Controlling Guessing Bias in the Dichotomous Rasch Model Applied to a Large-Scale, Vertically Scaled Testing Program.

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 consequence is that the proficiencies of the more proficient students are increased relative to those of the less proficient.

Is there a contradiction between the network and latent variable perspectives?

First, we question whether Cramer et al.'s proposed network model can provide a viable scientific foundation for investigating comorbidity without invoking latent variables in some form. Second, the authors' claim that the network perspective is radically different from a latent variable perspective rests upon an undemonstrated premise.

Understanding the unit in the Rasch model.

The purpose of this paper is to explain the role of the unit implicit in the dichotomous Rasch model in determining the multiplicative factor of separation between measurements in a specified frame of reference. The explanation is provided at two complementary levels: first, in terms of the algebra of the model in which the role of an implicit, multiplicative constant is made explicit; and second, at a more fundamental level, in terms of the classical definition of measurement in the physical sciences. The Rasch model is characterized by statistical sufficiency, which arises from the requirement of invariant comparisons within a specified frame of reference.