Computing conditional maximum likelihood estimates for generalized Rasch models using simple loglinear models with diagonals parameters
Computing conditional maximum likelihood estimates for generalized Rasch models using simple loglinear models with diagonals parameters
复制标题
使用具有对角线参数的简单对数线性模型计算广义 Rasch 模型的条件最大似然估计
DOI：
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发表时间：
1993
影响因子：
1
通讯作者：
A. Agresti
中科院分区：
文献类型：
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作者：
A. Agresti
Andersen（1973）提出的多反应项目的广义Rasch模型与拟对称对数线性模型有关。对数线性模型是通过将Rasch模型中的受试者参数视为随机效应获得的。拟合对数线性模型产生广义Rasch模型中项目参数的估计值，当受试者效应被视为固定时，这些估计值也是条件最大似然估计值。对于存在有序响应类别时自然适用的模型，相关对数线性模型是具有对角线参数的简单准对称模型。我们的结果推广了Tjur（1982）关于二值响应Rasch模型和对数线性模型之间联系的观察。
Generalized Rasch models for multiple-response items proposed by Andersen (1973) are related to quasi-symmetric loglinear models. The loglinear models are obtained by treating subject parameters in the Rasch models as random effects. Fitting the loglinear models yields estimates of item parameters in the generalized Rasch models that are also conditional maximum likelihood estimates when the subject effects are treated as fixed. For models that apply naturally when there are ordinal response categories, the related loglinear models are simple quasi-symmetric models having diagonals parameters. Our results generalize Tjur's (1982) observation about the connection between binary-response Rasch models and loglinear models.