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Statistical Power for Random Assignment Evaluations of Education Programs

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  • Peter Z. Schochet

Abstract

This article examines theoretical and empirical issues related to the statistical power of impact estimates for experimental evaluations of education programs. The author considers designs where random assignment is conducted at the school, classroom, or student level, and employs a unified analytic framework using statistical methods from the literature. Focusing on standardized test scores of elementary school students, this article discusses appropriate precision standards and, for each design, the required number of schools to achieve those standards using empirical values of intraclass correlations, regression R 2 values, and other parameters. Clustering effects vary by design but are typically large. Thus, large school samples are required for education trials, and many evaluations will only have sufficient power to detect precise impacts for relatively large subgroups of sites.

Suggested Citation

  • Peter Z. Schochet, 2008. "Statistical Power for Random Assignment Evaluations of Education Programs," Journal of Educational and Behavioral Statistics, , vol. 33(1), pages 62-87, March.
  • Handle: RePEc:sae:jedbes:v:33:y:2008:i:1:p:62-87
    DOI: 10.3102/1076998607302714
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    Citations

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    Cited by:

    1. Peter Z. Schochet, "undated". "Statistical Theory for the RCT-YES Software: Design-Based Causal Inference for RCTs," Mathematica Policy Research Reports a0c005c003c242308a92c02dc, Mathematica Policy Research.
    2. Joanne Lee & Peter Z. Schochet & Jillian Berk, "undated". "The External Review of Job Corps: Directions for Future Research," Mathematica Policy Research Reports 376221bbee0d4b40bda431a16, Mathematica Policy Research.
    3. Kimberly Boller & Sally Atkins-Burnett & Elizabeth M. Malone & Gail P. Baxter & Jerry West, "undated". "Compendium of Student, Teacher, and Classroom Measures Used in NCEE Evaluations of Educational Interventions, Volume I: Measures Selection Approaches and Compendium Development Methods," Mathematica Policy Research Reports 07a8a75ba5634b3a813e22bc6, Mathematica Policy Research.
    4. Laura Kimmey & Michael Anderson & Valerie Cheh & Evelyn Li & Catherine McLaughlin & Linda Barterian & Jay Crosson & Cara Stepanczuk & Lori Timmins & Jiaqi Li & Shannon Heitkamp & Christine Cheu & Tyle, "undated". "Evaluation of the Independence at Home Demonstration: An Examination of the First Four Years," Mathematica Policy Research Reports f92acd5d008b4cbc82f7e940e, Mathematica Policy Research.
    5. repec:mpr:mprres:6568 is not listed on IDEAS

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