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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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    Citations

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    Cited by:

    1. 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.
    2. 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.
    3. P. Givord & M. Suarez Castillo, 2019. "Excellence for all? Heterogeneity in high-schools’ value-added," Documents de Travail de l'Insee - INSEE Working Papers g2019-14, Institut National de la Statistique et des Etudes Economiques.
    4. 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.
    5. Jordan H. Rickles & Michael Seltzer, 2014. "A Two-Stage Propensity Score Matching Strategy for Treatment Effect Estimation in a Multisite Observational Study," Journal of Educational and Behavioral Statistics, , vol. 39(6), pages 612-636, December.
    6. Braun, Martin & Verdier, Valentin, 2023. "Estimation of spillover effects with matched data or longitudinal network data," Journal of Econometrics, Elsevier, vol. 233(2), pages 689-714.
    7. 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.
    8. Andrew McEachin & Allison Atteberry, 2017. "The Impact of Summer Learning Loss on Measures of School Performance," Education Finance and Policy, MIT Press, vol. 12(4), pages 468-491, Fall.
    9. Mika Kortelainen & Kalle Manninen, 2019. "Effectiveness of Private and Public High Schools: Evidence from Finland," CESifo Economic Studies, CESifo Group, vol. 65(4), pages 424-445.
    10. Susanna Loeb & Michael S. Christian & Heather Hough & Robert H. Meyer & Andrew B. Rice & Martin R. West, 2019. "School Differences in Social–Emotional Learning Gains: Findings From the First Large-Scale Panel Survey of Students," Journal of Educational and Behavioral Statistics, , vol. 44(5), pages 507-542, October.
    11. Cassandra M. Guarino & Michelle Maxfield & Mark D. Reckase & Paul N. Thompson & Jeffrey M. Wooldridge, 2015. "An Evaluation of Empirical Bayes’s Estimation of Value-Added Teacher Performance Measures," Journal of Educational and Behavioral Statistics, , vol. 40(2), pages 190-222, April.
    12. Katherine E. Castellano & Andrew D. Ho, 2015. "Practical Differences Among Aggregate-Level Conditional Status Metrics," Journal of Educational and Behavioral Statistics, , vol. 40(1), pages 35-68, February.
    13. 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.
    14. 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.
    15. 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.

    More about this item

    Keywords

    value-added models; School effects; teacher effects;
    All these keywords.

    JEL classification:

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

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