Estimating actor, partner, and interaction effects for dyadic data using PROC MIXED and HLM: A user-friendly guide
Estimating actor, partner, and interaction effects for dyadic data using PROC MIXED and HLM: A user-friendly guide
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DOI：
10.1111/1475-6811.00023
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发表时间：
2002-09-01
影响因子：
1.6
通讯作者：
Kashy, DA
中科院分区：
文献类型：
--
作者：
Campbell, L;Kashy, DA
从二分体的两个成员收集的数据提供了大量的机会，以及数据分析的挑战。参与者-合作伙伴相互依赖模型（APIM; Kashy & Kenny, 2000）是作为收集和分析二元数据的概念框架而发展起来的，主要是通过强调考虑二元成员之间存在的相互依赖的重要性。本文的目的是详细说明如何在二进研究中实现APIM，以及如何使用分层线性建模来估计其效果，包括SAS和HLM中的PROC MIXED（版本5.04;Raudenbush, Bryk, Cheong, & Congdon, 2001）。本文描述了APIM，并说明了数据集必须如何结构化才能使用所提出的数据分析方法。它还提供了评估模型所需的语法，指出了如何测试几种类型的交互，并描述了如何解释输出。
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.