The Social Relations Model (SRM) is a conceptual and mathematical model of interpersonal responses in dyads. The SRM permits estimation of responses of one to many (the actor effect) and the responses of many to the one (the partner effect) at the individual level of analysis. The SRM also permits estimation of the unique responses of actors and partners in specific dyadic arrangements (the relationship effect). During the four decades that the SRM has been used empirically, most attention was focused on estimation of variance and covariance of components. More recently, second stage modeling has occurred in which SRM effect estimates are used as variables in multivariate models. Consequently, it has become important to have good predictions of SRM actor, partner, and relationship effects. A method proposed by Warner, Kenny, and Stoto has been used to predict these effects. Here we propose an alternative matrix-based estimation method that predicts the latent SRM random effects from their conditional expected values given observed data. Analytic work and Monte Carlo simulations indicate that our conditional-expectation predictions of SRM effects are more valid and precise than the traditional predictions. They will improve second-stage Social Relations Modeling and also have practical uses as well (in, e.g., determining employee salary raises).

中文翻译：

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根据条件期望预测社会关系模型效应。

社会关系模型 (SRM) 是二元人际关系反应的概念和数学模型。SRM 允许在个体分析层面估计一对多的反应（演员效应）和许多对一个的反应（伙伴效应）。SRM 还允许估计参与者和合作伙伴在特定二元安排（关系效应）中的独特反应。在根据经验使用 SRM 的四个十年中，大多数注意力都集中在分量方差和协方差的估计上。最近，出现了第二阶段建模，其中 SRM 效应估计被用作多变量模型中的变量。因此，对 SRM 参与者、合作伙伴和关系效果进行良好预测变得很重要。Warner, Kenny 提出的一种方法，Stoto 已被用于预测这些影响。在这里，我们提出了一种替代的基于矩阵的估计方法，该方法根据给定观察数据的条件期望值预测潜在的 SRM 随机效应。分析工作和蒙特卡罗模拟表明，我们对 SRM 效应的条件预期预测比传统预测更有效和精确。它们将改进第二阶段的社会关系建模，并且也有实际用途（例如，确定员工加薪）。分析工作和蒙特卡罗模拟表明，我们对 SRM 效应的条件预期预测比传统预测更有效和精确。它们将改进第二阶段的社会关系建模，并且也有实际用途（例如，确定员工加薪）。分析工作和蒙特卡罗模拟表明，我们对 SRM 效应的条件预期预测比传统预测更有效和精确。它们将改进第二阶段的社会关系建模，并且也有实际用途（例如，确定员工加薪）。