Dyadic Reciprocity as a Function of Covariates

TL;DR

This paper introduces an extended multilevel Social Relations Model to analyze dyadic reciprocity as a function of covariates, accommodating binomial outcomes and enabling the computation of reciprocity correlations.

Contribution

It proposes a novel multilevel modeling approach that incorporates covariates and random effects to better understand dyadic reciprocity in social interactions.

Findings

Provides a method to estimate reciprocity as a function of covariates.

Allows computation of dyadic reciprocity correlation.

Can be integrated with other social network analysis models.

Abstract

Reciprocity in dyadic interactions is common and a topic of interest across disciplines. In some cases, reciprocity may be expected to be more or less prevalent among certain kinds of dyads. In response to interest among researchers in estimating dyadic reciprocity as a function of covariates, this paper proposes an extension to the multilevel Social Relations Model. The outcome variable is assumed to be a binomial proportion, as is commonly encountered in observational and archival research. The approach draws on principles of multilevel modeling to implement random intercepts and slopes that vary among dyads. The corresponding variance function permits the computation of a dyadic reciprocity correlation. The modeling approach can potentially be integrated with other statistical models in the field of social network analysis.