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A Comparison of PLS and ML Bootstrapping Techniques in SEM: A Monte Carlo Study

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New Perspectives in Partial Least Squares and Related Methods

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 56))

Abstract

Structural Equation Modeling (SEM) techniques have been extensively used in business and social science research to model complex relationships. The two most widely used estimation methods in SEM are the Maximum Likelihood (ML) and Partial Least Square (PLS). Both the estimation methods rely on Bootstrap re-sampling to a large extent. While PLS relies completely on Bootstrapping to obtain standard errors for hypothesis testing, ML relies on Bootstrapping under conditions in violation of the distributional assumptions. Even though Bootstrapping has several advantages, it may fail under certain conditions. In this Monte Carlo study, we compare the accuracy and efficiency of ML and PLS based Bootstrapping in SEM, while recovering the true estimates under various conditions of sample size and distributional assumptions. Our results suggest that researchers might benefit by using PLS based bootstrapping with smaller sample sizes. However, at larger sample sizes the use of ML based bootstrapping is recommended.

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Author information

Authors and Affiliations

  1. Joseph M. Katz Graduate School of Business, University of Pittsburgh, Pittsburgh, PA, USA

    Pratyush N. Sharma

  2. School of Education and Joseph M. Katz Graduate School of Business, University of Pittsburgh, Pittsburgh, PA, USA

    Kevin H. Kim

Authors
  1. Pratyush N. Sharma
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  2. Kevin H. Kim
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Corresponding author

Correspondence to Pratyush N. Sharma .

Editor information

Editors and Affiliations

  1. School of Behavioral & Brain Sciences, The University of Texas at Dallas, Richardson, Texas, USA

    Herve Abdi

  2. Department of Decision and Information Systems, University of Houston, Houston, Texas, USA

    Wynne W. Chin

  3. ESSEC Business School of Paris, Cergy-Pontoise Cedex, France

    Vincenzo Esposito Vinzi

  4. CNAM, Paris, USA

    Giorgio Russolillo

  5. Rouen Business School, Rouen, France

    Laura Trinchera

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Sharma, P.N., Kim, K.H. (2013). A Comparison of PLS and ML Bootstrapping Techniques in SEM: A Monte Carlo Study. In: Abdi, H., Chin, W., Esposito Vinzi, V., Russolillo, G., Trinchera, L. (eds) New Perspectives in Partial Least Squares and Related Methods. Springer Proceedings in Mathematics & Statistics, vol 56. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8283-3_13

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