Missing data is a common problem in datasets that are obtained by administration of educational and psychological tests. It is widely known that existence of missing observations in data can lead to serious problems such as biased parameter estimates and inflation of standard errors. Most of the missing data imputation methods are focused on…

Recent research has shown how the statistical bias in Rasch model difficulty estimates induced by guessing in multiple-choice items can be eliminated. Using vertical scaling of a high-profile national reading test, it is shown that the dominant effect of removing such bias is a nonlinear change in the unit of scale across the continuum. The…