IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/15390.html
   My bibliography  Save this paper

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

Author

Listed:
  • 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.

Suggested Citation

  • Ringle, Christian M. & Götz, Oliver & Wetzels, Martin & Wilson, Bradley, 2009. "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," MPRA Paper 15390, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:15390
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/15390/1/MPRA_paper_15390.pdf
    File Function: original version
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    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 & Francis Journals, vol. 26(4), pages 435-446.
    2. Wold, Herman, 1974. "Causal flows with latent variables : Partings of the ways in the light of NIPALS modelling," European Economic Review, Elsevier, vol. 5(1), pages 67-86, June.
    3. Allen Fleishman, 1978. "A method for simulating non-normal distributions," Psychometrika, Springer;The Psychometric Society, vol. 43(4), pages 521-532, December.
    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. 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.
    6. 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, Oxford University Press, vol. 30(2), pages 199-218, September.
    7. Albert Satorra & Peter Bentler, 2001. "A scaled difference chi-square test statistic for moment structure analysis," Psychometrika, Springer;The Psychometric Society, vol. 66(4), pages 507-514, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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. repec:spr:jknowl:v:8:y:2017:i:3:d:10.1007_s13132-015-0278-z is not listed on IDEAS
    3. repec:spr:soinre:v:135:y:2018:i:1:d:10.1007_s11205-016-1480-5 is not listed on IDEAS
    4. Jacqueline Beuningen & Hans Schmeets, 2013. "Developing a Social Capital Index for the Netherlands," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 113(3), pages 859-886, September.
    5. Meyer, Wiebke, 2012. "Motives for remitting from Germany to Kosovo," Studies on the Agricultural and Food Sector in Transition Economies, Leibniz Institute of Agricultural Development in Transition Economies (IAMO), volume 69, number 69.
    6. 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.
    7. Achcaoucaou, Fariza & Miravitlles, Paloma & León-Darder, Fidel, 2017. "Do we really know the predictors of competence-creating R&D subsidiaries? Uncovering the mediation of dual network embeddedness," Technological Forecasting and Social Change, Elsevier, vol. 116(C), pages 181-195.
    8. repec:spr:compst:v:33:y:2018:i:1:d:10.1007_s00180-017-0723-5 is not listed on IDEAS

    More about this item

    Keywords

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

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:15390. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Joachim Winter) or (Rebekah McClure). General contact details of provider: http://edirc.repec.org/data/vfmunde.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.