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In this study, the psychometric properties of three commonly used rating scales of test anxiety were examined, including the test anxiety inventory (TAI), the test anxiety scale (TAS) and the Friedman-Bendas Test Anxiety Scale (FAT). Under the framework of item response theory (IRT), the Bifactor multi-dimensional item response model was employed to compare the psychometric properties of the three scales. Results showed that the Bifactor structures were suitable for the three scales, which were then used in the subsequent Bifactor multidimensional item response theory analysis. Although the three commonly used TA scales were likely to measure the same underlying construct—test anxiety, they had very different psychometric properties. The findings of the Bifactor Multi-IRT provided suggestions for determining which scale to use in a given study design: the TAI and the FAT evaluated information at greatly overlapping ranges; however, the TAI, performing a litter better at the same levels of severity of TA, may be a good choice when we recruit those with various levels of TA severity to ensure a high precision. What’s more, FAT may be a good choice for measuring those with moderate TA severity. Meanwhile, the TAS provided more information at the lower level of TA symptomatology, which was to say, TAS was more suitable for epidemiological TA studies and for measuring those with lower TA severity.

Q -matrix is an essential component specifying the relationship between attributes and items, which plays a key role in cognitive diagnosis assessment. The Q -matrix is usually developed by domain experts and its specifications tend to be subjective and might have misspecifications. Many existing pieces of research concentrate on the validation of Q -matrix; however, few of them can be applied to saturated cognitive diagnosis models. This paper proposes a general and effective Q -matrix validation method by employing multiple logistic regression model. Simulation studies are carried out to investigate the performance of the proposed method and compare it with four existing methods. Simulation results indicate the proposed method outperforms the existing methods in terms of validation accuracy. In addition, a set of real data is used as an example to illustrate its application. Finally, we discuss the limitations of the current study and the directions of future studies.

Small-scale (e.g., classroom) assessment represents the most common and needed scenario for cognitive diagnostic testing. In such settings, polytomously scored items (e.g., constructed-response tasks) are widely used, as they provide more fine-grained measurement of students’ skills and cognitive processes. However, a significant gap remains between the current methods and pressing practical needs. On one hand, parametric cognitive diagnosis models capable of handling polytomous response data require large samples for stable estimation, making them unsuitable for small-scale classroom use. On the other hand, existing nonparametric classification methods, while robust in small samples, are largely confined to dichotomous (0/1) response data. There is a lack of dedicated nonparametric methods for polytomous responses, creating a disconnect between practical testing and diagnostic tools. To address this real-world necessity, this study proposes the seq-GNPED method. It extends the generalized nonparametric classification framework to polytomous data by introducing weighted ideal category response and a collapsed class iterative algorithm. Simulations and empirical applications confirm that seq-GNPED achieves robust and accurate diagnosis under small sample conditions where parametric models falter, effectively leveraging the informational richness of polytomous items. This work bridges a critical gap by providing a practical, nonparametric tool tailored for fine-grained, classroom-ready cognitive diagnosis.

We aimed to develop an item bank of computerized adaptive testing for eating disorders (CAT-ED) in Chinese university students to increase measurement precision and improve test efficiency. A total of 1,025 Chinese undergraduate respondents answered a series of questions about eating disorders in a paper-pencil test. A total of 133 items from four well-validated Chinese-version scales of eating disorders were used to construct the item bank of CAT-ED with the following analysis. First, unidimensionality, model fit, local independence, item fit, discrimination and differential item functioning (DIF) were tested. Then, two simulation studies were applied to test the CAT-ED’s effectivity and rationality by calculating concurrent criterion-related validity, sensitivity and specificity. The final item bank comprised 77 items, which met the requirements of local independence, item fit, high discrimination and no differential item functioning in CAT. The mean number of administered items in CAT with the stopping rule fixed at SE ≤ 0.3 was 11 items. The obtained results showed that CAT-ED had acceptable reliability, validity, sensitivity and specificity.

Simpler and more precise tools are needed to measure Taijin Kyofusho which is a culture-bound anxiety disorder in East Asian countries. This study aimed to develop and validate a short form and a computerized adaptive test (CAT) of Taijin Kyofusho Scale (TKS), as well as compare the measurement precision of the short form, the CAT version and its original version. Item Response Theory (IRT) method was used to develop static short form and to simulate CAT. The short form consisted of 12 items (a 61% reduction) and the CAT version consisted of average 11.72 items (a 62% reduction) from the original TKS, respectively. Both short form and CAT version have similar levels of accuracy and precision in comparison to the original scale at the group level. However, at the individual level, the CAT version can maintain a more consistent level of precision across the continuum of severity than the short form. The short form of the TKS is sufficient for an initial assessment or screening in the community population. And the CAT version of the TKS is more suitable for tailored treatments in the clinical practice, which could detect detailed changes in the severity of Taijin Kyofusho.

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.

The promotion of enhanced well-being among children and collaboration among families, schools, and communities is paramount and is a pressing concern in the global education sector. This necessitates that preschool teachers possess the necessary competencies for effective family-preschool partnerships (FPPs). This study explored the competencies necessary for Chinese kindergarten teachers to engage in FPP using behavioral event interviews with 30 participants. Thematic analysis identified key competency traits, and independent samples t-tests with Bonferroni correction compared collaboration competencies between outstanding and typical teachers, as well as across different career stages. Consequently, a comprehensive crisscrossing competency framework consisting of four quadrants was developed. This framework distinguishes between high-performance and general traits, as well as between stable and variable traits that may evolve across career stages. High-performance traits such as communication, expression, and relationship management should be prioritized in the training and recruitment of early childhood educators involved in FPP. In contrast, intrinsic qualities that foster successful FPP, such as child orientation, should be cultivated early and sustained throughout a teacher’s career. From a developmental perspective, this framework provides a crucial foundation for evaluating and training kindergarten teachers in the competencies essential for fostering effective FPP.

A common observation in ability assessment is that the probability of an examinee giving a correct response drops for end-of-test items due to low motivation, time limits or other factors. On the test-takers’ side, this change can be considered performance decline (PD), which can strongly affect test validity and bias respondents’ ability estimators. Currently, there is an increasing interest in the detection of PD among researchers and practitioners. Researchers and practitioners found that PD detection fails to achieve acceptable power, which is typically below 0.55. Change-point analysis (CPA), a well-developed statistical method, can be applied to item response sequences to identify whether an abrupt change exists. Existing CPA methods cannot be directly used to detect PD because they are appropriate for two-sided alternative hypotheses. To address these issues, this research firstly develops a CPA method based on Jensen-Shannon divergence to detect PD. Additionally, existing CPA statistics were converted into one-sided statistics to accommodate PD detection. Then, a simulation study was conducted to investigate the performance of the proposed method and compare it with modified CPA statistics. Results show that the proposed CPA method can detect PD with higher power while generating a well-controlled Type‐I error rate. Compared against modified CPA statistics, the proposed method exhibits an augmentation in power from 1.0% to 8.2%, with average of 5.7% and higher accuracy in locating the change point. Finally, the proposed method was applied to two real datasets to demonstrate its utility.

Cognitive diagnosis models (CDMs) have been used as psychometric tools in educational assessments to estimate students' strengths and weaknesses in terms of cognitive skills learned and skills that need study. In practice, it is not uncommon that questions can often be solved using more than one strategy, which requires CDMs capable of accommodating multiple strategies. However, existing parametric multi-strategy CDMs need a large sample size to produce a reliable estimation of item parameters and examinees' proficiency class memberships, which obstructs their practical applications. This article proposes a general nonparametric multi-strategy classification method with promising classification accuracy in small samples for dichotomous response data. The method can accommodate different strategy selection approaches and different condensation rules. Simulation studies showed that the proposed method outperformed the parametric CDMs when sample sizes were small. A set of real data was analyzed as well to illustrate the application of the proposed method in practice.

The ability to rapidly provide examinees with detailed and effective diagnostic information is a critical topic in psychology. Knowing what diagnostic criteria the examinees have met enables the practitioner to seek the solution to help them in a timely manner, and this can be achieved by cognitive diagnostic computerized adaptive testing (CD-CAT). However, the pervasive challenge of replenishing items in the CD-CAT item bank limits its practical application. Online calibration is a means to address item replenishment, but in CD-CAT, most existing online calibration methods that jointly calibrate the Q-matrix and item parameters of the new items are developed only for dichotomous responses and are time-consuming. Notably, previous studies pay no attention to polytomously scored items that are frequently observed in testing, even though they can offer additional evidence for the examinees’ diagnosis. To fill this gap, we propose a SCAD-based method (SCAD-EM) to calibrate the Q-matrix and item parameters of the new items with polytomous response data in order to promote the application of CD-CAT in practice. The performance of the SCAD-EM was investigated in two comprehensive simulation studies and compared against the revised single-item estimation method (SIE-BIC). Results indicated that the SCAD-EM produces a higher calibration accuracy for the category-level Q-matrix and is computationally more efficient across all conditions, but it produces a lower calibration accuracy for the item-level Q-matrix. An empirical study further demonstrated the utility of the SCAD-EM and the SIE-BIC methods in calibrating new items with a real dataset. The advantages of the proposed method, its limitations, and possible future research directions are offered at the end.