Measurement invariance testing in partial least squares structural equation modeling

When using structural equation modeling, comparison across time or groups can be misleading if measures are not invariant. Partial least squares structural equation modeling (PLS-SEM) is a method widely used in business research, but its ability to test for measurement invariance is limited. This study introduces a comprehensive approach for measurement invariance testing in reflective measurement models in PLS-SEM. The methodology diverges from the traditional measurement invariance of composite models (MICOM) approach and expands the possibilities of measurement invariance testing in three areas: 1) providing statistical tests to validate the comparison of latent means across groups; 2) measurement invariance testing in longitudinal studies; and 3) the ability to simultaneously assess measurement invariance across multiple groups. Additionally, this study proposes a strategy to address measurement invariance rejections in large-sample studies. The paper offers guidelines for the MI tests, and an empirical example illustrates their utility in facilitating experimental approaches in PLS-SEM.