Information Matrices in Latent-Variable Models
Information Matrices in Latent-Variable Models
复制标题
潜变量模型中的信息矩阵
DOI：
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发表时间：
1989
期刊：
影响因子：
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通讯作者：
K. Sheehan
中科院分区：
文献类型：
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作者：
R. Mislevy;K. Sheehan
潜变量模型中参数的费舍尔信息矩阵或期望信息矩阵由潜变量的值也可以观测到的信息自上而下地限定。这一上限与观测数据中的信息之间的差异是“缺失信息”。本文阐述了期望信息矩阵和相关信息矩阵的结构，并刻画了受访者利用附带变量恢复遗漏信息的程度。在项目反应理论模型的背景下，对研究结果进行了说明，并讨论了实际意义。
The Fisher, or expected, information matrix for the parameters in a latent-variable model is bounded from above by the information that would be obtained if the values of the latent variables could also be observed. The difference between this upper bound and the information in the observed data is the “missing information.” This paper explicates the structure of the expected information matrix and related information matrices, and characterizes the degree to which missing information can be recovered by exploiting collateral variables for respondents. The results are illustrated in the context of item response theory models, and practical implications are discussed.