The time has come:Toward Bayesian SEM estimation in tourism research

Assaf, A. George and Tsionas, Mike and Oh, Haemoon (2018) The time has come:Toward Bayesian SEM estimation in tourism research. Tourism Management, 64. pp. 98-109. ISSN 0261-5177

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Abstract

While the Bayesian SEM approach is now receiving a strong attention in the literature, tourism studies still heavily rely on the covariance-based approach for SEM estimation. In a recent special issue dedicated to the topic, Zyphur and Oswald (2013) used the term “Bayesian revolution” to describe the rapid growth of the Bayesian approach across multiple social science disciplines. The method introduces several advantages that make SEM estimation more flexible and powerful. We aim in this paper to introduce tourism researchers to the power of the Bayesian approach and discuss its unique advantages over the covariance-based approach. We provide first some foundations of Bayesian estimation and inference. We then present an illustration of the method using a tourism application. The paper also conducts a Monte Carlo simulation to illustrate the performance of the Bayesian approach in small samples and discuss several complicated SEM contexts where the Bayesian approach provides unique advantages.

Item Type:
Journal Article
Journal or Publication Title:
Tourism Management
Additional Information:
This is the author’s version of a work that was accepted for publication in Tourism Management. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Tourism Management, 64, 2018 DOI: 10.1016/j.tourman.2017.07.018
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/3300/3313
Subjects:
ID Code:
87413
Deposited By:
Deposited On:
22 Aug 2017 13:20
Refereed?:
Yes
Published?:
Published
Last Modified:
20 Sep 2020 04:21