A Mixed Effects Randomized Item Response Model
A Mixed Effects Randomized Item Response Model
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DOI：
10.3102/1076998607306451
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发表时间：
2008-12-01
影响因子：
2.4
通讯作者：
Wyrick, Cheryl
中科院分区：
文献类型：
--
作者：
Fox, J. -P.;Wyrick, Cheryl
随机反应技术确保单个项目的反应（表示为真实的项目反应）在观察它们之前是随机的，并观察到所谓的随机项目反应。在随机化的项目响应数据和真实的项目响应数据之间指定一种关系。真实的项目反应数据采用（非线性）混合效应和/或项目反应理论模型建模。虽然个体真实项目反应通过随机化反应被掩盖，但模型扩展可以计算个体真实项目反应概率和个体敏感行为/态度的估计及其与背景变量的关系，同时考虑到任何被调查者的聚类。结果来自大学酒精问题量表（CAPS），学生通过直接提问或随机回答技术接受采访。给出了一种马尔可夫链蒙特卡罗算法，用于同时估计给定层次结构二值化或多分随机化项目响应数据和背景变量的所有模型参数。
The randomized response technique ensures that individual item responses, denoted as true item responses, are randomized before observing them and so-called randomized item responses are observed. A relationship is specified between randomized item response data and true item response data. True item response data are modeled with a (non)linear mixed effects and/or item response theory model. Although the individual true item responses are masked through randomizing the responses, the model extension enables the computation of individual true item response probabilities and estimates of individuals' sensitive behavior/attitude and their relationships with background variables taking into account any clustering of respondents. Results are presented from a College Alcohol Problem Scale (CAPS) where students were interviewed via direct questioning or via a randomized response technique. A Markov Chain Monte Carlo algorithm is given for estimating simultaneously all model parameters given hierarchical structured binary, or polytomous randomized item response data and background variables.