An Alternative Method to the PLS Approach for Estimating Structural Equation Models
Keywords:
Observed variables, latent variables, Bayesian approach, convergence, simulation, recovery satisfaction modelAbstract
The purpose of this paper is to provide an alternative method to the Partial Least Squares (PLS) approach for estimating structural equation models. This method is based on the Bayesian approach in the step of latent variables estimation. The main advantage of the proposed method compared to the classical PLS approach is that the estimation of the latent variables and the model parameters is
given without any algorithm. The results obtained from different applications on simulated and on real data using a recovery satisfaction model show the great performance of our alternative method.
References
Ansari A, Jedidi K, Jagpal S. A hierarchical Bayesian methodology for treating heterogeneity in
structural equation models. Market Sci. 2000; 19(4): 328-347.
Bollen K. Structural equations with latent variables. New York: John Wiley & Sons; 1989.
Derquenne C, Hallais C. Une mthode alternative l’approche PLS: comparaison et application aux
modles conceptuels marketing. J Soc Fr Stat Rev Stat Appl. 2004; 52(3): 37-72.
Dunson DB. Bayesian latent variable models for clustered mixed outcomes. J R Stat Soc Series B
Stat Methodol. 2000; 62(2): 355-366.
Hair JF, Risher JJ, Sarstedt M, Ringle CM. When to use and how to report the results of PLS-SEM.
Eur Bus Rev. 2019; 31(1): 2-24.
Hair JF, Sarstedt M, Pieper TM, Ringle CM. The use of Partial Least Squares structural equation
modeling in strategic management research: a review of past practices and recommendations for
future applications. Long Range Plann. 2012; 45(5-6): 320-340.
Hair JJF, Howard MC, Nitzl C. Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. J Bus Res. 2020; 109: 101-110.
Henseler J. On the convergence of the Partial Least Squares path modeling algorithm. Comput Stat.
; 25(1): 107-120.
Henseler J. Partial least squares path modeling: Quo vadis?. Qual Quant. 2018; 52(1): 1-8.
Jedidi K, Ansari A. Bayesian structural equation models for multilevel data. In: Marcoulides GA,
Schumacker RE, editors. New developments and techniques in structural equation modeling.
New York: Psychology Press; 2001. p. 129-157.
Joreskog KG. A general method for estimating a linear structural equation system. ETS Res Bull. ¨
; 1970(2): 1-41.
Lee SY, Song XY. Evaluation of the Bayesian and maximum likelihood approaches in analyzing
structural equation models with small sample sizes. Multivar Behav Res. 2004; 39(4):653-686.
Lee SY, Song XY. Model comparison of nonlinear structural equation models with fixed covariates.
Psychometrika. 2003; 68(1): 27-47.
Lee SY. Structural equation modeling: A Bayesian approach. Chichester: John Wiley & Sons; 2007.
Lunn DJ, Thomas A, Best N, Spiegelhalter D. WinBUGS-a Bayesian modelling framework: concepts, structure, and extensibility. Stat Comput. 2000; 10(4): 325-337.
Nitzl C. The use of Partial Least Squares Structural Squation Modelling (PLS-SEM) in management
accounting research: Directions for future theory development. J Account Lit. 2016; 37(1): 19-
Ouazza A, Rhomari N, Zarrouk Z. A hybrid method combining the PLS and the Bayesian approaches
to estimate the Structural Equation Models. Electron J Appl Stat Anal. 2018; 11(2):577-607.
Sarstedt M, Ringle CM, Hair JF. Partial least squares structural equation modeling. In: Homburg C,
Klarmann M, Vomberg A, editors. Handbook of market research. Switzerland: Cham: Springer
International Publishing; 2021. p. 587-632.
Sarstedt M, Ringle CM, SmithD, Reams R, Hair JF. Partial Least Squares Structural Squation Modeling (PLS-SEM): A useful tool for family business researchers. J Fam Bus Strategy. 2004; 5(1):
-115.
Song XY, Lee SY. A tutorial on the Bayesian approach for analyzing structural equation models. J
Math Psychol. 2012; 56(3): 135-148.
Tenenhaus M, Amato S, Vinzi VE. A global goodness-of-fit index for PLS structural equation modelling. In: Tenenhaus M, Amato S, Vinzi VE, editors. Proceedings of the XLII SIS Scientific
Meeting; 2004. pp. 739-742.
Tenenhaus M. L’approche PLS. J Soc Fr Stat Rev Stat Appl. 1999; 47(2):5-40.
Tenenhaus M, Vinzi VE, Chatelin YM, Lauro C. PLS path modeling. Comput Stat Data Anal. 2005;
(1):159-205.
Vinzi VE, Chin WW, Henseler J, Wang H. Handbook of partial least squares. Berlin: Springer; 2010.
Vinzi VE, Trinchera L, Amato S. PLS path modeling: from foundations to recent developments and
open issues for model assessment and improvement. In: Vinzi VE, Chin WW, Henseler J, Wang
H, editors. Handbook of Partial Least Squares. Berlin: Springer; 2010. p. 47-82.
Wetzels M, Odekerken-Schroder G, Van Oppen C. Using PLS path modeling for assessing hierar- ¨
chical construct models: Guidelines and empirical illustration. MIS Q. 2009; 33(1): 177-195.
Wold H. Partial Least Squares. In: Kotz S, Johnson NL, editors. Encyclopedia of Statistical Sciences.
New York: Wiley; 1985. p. 581-591.
Wold H. Soft modelling: the basic design and some extensions. In: Joreskog KG, Wold H, editors. ¨
Systems under indirect observation, Part II. Amsterdam: North-Holland Publishing Company;
p. 1-54.
Zarrouk Z. Injustice, compensation et qualit de la relation: le cas d’une dfaillance volontaire: le
surbooking. PhD [dissertation]. Pau: University of Pau; 2008.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
