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.

中文翻译：

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关于逻辑回归和边际效应补救措施的难以忽视的事实


逻辑回归是公共行政研究的标准技术。然而，学者们应该注意关于逻辑回归的两个令人难以忽视的事实。首先，逻辑回归结果难以解释。原始系数以神秘的对数优势比例表示，优势比经常被误解为风险比。其次，逻辑回归结果是不可折叠的，这使得模型比较无效。对最近公共管理文章的回顾表明，这些令人难以忽视的事实仍然困扰着该学科。本文主张使用平均边际效应来考虑这两个难以忽视的事实。平均边际效应很容易理解，因为它们以概率尺度衡量效应大小。平均边际效应是可折叠的，因此有利于有效的模型比较。这些概念通过数据模拟和 2017 年当前人口调查的数据进行了说明。本文最后提出了改进研究实践的建议。