Bayesian statistics in educational research: a look at the current state of affairs

The ability of a scientific discipline to build cumulative knowledge depends on its predominant method of data analysis. A steady accumulation of knowledge requires approaches which allow researchers to consider results from comparable prior research. Bayesian statistics is especially relevant for establishing a cumulative scientific discipline, because the incorporation of background (or prior) knowledge is fundamentally anchored in its basic principles. The aim of the current systematic review is to provide insights into the current state of methodological affairs in educational research, with a focus on Bayesian statistics and the use of prior information. An analysis of publication histories of the 224 educational journals currently listed in the Thomson Reuters Journal Citation Report 2015 indicates that Bayesian statistics is primarily used to solve methodological problems, rather than used to build cumulative knowledge based on a combination of study results with comparable prior research. The utilisation of Bayesian statistics is motivated by its flexibility: models are estimated which would not be estimable with frequentist approaches, thus expanding the methodological repertoire of educational researchers and producing knowledge which otherwise would not have been available. Lastly, the predominant use of noninformative prior distributions indicates that one of the biggest advantages of Bayesian statistics, namely the combination of study results with comparable prior research, remains underutilised in educational research. Practical implications of these findings for educational research are illustrated and discussed.