University of Twente Proceedings

Predictive model selection in partial least squares path modeling (PLS-PM)

Sharma, Pratyush Nidhi and Sarstedt, Marko and Shmueli, Galit and Kim, Kevin H. (2015) Predictive model selection in partial least squares path modeling (PLS-PM). In: 2nd International Symposium on Partial Least Squares Path Modeling - The Conference for PLS Users., 16 June 2015 - 19 June 2015, Seville, Spain .

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Event:	2nd International Symposium on Partial Least Squares Path Modeling - The Conference for PLS Users., 16 June 2015 - 19 June 2015, Seville, Spain
Abstract:	Predictive model selection metrics are used to select models with the highest out-of-sample predictive power among a set of models. R2 and related metrics, which are heavily used in partial least squares path modeling, are often mistaken as predictive metrics. We introduce information theoretic model selection criteria that are designed for out-of-sample prediction and which do not require creating a holdout sample. Using a Monte Carlo study, we compare the performance of frequently used model evaluation criteria and information theoretic criteria in selecting the best predictive model under various conditions of sample size, effect size, loading patterns, and data distribution.
Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Partial Least Squares Path Modeling (PLS-PM), Structural Equation Modeling (SEM), Out-of-Sample Prediction, Model Selection, Monte Carlo Study
Link to this item:	https://doi.org/10.3990/2.336
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