Controlling for false negatives in agent-based models: a review of power analysis in organizational research

Davide Secchi and Raffaello Seri

Computational and Mathematical Organization Theory, 2017, vol. 23, issue 1, No 4, 94-121

Abstract: Abstract This article is concerned with the study of statistical power in agent-based modeling (ABM). After an overview of classic statistics theory on how to interpret Type-II error (whose occurrence is also referred to as a false negative) and power, the manuscript presents a study on ABM simulation articles published in management journals and other outlets likely to publish management and organizational research. Findings show that most studies are underpowered, with some being overpowered. After discussing the risks of under- and overpower, we present two formulas to approximate the number of simulation runs to reach an appropriate level of power. The study concludes with the importance for organizational behavior scholars to perform their models in an attempt to reach a power of 0.95 or higher at the 0.01 significance level.

Keywords: Statistical power; Agent-based modeling; Computational simulation; Effect size; Sample size; Organizational behavior research (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (12)

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DOI: 10.1007/s10588-016-9218-0

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