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The Challenges Presented by Measuring Up 2000
Crunching the Numbers: Looking Behind the Grades
Presentation of the "Best" Models for Each Performance Measure
Discussion of Model Results
Policy Conclusions and Suggestions for Future Research
Appendix I: Preliminary Measures Organized into Clusters of Influence
Appendix II: Final Variables Used in Analysisthor
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This paper presents the results of a statistical analysis of Measuring Up 2000, the first report card on state postsecondary education performance in the United States. Measuring Up 2000 represents an ambitious effort to synthesize data about postsecondary education performance for the 50 states. Despite the necessary limitations of measures of this sort, it provides an opportunity for analysts and policymakers to take the next steps: to continue to improve how we assess performance in higher education; to test the relationship between policy and performance; and to use these assessments to improve both policy and performance.

This paper explores one way to understand the bases for the grades in the report card. Using multivariate statistical analysis, this report tests the strength of the relationships between the grades and organizational, funding, and demographic/economic variables. The purpose is to learn what this kind of analysis reveals about the factors that predict overall performance and, in particular, to separate the degree to which policy and design-rather than environmental conditions-are associated with performance. By mapping these "predictor" variables, we hope to learn more about the types of interventions and policies that are most likely to influence performance for each measure. The results can be instructive about the nature of the measures, as well as helpful in suggesting directions for state-level policy research.

This paper begins with a general discussion of the challenges presented by Measuring Up 2000, and of the need to probe state-level aggregate data to learn more about how policy decisions are related to performance. The discussion then turns to a review of the design of the statistical model, and how the data were organized for the research. The results of the statistical analysis are presented, and the paper concludes with a discussion of the possible meaning of the results, including suggestions for future research.


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