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In this article, we introduce the concept of thinking meta-generatively, which we define as the direct integration of 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 other research synthesis techniques (e.g., meta-analysis) because it involves meta-synthesis not only across studies but also within studies. We describe how meta-generative thinking can be both maximized and optimized with respect to quantitative research data/findings via the use of Bayesian methodology that has been shown to be superior to null hypothesis significance testing, which is inherently flawed.
Prathiba Natesan, University of North Texas
Peter Boedeker, Baylor College of Medicine
Anthony J. Onwuegbuzie, Sam Houston State University