Great Expectations: Mining State Level Data for Baseline Comparisons to Augment Randomized Experimental Designs
Authors: Michael J. Bryant, Kimberly A. Hammond, Kathleen Bocian, Catherine A. Miller, Michael Rettig, Richard A. Cardullo

3. Design, Data & Analysis
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3. Design, Data & Analysis
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We utilized public domain data sets from the California Department of Education from 2003 to 2007 and modeled student achievement gains by using the percent of students within a school scoring proficient or advanced. This is a measure that focuses on changes at the top end of the student body. In addition we looked at movement out of the lower end (students scoring below basic) into higher proficiency levels. Our baselines were generated using a series of multi-level models.