IRTmodels are widely used but often rely on distributional assumptions about the latent variable. For a simple class of IRTmodels,\r\nthe Rasch models, conditional inference is feasible. This enables consistent estimation of item parameters without reference to the\r\ndistribution of the latent variable in the population. Traditionally, specialized software has been needed for this, but conditional\r\nmaximum likelihood estimation can be done using standard software for fitting generalized linear models. This paper describes an\r\nSAS macro %rasch cml that fits polytomous Rasch models. The macro estimates item parameters using conditional maximum\r\nlikelihood (CML) estimation and person locations using maximum likelihood estimator (MLE) and Warm�s weighted likelihood\r\nestimation (WLE). Graphical presentations are included: plots of item characteristic curves (ICCs), and a graphical goodness-offit-\r\ntest is also produced.
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