Tag
value_added_models
5 findings
EducationDemographic Covariate Inclusion in Value-Added ModelingThe incremental component removed by controlling for student demographic characteristics yielded an IV coefficient of -0.025 (SE = 0.521), demonstrating that removing demographic variables does not significantly alter post-randomization outcome predictions, though large standard errors leave the policy trade-off statistically imprecise.RCTEducationPeer Effects Overcontrolling in Value-Added ModelsThe component removed by peer effect controls predicted student achievement after random assignment with a coefficient of 1.150 (p < 0.01, SE = 0.336), proving it contains causal teacher effectiveness. In contrast, the component removed by individual baseline score controls yielded a coefficient of 0.047 (SE = 0.042), showing baseline score controls successfully remove non-teacher sorting noise.RCTEducationStudent Sorting Bias and Baseline Score AdjustmentPrior to randomization, the within-school standard deviation of teacher mean baseline math scores was 0.382 to 0.520. Evaluating instructors using unadjusted end-of-year student test scores yielded a severely biased predictive coefficient of 0.228 (SE = 0.042), significantly overstating true instructor impact.RCTEducationUnadjusted Status Score Bias in Performance EvaluationUnadjusted end-of-year score ratings yielded a predictive coefficient of 0.228 (p < 0.01, SE = 0.042), significantly overstating true teacher effectiveness differences compared to baseline-adjusted growth models (coefficient = 0.955).RCTEducationRandom Effects and Fixed Effects Value-Added EquivalenceTeacher effect estimates generated from random-effects specifications controlling for classroom section-mean covariates are highly correlated with teacher fixed-effects specifications because the vast majority of student baseline variance exists within classrooms rather than between classrooms.Observational Study