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Data generation for composite-based structural equation modeling methods

Author

Listed:
  • Rainer Schlittgen

    (University of Hamburg)

  • Marko Sarstedt

    (Otto-von-Guericke-University Magdeburg
    Monash University of Malaysia)

  • Christian M. Ringle

    (Hamburg University of Technology
    University of Waikato)

Abstract

Examining the efficacy of composite-based structural equation modeling (SEM) features prominently in research. However, studies analyzing the efficacy of corresponding estimators usually rely on factor model data. Thereby, they assess and analyze their performance on erroneous grounds (i.e., factor model data instead of composite model data). A potential reason for this malpractice lies in the lack of available composite model-based data generation procedures for prespecified model parameters in the structural model and the measurements models. Addressing this gap in research, we derive model formulations and present a composite model-based data generation approach. The findings will assist researchers in their composite-based SEM simulation studies.

Suggested Citation

  • Rainer Schlittgen & Marko Sarstedt & Christian M. Ringle, 2020. "Data generation for composite-based structural equation modeling methods," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(4), pages 747-757, December.
    • Handle: RePEc:spr:advdac:v:14:y:2020:i:4:d:10.1007_s11634-020-00396-6
    DOI: 10.1007/s11634-020-00396-6
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    References listed on IDEAS

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    4. Rainer Schlittgen, 2018. "Estimation of generalized structured component analysis models with alternating least squares," Computational Statistics, Springer, vol. 33(1), pages 527-548, March.
    5. 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.
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    7. Sarstedt, Marko & Hair, Joseph F. & Ringle, Christian M. & Thiele, Kai O. & Gudergan, Siegfried P., 2016. "Estimation issues with PLS and CBSEM: Where the bias lies!," Journal of Business Research, Elsevier, vol. 69(10), pages 3998-4010.
    8. Reinartz, Werner & Haenlein, Michael & Henseler, Jörg, 2009. "An empirical comparison of the efficacy of covariance-based and variance-based SEM," International Journal of Research in Marketing, Elsevier, vol. 26(4), pages 332-344.
    9. Marko Sarstedt & Christian M Ringle & Jun-Hwa Cheah & Hiram Ting & Ovidiu I Moisescu & Lacramioara Radomir, 2020. "Structural model robustness checks in PLS-SEM," Tourism Economics, , vol. 26(4), pages 531-554, June.
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    Cited by:

    1. Pasquale Dolce & Cristina Davino & Domenico Vistocco, 2022. "Quantile composite-based path modeling: algorithms, properties and applications," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(4), pages 909-949, December.

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