Suchergebnisse

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.

Abstract. Background: As more and more people suffer from sleep disorders, the need to develop an efficient, inexpensive, and accurate assessment tool for screening sleep disorders has become more urgent. Aim: The aim of the current study was to develop a system allowing computerized adaptive testing for sleep disorders (CAT-SD). Methods: A large sample ( N = 1,304) was recruited to construct an item bank for CAT-SD and to investigate the psychometric characteristics of CAT-SD. First, analyses of unidimensionality, model fit, item fit, item discrimination parameters, and differential item functioning (DIF) were conducted to construct a final item pool to meet the requirements of item response theory measurement. Then, a simulated CAT study with real data was performed to investigate the psychometric characteristics of CAT-SD, including the reliability, validity, and predictive utility (sensitivity and specificity). Results: The final unidimensional item bank of the CAT-SD had good item fit, high discrimination, and no DIF. Moreover, it had acceptable reliability, validity, and predictive utility. Limitations: Non-statistical assembly constraints, execution environment, construction of item bank, criterion-related validity, and predictive utility (sensitivity and specificity) of CAT-SD, and sample representativeness are discussed. Conclusions: The CAT-SD could be used as an effective and accurate assessment tool for measuring the sleep disorders in individuals and offers a novel approach to the screening of sleep disorders utilizing psychological scales.

For various reasons, respondents to forced-choice assessments (typically used for noncognitive psychological constructs) may respond randomly to individual items due to indecision or globally due to disengagement. Thus, random responding is a complex source of measurement bias and threatens the reliability of forced-choice assessments, which are essential in high-stakes organizational testing scenarios, such as hiring decisions. The traditional measurement models rely heavily on nonrandom, construct-relevant responses to yield accurate parameter estimates. When survey data contain many random responses, fitting traditional models may deliver biased results, which could attenuate measurement reliability. This study presents a new forced-choice measure-based mixture item response theory model (called M-TCIR) for simultaneously modeling normal and random responses (distinguishing completely and incompletely random). The feasibility of the M-TCIR was investigated via two Monte Carlo simulation studies. In addition, one empirical dataset was analyzed to illustrate the applicability of the M-TCIR in practice. The results revealed that most model parameters were adequately recovered, and the M-TCIR was a viable alternative to model both aberrant and normal responses with high efficiency.

In recent decades, multidimensional forced-choice (MFC) tests have gained widespread popularity in organizational settings due to their effectiveness in reducing response biases. Detecting differential item functioning (DIF) is crucial in developing MFC tests, as it relates to test fairness and validity. However, existing methods appear insufficient for detecting DIF induced by the interaction between multiple covariates. Furthermore, for multi-category, ordered or continuous covariates, existing approaches often dichotomize them using a-priori cutoffs, commonly using the median of the covariates. This may lead to information loss and reduced power in detecting MFC DIF. To address these limitations, we propose a method to identify both main effect DIF and interactive DIF. This method can automatically search for the optimal cutoffs for ordered or continuous covariates without pre-defined cutoffs. We introduce the rationale behind the proposed method and evaluate its performance through three Monte Carlo simulation studies. Results demonstrate that the proposed method effectively identifies various DIF forms in MFC tests, thereby increasing detection power. Finally, we provide an empirical application to illustrate the practical applicability of the proposed method.