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extended · article · 2009

Using paired comparison matrices to estimate parameters of the partial credit Rasch measurement model for rater-mediated assessments.

Mary Garner; George Engelhard Jr.; Gerhard H. Fischer

The purpose of this paper is to describe a technique for estimating the parameters of a Rasch model that accommodates ordered categories and rater severity. The technique builds on the conditional pairwise algorithm described by Choppin (1968, 1985) and represents an extension of a conditional algorithm described by Garner and Engelhard (2000, 2002) in which parameters appear as the eigenvector of a matrix derived from paired comparisons. The algorithm is used successfully to recover parameters from a simulated data set. No one has previously described such an extension of the pairwise algorithm to a Rasch model that includes both ordered categories and rater effects. The paired comparisons technique has importance for several reasons: it relies on the separability of parameters that is true only for the Rasch measurement model; it works in the presence of missing data; it makes transpar...

Partial Credit ModelCAT
APA citation

Mary Garner, George Engelhard Jr., & Gerhard H. Fischer (2009). Using paired comparison matrices to estimate parameters of the partial credit Rasch measurement model for rater-mediated assessments.. PubMed, 10(1), 30-41. https://pubmed.ncbi.nlm.nih.gov/19299883