文章基本信息

标题：Conditional Maximum Likelihood Estimation in Polytomous Rasch Models Using SAS
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作者：Karl Bang Christensen
期刊名称：ISRN Computational Mathematics
电子版ISSN：2090-7842
出版年度：2013
卷号：2013
DOI：10.1155/2013/617475
出版社：Hindawi Publishing Corporation
摘要：IRT models are widely used but often rely on distributional assumptions about the latent variable. For a simple class of IRT models, the Rasch models, conditional inference is feasible. This enables consistent estimation of item parameters without reference to the distribution of the latent variable in the population. Traditionally, specialized software has been needed for this, but conditional maximum likelihood estimation can be done using standard software for fitting generalized linear models. This paper describes an SAS macro %rasch_cml that fits polytomous Rasch models. The macro estimates item parameters using conditional maximum likelihood (CML) estimation and person locations using maximum likelihood estimator (MLE) and Warm's weighted likelihood estimation (WLE). Graphical presentations are included: plots of item characteristic curves (ICCs), and a graphical goodness-of-fit-test is also produced.