Optimal Design for Count Data with Binary Predictors in Item Response Theory

Optimal Design for Count Data with Binary Predictors in Item Response Theory
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项目反应理论中二元预测计数数据的优化设计

DOI：
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发表时间：
2013
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通讯作者：
R. Schwabe
R. Schwabe
中科院分区：
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文献类型：
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作者：
Ulrike Graßhoff;H. Holling;R. Schwabe

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拉什泊松计数模型 (RPCM) 可以分析代表人类智力基本组成部分的心理速度。 RPCM 的扩展版本结合了协变量来解释难度，为现代基于规则的项目生成提供了一种方法。在对扩展 RPCM 进行简短介绍后，我们为此模型开发了局部 D 最优校准设计。为此，RPCM 被嵌入到特定的广义线性模型中。 Finally, the robustness of the derived designs is investigated.
The Rasch Poisson counts model (RPCM) allows for the analysis of mental speed which represents a basic component of human intelligence. An extended version of the RPCM, which incorporates covariates in order to explain the difficulty, provides a means for modern rule-based item generation. After a short introduction to the extended RPCM we develop locally D-optimal calibration designs for this model. To this end the RPCM is embedded in a particular generalized linear model. Finally, the robustness of the derived designs is investigated.