We developed the maximum limitation global discrimination index (MLGDI) from MDGDI, which allows for both attribute coverage balance and item exposure control. MLGDI can realize the attribute coverage balance and exposure control of the item. Our simulation study aimed to evaluate the performance of our new method against maximum global discrimination index (GDI), modified maximum GDI (MMGDI), standardized weighted deviation GDI (SWDGDI), and constraint progressive with SWDGDI (CP_SWDGDI). The results indicated that (1a) under the condition of realizing the attribute coverage balance, MDGDI had the highest attribute classification accuracy; (1b) when the selection strategy accommodated the practical constraints of the attribute coverage balance and item exposure control, MLGDI had the highest attribute classification accuracy; (2) adding the item exposure control mechanism to the item selection method reduces the classification accuracy of the attributes of the item selection method; and (3) compared with GDI, MMGDI, SWDGDI, CP_SWDGDI, and MDGDI, MLGDI can better achieve the attribute-coverage requirement, control item exposure rate, and attribute correct classification rate.

A Q-matrix, which reflects how attributes are measured for each item, is necessary when applying a cognitive diagnosis model to an assessment. In most cases, the Q-matrix is constructed by experts in the field and may be subjective and incorrect. One efficient method to refine the Q-matrix is to employ a suitable statistic that is calculated using response data. However, this approach is limited by its need to estimate all items in the Q-matrix even if only some are incorrect. To address this challenge, this study proposes an item fit statistic root mean square error approximation (RMSEA) for validating a Q-matrix with the deterministic inputs, noisy, “and” (DINA) model. Using a search algorithm, two simulation studies were performed to evaluate the effectiveness and efficiency of the proposed method at recovering Q-matrices. Results showed that using RMSEA can help define attributes in a Q-matrix. A comparison with the existing Delta method and residual sum of squares (RSS) method revealed that the proposed method had higher mean recovery rates and can be used to identify and correct Q-matrix misspecifications. When no error exists in the Q-matrix, the proposed method does not modify the correct Q-matrix.

Self-efficacy plays a crucial role in an individual’s success. This study investigated the psychometric properties of the Chinese version of the general self-efficacy scale (GSES-C) for senior elementary school students using item response theory. A total of 568 students—271 boys and 297 girls aged 10 to 14 years (M = 11.81, SD = .69)—were recruited from three elementary schools in East China. Exploratory factor analysis was used to detect dimensionality. A grade response model (GRM) was used to analyze item parameters, differential item functioning (DIF) regarding gender and grade variables, and item information. The results confirmed the unidimensionality and local independence of the scale. The test for model–data fit indicated that the GRM fit the data and that all items fit the model after adjusting for significance level. Furthermore, the test of DIF detected no significant DIF regarding gender or grade variables. Additionally, most items provided acceptable information, with the exceptions of items 2 and 3, which provided little item information for the survey across the entire scale. This study demonstrated that the GSES-C has acceptable psychometric properties and constitutes an excellent method for measuring general self-efficacy from very low to high levels; therefore, it can be used to measure the general self-efficacy of most senior elementary school students after the modification of some items.

Supplemental material, Online_Appendix_(3) for Q-Matrix Refinement Based on Item Fit Statistic RMSEA by Chunhua Kang, Yakun Yang and Pingfei Zeng in Applied Psychological Measurement