Estimating Actor, Partner, and Interaction Effects for Dyadic Data Using PROC MIXED and HLM: A User–Friendly Guide

TLDR

Dyadic data offer rich opportunities but pose analytic challenges, prompting the development of the Actor–Partner Interdependence Model to account for interdependence between partners. This paper aims to explain how to implement the APIM and estimate its actor, partner, and interaction effects using hierarchical linear modeling with PROC MIXED in SAS and HLM. It details the required data structure, provides syntax for estimating the model, shows how to test various interaction types, and explains how to interpret the resulting output.

Abstract

Data collected from both members of a dyad provide abundant opportunities as well as data analytic challenges. The Actor–Partner Interdependence Model (APIM; Kashy & Kenny, 2000) was developed as a conceptual framework for collecting and analyzing dyadic data, primarily by stressing the importance of considering the interdependence that exists between dyad members. The goal of this paper is to detail how the APIM can be implemented in dyadic research, and how its effects can be estimated using hierarchical linear modeling, including PROC MIXED in SAS and HLM (version 5.04; Raudenbush, Bryk, Cheong, & Congdon, 2001). The paper describes the APIM and illustrates how the data set must be structured to use the data analytic methods proposed. It also presents the syntax needed to estimate the model, indicates how several types of interactions can be tested, and describes how the output can be interpreted.