Department of Data Analysis, Faculty of Psychology and Educational Sciences, Universiteit Gent, Gent, Belgium.
Psychol Methods. 2013 Jun;18(2):220-36. doi: 10.1037/a0030640. Epub 2013 Mar 4.
When 2 people interact in a relationship, the outcome of each person can be affected by both his or her own inputs and his or her partner's inputs. For Gaussian dyadic outcomes, linear mixed models taking into account the correlation within dyads are frequently used to estimate actor's and partner's effects based on the actor-partner interdependence model. In this article, we explore the potential of generalized linear mixed models (GLMMs) for the analysis of non- Gaussian dyadic outcomes. Several approximation techniques that are available in standard software packages for these GLMMs are investigated. Despite the different modeling options related to these different techniques, none of these have an overall satisfactory performance in estimating actor and partner effects and the within-dyad correlation, especially when the latter is negative and/or the number of dyads is small. An approach based on generalized estimating equations for the analysis of non-Gaussian dyadic data turns out to be an interesting alternative.
当两个人在一段关系中互动时，每个人的结果都可能受到自身投入和伴侣投入的影响。对于高斯二元结果，通常使用考虑到对子内相关性的线性混合模型，根据演员-伙伴相互依赖模型来估计演员和伙伴的效果。在本文中，我们探讨了广义线性混合模型（GLMM）分析非高斯二元结果的潜力。研究了这些 GLMM 中标准软件包中可用的几种近似技术。尽管与这些不同技术相关的建模选项不同，但这些技术在估计演员和伙伴的效果以及对子内相关性方面都没有一个令人满意的总体性能，尤其是当后者为负且/或对子的数量较小时。基于广义估计方程分析非高斯二元数据的方法是一种很有前途的替代方法。
