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semPLS: Structural Equation Modeling Using Partial Least Squares

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  • Monecke, Armin
  • Leisch, Friedrich

Abstract

Structural equation models (SEM) are very popular in many disciplines. The partial least squares (PLS) approach to SEM offers an alternative to covariance-based SEM, which is especially suited for situations when data is not normally distributed. PLS path modelling is referred to as soft-modeling-technique with minimum demands regarding mea- surement scales, sample sizes and residual distributions. The semPLS package provides the capability to estimate PLS path models within the R programming environment. Different setups for the estimation of factor scores can be used. Furthermore it contains modular methods for computation of bootstrap confidence intervals, model parameters and several quality indices. Various plot functions help to evaluate the model. The well known mobile phone dataset from marketing research is used to demonstrate the features of the package.

Suggested Citation

  • Monecke, Armin & Leisch, Friedrich, 2012. "semPLS: Structural Equation Modeling Using Partial Least Squares," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i03).
    • Handle: RePEc:jss:jstsof:v:048:i03
    DOI: http://hdl.handle.net/10.18637/jss.v048.i03
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    1. Marko Sarstedt & Christian Ringle, 2010. "Treating unobserved heterogeneity in PLS path modeling: a comparison of FIMIX-PLS with different data analysis strategies," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(8), pages 1299-1318.
    2. Jarvis, Cheryl Burke & MacKenzie, Scott B & Podsakoff, Philip M, 2003. "A Critical Review of Construct Indicators and Measurement Model Misspecification in Marketing and Consumer Research," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 30(2), pages 199-218, September.
    3. Temme, Dirk & Kreis, Henning & Hildebrandt, Lutz, 2006. "PLS path modeling: A software review," SFB 649 Discussion Papers 2006-084, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    4. Marko Sarstedt & Jan-Michael Becker & Christian M. Ringle & Manfred Schwaiger, 2011. "Uncovering and Treating Unobserved Heterogeneity with FIMIX-PLS: Which Model Selection Criterion Provides an Appropriate Number of Segments?," Schmalenbach Business Review (sbr), LMU Munich School of Management, vol. 63(1), pages 34-62, January.
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