Partial least squares structural equation modeling (PLS-SEM)įirst-generation multivariate data analysis techniques, such as multiple regression, logistic regression, and analysis of variance, belong to the core set of statistical methods employed by researchers to empirically test hypothesized relationships between variables of interest.The chapter also describes considerations when using PLS-SEM and highlights situations that favor its use compared to CB-SEM. This chapter offers a concise overview of PLS-SEM’s key characteristics and discusses the main differences compared to CB-SEM. PLS-SEM is also useful for confirming measurement models. Whereas CB-SEM is primarily used to confirm theories, PLS represents a causal–predictive approach to SEM that emphasizes prediction in estimating models, whose structures are designed to provide causal explanations. To estimate structural equation models, researchers generally draw on two methods: covariance-based SEM (CB-SEM) and partial least squares SEM (PLS-SEM). Structural equation modeling is a multivariate data analysis method for analyzing complex relationships among constructs and indicators.
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