We compared coefficient alpha with five alternatives (omega total, omega RT, omega h, GLB, and coefficient H) in two simulation studies. Results showed for unidimensional scales, (a) all indices except omega h performed similarly well for most conditions; (b) alpha is still good; (c) GLB and coefficient H overestimated reliability with small…

Survey instruments and assessments are frequently used in many domains of social science. When the constructs that these assessments try to measure become multifaceted, multidimensional item response theory (MIRT) provides a unified framework and convenient statistical tool for item analysis, calibration, and scoring. However, the computational…

This study investigates the effect of several design and administration choices on item exposure and person/item parameter recovery under a multistage test (MST) design. In a simulation study, we examine whether number-correct (NC) or item response theory (IRT) methods are differentially effective at routing students to the correct next stage(s)…

Often, important decisions regarding accountability and placement of students in performance categories are made on the basis of test scores generated from tests, therefore, it is important to evaluate the validity of the inferences derived from test results. One of the threats to the validity of such inferences is aberrant responding. Several…

Longitudinal data analysis assumes that scales meet the assumption of longitudinal measurement invariance (i.e., that scales function equivalently across measurement occasions). This simulation study examines the impact of violations to the assumption of longitudinal measurement invariance on growth models and whether modeling the invariance…

In large-scale testing, the use of multigroup approaches is limited for assessing differential item functioning (DIF) across multiple variables as DIF is examined for each variable separately. In contrast, the item response theory with covariate (IRT-C) procedure can be used to examine DIF across multiple variables (covariates) simultaneously. To…

This study examines the effect of differential item functioning (DIF) items on test equating through multilevel item response models (MIRMs) and traditional IRMs. The performances of three different equating models were investigated under 24 different simulation conditions, and the variables whose effects were examined included sample size, test…

This simulation study compared the performances (Type I error and power) of Mantel-Haenszel (MH), SIBTEST, and item response theory-likelihood ratio (IRT-LR) methods under certain conditions. Manipulated factors were sample size, ability differences between groups, test length, the percentage of differential item functioning (DIF), and underlying…

The purpose of this simulation study was to establish general effect size guidelines for interpreting the results of differential bundle functioning (DBF) analyses using simultaneous item bias test (SIBTEST). Three factors were manipulated: number of items in a bundle, test length, and magnitude of uniform differential item functioning (DIF)…

This study elaborates the Rasch differential item functioning (DIF) model formulation under the marginal maximum likelihood estimation context. Also, the Rasch DIF model performance was examined and compared with the Mantel-Haenszel (MH) procedure in small sample and short test length conditions through simulations. The theoretically known…

A primary concern with testing differential item functioning (DIF) using a traditional point-null hypothesis is that a statistically significant result does not imply that the magnitude of DIF is of practical interest. Similarly, for a given sample size, a non-significant result does not allow the researcher to conclude the item is free of DIF. To…

In DIF studies, a Type I error refers to the mistake of identifying non-DIF items as DIF items, and a Type I error rate refers to the proportion of Type I errors in a simulation study. The possibility of making a Type I error in DIF studies is always present and high possibility of making such an error can weaken the validity of the assessment.…

Douglas, Roussos, and Stout introduced the concept of differential bundle functioning (DBF) for identifying the underlying causes of differential item functioning (DIF). In this study, reference group was simulated to have higher mean ability than the focal group on a nuisance dimension, resulting in DIF for each of the multidimensional items…

N. S. Raju, W. J. van der Linden, and P. F. Fleer (in press) have proposed an item response theory-based, parametric procedure for the detection of differential item functioning (DIF)/differential test functioning (DTF) known as differential functioning of item and test (DFIT). DFIT can be used with dichotomous, polytomous, or multidimensional…

Eight independent variables (differential item functioning [DIF] detection method, purification procedure, item response model, mean latent trait difference between groups, test length, DIF pattern, magnitude of DIF, and percentage of DIF items) were manipulated, and two dependent variables (Type I error and power) were assessed through…