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Description: Some of the most influential theories in organizational sciences explicitly describe a dynamic process. Yet the inherent complexity of such theories makes them difficult to test. Dynamic theories often describe multiple sub-processes that interact reciprocally over time, at different levels of analysis, and over different time scales. Computational (i.e., mathematical) modeling is increasingly advocated as a method for developing and testing theories of this type. In organizational sciences however, computational models are more commonly used for theory development, rather than theory testing, and the efforts that have been made to test models empirically are often indirect. We argue that the full potential of computational modeling as a tool for testing dynamic theory is yet to be realized. In this paper, we demonstrate an approach to testing dynamic theory using computational modeling. The approach uses simulations to generate model predictions, and Bayesian parameter estimation to fit models to empirical data and to facilitate model comparisons. This approach enables a direct integration between theory, model, and data, that we believe enables a more rigorous test of theory.