Start Date
14-12-2012 12:00 AM
Description
Partial Least Squares (PLS) based Structural Equation Modeling (SEM) has become increasingly popular in Management Information Systems (MIS) research to model complex relationships and to make valid inferences from the restricted sample to the larger population. Given the larger goal of creating generalizable theories in MIS research, we argue that the lack of model selection criteria in PLS that penalize model complexity might be causing researchers to select unnecessarily complex but highly fitting models that may not generalize to other samples. We introduce several Information Theoretic (IT) model selection criteria in the PLS context that penalize model complexity but reward high fit, and therefore guide researchers to select a parsimonious and generalizable model. In this Monte Carlo study, we compare their performance to the currently existing PLS indices, in selecting the best model among a set of competing models under various conditions of sample size, effect size and data distribution.
Recommended Citation
Sharma, Pratyush Nidhi and Kim, Kevin Hyunkyung, "Model Selection in Information Systems Research Using Partial Least Squares Based Structural Equation Modeling" (2012). ICIS 2012 Proceedings. 6.
https://aisel.aisnet.org/icis2012/proceedings/ResearchMethods/6
Model Selection in Information Systems Research Using Partial Least Squares Based Structural Equation Modeling
Partial Least Squares (PLS) based Structural Equation Modeling (SEM) has become increasingly popular in Management Information Systems (MIS) research to model complex relationships and to make valid inferences from the restricted sample to the larger population. Given the larger goal of creating generalizable theories in MIS research, we argue that the lack of model selection criteria in PLS that penalize model complexity might be causing researchers to select unnecessarily complex but highly fitting models that may not generalize to other samples. We introduce several Information Theoretic (IT) model selection criteria in the PLS context that penalize model complexity but reward high fit, and therefore guide researchers to select a parsimonious and generalizable model. In this Monte Carlo study, we compare their performance to the currently existing PLS indices, in selecting the best model among a set of competing models under various conditions of sample size, effect size and data distribution.