Many of the topics most central to the social sciences involve nominal groupings or ordinal rankings. There are many cases in which a summary of a nominal or ordinal independent variable's effect, or the effect on a nominal or ordinal outcome, is needed and useful for interpretation. For example, for nominal or ordinal independent variables, a single summary measure is useful to compare the effect sizes of different variables in a single model or across multiple models, as with mediation. For nominal or ordinal dependent variables, there are often an overwhelming number of effects to examine and understanding the holistic effect of an independent variable or how effect sizes compare within or across models is difficult. In this project, we propose two new summary measures using marginal effects (MEs). For nominal and ordinal independent variables, we propose ME inequality as a summary measure of a nominal or ordinal independent variable's holistic effect. For nominal and ordinal outcome models, we propose a total ME measure that quantifies the comprehensive effect of an independent variable across all outcome categories. The added benefits of our methods are both intuitive and substantively meaningful effect size metrics and approaches that can be applied across a wide range of models, including linear, nonlinear, categorical, multilevel, longitudinal, and more.

中文翻译：

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名义型变量和有序型变量的不等式和总效应汇总测度


社会科学的许多最核心的主题都涉及名义分组或序数排名。在许多情况下，需要总结名义型或序数自变量的影响，或对名义型或序数结果的影响，这对于解释是有用的。例如，对于名义型或有序型自变量，与中介一样，单个汇总度量可用于比较单个模型或多个模型之间不同变量的效应大小。对于名义型或有序型因变量，通常有大量的效应需要检查，并且很难理解自变量的整体效应或模型内或模型间效应大小的比较。在这个项目中，我们提出了两个使用边际效应 （ME） 的新总结措施。对于名义型和有序型自变量，我们建议 ME 不等式作为名义型或有序型自变量整体效应的汇总度量。对于名义和顺序结果模型，我们提出了一个总 ME 测量，用于量化自变量在所有结果类别中的综合影响。我们方法的额外好处是直观且具有实质性意义的效应大小指标和方法，可以应用于各种模型，包括线性、非线性、分类、多级、纵向等。