An iterative procedure for linking metrics and assessing item bias in item response theory

Abstract

The presence of biased items may seriously affect methods used to link metrics in item response theory. An iterative procedure designed to minimize this methodological problem was examined in a monte carlo investigation using the two-parameter item response model. The iterative procedure links the scales of independently calibrated parameter estimates using only those items identified as unbiased. Two methods for transforming parameter estimates to a common metric were incorporated into the iterative procedure. The first method links scales by equating the first two moments of the distributions of estimated item difficulties. The second method determines the linking transformation by minimizing differences across IRT characteristic curve estimates. Results indicate that iterative linking provides a substantial improvement in item bias detection over the noniterative approach. Index terms: Item bias, Item response theory, Iterative method, Linking, Metric linking, Two-parameter item response model.