What to Do If Measurement Invariance Does Not Hold? Let's Look at the Practical Significance

Abstract

Measurement invariance—that a test measures the same construct in the same way across subgroups—needs to hold for subgroup comparisons to be meaningful. There has been tremendous growth in measurement invariance research in the past decade. However, such research usually only provided binary conclusions of whether invariance holds or not, which gave little practical guidance on subsequent test usages in research and assessment settings. This presentation will illustrate these issues with an in-progress research synthesis of 32 invariance studies of a depression scale across genders. I will then provide suggestions on effect size indices that provide more information on the magnitude of noninvariance, and share a related Bayesian procedure for establishing invariance at the composite score level. Finally, I will discuss a procedure for quantifying how noninvariance affects the test’s selection/diagnostic accuracy.

Yuan Bo 袁博

Associate Professor of Psychology (Social Psychology)

My research examines the nature and dynamics of social norms, namely how norms may emerge and become stable, why norms may suddenly change, how is it possible that inefficient or unpopular norms survive, and what motivates people to obey norms. I combines laboratory and simulation experiments to test theoretical predictions and build empirically-grounded models of social norms and their dynamics.