Quantifying the impact of partial measurement invariance in diagnostic research: An application to addiction research

Establishing measurement invariance, or that an instrument measures the same construct(s) in the same way across subgroups of respondents, is crucial in efforts to validate social and behavioral instruments. Although substantial previous research has focused on detecting the presence of noninvariance, less attention has been devoted to its practical significance and even less has been paid to its possible impact on diagnostic accuracy. In this article, we draw additional attention to the importance of measurement invariance and advance diagnostic research by introducing a novel approach for quantifying the impact of noninvariance with binary items (e.g., the presence or absence of symptoms). We illustrate this approach by testing measurement invariance and evaluating diagnostic accuracy across age groups using DSM alcohol use disorder items from a public national data set. By providing researchers with an easy-to-implement R program for examining diagnostic accuracy with binary items, this article sets the stage for future evaluations of the practical significance of partial invariance. Future work can extend our framework to include ordinal and categorical indicators, other measurement models in item response theory, settings with three or more groups, and via comparison to an external, "gold-standard" validator.