Multicollinearity: An Overview and Introduction of Ridge PLS-SEM Estimation

Sandra Streukens () and Sara Leroi-Werelds ()
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Sandra Streukens: Hasselt University, Department of Marketing and Strategy
Sara Leroi-Werelds: Hasselt University, Department of Marketing and Strategy

Chapter Chapter 7 in Partial Least Squares Path Modeling, 2023, pp 183-207 from Springer

Abstract: Abstract MulticollinearityCollinearitymulticollinearity, orMulticollinearity the existence of excessive correlations among (combinations of) predictor variables, is a commonly encountered phenomenon that affects (PLS-SEM)Partial Least Squares (PLS)structural equation model (PLS-SEM) parameter estimatesParameter estimates. This chapter provides an extensive overview ofMulticollinearity multicollinearityCollinearitymulticollinearity, its consequences, detection, and possible solutions. Critical to this overview is the explicit distinction among three types ofMulticollinearity multicollinearityCollinearitymulticollinearity: canonical structuralCollinearitymulticollinearity multicollinearityCanonical structural multicollinearity, numericalMulticollinearity multicollinearityCollinearitymulticollinearity, and common-factorCommon-factor multicollinearity multicollinearityCollinearitymulticollinearity. In addition, ridgePartial Least Squares (PLS)structural equation model (PLS-SEM) PLS-SEM—an approach that combines the principles of ridge regression and PLS-SEM modeling—is introduced as an effective approach to mitigate the effects of canonical structuralCollinearitymulticollinearity multicollinearityCanonical structural multicollinearity on estimation results.

Keywords: Multicollinearity; Ridge estimation; PLS-SEM; Ridge PLS-SEM; VIF (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/978-3-031-37772-3_7

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