An Algorithm for Testing Unidimensionality and Clustering Items in Rasch Measurement

A new approach to identify item clusters fitting the Rasch model is described and evaluated using simulated and real data. The proposed method is based on hierarchical cluster analysis and constructs clusters of items that show a good fit to the Rasch model. It thus gives an estimate of the number of independent scales satisfying the postulates of sufficiency of total number of correctly answered items for a person's proficiency, unidimensionality, and local independence that can be constructed from an item set. The method is also compared with the application of a principal components analysis based on tetrachoric correlations. In general, the proposed method was shown to provide practically usable results especially for large person samples.