Adopting a Meta-Generative Way of Thinking in the Field of Education via the Use of Bayesian Methods: A Multimethod Approach in a Post-Truth and COVID-19 Era1

Abstract

In this editorial, we introduce the multimethod concept of thinking meta-generatively, which we define as directly integrating findings from the extant literature during the data collection, analysis, and interpretation phases of primary studies. We demonstrate that meta-generative thinking goes further than do other research synthesis techniques (e.g., meta-analysis) because it involves meta-synthesis not only across studies but also within studies—thereby representing a multimethod approach. We describe how meta-generative thinking can be maximized/optimized with respect to quantitative research data/findings via the use of Bayesian methodology that has been shown to be superior to the inherently flawed null hypothesis significance testing. We contend that Bayesian meta-generative thinking is essential, given the potential for divisiveness and far-reaching sociopolitical, educational, and health policy implications of findings that lack generativity in a post-truth and COVID-19 era.