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

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