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The Use of an Identifiability-Based Strategy for the Interpretation of Parameters in the 1PL-G and Rasch Models

机译:利用基于标识的策略来解释1PL-G和RASCH模型中的参数

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Using the well-known strategy in which parameters are linked to the sampling distribution via an identification analysis, we offer an interpretation of the item parameters in the one-parameter logistic with guessing model (1PL-G) and the nested Rasch model. The interpretations are based on measures of informativeness that are defined in terms of odds of correctly answering the items. It is shown that the interpretation of what is called the difficulty parameter in the random-effects 1PL-G model differs from that of the item parameter in a random-effects Rasch model. It is also shown that the traditional interpretation of the guessing parameter in the 1PL-G model changes, depending on whether fixed-effects or random-effects versions of both models are considered.
机译:使用众所周知的策略,其中参数通过识别分析将参数链接到采样分布,我们提供了用猜测模型(1pl-g)和嵌套的Rasch模型中的一个参数逻辑中的项目参数的解释。 该解释是基于信息性的措施,这些信息是在正确回答物品的几率方面定义的。 结果表明,在随机效应Rasch模型中,对随机效应1pl-g模型中所谓的难度参数的解释不同。 还表明,根据两种模型的固定效应或随机效应版本是否考虑了1PL-G模型的猜测参数的传统解释。

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