The analysis of (negatively) correlated non-Gaussian dyadic outcomes: non-multilevel-based alternatives?

Abstract

When two 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 paper, we explore the potential of generalized linear mixed models (GLMMs) for the analysis of nonGaussian 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. In contrast, a generalized estimating equations approach for the analysis of non-Gaussian dyadic data turns out to perform well in all situations considered.