The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
An Introduction to Structural Equation Modeling
Open accessAbstract
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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Statistical power analyses using G∗Power 3.1: tests for correlation and regression analyses
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PLS-regression: a basic tool of chemometrics
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An assessment of the use of partial least squares structural equation modeling in marketing research
Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-011-0261-6 · Cited by 8,088
Self-citationModern Methods for Business Research
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Hair, J. F., Howard, M. C., & Nitzl, C. (2020) · Journal of Business Research · doi:10.1016/j.jbusres.2019.11.069 · Cited by 5,446
Self-citationPLS-SEM or CB-SEM: updated guidelines on which method to use
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Self-citationCommon Beliefs and Reality About PLS: Comments on Rönkkö and Evermann (2013)
Henseler, J., Dijkstra, T. K., Sarstedt, M., Ringle, C. M., Diamantopoulos, A., Straub, D. W., Ketchen, D. J., Hair, J. F., Hult, G. T. M., & Calantone, R. J. (2014) · Organizational Research Methods · doi:10.1177/1094428114526928 · Cited by 3,332
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Metadata
- Authors
- Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S.
- Venue
- Classroom Companion: Business
- Publisher
- Springer International Publishing
- Published
- 2021
- Pages
- 1-29
- ISSN
- 2662-2866 (ISSN-L)
- 2662-2874
- OpenAlex
- W3209981475
- Cited by
- 1,219
- References
- 18
- FWCI
- 351.92
- Type
- book chapter
- Language
- en
- Open access
- hybrid
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