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Conceptualization of magnitude-based hypotheses (MBHs) reveals key methodological challenges, suggesting improvements for data evaluation.
Our paper provides a conceptualization of magnitude-based hypotheses (MBHs). We define an MBH as a specific type of hypothesis that tests for relative differences in the independent impact (i.e., effect size difference) of at least two explanatory variables on a given outcome. We reviewed 1,715 articles across eight leading management journals and found that nearly 10% (165) of articles feature an MBH, employing 41 distinct methodological approaches to test them. However, approximately 40% of these papers show missteps in the post-estimation process required to evaluate MBHs. To address this issue, we offer a conceptual framework, an empirical illustration using Bayesian analysis and frequentist statistics, and a decision-tree guideline that outlines key steps for evaluating MBHs. Overall, we contribute a framework for applying MBHs, demonstrating how they can shift theoretical inquiry from binary questions of whether an effect exists, to more comparative questions about how much a construct matters,compared to what, and under which conditions.
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Blevins et al. (2025) studied this question.
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