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Treating Unobserved Heterogeneity in PLS-SEM: A Multi-method Approach

  • Marko SarstedtEmail author
  • Christian M. Ringle
  • Joseph F. Hair

Abstract

Accounting for unobserved heterogeneity has become a key concern to ensure the validity of results when applying partial least squares structural equation modeling (PLS-SEM). Recent methodological research in the field has brought forward a variety of latent class techniques that allow for identifying and treating unobserved heterogeneity. This chapter raises and discusses key aspects that are fundamental to a full and adequate understanding of how to apply these techniques in PLS-SEM. More precisely, in this chapter, we introduce a systematic procedure for identifying and treating unobserved heterogeneity in PLS path models using a combination of latent class techniques. The procedure builds on the FIMIX-PLS method to decide if unobserved heterogeneity has a critical impact on the results. Based on these outcomes, researchers should use more recently developed latent class methods, which have been shown to perform superior in recovering the segment-specific model estimates. After introducing these techniques, the chapter continues by discussing the means to identify explanatory variables that characterize the latent segments. Our discussion also broaches the issue of measurement invariance testing, which is a fundamental requirement for a subsequent comparison of parameters across groups by means of a multigroup analysis.

Notes

Acknowledgments

This chapter builds on the articles published by Hair et al. (2016) and Matthews et al. (2016) in the European Business Review journal, the article by Sarstedt et al. (2016b) in Annals of Tourism Research, and the chapter on uncovering unobserved heterogeneity in the book on PLS-SEM advances by Hair et al. (2017a). This chapter refers to the use of the statistical software SmartPLS (http://www.smartpls.com). Ringle acknowledges a financial interest in SmartPLS.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Marko Sarstedt
    • 1
    • 2
    Email author
  • Christian M. Ringle
    • 3
    • 2
  • Joseph F. Hair
    • 4
  1. 1.Institute of MarketingOtto-von-Guericke University MagdeburgMagdeburgGermany
  2. 2.Faculty of Business and LawUniversity of NewcastleCallaghanAustralia
  3. 3.Institute of Human Resource Management and Organizations (HRMO)Hamburg University of Technology (TUHH)HamburgGermany
  4. 4.University of South AlabamaMobileUSA

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