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Assumptions of Value-Added Models for Estimating School Effects

Author

Listed:
  • Sean F. Reardon

    () (School of Education, Stanford University)

  • Stephen W. Raudenbush

    () (Department of Sociology, University of Chicago)

Abstract

The ability of school (or teacher) value-added models to provide unbiased estimates of school (or teacher) effects rests on a set of assumptions. In this article, we identify six assumptions that are required so that the estimands of such models are well defined and the models are able to recover the desired parameters from observable data. These assumptions are (1) manipulability, (2) no interference between units, (3) interval scale metric, (4) homogeneity of effects, (5) strongly ignorable assignment, and (6) functional form. We discuss the plausibility of these assumptions and the consequences of their violation. In particular, because the consequences of violations of the last three assumptions have not been assessed in prior literature, we conduct a set of simulation analyses to investigate the extent to which plausible violations of them alter inferences from value-added models. We find that modest violations of these assumptions degrade the quality of value-added estimates but that models that explicitly account for heterogeneity of school effects are less affected by violations of the other assumptions. © 2009 American Education Finance Association

Suggested Citation

  • Sean F. Reardon & Stephen W. Raudenbush, 2009. "Assumptions of Value-Added Models for Estimating School Effects," Education Finance and Policy, MIT Press, vol. 4(4), pages 492-519, October.
  • Handle: RePEc:tpr:edfpol:v:4:y:2009:i:4:p:492-519
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    File URL: http://www.mitpressjournals.org/doi/pdf/10.1162/edfp.2009.4.4.492
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    Citations

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    Cited by:

    1. Papay, John P. & Kraft, Matthew A., 2015. "Productivity returns to experience in the teacher labor market: Methodological challenges and new evidence on long-term career improvement," Journal of Public Economics, Elsevier, vol. 130(C), pages 105-119.
    2. Jorge Manzi & Ernesto San Martín & Sébastien Van Bellegem, 2014. "School System Evaluation by Value Added Analysis Under Endogeneity," Psychometrika, Springer;The Psychometric Society, vol. 79(1), pages 130-153, January.
    3. Lindsay Fox, 2016. "Playing to Teachers’ Strengths: Using Multiple Measures of Teacher Effectiveness to Improve Teacher Assignments," Education Finance and Policy, MIT Press, vol. 11(1), pages 70-96, Winter.
    4. Gary Henry & Roderick Rose & Doug Lauen, 2014. "Are value-added models good enough for teacher evaluations? Assessing commonly used models with simulated and actual data," Investigaciones de Economía de la Educación volume 9,in: Adela García Aracil & Isabel Neira Gómez (ed.), Investigaciones de Economía de la Educación 9, edition 1, volume 9, chapter 20, pages 383-405 Asociación de Economía de la Educación.
    5. Condie, Scott & Lefgren, Lars & Sims, David, 2014. "Teacher heterogeneity, value-added and education policy," Economics of Education Review, Elsevier, vol. 40(C), pages 76-92.

    More about this item

    Keywords

    value-added models; School effects; teacher effects;

    JEL classification:

    • I20 - Health, Education, and Welfare - - Education - - - General
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education

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