Abstract
The purpose of this study is to describe a Many-Faceted Rasch (FACETS) model for the measurement of writing ability. The FACETS model is a multivariate extension of Rasch measurement models that can be used to provide a framework for calibrating both raters and writing tasks within the context of writing assessment. The use of the FACETS model for solving measurement problems encountered in the large-scale assessment of writing ability is presented here. A random sample of 1,000 students from a statewide assessment of writing ability is used to illustrate the FACETS model. The data suggest that there are significant differences in rater severity, even after extensive training. Small, but statistically significant, differences in writing- task difficulty were also found. The FACETS model offers a promising approach for addressing measurement problems encountered in the large- scale assessment of writing ability through written compositions.
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