Abstract
Since the introduction of covariance-based structural equation modeling (SEM) by Joreskog in 1973, this technique has been received with considerable interest among empirical researchers. However, the predominance of LISREL, certainly the most well-known tool to perform this kind of analysis, has led to the fact that not all researchers are aware of alternative techniques for SEM, such as partial least squares (PLS) analysis. Therefore, the objective of this article is to provide an easily comprehensible introduction to this technique, which is particularly suited to situations in which constructs are measured by a very large number of indicators and where maximum likelihood covariance-based SEM tools reach their limit. Because this article is intended as a general introduction, it avoids mathematical details as far as possible and instead focuses on a presentation of PLS, which can be understood without an in-depth knowledge of SEM.
Keywords
LISRELCovariancePartial least squares regressionStructural equation modelingComputer scienceAnalysis of covarianceLimit (mathematics)EconometricsManagement scienceStatisticsApplied mathematicsMathematicsMachine learningEngineering
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Publication Info
- Year
- 2004
- Type
- article
- Volume
- 3
- Issue
- 4
- Pages
- 283-297
- Citations
- 1649
- Access
- Closed
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Cite This
Michael Haenlein,
Andreas Kaplan
(2004).
A Beginner's Guide to Partial Least Squares Analysis.
Understanding Statistics
, 3
(4)
, 283-297.
https://doi.org/10.1207/s15328031us0304_4
Identifiers
- DOI
- 10.1207/s15328031us0304_4