Testing measurement invariance of composites using partial least squares

Jörg Henseler, Christian M. Ringle, Marko Sarstedt

Research output: Contribution to journalArticle

414 Citations (Scopus)

Abstract

Purpose – Research on international marketing usually involves comparing different groups of respondents. When using structural equation modeling (SEM), group comparisons can be misleading unless researchers establish the invariance of their measures. While methods have been proposed to analyze measurement invariance in common factor models, research lacks an approach in respect of composite models. The purpose of this paper is to present a novel three-step procedure to analyze the measurement invariance of composite models (MICOM) when using variance-based SEM, such as partial least squares (PLS) path modeling. Design/methodology/approach – A simulation study allows us to assess the suitability of the MICOM procedure to analyze the measurement invariance in PLS applications. Findings – The MICOM procedure appropriately identifies no, partial, and full measurement invariance. Research limitations/implications – The statistical power of the proposed tests requires further research, and researchers using the MICOM procedure should take potential type-II errors into account. Originality/value – The research presents a novel procedure to assess the measurement invariance in the context of composite models. Researchers in international marketing and other disciplines need to conduct this kind of assessment before undertaking multigroup analyses. They can use MICOM procedure as a standard means to assess the measurement invariance.

Original languageEnglish
Pages (from-to)405-431
Number of pages27
JournalInternational Marketing Review
Volume33
Issue number3
DOIs
Publication statusPublished - 9 May 2016

Keywords

  • Composite models
  • Measurement
  • Measurement invariance
  • Methodology
  • MICOM
  • Multigroup
  • Partial least squares
  • Path modelling
  • Permutation test
  • Structural equation modelling
  • Variance-based SEM

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