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

Pratyush Nidhi Sharma, Galit Shmueli, Marko Sarstedt, Kevin H. Kim

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review


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.
Original languageEnglish
Title of host publicationProceedings of the 2nd International Symposium on Partial Least Squares Path Modeling
Subtitle of host publicationThe Conference for PLS Users
EditorsJörg Henseler, Christian Ringle, José Roldán, Gabriel Cepeda
Place of PublicationEnschede
PublisherUnivesity of Twente
Number of pages6
ISBN (Print)9789036540568
Publication statusPublished - 2015
Externally publishedYes
Event2015 PLS User Conference: 2nd International Symposium on Partial Least Squares Path Modeling - The Conference for PLS Users - Seville, Spain
Duration: 16 Jun 201519 Jun 2015


Conference2015 PLS User Conference

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