In this paper we reimagine linear regression modeling as a relational method for cultural analysis. Drawing on the dual-to-regression analytic approach (Schoon, Melamed & Breiger, 2024), we argue that the fundamental building blocks in a regression equation are not single variables, but configurations of variables manifested by clusters of cases. In a study of peer effects and achievement in an academic institution, we show how the regression model itself may be understood as positing a network of pairwise influence relations among social actors that produces the outcome as modeled by the regression. Moreover, this network is appropriate for studying homophily (the tendency for individuals with similar characteristics to have social network connections). We push the new, case-oriented thinking about the regression model of Schoon et al. by incorporating information on networks of social relations connecting the cases. We find that, when profile similarity boosts academic performance, high-density social network clusters are discovered. We demonstrate that it is sometimes useful to consider configurations of cases as the “variables” in a regression model. We argue that this methodological innovation has a distinctive pragmatic value and strong theoretical motivation in the specific empirical context of our study, and beyond.

中文翻译：

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文化作为类别的配置：通过对偶回归模型分析对等效应

在本文中，我们将线性回归建模重新构想为一种用于文化分析的关系方法。借鉴对偶回归分析方法（Schoon， Melamed & Breiger， 2024），我们认为回归方程的基本组成部分不是单个变量，而是由案例集群表现出的变量配置。在对学术机构的同伴效应和成就的研究中，我们展示了回归模型本身如何被理解为假设社会行为者之间的成对影响关系网络，该网络产生了回归建模的结果。此外，这个网络适合研究同性恋（具有相似特征的个体具有社交网络联系的趋势）。我们通过整合连接案例的社会关系网络的信息，推动了关于 Schoon 等人的回归模型的新的、面向案例的思考。我们发现，当个人资料相似性提高学习成绩时，会发现高密度的社交网络集群。我们证明，在回归模型中将案例配置视为 “变量” 有时是有用的。我们认为，这种方法的创新在我们研究的特定实证背景下以及更远的地方具有独特的实用价值和强大的理论动机。