Do learning rates differ by race/ethnicity over kindergarten? Reconciling results across gain score, first-difference, and random effects models

David M. Quinn and Joe McIntyre

Economics of Education Review, 2017, vol. 59, issue C, 81-86

Abstract: The question of whether students’ school-year learning rates differ by race/ethnicity is important for monitoring educational inequality. Researchers applying different modeling strategies to the same data (the ECLS-K:99) have reached contrasting conclusions on this question. We outline the similarities and differences across three common approaches to estimating gains and heterogeneity in gains: 1) a gain score model (with intercept), 2) a first-difference (FD) model (in some cases equivalent to regression-through-the-origin [RTO] and student fixed effects models), and 3) a student random effects (RE) model. We show via simulation that FD/RTO and RE models produce estimates of learning rates – and group differences in learning rates – with more favorable RMSD compared to the gain score model with intercept. Using data from the ECLS-K:99, we demonstrate that these precision differences lead to contrasting inferences regarding learning rate heterogeneity, and likely explain the inconsistencies across previous studies.

Keywords: Heterogeneity in learning rates; Gain score model; Regression-through-the-origin; First-difference model; Student fixed effects model; Student random effects model; Simulation; ECLS-K:99 (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecoedu:v:59:y:2017:i:c:p:81-86

DOI: 10.1016/j.econedurev.2017.06.006

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