Partial least squares path modeling

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Abstract

Structural equation modeling (SEM) is a family of statistical techniques that has become very popular in marketing. Its ability to model latent variables, to take various forms of measurement error into account, and to test entire theories makes it useful for a plethora of research questions. It does not come as a surprise that some of the most cited scholarly articles in the marketing domain are about SEM (e.g., Bagozzi and Yi 1988; Fornell and Larcker 1981), and that SEM is covered by two contributions within this volume. The need for two contributions arises from the SEM family tree having two major branches (Reinartz et al. 2009): covariance-based SEM (which is presented in Chap. 11) and variance-based SEM, which is presented in this chapter.
Original languageEnglish
Title of host publication Advanced Methods for Modeling Markets
EditorsPeter S. H. Leeflang, Jaap E. Wieringa, Tammo H. A. Bijmolt, Pauwels Koen H.
Place of PublicationHeidelberg
PublisherSpringer
Pages361-381
ISBN (Electronic)978-3-319-53469-5
ISBN (Print)978-3-319-53467-1
DOIs
Publication statusPublished - 2017

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Henseler, J. (2017). Partial least squares path modeling. In P. S. H. Leeflang, J. E. Wieringa, T. H. A. Bijmolt, & P. Koen H. (Eds.), Advanced Methods for Modeling Markets (pp. 361-381). [12] Heidelberg: Springer. https://doi.org/10.1007/978-3-319-53469-5