 Correcting for Test Score Measurement Error in ANCOVA Models for Estimating Treatment EffectsJ. R. Lockwood and Daniel F. McCaffrey Journal of Educational and Behavioral Statistics, 2014, vol. 39, issue 1, 22-52 Abstract: A common strategy for estimating treatment effects in observational studies using individual student-level data is analysis of covariance (ANCOVA) or hierarchical variants of it, in which outcomes (often standardized test scores) are regressed on pretreatment test scores, other student characteristics, and treatment group indicators. Measurement error in the prior test scores, which typically is both large and heteroscedastic, is regularly overlooked in empirical analyses and may erode the ability of regression models to adjust for student factors and may result in biased treatment effect estimates. We develop extensions of method-of-moments, Simulation-Extrapolation, and latent regression approaches to correcting for measurement error using the conditional standard errors of measure of test scores, and demonstrate their effectiveness relative to simpler alternatives using both simulation and a case study of teacher value-added effect estimation using longitudinal data from a large suburban school district. Keywords: errors-in-variables regression; value-added models; simulation-extrapolation; deconvolution; latent variable regression (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:39:y:2014:i:1:p:22-52 Access Statistics for this article More articles in Journal of Educational and Behavioral Statistics |
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