A Practical Guide to Specifying Random Effects in Longitudinal Dyadic Multilevel Modeling

Analyzing over-time dyadic data can be challenging, particularly when using multilevel models with complex random-effect structures. In this tutorial, we discuss the best practices of model specification for longitudinal dyadic multilevel modeling, providing a practical guide to specifying (and respecifying) random effects with both theoretical and practical considerations in mind. We begin by defining random effects in the context of repeated-measures dyadic data and address common issues such as nonconvergence. Then, using two models—the dyadic growth-curve and the stability and influence model—we demonstrate how to apply these guidelines in both SAS and R. The dyadic growth-curve model provides a straightforward example, whereas the stability and influence model illustrates common challenges when dealing with complex random-effect structures and convergence issues. In the first exercise, we explain how to customize the variance-covariance matrix for these analyses in SAS. In the second exercise, we adapt these analyses for R and discuss how to implement the sum-and-difference approach for indistinguishable dyads. We conclude with a discussion of alternative models and go over the utility of data simulation during study design, helping readers plan and select the best approach for their research.