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Estimating Actor, Partner, and Interaction Effects for Dyadic Data Using PROC MIXED and HLM: A User–Friendly Guide
Author(s) -
Campbell Lorne,
Kashy Deborah A.
Publication year - 2002
Publication title -
personal relationships
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.81
H-Index - 83
eISSN - 1475-6811
pISSN - 1350-4126
DOI - 10.1111/1475-6811.00023
Subject(s) - dyad , multilevel model , set (abstract data type) , psychology , syntax , data set , computer science , social psychology , artificial intelligence , programming language , machine learning
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.