The Comparison of Classical and Bayesian Structural Equation Models Through Ordered Categorical Data: A Case Study of Banking Service Quality

Year 2023, Volume: 36 Issue: 3, 1392 - 1402, 01.09.2023

Gizem Erkan Murat Doğan Hüseyin Tatlıdil

https://doi.org/10.35378/gujs.814405

Abstract

This study aims to compare classical Structural Equation Modeling (SEM) and Bayesian Structural Equation Modeling (BSEM) in terms of ordered categorical data. In order to show the relationship between service dimensions and banks’ customers’ satisfactions, a data were analyzed with classical SEM and BSEM parameter estimation methods. In the Banking Service Quality Scale (SERVQUAL), which consists of sequential categorical data, classical SEM and BSEM were compared to evaluate customer satisfaction. In classical SEM, parameter estimations were made according to the Maximum Likelihood (ML) estimation method. In most of the studies using SERVQUAL in the literature, the results found in previous studies could not be used as prior informative because the service dimensions consisted of different number of factors. For this reason, considering that the results could yield similar results with the ML estimation method due to the high sample size, the use of conjugate prior was preferred instead of the non-informative prior due to the ordinal categorical nature of the data in the BSEM analysis. Since the questionnaire used in the study had a Likert type scale structure, the threshold values were calculated for ordered categorical data and used as prior informative. Thus, by using the threshold values obtained from the data set, a faster convergence of the parameters was achieved. As a result, service dimensions affecting satisfaction according to the ML parameter estimation method were found, Assurance, Physical Appearance, and Accessibility. In addition to these, Reliability as a service dimension was found to be also statistically significant in BSEM.

Keywords

Bayesian structural, Equation modelling, Threshold approach, Servqual

References

• [1] Erkan, G., “Comparison of Parameter Estimation in Classic and Bayesian Structural Equation Models: An Application with Ordered Categorical Data”, MSc. Thesis, Hacettepe University Graduate School of Science and Engineering, 1-70, (2019).
• [2] Palomo, J., Dunson, D. B., and Bollen, K., Bayesian Structural Equation Modelling, Handbook of Latent Variable and Related Models, Sik-Yum Lee, North-Holland, 163-188, (2007).
• [3] Yanaur, F., “The Estimation Process in Bayesian Structural Equation Modelling Approach”, Journal of Physics: Conference Series, 495(1): 12-47, (2014).
• [4] Lee, S. Y., Structural Equation Modeling: A Bayesian Approach, John Wiley and Sons, (2007).
• [5] Lee, S. Y., Song, X. Y., Basic And Advanced Bayesian Structural Equation Modeling: With Applications in the Medical and Behavioral Sciences, John Wiley and Sons, (2012).
• [6] Demeyer, S., Fischer, N., Saporta, G., “Contributions to Bayesian Structural Equation Modeling”, 19th International Conference on Computational Statistics, Paris, 469-476, (2010).
• [7] Song, X. Y., Xia, Y. M., Pan, J. H., Lee, S. Y.. “Model Comparison of Bayesian Semiparametric and Parametric Structural Equation Models”, Structural Equation Modeling, 18(1): 55-72, (2011).
• [8] Ozechowski, T. J., "Empirical Bayes MCMC Estimation for Modeling Treatment Processes, Mechanisms of Change, and Clinical Outcomes in Small Samples", Journal of Consulting and Clinical Psychology, 82(5): 854-867, (2014).
• [9] Stenling, A., Ivarsson, A., Johnson, U., and Lindwall, M., "Bayesian Structural Equation Modeling in Sport and Exercise Psychology", Journal of Sport and Exercise Psychology, 37(4): 410-420, (2015).
• [10] Kim, J., Park, J., “Bayesian Structural Equation Modeling for Coastal Management: The Case of The Saemangeum Coast of Korea for Water Quality Improvements”, Ocean and Coastal Management, 136: 120-132, (2017).
• [11] Harindranath, R. M., Jayanth, J., “Bayesian Structural Equation Modelling Tutorial for Novice Management Researchers”, Management Research Review, 41: 1254-1270, (2018).
• [12] Altındağ, İ., “Bayesian Non Linear Structural Equation Model”, PhD Thesis, Selçuk University, The Graduate School Of Natural And Applied Science, Konya, 1-141, (2015).
• [13] Lee, S. Y., Song, X. Y., “Evaluation of the Bayesian and Maximum Likelihood Approaches in Analyzing Structural Equation Models with Small Sample Sizes”, Multivariate Behavioral Research, 39(4): 653-686, (2004).
• [14] Rupp, A. A., Dey, D. K., Zumbo, B. D., “To Bayes or not to Bayes, from whether to when: Applications of Bayesian Methodology to Modeling”, Structural Equation Modeling, 11(3): 424-451, (2004).
• [15] Kruschke, J. K., Aguinis, H., and Joo, H., “The Time has Come: Bayesian Methods for Data Analysis in the Organizational Sciences”, Organizational Research Methods, 15(4): 722-752, (2012).
• [16] Palomo, J., Dunson, D. B., Bollen, K., Bayesian Structural Equation Modelling, Handbook of Latent Variable and Related Models, Sik-Yum Lee, North-Holland, 163-188, (2007).
• [17] Zyphur, M. J., Oswald, F. L., "Bayesian Estimation and Inference: A User’s Guide”, Journal of Management, 41: 390-420, (2013).
• [18] Muthén, B., Asparouhov, T., “Bayesian Structural Equation Modeling: A More Flexible Representation of Substantive Theory”, Psychological Methods, 17(3): 313-335, (2012).
• [19] Yılmaz, V., Ari, E., Gürbüz, H., “Investigating the Relationship Between Service Quality Dimensions, Customer Satisfaction and Loyalty in Turkish Banking Sector”, International Journal of Bank Marketing, 36(3): 423-440, (2018).
• [20] Amin, M., Isa, Z., “An Examination of the Relationship Between Service Quality Perception and Customer Satisfaction”, International Journal of Islamic and Middle Eastern Finance and Management, 1(3): 191-209, (2018).
• [21] Arasli, H., Katircioglu, S. T., Mehtap‐Smadi, S., “A Comparison of Service Quality in the Banking Industry”, International Journal of Bank Marketing, 23(7): 508-526, (2005).
• [22] Caruana, A., “Service Loyalty - The Effects of Service Quality and the Decreasing Role of Customer Satisfaction”, European Journal of Marketing, 36(7): 811-828, (2005).
• [23] Jöreskog, K. G., Sörbom, D., “PRELIS 2 User's Reference Guide: A Program for Multivariate Data Screening and Data Summarization: A Preprocessor for LISREL”, Scientific Software International, (1996).
• [24] Jöreskog, K. G., “LISREL”, Encyclopedia of Statistical Sciences, John Wiley and Sons, (2006).
• [25] Lee, S. Y., Song, X. Y., Skevington, S., Hao, Y. T., “Application of Structural Equation Models to Quality of Life”, Structural Equation Modeling, 12(3): 435-453, (2005).
• [26] Yanuar, F., Ibrahim, K., and Jemain, A. A., “Bayesian Structural Equation Modeling for the Health Index”, Journal of Applied Statistics, 40(6): 1254-1269, (2013).
• [27] Lee, S. Y., Handbook of Latent Variable and Related Models, Elsevier, (2011).