Work

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

Year: 2016

Type: article

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.

Source: Computational and Mathematical Organization Theory

DOI: 10.1007/s10588-016-9218-0

Authors: Davide Secchi, Raffaello Seri

Institutions: University of Southern Denmark, University of Insubria

Language: English

Topic: Simulation Techniques and Applications

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

Open Access status: hybrid

Estimated APC paid: $2,990

Funder: Ministero dell’Istruzione, dell’Università e della Ricerca