Abstract

The Bayesian estimation method has recently received a lot of attention in the social sciences. The Bayesian method has a special factor of prior distribution that can reflect researchers’ background knowledge in the estimation process. The specification of the prior distribution affects the overall estimation. Despite prior distribution being the most important factor in Bayesian analysis, there is a lack of methodological research for understanding and appropriately specifying the prior distribution. Therefore, the present study tries to help researchers to apply the prior distribution to their estimation by addressing the importance of the prior distribution and the overall content of the prior specification. First, we explore the method that researchers do not directly specify the prior distribution. This method means selecting the default prior distribution automatically provided by the program, and if you want to use this option, you must know exactly what kind of the default prior distribution is actually provided. For this, we discuss the default priors of frequently used programs, as well as the known problem of the default priors. Second, we address the method that researchers do specify the prior distribution by themseleves. The prior distributions that can be directly specified include noninformative prior distributions and informative prior distributions. Which prior distribution to use is determined by the presence of prior information on parameters. This study deals with the necessity of noninformative prior distributions and the proposed method when specifying them, provides studies that can be referenced when specifying informative prior distributions, and explores criteria that can be referenced for the select of informativeness by synthesizing the criteria across many studies. We provide practical help through data examples applying the methods discussed in the text, and finally discuss the significance and limitations of the present study.