The Impact of the Number of Dyads on Estimation of Dyadic Data Analysis Using Multilevel Modeling

Abstract

Abstract. Dyadic data often appear in social and behavioral research, and multilevel models (MLMs) can be used to analyze them. For dyadic data, the group size is 2, which is the minimum group size we could have for fitting a multilevel model. This Monte Carlo study examines the effects of the number of dyads, the intraclass correlation (ICC), the proportion of singletons, and the missingness mechanism on convergence, bias, coverage rates, and Type I error rates of parameter estimates of dyadic data analysis using MLMs. Results showed that the estimation of variance components could have nonconvergence problems, nonignorable bias, and deviated coverage rates from nominal values when ICC is low, the proportion of singletons is high, and/or the number of dyads is small. More dyads helped obtain more reliable and valid estimates. Sample size guidelines based on the simulation model are given and discussed.