Rasch Model Estimation: Further Topics

John M. Linacre

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Rasch Model Estimation: Further Topics

机译：Rasch模型估计：更多主题

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Building on Wright and Masters (1982), several Rasch estimation methods are briefly described, including Marginal Maximum Likelihood Estimation (MMLE) and minimum chi-square methods. General attributes of Rasch estimation algorithms are discussed, including the handling of missing data, precision and accuracy, estimate consistency, bias and symmetry. Reasons for, and the implications of, measure misestimation are explained, including the effect of loose convergence criteria, and failure of Newton-Raphson iteration to converge. Alternative parameterizations of rating scales broaden the scope of Rasch measurement methodology.

机译：在Wright和Masters（1982）的基础上，简要描述了几种Rasch估计方法，包括边际最大似然估计（MMLE）和最小卡方方法。讨论了Rasch估计算法的一般属性，包括缺失数据的处理，精度和准确性，估计一致性，偏差和对称性。解释了度量估计错误的原因及其含义，包括宽松的收敛准则的影响以及牛顿-拉夫森迭代的收敛失败。评定量表的替代参数设置扩大了Rasch测量方法的范围。

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《Journal of applied measurement》 |2004年第1期|p.95-110|共16页
作者
John M. Linacre;
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University of the Sunshine Coast Australia;

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正文语种 eng
中图分类一般工业技术;
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Rasch Model Estimation: Further Topics

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