Using Data Augmentation and Markov Chain Monte Carlo for the Estimation of Unfolding Response Models
Using Data Augmentation and Markov Chain Monte Carlo for the Estimation of Unfolding Response Models
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使用数据增强和马尔可夫链蒙特卡罗来估计展开响应模型
DOI：
10.3102/10769986028003195
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发表时间：
2003
影响因子：
2.4
通讯作者：
B. Junker
中科院分区：
文献类型：
--
作者：
Matthew S. Johnson;B. Junker
展开反应模型是一类假设单峰项目反应函数的项目反应理论（IRT）模型，常用于态度的测量。Verhelst和Verstralen（1993）以及Andrich和Luo（1993）利用潜在反应模型（LRM; Maris, 1995）将观察到的反应与更常见的单调IRT模型联系起来，独立开发了展开反应模型。本文推广了他们的方法，并提出了一种用于估计任何展开响应模型的数据扩充方案。介绍了展开模型参数贝叶斯估计的两种马尔可夫链蒙特卡罗（MCMC）估计方法；一种是直接实现MCMC，另一种是利用数据增强方法。我们使用估计程序来分析三个数据集，一个是模拟的，另外两个来自真实的态度调查。
Unfolding response models, a class of item response theory (IRT) models that assume a unimodal item response function (IRF), are often used for the measurement of attitudes. Verhelst and Verstralen (1993)and Andrich and Luo (1993) independently developed unfolding response models by relating the observed responses to a more common monotone IRT model using a latent response model (LRM; Maris, 1995). This article generalizes their approach, and suggests a data augmentation scheme for the estimation of any unfolding response model. The article introduces two Markov chain Monte Carlo (MCMC) estimation procedures for the Bayesian estimation of unfolding model parameters; one is a direct implementation of MCMC, and the second utilizes the data augmentation method. We use the estimation procedure to analyze three data sets, one simulated, and two from real attitudinal surveys.
DOI：
10.1109/tpami.1984.4767596
发表时间：
1984-01-01
影响因子：
23.6
作者：
GEMAN, S;GEMAN, D
通讯作者：
GEMAN, D