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.