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On the Use of Formative Measurement Specifications in Structural Equation Modeling: A Monte Carlo Simulation Study to Compare Covariance-Based and Partial Least Squares Model Estimation Methodologies

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  • Ringle, Christian M.
  • Götz, Oliver
  • Wetzels, Martin
  • Wilson, Bradley

Abstract

The broader goal of this paper is to provide social researchers with some analytical guidelines when investigating structural equation models (SEM) with predominantly a formative specification. This research is the first to investigate the robustness and precision of parameter estimates of a formative SEM specification. Two distinctive scenarios (normal and non-normal data scenarios) are compared with the aid of a Monte Carlo simulation study for various covariance-based structural equation modeling (CBSEM) estimators and various partial least squares path modeling (PLS-PM) weighting schemes. Thus, this research is also one of the first to compare CBSEM and PLS-PM within the same simulation study. We establish that the maximum likelihood (ML) covariance-based discrepancy function provides accurate and robust parameter estimates for the formative SEM model under investigation when the methodological assumptions are met (e.g., adequate sample size, distributional assumptions, etc.). Under these conditions, ML-CBSEM outperforms PLS-PM. We also demonstrate that the accuracy and robustness of CBSEM decreases considerably when methodological requirements are violated, whereas PLS-PM results remain comparatively robust, e.g. irrespective of the data distribution. These findings are important for researchers and practitioners when having to choose between CBSEM and PLS-PM methodologies to estimate formative SEM in their particular research situation.

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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 15390.

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Date of creation: 2009
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Handle: RePEc:pra:mprapa:15390

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Keywords: PLS; path modeling; covariance structure analysis; structural equation modeling; formative measurement; simulation study;

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  1. Claes Cassel & Peter Hackl & Anders Westlund, 1999. "Robustness of partial least-squares method for estimating latent variable quality structures," Journal of Applied Statistics, Taylor and Francis Journals, vol. 26(4), pages 435-446.
  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, University of Chicago Press, vol. 30(2), pages 199-218, September.
  3. Don Y. Lee, 2001. "The effects of entrepreneurial personality, background and network activities on venture growth," Journal of Management Studies, Wiley Blackwell, vol. 38(4), pages 583-602, 06.
  4. Albert Satorra, 1990. "Robustness issues in structural equation modeling: a review of recent developments," Quality & Quantity: International Journal of Methodology, Springer, vol. 24(4), pages 367-386, November.
  5. C. Vale & Vincent Maurelli, 1983. "Simulating multivariate nonnormal distributions," Psychometrika, Springer, vol. 48(3), pages 465-471, September.
  6. Albert Satorra & Peter M. Bentler, 1999. "A scaled difference chi-square test statistic for moment structure analysis," Economics Working Papers 412, Department of Economics and Business, Universitat Pompeu Fabra.
  7. Tenenhaus, Michel & Vinzi, Vincenzo Esposito & Chatelin, Yves-Marie & Lauro, Carlo, 2005. "PLS path modeling," Computational Statistics & Data Analysis, Elsevier, vol. 48(1), pages 159-205, January.
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Cited by:
  1. Meyer, Wiebke & Mollers, Judith & Buchenrieder, Gertrud, 2012. "A behavioural approach to remittances analysis," 2012 Conference, August 18-24, 2012, Foz do Iguacu, Brazil 126428, International Association of Agricultural Economists.

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