%0 Journal Article
%T Accounting for Student Disadvantage in Value
%A Cory Koedel
%A Eric Parsons
%A Li Tan
%J Journal of Educational and Behavioral Statistics
%@ 1935-1054
%D 2019
%R 10.3102/1076998618803889
%X We study the relative performance of two policy-relevant value-added models¡ªa one-step fixed effect model and a two-step aggregated residuals model¡ªusing a simulated data set well grounded in the value-added literature. A key feature of our data generating process is that student achievement depends on a continuous measure of economic disadvantage. This is a realistic condition that has implications for model performance because researchers typically have access to only a noisy, binary measure of disadvantage. We find that one- and two-step value-added models perform similarly across a wide range of student and teacher sorting conditions, with the two-step model modestly outperforming the one-step model in conditions that best match observed sorting in real data. A reason for the generally superior performance of the two-step model is that it better handles the use of an error-prone, dichotomous proxy for student disadvantage
%K accountability
%K econometric analysis
%K educational policy
%K evaluation
%K policy analysis
%K teacher research
%U https://journals.sagepub.com/doi/full/10.3102/1076998618803889