Multidimensionality and item bias in item response theory

Abstract

This paper demonstrates empirically how item bias indexes based on item response theory (IRT) identify bias that results from multidimensionality. When a test is multidimensional (MD) with a primary trait and a nuisance trait that affects a small portion of the test, item bias is defined as a mean difference on the nuisance trait between two groups. Results from a simulation study showed that although IRT-based bias indexes clearly distinguished multidimensionality from item bias, even with the presence of a between-group difference on the primary trait, the bias detection rate depended on the degree to which the item measured the nuisance trait, the values of MD discrimination, and the number of MD items. It was speculated that bias defined from the MD perspective was more likely to be detected when the test data met the essential unidimensionality assumption. Index terms: item bias, multidimensionality; item response theory, item bias, mean differences, multidimensionality; multidimensionality; mean differences in IRT.