Multidimensional Extension of Multiple Indicators Multiple Causes Models to Detect DIF

A number of studies have found multiple indicators multiple causes (MIMIC) models to be an effective tool in detecting uniform differential item functioning (DIF) for individual items and item bundles. A recently developed MIMIC-interaction model is capable of detecting both uniform and nonuniform DIF in the unidimensional item response theory (IRT) framework. The goal of the current study is to extend the MIMIC-interaction model for detecting DIF in the context of multidimensional IRT modelling and examine the performance of the multidimensional MIMIC-interaction model under various simulation conditions with respect to Type I error and power rates. Simulation conditions include DIF pattern and magnitude, test length, correlation between latent traits, sample size, and latent mean differences between focal and reference groups. The results of this study indicate that power rates of the multidimensional MIMIC-interaction model under uniform DIF conditions were higher than those of nonuniform DIF conditions. When anchor item length and sample size increased, power for detecting DIF increased. Also, the equal latent mean condition tended to produce higher power rates than the different mean condition. Although the multidimensional MIMIC-interaction model was found to be a reasonably useful tool for identifying uniform DIF, the performance of the model in detecting nonuniform DIF appeared to be questionable.

Examining Sources of DIF in Psychological and Educational Assessment Using Multilevel Logistic Regression

In the last three decades, important progress has been made toward more efficient statistical techniques for detecting Differential Item Functioning (DIF). However, the findings are scant when it comes to explaining DIF. Multilevel regression models can expand the knowledge of DIF causes, specifying a DIF parameter that varies randomly over items and testing hypotheses on sources of DIF shared by item bundles. The present study uses multilevel logistic regression to identify the item characteristics that could explain the presence of DIF in short tests or questionnaires, which are usually used in psychological and educational assessment. The usefulness of the approach is tested on measurements of the attitudes toward science of Spanish and English pupils obtained from the OECD Programme for International Student Assessment database.