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Segmentation of PLS path models by iterative reweighted regressions

Author

Listed:
  • Schlittgen, Rainer
  • Ringle, Christian M.
  • Sarstedt, Marko
  • Becker, Jan-Michael

Abstract

Uncovering unobserved heterogeneity is a requirement to obtain valid results when using structural equation modeling (SEM). Conventional segmentation methods usually fail in an SEM context because they account for the indicator data, but not for the latent variables and their relationships in the structural model. This research introduces a new segmentation approach to variance-based SEM using partial least squares path modeling (PLS). The iterative reweighted regressions segmentation method for PLS (PLS-IRRS) effectively identifies and treats unobserved heterogeneity in data sets. Compared to existing alternatives, PLS-IRRS is multiple times faster while delivering results of the same quality. Researchers should therefore routinely use PLS-IRRS to address the critical issue of unobserved heterogeneity in PLS.

Suggested Citation

  • Schlittgen, Rainer & Ringle, Christian M. & Sarstedt, Marko & Becker, Jan-Michael, 2016. "Segmentation of PLS path models by iterative reweighted regressions," Journal of Business Research, Elsevier, vol. 69(10), pages 4583-4592.
    • Handle: RePEc:eee:jbrese:v:69:y:2016:i:10:p:4583-4592
    DOI: 10.1016/j.jbusres.2016.04.009
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    References listed on IDEAS

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