Use of Bayesian Estimation in the Context of Fully Integrated Mixed Methods Models

Bayesian methods have gained popularity in the recent decades given the advances in statistics theory and computing solutions. The purpose of this chapter is to extend Bayesian thinking in the context of fully integrated mixed methods models. Bayesian methods naturally integrate findings from previous studies in the form of priors in order to estimate the posterior distributions of the parameters. This situates both the data and the findings in the context of existing research and, therefore, extends the body of knowledge more seamlessly. Furthermore, we are able to use both qualitative and quantitative data and findings for Bayesian analysis. I discuss how Bayesian thinking is a fully integrated mixed methods model and the advantages of such an approach. I also present an example to further aid readers in this process.