A RANDOM-EFFECTS ORDINAL REGRESSION-MODEL FOR MULTILEVEL ANALYSIS
A RANDOM-EFFECTS ORDINAL REGRESSION-MODEL FOR MULTILEVEL ANALYSIS
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DOI：
10.2307/2533433
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发表时间：
1994-12-01
期刊：
影响因子：
1.9
通讯作者：
GIBBONS, RD
中科院分区：
文献类型：
--
作者：
HEDEKER, D;GIBBONS, RD
本文提出了一种随机效应有序回归模型，用于分析聚类或纵向有序反应数据。该模型是为概率单位和逻辑响应函数开发的。阈值的概念，其中它是假设所观察到的有序类别是由一个潜在的不可观察的连续响应，遵循线性回归模型，将随机效应的值。一个最大边际似然（MML）的解决方案是使用高斯-厄米特积分的随机效应的分布进行数值积分。一个数据集的分析，其中学生被聚集或嵌套在教室内被用来说明功能的随机效应分析的聚类有序数据，而一个纵向数据集的分析，其中精神病患者被反复评为他们的严重程度被用来说明功能的随机效应方法的纵向有序数据。
A random-effects ordinal regression model is proposed for analysis of clustered or longitudinal ordinal response data. This model is developed for both the probit and logistic response functions. The threshold concept is used, in which it is assumed that the observed ordered category is determined by the value of a latent unobservable continuous response that follows a linear regression model incorporating random effects. A maximum marginal likelihood (MML) solution is described using Gauss-Hermite quadrature to numerically integrate over the distribution of random effects. An analysis of a dataset where students are clustered or nested within classrooms is used to illustrate features of random-effects analysis of clustered ordinal data, while an analysis of a longitudinal dataset where psychiatric patients are repeatedly rated as to their severity is used to illustrate features of the random-effects approach for longitudinal ordinal data.