The Construction of Patient Loyalty Model Using Bayesian Structural Equation Modeling Approach

Astari Rahmadita, Ferra Yanuar, Dodi Devianto


The information on the health status of an individual is often gathered based on a health survey. Patient assessment on the quality of hospital services is important as a reference in improving the service so that it can increase a patient satisfaction and patient loyalty. The concepts of health service are often involve multivariate factors with multidimensional sructure of corresponding factors. One of the methods that can be used to model such these variables is SEM (Structural Equation Modeling). Structural Equation Modelling (SEM) is a multivariate method that incorporates ideas from regression, path-analysis and factor analysis. A Bayesian approach to SEM may enable models that reflect hypotheses based on complex theory. Bayesian SEM is used to construct the model for describing the patient loyalty at Puskesmas in Padang City. The convergence test with the history of trace plot, density plot and the model consistency test were performed with three different prior types. Based on Bayesian SEM approach, it is found that the quality of service and patient satisfaction significantly related to the patient loyalty.


Bayesian methods; patient loyalty; structural equation modeling

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A. Ansari, K. Jedidi, and S. Jagpal, A hierarchical Bayesian Methodology for Treating Heterogeneity in Structural Equation Models, Market. Sci, Vol. 19, pp. 328–347, 2000.

K.A. Bollen, Structural Equations with Latent Variables, John Wiley & Sons, New York, NY, 1989.

G.E.P. Box and G.C. Tiao, Bayesian Inference in Statistical Analysis, Addision Wesley Company. Inc: Philippines, 1973.

J.T. Bowen and S.L. Chen, The Relationship Between Customer Loyalty and Customer Satisfaction, International Journal of Contemporary Hospitality Management, Vol. 13 Issue: 5, pp.213-217, 2001.

S.Y. Lee, Structural Equation Modeling, A Bayesian Approach. Hongkong, John Wiley & Sons, Inc, 2007.

S.Y. Lee and J.Q. Shi, Bayesian Analysis of Structural Equation Model with Fixed Covariates, Structural Equation Modeling: A Multidisciplinary Journal, 7:3, pp. 411–430, 2000.

S.Y. Lee and X.Y. Song, Evaluation of the Bayesian and Maximum Likelihood Approaches in Analyzing Structural Equation Models with Small Sample Sizes, Multivariate Behavioral Research. Vol. 39, pp. 653–686, 2004.

A.A. Mattjik and I.M. Sumertajaya, Sidik Peubah Ganda. Institut Pertanian Bogor. Bogor, 2011.

I. Ntzoufras, Bayesian Modeling Using WINBugs, John Wiley Sons, Inc: New Jersey, 2009.

A. Parasuraman, V.A. Zeithaml and L.L. Berry, Reassessment of Expectation as a Comparison Standard in Measuring Service Quality: Implications for Further Research. Journal of Marketing, Vol. 58, pp. 111-124, 1994.

J.B. Ullman, Structural Equation Modeling : Reviewing The Basics and Moving Forward. Journal of Personality Assessment, 87 (1) :35-50, 2006

F. Yanuar, K. Ibrahim and A.A. Jemain, Bayesian Structural Modeling for The Health Index. Journal of Applied Statistics, 40(6): 1254-1269, 2013.

F. Yanuar, D. Devianto and S. Marisa, Consistency Test of Reliability Index in SEM Model. Applied Mathematical Sciences, Vol. 9: 5283-5292, 2015.



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