This paper addresses the issue of measurement invariance in composite models when using partial least squares (PLS) path modeling in structural equation modeling (SEM). The authors propose a novel three-step procedure, called Measurement Invariance of Composite Models (MICOM), to assess measurement invariance in composite models. The MICOM procedure consists of three hierarchical steps: configural invariance, compositional invariance, and the equality of composite mean values and variances. The authors validate the MICOM procedure through a simulation study and an empirical example. The simulation study demonstrates that the MICOM procedure appropriately identifies no, partial, and full measurement invariance. The empirical example, based on a corporate reputation model, shows that the MICOM procedure can effectively identify measurement invariance in real-world data. The authors conclude that the MICOM procedure is a valuable tool for researchers in international marketing and other disciplines to ensure the validity of multigroup analyses.This paper addresses the issue of measurement invariance in composite models when using partial least squares (PLS) path modeling in structural equation modeling (SEM). The authors propose a novel three-step procedure, called Measurement Invariance of Composite Models (MICOM), to assess measurement invariance in composite models. The MICOM procedure consists of three hierarchical steps: configural invariance, compositional invariance, and the equality of composite mean values and variances. The authors validate the MICOM procedure through a simulation study and an empirical example. The simulation study demonstrates that the MICOM procedure appropriately identifies no, partial, and full measurement invariance. The empirical example, based on a corporate reputation model, shows that the MICOM procedure can effectively identify measurement invariance in real-world data. The authors conclude that the MICOM procedure is a valuable tool for researchers in international marketing and other disciplines to ensure the validity of multigroup analyses.