How Rare Is Rare? How Common Is Common? Empirical Issues Associated With Binary Dependent Variables With Rare Or Common Event Rates

The use of logit and probit models when examining binary dependent variables including those in the form 0/ 1 (i.e., dummy variables), yes/no, and true/false (hereafter binary DVs) is commonplace. Yet, the appropriateness and effectiveness of such models are challenged when the event rate of a binary DV is rare or common. To better understand the impact on the field of strategy, we undertook a literature review and assessed recently published research in the Strategic Management Journal. We then utilized Monte Carlo simulations with results showing that as event rates become rarer or more common, issues including biased coefficients, standard error inflation, low statistical power to detect significant effects, and model convergence failure increasingly arise. In addition, small sample sizes amplified these empirical issues. Using a strategy example study, we also show how various analytic tools can lead to different findings when empirical models face an extreme event rate with small sample sizes. Based on our findings, we provide step-by-step guidance for strategy researchers going forward.

Automated summary

This article examines the challenges faced by logit and probit models when studying binary dependent variables in strategic management research. It finds that the appropriateness and effectiveness of these models are compromised when the event rate of the dependent variable is rare or common. The study highlights issues such as biased coefficients, standard error inflation, low statistical power, and model convergence failure. It also emphasizes the amplification of these issues with small sample sizes. The findings provide step-by-step guidance for strategy researchers in dealing with extreme event rates and small sample sizes.