PERFORMANCE DATA - 3 COMPARISON METHODS

Performance data need a context to meaningfully interpret the data. On e method of providing context for an individual unit's performance is to compare it with other similar units. This study compares three meth ods for selecting similar units: cluster groupings, index groups, and benchmark groups. Each of the three methods is evaluated on a number o f criteria, primarily the minimization of within-group variance. Bench mark groups are the best at reducing the variation within the selected groups, and they resist attempts to ''label '' the groupings. Cluster groups are a close second to benchmarks in the minimization of variab ility within groups and are considerably easier to compute and adminis ter However clustering allows labeling that could stigmatize the group s and threshold effects that might influence judgments about performan ce. Index groups, while simple, do not perform well on any of the othe r criteria.