IDEAS home Printed from https://ideas.repec.org/p/iza/izadps/dp7973.html
   My bibliography  Save this paper

A Comparison of Growth Percentile and Value-Added Models of Teacher Performance

Author

Listed:
  • Guarino, Cassandra M.

    (University of California, Riverside)

  • Reckase, Mark D.

    (Michigan State University)

  • Stacy, Brian

    (World Bank)

  • Wooldridge, Jeffrey M.

    (Michigan State University)

Abstract

School districts and state departments of education frequently must choose between a variety of methods to estimating teacher quality. This paper examines under what circumstances the decision between estimators of teacher quality is important. We examine estimates derived from growth percentile measures and estimates derived from commonly used value-added estimators. Using simulated data, we examine how well the estimators can rank teachers and avoid misclassification errors under a variety of assignment scenarios of teachers to students. We find that growth percentile measures perform worse than value-added measures that control for prior year student test scores and control for teacher fixed effects when assignment of students to teachers is nonrandom. In addition, using actual data from a large diverse anonymous state, we find evidence that growth percentile measures are less correlated with value-added measures with teacher fixed effects when there is evidence of nonrandom grouping of students in schools. This evidence suggests that the choice between estimators is most consequential under nonrandom assignment of teachers to students, and that value-added measures controlling for teacher fixed effects may be better suited to estimating teacher quality in this case.

Suggested Citation

  • Guarino, Cassandra M. & Reckase, Mark D. & Stacy, Brian & Wooldridge, Jeffrey M., 2014. "A Comparison of Growth Percentile and Value-Added Models of Teacher Performance," IZA Discussion Papers 7973, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp7973
    as

    Download full text from publisher

    File URL: https://docs.iza.org/dp7973.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Thomas J. Kane & Douglas O. Staiger, 2002. "The Promise and Pitfalls of Using Imprecise School Accountability Measures," Journal of Economic Perspectives, American Economic Association, vol. 16(4), pages 91-114, Fall.
    2. Roland Fryer & Steven Levitt & John List & Sally Sadoff, 2012. "Enhancing the Efficacy of Teacher Incentives through Loss Aversion: A Field Experiment," Framed Field Experiments 00591, The Field Experiments Website.
    3. Sass, Tim R. & Semykina, Anastasia & Harris, Douglas N., 2014. "Value-added models and the measurement of teacher productivity," Economics of Education Review, Elsevier, vol. 38(C), pages 9-23.
    4. Daniel Aaronson & Lisa Barrow & William Sander, 2007. "Teachers and Student Achievement in the Chicago Public High Schools," Journal of Labor Economics, University of Chicago Press, vol. 25(1), pages 95-135.
    5. 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.
    6. Gadi Barlevy & Derek Neal, 2012. "Pay for Percentile," American Economic Review, American Economic Association, vol. 102(5), pages 1805-1831, August.
    7. Brian A. Jacob & Lars Lefgren, 2008. "Can Principals Identify Effective Teachers? Evidence on Subjective Performance Evaluation in Education," Journal of Labor Economics, University of Chicago Press, vol. 26(1), pages 101-136.
    8. Roger Koenker & Kevin F. Hallock, 2001. "Quantile Regression," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 143-156, Fall.
    9. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, December.
    10. Eric A. Hanushek, 1979. "Conceptual and Empirical Issues in the Estimation of Educational Production Functions," Journal of Human Resources, University of Wisconsin Press, vol. 14(3), pages 351-388.
    11. Petra E. Todd & Kenneth I. Wolpin, 2003. "On The Specification and Estimation of The Production Function for Cognitive Achievement," Economic Journal, Royal Economic Society, vol. 113(485), pages 3-33, February.
    12. 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.
    13. Steven Dieterle & Cassandra M. Guarino & Mark D. Reckase & Jeffrey M. Wooldridge, 2015. "How do Principals Assign Students to Teachers? Finding Evidence in Administrative Data and the Implications for Value Added," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 34(1), pages 32-58, January.
    14. Daniel F. McCaffrey & J. R. Lockwood & Daniel Koretz & Thomas A. Louis & Laura Hamilton, 2004. "Models for Value-Added Modeling of Teacher Effects," Journal of Educational and Behavioral Statistics, , vol. 29(1), pages 67-101, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. Lee Crawfurd, 2017. "School Management and Public–Private Partnerships in Uganda," Journal of African Economies, Centre for the Study of African Economies, vol. 26(5), pages 539-560.
    3. Pauline Givord & Milena Suarez Castillo, 2021. "What Makes a Good High School? Measuring School Effects beyond the Average," Economie et Statistique / Economics and Statistics, Institut National de la Statistique et des Etudes Economiques (INSEE), issue 528-529, pages 29-45.
    4. repec:hal:spmain:info:hdl:2441/1dniduq06u8se8q5enfvnorti9 is not listed on IDEAS
    5. 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.
    6. 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.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    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. 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.
    3. Koedel, Cory & Mihaly, Kata & Rockoff, Jonah E., 2015. "Value-added modeling: A review," Economics of Education Review, Elsevier, vol. 47(C), pages 180-195.
    4. 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.
    5. Douglas O. Staiger & Jonah E. Rockoff, 2010. "Searching for Effective Teachers with Imperfect Information," Journal of Economic Perspectives, American Economic Association, vol. 24(3), pages 97-118, Summer.
    6. Gershenson, Seth, 2021. "Identifying and Producing Effective Teachers," IZA Discussion Papers 14096, Institute of Labor Economics (IZA).
    7. Steven Dieterle & Cassandra M. Guarino & Mark D. Reckase & Jeffrey M. Wooldridge, 2015. "How do Principals Assign Students to Teachers? Finding Evidence in Administrative Data and the Implications for Value Added," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 34(1), pages 32-58, January.
    8. 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.
    9. Stacy, Brian, 2014. "Ranking Teachers when Teacher Value-Added is Heterogeneous Across Students," EconStor Preprints 104743, ZBW - Leibniz Information Centre for Economics.
    10. 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.
    11. Nirav Mehta, 2019. "Measuring quality for use in incentive schemes: The case of “shrinkage” estimators," Quantitative Economics, Econometric Society, vol. 10(4), pages 1537-1577, November.
    12. Azam, Mehtabul & Kingdon, Geeta Gandhi, 2015. "Assessing teacher quality in India," Journal of Development Economics, Elsevier, vol. 117(C), pages 74-83.
    13. Tahir Andrabi & Jishnu Das & Asim Ijaz Khwaja & Tristan Zajonc, 2011. "Do Value-Added Estimates Add Value? Accounting for Learning Dynamics," American Economic Journal: Applied Economics, American Economic Association, vol. 3(3), pages 29-54, July.
    14. 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.
    15. Hanushek, Eric A. & Rivkin, Steven G. & Schiman, Jeffrey C., 2016. "Dynamic effects of teacher turnover on the quality of instruction," Economics of Education Review, Elsevier, vol. 55(C), pages 132-148.
    16. Peter Z. Schochet & Hanley S. Chiang, 2013. "What Are Error Rates for Classifying Teacher and School Performance Using Value-Added Models?," Journal of Educational and Behavioral Statistics, , vol. 38(2), pages 142-171, April.
    17. Marine de Talancé, 2015. "Better Teachers, Better Results? Evidence from Rural Pakistan," Working Papers DT/2015/21, DIAL (Développement, Institutions et Mondialisation).
    18. Harris, Douglas N. & Sass, Tim R., 2014. "Skills, productivity and the evaluation of teacher performance," Economics of Education Review, Elsevier, vol. 40(C), pages 183-204.
    19. repec:pri:cepsud:170rothstein is not listed on IDEAS
    20. Figlio, D. & Karbownik, K. & Salvanes, K.G., 2016. "Education Research and Administrative Data," Handbook of the Economics of Education,, Elsevier.
    21. Mariesa Herrmann & Elias Walsh & Eric Isenberg & Alexandra Resch, 2013. "Shrinkage of Value-Added Estimates and Characteristics of Students with Hard-to-Predict Achievement Levels," Mathematica Policy Research Reports 2b140369be0242ac83eeb5b0a, Mathematica Policy Research.

    More about this item

    Keywords

    teacher quality; teacher labor markets; teacher value-added;
    All these keywords.

    JEL classification:

    • I20 - Health, Education, and Welfare - - Education - - - General
    • J08 - Labor and Demographic Economics - - General - - - Labor Economics Policies
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • J45 - Labor and Demographic Economics - - Particular Labor Markets - - - Public Sector Labor Markets

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:iza:izadps:dp7973. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Holger Hinte (email available below). General contact details of provider: https://edirc.repec.org/data/izaaade.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.