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have been replaced by more sophisticated statistical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.
Millsap, Roger E.; Everson, Howard T..
Methodology review: Statistical approaches for assessing measurement bias.
Retrieved from the University of Minnesota Digital Conservancy,
Content distributed via the University of Minnesota's Digital
Conservancy may be subject to additional license and use
restrictions applied by the depositor.