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An Evaluation of Bias in Three Measures of Teacher Quality: Value-Added, Classroom Observations, and Student Surveys

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Listed:
  • Andrew Bacher-Hicks
  • Mark J. Chin
  • Thomas J. Kane
  • Douglas O. Staiger

Abstract

There are three primary measures of teaching performance: student test-based measures (i.e., value added), classroom observations, and student surveys. Although all three types of measures could be biased by unmeasured traits of the students in teachers’ classrooms, prior research has largely focused on the validity of value-added measures. We conduct an experiment involving 66 mathematics teachers in four school districts and test the validity of all three types of measures. Specifically, we test whether a teacher’s performance on each measure under naturally occurring (i.e., non-experimental) settings predicts performance following random assignment of that teacher to a class of students. Combining our results with those from two previous experiments, we provide further evidence that value-added measures are unbiased predictors of teacher performance. In addition, we provide the first evidence that classroom observation scores are unbiased predictors of teacher performance on a rubric measuring the quality of mathematics instruction. Unfortunately, we lack the statistical power to reach any similar conclusions regarding the predictive validity of a teacher’s student survey responses.

Suggested Citation

  • Andrew Bacher-Hicks & Mark J. Chin & Thomas J. Kane & Douglas O. Staiger, 2017. "An Evaluation of Bias in Three Measures of Teacher Quality: Value-Added, Classroom Observations, and Student Surveys," NBER Working Papers 23478, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:23478
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    References listed on IDEAS

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    1. Thomas J. Kane & Douglas O. Staiger, 2008. "Estimating Teacher Impacts on Student Achievement: An Experimental Evaluation," NBER Working Papers 14607, National Bureau of Economic Research, Inc.
    2. M. Caridad Araujo & Pedro Carneiro & Yyannú Cruz-Aguayo & Norbert Schady, 2016. "Teacher Quality and Learning Outcomes in Kindergarten," The Quarterly Journal of Economics, Oxford University Press, vol. 131(3), pages 1415-1453.
    3. A. Colin Cameron & Jonah B. Gelbach & Douglas L. Miller, 2008. "Bootstrap-Based Improvements for Inference with Clustered Errors," The Review of Economics and Statistics, MIT Press, vol. 90(3), pages 414-427, August.
    4. Raj Chetty & John N. Friedman & Jonah E. Rockoff, 2014. "Measuring the Impacts of Teachers I: Evaluating Bias in Teacher Value-Added Estimates," American Economic Review, American Economic Association, vol. 104(9), pages 2593-2632, September.
    5. Jonah E. Rockoff, 2004. "The Impact of Individual Teachers on Student Achievement: Evidence from Panel Data," American Economic Review, American Economic Association, vol. 94(2), pages 247-252, May.
    6. M. Caridad Araujo & Pedro Carneiro & Yyannú Cruz-Aguayo & Norbert Schady, 2016. "Teacher Quality and Learning Outcomes in Kindergarten," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(3), pages 1415-1453.
    7. Kane, Thomas J. & Rockoff, Jonah E. & Staiger, Douglas O., 2008. "What does certification tell us about teacher effectiveness? Evidence from New York City," Economics of Education Review, Elsevier, vol. 27(6), pages 615-631, December.
    8. Andrew Bacher-Hicks & Thomas J. Kane & Douglas O. Staiger, 2014. "Validating Teacher Effect Estimates Using Changes in Teacher Assignments in Los Angeles," NBER Working Papers 20657, National Bureau of Economic Research, Inc.
    9. Jesse Rothstein, 2010. "Teacher Quality in Educational Production: Tracking, Decay, and Student Achievement," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 125(1), pages 175-214.
    10. Brian A. Jacob & Lars Lefgren, 2005. "Principals as Agents: Subjective Performance Measurement in Education," NBER Working Papers 11463, National Bureau of Economic Research, Inc.
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    Cited by:

    1. Filmer,Deon P. & Nahata,Vatsal & Sabarwal,Shwetlena, 2021. "Preparation, Practice, and Beliefs : A Machine Learning Approach to Understanding Teacher Effectiveness," Policy Research Working Paper Series 9847, The World Bank.
    2. Briole, Simon & Maurin, Eric, 2019. "Does Evaluating Teachers Make a Difference?," IZA Discussion Papers 12307, Institute of Labor Economics (IZA).
    3. Esteban M. Aucejo & Patrick Coate & Jane Cooley Fruehwirth & Sean Kelly & Zachary Mozenter, 2018. "Teacher effectiveness and classroom composition," CEP Discussion Papers dp1574, Centre for Economic Performance, LSE.
    4. Jacob, Brian A. & Rockoff, Jonah E. & Taylor, Eric S. & Lindy, Benjamin & Rosen, Rachel, 2018. "Teacher applicant hiring and teacher performance: Evidence from DC public schools," Journal of Public Economics, Elsevier, vol. 166(C), pages 81-97.
    5. David Blazar, 2018. "Validating Teacher Effects on Students’ Attitudes and Behaviors: Evidence from Random Assignment of Teachers to Students," Education Finance and Policy, MIT Press, vol. 13(3), pages 281-309, Summer.
    6. Asma Benhenda, 2018. "Teacher Screening, On the Job Evaluations and Performancee," DoQSS Working Papers 18-06, Quantitative Social Science - UCL Social Research Institute, University College London.
    7. 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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    More about this item

    JEL classification:

    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity

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