Bayesian analysis offers strategy scholars numerous benefits. In addition to aligning empirical and theoretical endeavors by incorporating prior knowledge, the Bayesian approach allows researchers to estimate and visualize relationships that reflect the probability distributions many strategy researchers mistakenly interpret from conventional techniques. Yet, strategy scholars have proven hesitant to adopt Bayesian methods. We suggest that this is because there is no accessible template for employing the technique with the types of data strategy researchers tend to encounter. The central objective of our research is to synthesize disparate contributions from the Bayesian literature that are relevant for strategy scholarship, especially for nested data. We provide an intuitive overview of Bayesian thinking and illustrate how scholars can employ Bayesian techniques to analyze nested data using an example dataset involving CEO compensation. Our results show how using Bayesian models may lead to substantively different interpretations and conclusions compared to traditional approaches based on frequentist techniques.
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