Paper on predictive model selection accepted
We are happy to announce that the paper “Prediction-oriented model selection in partial least squares path modeling,” authored by Pratyush N. Sharma (University of Delaware), Galit Shmueli (National Tsing Hua University), Marko Sarstedt (OVGU), Nicholas Danks (National Tsing Hua University), and Soumya Ray (National Tsing Hua University) has been accepted for publication in Decision Sciences. In our paper, we compare the performance of standard PLS-SEM-based model evaluation and model selection criteria derived from Information Theory, in terms of selecting the best predictive model among a cohort of competing models. We use Monte Carlo simulation to study this question under various sample sizes, effect sizes, item loadings, and model setups. Specifically, we explore whether, and when, the in-sample measures such as the model selection criteria can substitute for out-of-sample criteria that require a holdout sample. Such a substitution is advantageous when creating a holdout causes considerable loss of statistical and predictive power due to an overall small sample. Based on our results, we identify a set of criteria that researchers should use when not having the luxury of a holdout sample, and the goal is selecting correctly specified models with low prediction error. We also illustrate the model selection criteria’s practical utility using a well-known corporate reputation model.