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.
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.