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PLS-MGA: A Non-Parametric Approach to Partial Least Squares-based Multi-Group Analysis

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Challenges at the Interface of Data Analysis, Computer Science, and Optimization

Abstract

This paper adds to an often applied extension of Partial Least Squares (PLS) path modeling, namely the comparison of PLS estimates across subpopulations, also known as multi-group analysis. Existing PLS-based approaches to multi-group analysis have the shortcoming that they rely on distributional assumptions. This paper develops a non-parametric PLS-based approach to multi-group analysis: PLS-MGA. Both the existing approaches and the new approach are applied to a marketing example of customer switching behavior in a liberalized electricity market. This example provides first evidence of favorable operation characteristics of PLS-MGA.

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Notes

  1. 1.

    This notation of the Welch-Satterthwaite equation was derived by Nitzl (2010). Note that the formula proposed by Chin (2000) is incorrect.

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Author information

Authors and Affiliations

  1. Institute for Management Research, Radboud University Nijmegen, Thomas van Aquinostraat 3, 6525 GD, Nijmegen, The Netherlands

    Jörg Henseler

Authors
  1. Jörg Henseler
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Correspondence to Jörg Henseler .

Editor information

Editors and Affiliations

  1. Fak. Wirtschaftswissenschaften, Inst. Entscheidungstheorieund, Universität Karlsruhe (TH), Kaiserstr. 12, Karlsruhe, 76128, Germany

    Wolfgang A. Gaul

  2. Insitute for Information Systems, and Management (IISM), Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, Karlsruhe, 76131, Baden-Württemberg, Germany

    Andreas Geyer-Schulz

  3. , Information Systems, University ofHildesheim, Marienburger Platz 22, Hildesheim, 31141, Germany

    Lars Schmidt-Thieme

  4. Institute for Information Systems, and Management (IISM), Karlsruhe Institute of Technology (KIT), Kaiserstraße 12, Karlsruhe, 76128, Germany

    Jonas Kunze

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© 2012 Springer-Verlag Berlin Heidelberg

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Henseler, J. (2012). PLS-MGA: A Non-Parametric Approach to Partial Least Squares-based Multi-Group Analysis. In: Gaul, W., Geyer-Schulz, A., Schmidt-Thieme, L., Kunze, J. (eds) Challenges at the Interface of Data Analysis, Computer Science, and Optimization. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24466-7_50

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