Using multilevel models to analyze couple and family treatment data: Basic and advanced issues
Using multilevel models to analyze couple and family treatment data: Basic and advanced issues
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DOI：
10.1037/0893-3200.19.1.98
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发表时间：
2005-03-01
影响因子：
2.7
通讯作者：
Atkins, DC
中科院分区：
文献类型：
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作者：
Atkins, DC
夫妻和家庭治疗数据对统计分析提出了特别的挑战。伴侣和家庭成员往往彼此比其他人更相似，这在数据分析中提出了有趣的可能性，但也导致了经典统计方法的重大问题。本文提出了多水平模型(也称为分层线性模型、混合效应模型或随机系数模型)，作为一种灵活的分析方法来分析夫妻和家庭纵向数据。本文回顾了多层次、多层次、多层次的基本、性质。模型，但主要关注三个重要的扩展：丢失数据、电源。和样本大小，以及耦合数据的替代表示。信息以教程的形式提供，Web附录提供了带有SPSS和R代码的数据集，以重现示例。
Couple and family treatment data present particular challenges to statistical analyses. Partners and family members tend to be more similar to one another than to other individuals, which raises interesting possibilities in the data analysis but also causes significant problems with classical, statistical methods. The present article presents multilevel models (also called hierarchical linear models, mixed-effects models, or random coefficient models) as a flexible analytic approach to couple and family longitudinal data. The article reviews basic, properties of multilevel,. models but focuses primarily on 3 important extensions: missing data, power. and sample size, and alternative representations of couple data. Information is presented as a tutorial, with a Web appendix providing datasets with SPSS and R code to reproduce the examples.