LURKING INFERENTIAL MONSTERS? QUANTIFYING SELECTION BIAS IN EVALUATIONS OF SCHOOL PROGRAMS

• 成果类型: Article
• 署名作者: Weidmann, Ben; Miratrix, Luke
• 署名单位: Harvard University
• 刊物名称: JOURNAL OF POLICY ANALYSIS AND MANAGEMENT
• ISSN/ISSBN: 1520-7151
• DOI: 10.1002/pam.22236
• 发表日期: 2021
• 页码: 964-+
• 关键词: econometric evaluations; MULTIVARIATE; DESIGN; reduction; validity

摘要

This study examines whether unobserved factors substantially bias education evaluations that rely on the Conditional Independence Assumption. We add 14 new within-study comparisons to the literature, all from primary schools in England. Across these 14 studies, we generate 42 estimates of selection bias using a simple approach to observational analysis. A meta-analysis of these estimates suggests that the distribution of underlying bias is centered around zero. The mean absolute value of estimated bias is 0.03 sigma, and none of the 42 estimates are larger than 0.11 sigma. Results are similar for math, reading, and writing outcomes. Overall, we find no evidence of substantial selection bias due to unobserved characteristics. These findings may not generalize easily to other settings or to more radical educational interventions, but they do suggest that non-experimental approaches could play a greater role than they currently do in generating reliable causal evidence for school education.