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Correcting for Test Score Measurement Error in ANCOVA Models for Estimating Treatment Effects

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  • J. R. Lockwood
  • Daniel F. McCaffrey

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.

Suggested Citation

  • J. R. Lockwood & Daniel F. McCaffrey, 2014. "Correcting for Test Score Measurement Error in ANCOVA Models for Estimating Treatment Effects," Journal of Educational and Behavioral Statistics, , vol. 39(1), pages 22-52, February.
    • Handle: RePEc:sae:jedbes:v:39:y:2014:i:1:p:22-52
    DOI: 10.3102/1076998613509405
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    References listed on IDEAS

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    1. Jesse Rothstein, 2009. "Student Sorting and Bias in Value-Added Estimation: Selection on Observables and Unobservables," Education Finance and Policy, MIT Press, vol. 4(4), pages 537-571, October.
    2. Sophia Rabe-Hesketh & Anders Skrondal & Andrew Pickles, 2004. "GLLAMM Manual," U.C. Berkeley Division of Biostatistics Working Paper Series 1160, Berkeley Electronic Press.
    3. Brian Junker & Lynne Schofield & Lowell Taylor, 2012. "The use of cognitive ability measures as explanatory variables in regression analysis," IZA Journal of Labor Economics, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 1(1), pages 1-19, December.
    4. Dale Ballou, 2009. "Test Scaling and Value-Added Measurement," Education Finance and Policy, MIT Press, vol. 4(4), pages 351-383, October.
    5. White, Halbert, 1982. "Maximum Likelihood Estimation of Misspecified Models," Econometrica, Econometric Society, vol. 50(1), pages 1-25, January.
    6. Devanarayan, Viswanath & Stefanski, Leonard A., 2002. "Empirical simulation extrapolation for measurement error models with replicate measurements," Statistics & Probability Letters, Elsevier, vol. 59(3), pages 219-225, October.
    7. Daniel F. McCaffrey & J. R. Lockwood & Claude M. Setodji, 2013. "Inverse probability weighting with error-prone covariates," Biometrika, Biometrika Trust, vol. 100(3), pages 671-680.
    8. Cory Koedel & Julian Betts, 2007. "Re-Examining the Role of Teacher Quality In the Educational Production Function," Working Papers 0708, Department of Economics, University of Missouri.
    9. Kata Mihaly & Daniel F. McCaffrey & J. R. Lockwood & Tim R. Sass, 2010. "Centering and reference groups for estimates of fixed effects: Modifications to felsdvreg," Stata Journal, StataCorp LP, vol. 10(1), pages 82-103, March.
    10. Donald Boyd & Hamilton Lankford & Susanna Loeb & James Wyckoff, 2012. "Measuring Test Measurement Error: A General Approach," NBER Working Papers 18010, National Bureau of Economic Research, Inc.
    11. Koedel Cory & Leatherman Rebecca & Parsons Eric, 2012. "Test Measurement Error and Inference from Value-Added Models," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 12(1), pages 1-37, November.
    12. Roger Millsap, 2007. "Invariance in Measurement and Prediction Revisited," Psychometrika, Springer;The Psychometric Society, vol. 72(4), pages 461-473, December.
    13. Derek C. Briggs & Jonathan P. Weeks, 2009. "The Sensitivity of Value-Added Modeling to the Creation of a Vertical Score Scale," Education Finance and Policy, MIT Press, vol. 4(4), pages 384-414, October.
    14. Jean-Paul Fox & Cees Glas, 2003. "Bayesian modeling of measurement error in predictor variables using item response theory," Psychometrika, Springer;The Psychometric Society, vol. 68(2), pages 169-191, June.
    15. Silvia Bianconcini & Silvia Cagnone, 2012. "A General Multivariate Latent Growth Model With Applications to Student Achievement," Journal of Educational and Behavioral Statistics, , vol. 37(2), pages 339-364, April.
    16. Kilchan Choi & Michael Seltzer, 2010. "Modeling Heterogeneity in Relationships Between Initial Status and Rates of Change: Treating Latent Variable Regression Coefficients as Random Coefficients in a Three-Level Hierarchical Model," Journal of Educational and Behavioral Statistics, , vol. 35(1), pages 54-91, February.
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    Cited by:

    1. Stacy, Brian & Guarino, Cassandra & Wooldridge, Jeffrey, 2018. "Does the precision and stability of value-added estimates of teacher performance depend on the types of students they serve?," Economics of Education Review, Elsevier, vol. 64(C), pages 50-74.
    2. Hwanhee Hong & Kara E. Rudolph & Elizabeth A. Stuart, 2017. "Bayesian Approach for Addressing Differential Covariate Measurement Error in Propensity Score Methods," Psychometrika, Springer;The Psychometric Society, vol. 82(4), pages 1078-1096, December.
    3. Backes, Ben & Cowan, James & Goldhaber, Dan & Koedel, Cory & Miller, Luke C. & Xu, Zeyu, 2018. "The common core conundrum: To what extent should we worry that changes to assessments will affect test-based measures of teacher performance?," Economics of Education Review, Elsevier, vol. 62(C), pages 48-65.
    4. Koedel, Cory & Mihaly, Kata & Rockoff, Jonah E., 2015. "Value-added modeling: A review," Economics of Education Review, Elsevier, vol. 47(C), pages 180-195.
    5. J. R. Lockwood & Daniel F. McCaffrey, 2017. "Simulation-Extrapolation with Latent Heteroskedastic Error Variance," Psychometrika, Springer;The Psychometric Society, vol. 82(3), pages 717-736, September.

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