IDEAS home Printed from https://ideas.repec.org/a/sae/jedbes/v36y2011i5p616-637.html
   My bibliography  Save this article

The Persistence of School-Level Value-Added

Author

Listed:
  • Derek C. Briggs
  • Jonathan P. Weeks

Abstract

Using longitudinal data for an entire state from 2004 to 2008, this article describes the results from an empirical investigation of the persistence of value-added school effects on student achievement in reading and math. It shows that when schools are the principal units of analysis rather than teachers, the persistence of estimated school effects across grades can only be reasonably identified by placing strong constraints on the variable persistence model implemented by Lockwood, McCaffrey, Mariano, and Setodji. In general, there are relatively strong correlations between the school effects estimated using these constrained models and a reference model that assumes full persistence. These correlations vary somewhat by grade and the underlying test subject. The results from this study indicate cautious support for previous findings that the assumption of full persistence for cumulative value-added effects may be untenable, and evidence is also presented, which indicates a strong interaction by test subject. However, the practical impact of violating the assumption of full persistence appears to be smaller in the context of schools than it is for teachers.

Suggested Citation

  • Derek C. Briggs & Jonathan P. Weeks, 2011. "The Persistence of School-Level Value-Added," Journal of Educational and Behavioral Statistics, , vol. 36(5), pages 616-637, October.
  • Handle: RePEc:sae:jedbes:v:36:y:2011:i:5:p:616-637
    DOI: 10.3102/1076998610396887
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.3102/1076998610396887
    Download Restriction: no

    File URL: https://libkey.io/10.3102/1076998610396887?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Derek C. Briggs & Jonathan P. Weeks, 2009. "The Sensitivity of Value-Added Modeling to the Creation of a Vertical Score Scale," Education Finance and Policy, MIT Press, vol. 4(4), pages 384-414, October.
    2. Douglas N. Harris, 2009. "Would Accountability Based on Teacher Value Added Be Smart Policy? An Examination of the Statistical Properties and Policy Alternatives," Education Finance and Policy, MIT Press, vol. 4(4), pages 319-350, October.
    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. Karl, Andrew T. & Yang, Yan & Lohr, Sharon L., 2013. "Efficient maximum likelihood estimation of multiple membership linear mixed models, with an application to educational value-added assessments," Computational Statistics & Data Analysis, Elsevier, vol. 59(C), pages 13-27.
    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. Heikki Pursiainen & Mika Kortelainen & Jenni Pääkkönen, 2014. "Impact of School Quality on Educational Attainment - Evidence from Finnish High Schools," ERSA conference papers ersa14p711, European Regional Science Association.
    4. 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.

    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. 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.
    2. Cook, Jason B. & Mansfield, Richard K., 2016. "Task-specific experience and task-specific talent: Decomposing the productivity of high school teachers," Journal of Public Economics, Elsevier, vol. 140(C), pages 51-72.
    3. Derek C. Briggs & Ben Domingue, 2013. "The Gains From Vertical Scaling," Journal of Educational and Behavioral Statistics, , vol. 38(6), pages 551-576, December.
    4. Figlio, D. & Karbownik, K. & Salvanes, K.G., 2016. "Education Research and Administrative Data," Handbook of the Economics of Education,, Elsevier.
    5. J. R. Lockwood & Daniel F. McCaffrey, 2014. "Correcting for Test Score Measurement Error in ANCOVA Models for Estimating Treatment Effects," Journal of Educational and Behavioral Statistics, , vol. 39(1), pages 22-52, February.
    6. Guillermo Montes, 2012. "Using Artificial Societies to Understand the Impact of Teacher Student Match on Academic Performance: The Case of Same Race Effects," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 15(4), pages 1-8.
    7. Ben Ost, 2014. "How Do Teachers Improve? The Relative Importance of Specific and General Human Capital," American Economic Journal: Applied Economics, American Economic Association, vol. 6(2), pages 127-151, April.
    8. Derek Neal, 2011. "The Design of Performance Pay in Education," NBER Working Papers 16710, National Bureau of Economic Research, Inc.
    9. 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.

    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:sae:jedbes:v:36:y:2011:i:5:p:616-637. 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: SAGE Publications (email available below). General contact details of provider: .

    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.