A comparative study on the performance of maximum likelihood, generalized least square, scale-free least square, partial least square and consistent partial least square estimators in structural equation modeling

Zulkifli, R.; Aimran, N.; Deni, S.; Badarisam, F.

Growing Science » International Journal of Data and Network Science » A comparative study on the performance of maximum likelihood, generalized least square, scale-free least square, partial least square and consistent partial least square estimators in structural equation modeling

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International Journal of Data and Network Science

ISSN 2561-8156 (Online) - ISSN 2561-8148 (Print) Quarterly Publication Volume 6 Issue 2 pp. 391-400 , 2022

A comparative study on the performance of maximum likelihood, generalized least square, scale-free least square, partial least square and consistent partial least square estimators in structural equation modeling Pages 391-400 Download PDF

Authors: Raudhah Zulkifli, Nazim Aimran, Sayang Mohd Deni, Fatin Najihah Badarisam

DOI: 10.5267/j.ijdns.2021.12.015

Keywords: Consistent partial least squares, Generalized least squares, Maximum likelihood, Scale-free least squares, Structural equation modeling

Abstract: Structural equation modeling offers various estimation methods for estimating parameters. The most used method in covariance-based structural equation modeling (CB-SEM) is the maximum likelihood (ML) estimator. The ML estimator is typically used when fitting models with normally distributed data. The growth of partial least squares path modeling (PLS-PM), including consistent partial least squares (PLSc), has also been noticed by researchers in the SEM fields. The PLSc has elevated interest in the scholastic setting in measuring the performance of various estimation methods in structural equation modeling. The choice of estimation methods has substantial impact in yielding parameter estimates. There could be a trade-off among the estimation methods’ ability to deal with different types of data based on the model tested. Accordingly, this study aims to compare the performance of ML, generalized least squares (GLS), and scale-free least squares (SFLS) for CB-SEM as well as partial least squares (PLS) and consistent partial least squares (PLSc). Multivariate normal data were generated using Monte Carlo simulation with pre-determined population parameters and sample sizes using R Programming packages. To produce the estimated values, data analysis was performed using AMOS and SmartPLS for CB-SEM and PLS-SEM, respectively. The findings illustrate notable similarities between CB-SEM (ML) and PLS-SEM results when the true indicator loading is certainly high.

How to cite this paper

 Zulkifli, R., Aimran, N., Deni, S & Badarisam, F. (2022). A comparative study on the performance of maximum likelihood, generalized least square, scale-free least square, partial least square and consistent partial least square estimators in structural equation modeling.International Journal of Data and Network Science, 6(2), 391-400.

Refrences

Afthanorhan, A., Awang, Z., & Aimran, N. (2020). An extensive comparison of cb-sem and pls-sem for reliability and va-lidity. International Journal of Data and Network Science, 4(4), 357–364.
Afthanorhan, A., Awang, Z., Aimran, N., & Arifin, J. (2021). An Extensive Comparison Between CBSEM and Consistent PLS-SEM On Producing the Estimates of Construct Correlation in Applied Research. Journal of Physics: Conference Series, 1874(1), 012083.
Aimran, A. N., Ahmad, S., Afthanorhan, A., & Awang, Z. (2017a). The assessment of the performance of covariance-based structural equation modeling and partial least square path modeling. AIP Conference Proceedings, 1842.
Aimran, A. N., Ahmad, S., Afthanorhan, A., & Awang, Z. (2017b). The development of comparative bias index. AIP Con-ference Proceedings, 1870.
Ainur, A. K., Sayang, M. D., Jannoo, Z., & Yap, B. W. (2017). Sample size and non-normality effects on goodness of fit measures in structural equation models. Pertanika Journal of Science and Technology, 25(2), 575–586.
Andreassen, T. W., Lorentzen, B. G., & Olsson, U. H. (2006). The impact of non-normality and estimation methods in SEM on satisfaction research in marketing. Quality and Quantity, 40(1), 39–58.
Arbuckle, J. L. (2011). IBM SPSS Amos 20 user’s guide. Amos Development Corporation, SPSS Inc.
Awang, Z. (2015). SEM made simple: A gentle approach to learning Structural Equation Modeling. MPWS Rich Publica-tion.
Dijkstra, T. K. (2010). Latent Variables and Indices: Herman Wold’s Basic Design and Partial Least Squares. in Handbook of Partial Least Squares: Concepts, Methods, and Applications,V. E. Vinzi, W. W. Chin, J. Henseler, and H. Wang (eds.), New York: Springer, pp. 23-46.
Dijkstra, T. K., & Henseler, J. (2015). Consistent partial least squares path modeling. MIS Quarterly: Management Infor-mation Systems, 39(2), 297–316.
Hair., Matthews, L. M., Matthews, R. L., & Sarstedt, M. (2017). PLS-SEM or CB-SEM: updated guidelines on which method to use. International Journal of Multivariate Data Analysis, 1(2), 107.
Hair, Risher, J. J., Sarstedt, M., & Ringle, C. M. (2018). The Results of PLS-SEM Article information. European Business Review, 31(1), 2–24.
Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international mar-keting. In R. R. Sinkovics & P. N. Ghauri (Eds.), Advances in international marketing (Vol. 20, pp. 277–320). Bingley: Emerald.
Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: Updated guidelines. Industrial Management and Data Systems, 116(1), 2–20.
Maydeu-Olivares, A. (2017). Maximum likelihood estimation of structural equation models for continuous data: Stand-ard errors and goodness of fit. Structural Equation Modeling: A Multidisciplinary Journal, 24(3), 383-394.
McDonald, R. P. (1996). Path analysis with composite variables. Multivariate Behavioral Research, 31(2), 239–270.
Newsom, J.T. (2018). Alternative Estimation Methods Remember. Psy 523/623 Structural Equation Modeling, 1-3.
Rahlin, N. A., Awang, Z., Afthanorhan, A., & Aimran, N. (2019). Antecedents and consequences of employee safety cli-mate in the small manufacturing enterprises: Translation, validation and application of the generic safety climate questionnaire. International Journal of Innovation, Creativity and Change, 7(10), 307–328.
Sarstedt, M., Ringle, C.M., Henseler, J., & Hair, J.F. (2014). On the emancipation of PLS-SEM: a useful tool for family business researchers. Journal of Family Business Strategy, 5(1), 105–115.
Schamberger, T., Schuberth, F., Henseler, J., & Dijkstra, T. K. (2020). Robust partial least squares path modeling. Behav-iormetrika, 47(1), 307–334.