Hierarchical modeling of sequential behavioral data:: An empirical Bayesian approach
Hierarchical modeling of sequential behavioral data:: An empirical Bayesian approach
复制标题
DOI：
10.1037/1082-989x.7.2.262
复制
发表时间：
2002-06-01
影响因子：
7
通讯作者：
Muthén, BO
中科院分区：
文献类型：
--
作者：
Dagne, GA;Howe, GW;Muthén, BO
作者回顾了测量行为序列中两个行为之间的偶然性强度的常用方法，二项式z得分和调整的单元残差，并指出了这些方法的一些局限性，他们提出了一种新的方法，在分层建模的背景下使用对数比值比和经验贝叶斯估计，一种不受这些局限性约束的方法。一系列的分层模型来测试行为序列的平稳性，序列的同质性在整个样本的情节，以及协变量是否可以解释整个样本的序列的变化。这些模型被应用到从254对夫妇的行为相互作用的研究，以说明其使用的观察数据。
The authors review the common methods for measuring strength of contingency between 2 behaviors in a behavioral sequence, the binomial z score and the adjusted cell residual, and point out a number of limitations of these approaches, They present a new approach using log odds ratios and empirical Bayes estimation in the context of hierarchical modeling, an approach not constrained by these limitations. A series of hierarchical models is presented to test the stationarity of behavioral sequences, the homogeneity of sequences across a sample of episodes, and whether covariates can account for variation in sequences across the sample. These models are applied to observational data taken from a study of the behavioral interactions of 254 couples to illustrate their use.