Using Cluster Analysis To Facilitate the Standard Setting Process.

The most popular methods for setting passing scores and other standards on educational tests rely heavily on subjective judgment. This paper presents and evaluates a new procedure for setting and evaluating standards on tests based on cluster analysis of test data. The clustering procedure was applied to a statewide mathematics proficiency test administered to 818 seventh-grade students in a small urban/suburban school district. Content area subscores were derived from the test specifications to serve as clustering variables. Subsequent course grades in mathematics were used to validate the cluster solutions and the stability of the solutions were evaluated using two random samples. The three-cluster (K-means) solution provided relatively homogeneous groupings of students that were consistent across the two samples and were congruent with school mathematics grades. Standards for "intervention," "proficient," and "excellent" levels of student performance were derived from these results. These standards were similar to those established by the local school district. The results suggest that cluster analytic techniques may be useful for helping set standards on educational tests, as well as for evaluating standards set by other methods. Suggestions for future research are provided. (Contains 2 figures, 7 tables, and 23 references..) (Author/SLD) ******************************************************************************** Reproductions supplied by EDRS are the best that can be made from the original document. ********************************************************************************