Comparing Bayesian and Maximum Likelihood Methods in Structural Equation Modelling of University Student Satisfaction: An Empirical Analysis

Author:

Asosega Killian Asampana12ORCID,Iddrisu Wahab Abdul13ORCID,Tawiah Kassim12ORCID,Opoku Alex Akwasi1,Okyere Eric1ORCID

Affiliation:

1. Department of Mathematics and Statistics, University of Energy and Natural Resources, Sunyani, Ghana

2. Department of Statistics and Actuarial Science, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana

3. Department of Mathematics and Statistics, Ghana Communication Technology University (GCTU), Accra, Ghana

Abstract

Students’ satisfaction in the university environment is essential to both the student (customer) and management of the university. Satisfied students are determined to succeed in their academics, and this sustains their loyalty and trust, which results in an improved image and esteem of the university. This study examined the level of students’ satisfaction with campus facilities and infrastructure, campus social life, student support services, and the quality of academics in the University of Energy and Natural Resources (UENR) in Ghana and further investigated how students’ satisfaction with the above four areas of the university environment affect each other. A questionnaire was administered to continuous students in UENR, and the collected data were analysed using structural equation modelling within the maximum likelihood and Bayesian frameworks whose results and performance were compared. Results showed that students’ satisfaction levels with available campus facilities, campus social life, and student support services were low but were fairly satisfied with the quality of academics. Both maximum likelihood and Bayesian techniques showed positive significant effects of students’ satisfaction with campus facilities and infrastructure on satisfaction with campus social life, students’ support services, and academics. Moreover, students’ satisfaction with social life was positively associated with their satisfaction with academics and student support services. Although both estimation methods obtained similar estimates and inferences, the Bayesian SEM outperformed the ML-SEM based on the recommended fit indices. Findings of the study highlight the significant effects of satisfaction with campus facilities and student support services on students’ satisfaction with academics and the university environment at large. The study further underpins the important role of the availability of adequate facilities and quality students’ services in improving and sustaining satisfaction.

Publisher

Hindawi Limited

Subject

Education

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