Differential item functioning analysis with ordinal logistic regression techniques. DIFdetect and difwithpar.

Differential Item Functioning (DIF) in Terms of Gender in the Reading Comprehension Subtest of a High-Stakes Test

Validation is an important enterprise especially when a test is a high stakes one. Demographic variables like gender and field of study can affect test results and interpretations. Differential Item Functioning (DIF) is a way to make sure that a test does not favor one group of test takers over the others. This study investigated DIF in terms of gender in the reading comprehension subtest (35 i...

A Handbook on the Theory and Methods of Differential Item Functioning (DIF) LOGISTIC REGRESSION MODELING AS A UNITARY FRAMEWORK FOR BINARY AND LIKERT-TYPE (ORDINAL) ITEM SCORES

A Simulation Study to Assess the Effect of the Number of Response Categories on the Power of Ordinal Logistic Regression for Differential Item Functioning Analysis in Rating Scales

Objective. The present study uses simulated data to find what the optimal number of response categories is to achieve adequate power in ordinal logistic regression (OLR) model for differential item functioning (DIF) analysis in psychometric research. Methods. A hypothetical ten-item quality of life scale with three, four, and five response categories was simulated. The power and type I error ra...

Extension of an iterative hybrid ordinal logistic regression/item response theory approach to detect and account for differential item functioning in longitudinal data.

Many constructs are measured using multi-item data collection instruments. Differential item functioning (DIF) occurs when construct-irrelevant covariates interfere with the relationship between construct levels and item responses. DIF assessment is an active area of research, and several techniques are available to identify and account for DIF in cross-sectional settings. Many studies include ...

Assessment of Differential Item Functioning in Health-Related Outcomes: A Simulation and Empirical Analysis with Hierarchical Polytomous Data

Background The purpose of this study was to evaluate the effectiveness of two methods of detecting differential item functioning (DIF) in the presence of multilevel data and polytomously scored items. The assessment of DIF with multilevel data (e.g., patients nested within hospitals, hospitals nested within districts) from large-scale assessment programs has received considerable attention but ...