Inconvenient truths about logistic regression and the remedy of marginal effects
本文指出逻辑回归在公共管理研究中存在系数难解释和不可折叠两大问题，并推荐使用平均边际效应来解决，通过数据模拟和2017年当前人口调查数据加以说明。
Abstract Logistic regression is a standard technique in public administration research. However, there are two inconvenient truths about logistic regression of which scholars should be aware. First, logistic regression results are difficult to interpret. Raw coefficients are expressed in an enigmatic log odds scale and odds ratios are regularly misinterpreted as risk ratios. Second, logistic regression results are non‐collapsible, which renders model comparisons invalid. A review of recent public administration articles reveals that these inconvenient truths still plague the discipline. This paper advocates the use of average marginal effects to reckon with both inconvenient truths. Average marginal effects are easy to comprehend because they measure effect sizes on a probability scale. And average marginal effects are collapsible, and hence facilitate valid model comparisons. These concepts are illustrated using data simulations and data from the 2017 Current Population Survey. The paper concludes with suggestions for improved research practice.