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Controlling for Student Background in Value-Added Assessment of Teachers

Author

Listed:
  • Dale Ballou
  • William Sanders
  • Paul Wright

Abstract

The Tennessee Value-Added Assessment System measures teacher effectiveness on the basis of student gains, implicitly controlling for socioeconomic status and other background factors that influence initial levels of achievement. The absence of explicit controls for student background has been criticized on the grounds that these factors influence gains as well. In this research we modify the TVAAS by introducing commonly used controls for student SES and demographics. The introduction of controls at the student level has a negligible impact on estimated teacher effects in the TVAAS, though not in a simple fixed effects estimator with which the TVAAS is compared. The explanation lies in the TVAAS’s exploitation of the covariance of tests in different subjects and grades, whereby a student’s history of test performance substitutes for omitted background variables.

Suggested Citation

  • Dale Ballou & William Sanders & Paul Wright, 2004. "Controlling for Student Background in Value-Added Assessment of Teachers," Journal of Educational and Behavioral Statistics, , vol. 29(1), pages 37-65, March.
    • Handle: RePEc:sae:jedbes:v:29:y:2004:i:1:p:37-65
    DOI: 10.3102/10769986029001037
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    Citations

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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. Dan Goldhaber & Roddy Theobald, 2013. "Managing the Teacher Workforce in Austere Times: The Determinants and Implications of Teacher Layoffs," Education Finance and Policy, MIT Press, vol. 8(4), pages 494-527, October.
    3. Sean Corcoran & Dan Goldhaber, 2013. "Value Added and Its Uses: Where You Stand Depends on Where You Sit," Education Finance and Policy, MIT Press, vol. 8(3), pages 418-434, July.
    4. Jesse Rothstein, 2007. "Do Value-Added Models Add Value? Tracking, Fixed Effects, and Causal Inference," Working Papers 1036, Princeton University, Department of Economics, Center for Economic Policy Studies..
    5. Eric A. Hanushek & John F. Kain & Daniel M. O'Brien & Steven G. Rivkin, 2005. "The Market for Teacher Quality," NBER Working Papers 11154, National Bureau of Economic Research, Inc.
    6. Brehm, Margaret & Imberman, Scott A. & Lovenheim, Michael F., 2017. "Achievement effects of individual performance incentives in a teacher merit pay tournament," Labour Economics, Elsevier, vol. 44(C), pages 133-150.
    7. Julie Berry Cullen & Cory Koedel & Eric Parsons, 2021. "The Compositional Effect of Rigorous Teacher Evaluation on Workforce Quality," Education Finance and Policy, MIT Press, vol. 16(1), pages 7-41, Winter.
    8. Mueller, Steffen, 2013. "Teacher experience and the class size effect — Experimental evidence," Journal of Public Economics, Elsevier, vol. 98(C), pages 44-52.
    9. Brendan Houng & Moshe Justman, 2013. "Comparing Least-Squares Value-Added Analysis and Student Growth Percentile Analysis for Evaluating Student Progress and Estimating School Effects," Melbourne Institute Working Paper Series wp2013n07, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    10. Josh Kinsler, 2016. "Teacher Complementarities in Test Score Production: Evidence from Primary School," Journal of Labor Economics, University of Chicago Press, vol. 34(1), pages 29-61.
    11. Cassandra M. Guarino & Mark D. Reckase & Jeffrey M. Woolrdige, 2014. "Can Value-Added Measures of Teacher Performance Be Trusted?," Education Finance and Policy, MIT Press, vol. 10(1), pages 117-156, November.
    12. Garritt L. Page & Ernesto San Martín & Javiera Orellana & Jorge González, 2017. "Exploring complete school effectiveness via quantile value added," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(1), pages 315-340, January.
    13. Moshe Justman & Brendan Houng, 2013. "A Comparison Of Two Methods For Estimating School Effects And Tracking Student Progress From Standardized Test Scores," Working Papers 1316, Ben-Gurion University of the Negev, Department of Economics.
    14. Dan Goldhaber & Michael Hansen, 2013. "Is it Just a Bad Class? Assessing the Long-term Stability of Estimated Teacher Performance," Economica, London School of Economics and Political Science, vol. 80(319), pages 589-612, July.
    15. repec:pri:cepsud:159rothstein is not listed on IDEAS
    16. Vosters, Kelly N. & Guarino, Cassandra M. & Wooldridge, Jeffrey M., 2018. "Understanding and evaluating the SAS® EVAAS® Univariate Response Model (URM) for measuring teacher effectiveness," Economics of Education Review, Elsevier, vol. 66(C), pages 191-205.
    17. Marianne Bitler & Sean Corcoran & Thurston Domina & Emily Penner, 2019. "Teacher Effects on Student Achievement and Height: A Cautionary Tale," NBER Working Papers 26480, National Bureau of Economic Research, Inc.

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