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Can Nonexperimental Estimates Replicate Estimates Based on Random Assignment in Evaluations of School Choice? A Within-Study Comparison
by
Bifulco, Robert
in
Academic achievement
/ Admission (School)
/ Analytical estimating
/ Assignment
/ Bias
/ Comparative Analysis
/ Computation
/ Connecticut
/ Connecticut Mastery Testing Program
/ Decision
/ Density estimation
/ Districts
/ Educational evaluation
/ Educational institutions
/ Estimating techniques
/ Estimation
/ Estimation bias
/ Estimation methods
/ Estimators
/ Evaluation
/ Experimental methods
/ Grade 8
/ Groups
/ Lotteries
/ Magnet Schools
/ Methods for Policy Analysis
/ Middle School Students
/ Outcome Measures
/ Physical characteristics
/ Policy analysis
/ Political science
/ Pretreatment
/ Reading Tests
/ Replication (Evaluation)
/ School Choice
/ Schools
/ Scores
/ Statistical Analysis
/ Statistical Bias
/ Students
/ Studies
/ Test bias
/ Test scores
2012
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Can Nonexperimental Estimates Replicate Estimates Based on Random Assignment in Evaluations of School Choice? A Within-Study Comparison
by
Bifulco, Robert
in
Academic achievement
/ Admission (School)
/ Analytical estimating
/ Assignment
/ Bias
/ Comparative Analysis
/ Computation
/ Connecticut
/ Connecticut Mastery Testing Program
/ Decision
/ Density estimation
/ Districts
/ Educational evaluation
/ Educational institutions
/ Estimating techniques
/ Estimation
/ Estimation bias
/ Estimation methods
/ Estimators
/ Evaluation
/ Experimental methods
/ Grade 8
/ Groups
/ Lotteries
/ Magnet Schools
/ Methods for Policy Analysis
/ Middle School Students
/ Outcome Measures
/ Physical characteristics
/ Policy analysis
/ Political science
/ Pretreatment
/ Reading Tests
/ Replication (Evaluation)
/ School Choice
/ Schools
/ Scores
/ Statistical Analysis
/ Statistical Bias
/ Students
/ Studies
/ Test bias
/ Test scores
2012
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Can Nonexperimental Estimates Replicate Estimates Based on Random Assignment in Evaluations of School Choice? A Within-Study Comparison
by
Bifulco, Robert
in
Academic achievement
/ Admission (School)
/ Analytical estimating
/ Assignment
/ Bias
/ Comparative Analysis
/ Computation
/ Connecticut
/ Connecticut Mastery Testing Program
/ Decision
/ Density estimation
/ Districts
/ Educational evaluation
/ Educational institutions
/ Estimating techniques
/ Estimation
/ Estimation bias
/ Estimation methods
/ Estimators
/ Evaluation
/ Experimental methods
/ Grade 8
/ Groups
/ Lotteries
/ Magnet Schools
/ Methods for Policy Analysis
/ Middle School Students
/ Outcome Measures
/ Physical characteristics
/ Policy analysis
/ Political science
/ Pretreatment
/ Reading Tests
/ Replication (Evaluation)
/ School Choice
/ Schools
/ Scores
/ Statistical Analysis
/ Statistical Bias
/ Students
/ Studies
/ Test bias
/ Test scores
2012
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Can Nonexperimental Estimates Replicate Estimates Based on Random Assignment in Evaluations of School Choice? A Within-Study Comparison
Journal Article
Can Nonexperimental Estimates Replicate Estimates Based on Random Assignment in Evaluations of School Choice? A Within-Study Comparison
2012
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Overview
The ability of nonexperimental estimators to match impact estimates derived from random assignment is examined using data from the evaluation of two interdistnct magnet schools. As in previous within-study comparisons, nonexperimental estimates differ from estimates based on random assignment when nonexperimental estimators are implemented without pretreatment measures of academic performance. With comparison groups consisting of students drawn from the same districts or districts with similar student body characteristics as the districts where treatment group students reside, using pretreatment test scores reduces the bias in nonexperimental methods between 64 and 96 percent. Adding pretreatment test scores does not achieve as much bias reduction when the comparison group consists of students drawn from districts with different student body characteristics than the treatment group students' districts. The results suggest that using pretreatment outcome measures and comparison groups that are geographically aligned with the treatment group greatly improves the performance of nonexperimental estimators.
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