How Rare Is Rare? How Common Is Common? Empirical Issues Associated With Binary Dependent Variables With Rare Or Common Event Rates
研究了当二元因变量的事件率极端（罕见或常见）时，logit和probit模型会出现系数偏误、标准误膨胀、统计力低和模型不收敛等问题，小样本会加剧这些问题，并为战略管理研究者提供了应对指南。
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.