Methodology review: Statistical approaches for assessing measurement bias

Abstract

Statistical methods developed over the last decade for detecting measurement bias in psychological and educational tests are reviewed. Earlier methods for assessing measurement bias generally have been replaced by more sophisticated statistical techniques, such as the Mantel-Haenszel procedure, the standardization approach, logistic regression models, and item response theory approaches. The review employs a conceptual framework that distinguishes methods of detecting measurement bias based on either observed or unobserved conditional invariance models. Although progress has been made in the development of statistical methods for detecting measurement bias, issues related to the choice of matching variable, the nonuniform nature of measurement bias, the suitability of current approaches for new and emerging performance assessment methods, and insights into the causes of measurement bias remain elusive. Clearly, psychometric solutions to the problems of measurement bias will further understanding of the more central issue of construct validity. The continuing development of statistical methods for detecting and understanding the causes of measurement bias will continue to be an important scientific challenge. Index terms: bias detection, differential item functioning, item bias, measurement bias, test bias.