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A Model for Teacher Effects From Longitudinal Data Without Assuming Vertical Scaling

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  • Louis T. Mariano
  • Daniel F. McCaffrey
  • J. R. Lockwood

Abstract

There is an increasing interest in using longitudinal measures of student achievement to estimate individual teacher effects. Current multivariate models assume each teacher has a single effect on student outcomes that persists undiminished to all future test administrations (complete persistence [CP]) or can diminish with time but remains perfectly correlated (variable persistence [VP]). However, when state assessments do not use a vertical scale or the evolution of the mix of topics present across a sequence of vertically aligned assessments changes as students advance in school, these assumptions of persistence may not be consistent with the achievement data. We develop the “generalized persistence†(GP) model, a Bayesian multivariate model for estimating teacher effects that accommodates longitudinal data that are not vertically scaled by allowing less than perfect correlation of a teacher’s effects across test administrations. We illustrate the model using mathematics assessment data.

Suggested Citation

  • Louis T. Mariano & Daniel F. McCaffrey & J. R. Lockwood, 2010. "A Model for Teacher Effects From Longitudinal Data Without Assuming Vertical Scaling," Journal of Educational and Behavioral Statistics, , vol. 35(3), pages 253-279, June.
    • Handle: RePEc:sae:jedbes:v:35:y:2010:i:3:p:253-279
    DOI: 10.3102/1076998609346967
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    References listed on IDEAS

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    Citations

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    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. Andrew T. Karl & Yan Yang & Sharon L. Lohr, 2013. "A Correlated Random Effects Model for Nonignorable Missing Data in Value-Added Assessment of Teacher Effects," Journal of Educational and Behavioral Statistics, , vol. 38(6), pages 577-603, December.
    3. 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.
    4. Jennifer Broatch & Sharon Lohr, 2012. "Multidimensional Assessment of Value Added by Teachers to Real-World Outcomes," Journal of Educational and Behavioral Statistics, , vol. 37(2), pages 256-277, April.
    5. Derek C. Briggs & Ben Domingue, 2013. "The Gains From Vertical Scaling," Journal of Educational and Behavioral Statistics, , vol. 38(6), pages 551-576, December.
    6. Minjeong Jeon & Sophia Rabe-Hesketh, 2012. "Profile-Likelihood Approach for Estimating Generalized Linear Mixed Models With Factor Structures," Journal of Educational and Behavioral Statistics, , vol. 37(4), pages 518-542, August.
    7. Karl, Andrew T. & Yang, Yan & Lohr, Sharon L., 2014. "Computation of maximum likelihood estimates for multiresponse generalized linear mixed models with non-nested, correlated random effects," Computational Statistics & Data Analysis, Elsevier, vol. 73(C), pages 146-162.
    8. 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.
    9. 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.
    10. Stephen Lipscomb & Bing-ru Teh & Brian Gill & Hanley Chiang & Antoniya Owens, "undated". "Teacher and Principal Value-Added: Research Findings and Implementation Practices," Mathematica Policy Research Reports b024faae6179407da5b887263, Mathematica Policy Research.
    11. Savitsky, Terrance & Paddock, Susan, 2014. "Bayesian Semi- and Non-Parametric Models for Longitudinal Data with Multiple Membership Effects in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 57(i03).
    12. repec:mpr:mprres:6941 is not listed on IDEAS

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