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An Introduction to Structural Equation Modeling

Joseph F. HairMitchell College of Business, University of South Alabama, Mobile, USAG. Tomas M. HultBroad College of Business, Michigan State University, East Lansing, USAChristian M. RingleDepartment of Management Science and Technology, Hamburg University of Technology, Hamburg, GermanyMarko SarstedtBabeș-Bolyai University, Faculty of Economics and Business Administration, Cluj, RomaniaNicholas P. DanksTrinity Business School, Trinity College, Dublin, IrelandSoumya RayNational Tsing Hua University, Hsinchu, Taiwan

2021en

ABI

Annotatsiya

Abstract Structural equation modeling is a multivariate data analysis method for analyzing complex relationships among constructs and indicators. To estimate structural equation models, researchers generally draw on two methods: covariance-based SEM (CB-SEM) and partial least squares SEM (PLS-SEM). 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. PLS-SEM is also useful for confirming measurement models. This chapter offers a concise overview of PLS-SEM’s key characteristics and discusses the main differences compared to CB-SEM. The chapter also describes considerations when using PLS-SEM and highlights situations that favor its use compared to CB-SEM.

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DOI: 10.1007/978-3-030-80519-7_1

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