Determining the number of dimensions is extremely important in applying item response theory (IRT) models to data. Traditional and revised parallel analyses have been proposed within the factor analysis framework, and both have shown some promise in assessing dimensionality. However, their performance in the IRT framework has not been…

Purpose: This study aims to investigate the orthogonality assumption, which restricts the use of Bifactor item response theory under different conditions. Method: Data of the study have been obtained in accordance with the Bifactor model. It has been produced in accordance with two different models (Model 1 and Model 2) in a simulated way.…

If classification in a limited number of categories is the purpose of testing, computerized adaptive tests (CATs) with algorithms based on sequential statistical testing perform better than estimation-based CATs (e.g., Eggen & Straetmans, 2000). In these computerized classification tests (CCTs), the Sequential Probability Ratio Test (SPRT) (Wald,…

Most computerized adaptive tests (CAT) have been studied under the framework of unidimensional item response theory. However, many psychological variables are multidimensional and might benefit from using a multidimensional approach to CAT. In addition, a number of psychological variables (e.g., quality of life, depression) can be conceptualized…

This study compares the ability of the multiple indicators, multiple causes (MIMIC) confirmatory factor analysis model to correctly identify cases of differential item functioning (DIF) with more established methods. Although the MIMIC model might have application in identifying DIF for multiple grouping variables, there has been little…