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2017 | OriginalPaper | Buchkapitel

12. Partial Least Squares Path Modeling

verfasst von : Jörg Henseler

Erschienen in: Advanced Methods for Modeling Markets

Verlag: Springer International Publishing

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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.

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Vorheriges Kapitel Structural Equation Modeling

Nächstes Kapitel Mixture Models

This assumption should be relaxed in the case of non-recursive models (Dijkstra and Henseler 2015a).

For an application of the NFI, see Ziggers and Henseler (2016).

Interestingly, the methodological literature on factor models hardly mentions what to do if the test rejects a factor model. Some researchers suggest considering a composite model as an alternative, because it is less restrictive (Henseler et al. 2014) and not subject to factor indeterminacy (Rigdon 2012). Others suggest allowing small deviations without principally questioning the factor model (see Asparouhov et al. 2015).

The AVE must be calculated based on consistent loadings, otherwise the assessment of convergent and discriminant validity based on the AVE is meaningless.

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Titel: Partial Least Squares Path Modeling
verfasst von: Jörg Henseler
Verlag: Springer International Publishing
Buch: Advanced Methods for Modeling Markets
Print ISBN: 978-3-319-53467-1

Electronic ISBN: 978-3-319-53469-5

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-53469-5_12