Model selection uncertainty and multimodel inference in partial least squares structural equation modeling (PLS-SEM)

Comparing alternative explanations for behavioral phenomena is central to the process of scientific inquiry. Recent research has emphasized the efficacy of Information Theoretic model selection criteria in partial least squares structural equation modeling (PLS-SEM), which has gained massive dissemination in a variety of fields. However, selecting one model over others based on model selection criteria may lead to a false sense of confidence as differences in the criteria values are often small. To overcome this limitation researchers have proposed Akaike weights, whose efficacy however, has not been assessed in the PLS-SEM context yet. Addressing this gap in research, we analyze the efficacy of Akaike weights in PLS-SEM-based model comparison tasks. We find that Akaike weights derived from BIC and GM are well suited for separating incorrectly specified from correctly specified models, and that Akaike weights based on AIC are useful for creating model-averaged predictions under conditions of model selection uncertainty.