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Exploring Student-Teacher Interactions in Longitudinal Achievement Data

Author

Listed:
  • J. R. Lockwood

    () (RAND Corporation)

  • Daniel F. McCaffrey

    () (RAND Corporation)

Abstract

This article develops a model for longitudinal student achievement data designed to estimate heterogeneity in teacher effects across students of different achievement levels. The model specifies interactions between teacher effects and students' predicted scores on a test, estimating both average effects of individual teachers and interaction terms indicating whether individual teachers are differentially effective with students of different predicted scores. Using various longitudinal data sources, we find evidence of these interactions that is of relatively consistent but modest magnitude across different contexts, accounting for about 10 percent of the total variation in teacher effects across all students. However, the amount that the interactions matter in practice depends on the heterogeneity of the groups of students taught by different teachers. Using empirical estimates of the heterogeneity of students across teachers, we find that the interactions account for about 3–4 percent of total variation in teacher effects on different classes, with somewhat larger values in middle school mathematics. Our findings suggest that ignoring these interactions is not likely to introduce appreciable bias in estimated teacher effects for most teachers in most settings. The results of this study should be of interest to policy makers concerned about the validity of value-added teacher effect estimates. © 2009 American Education Finance Association

Suggested Citation

  • J. R. Lockwood & Daniel F. McCaffrey, 2009. "Exploring Student-Teacher Interactions in Longitudinal Achievement Data," Education Finance and Policy, MIT Press, vol. 4(4), pages 439-467, October.
  • Handle: RePEc:tpr:edfpol:v:4:y:2009:i:4:p:439-467
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    File URL: http://www.mitpressjournals.org/doi/pdf/10.1162/edfp.2009.4.4.439
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    Citations

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    Cited by:

    1. Figlio, D. & Karbownik, K. & Salvanes, K.G., 2016. "Education Research and Administrative Data," Handbook of the Economics of Education, Elsevier.
    2. repec:hrv:faseco:30749606 is not listed on IDEAS
    3. 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.
    4. Stacy, Brian, 2014. "Ranking Teachers when Teacher Value-Added is Heterogeneous Across Students," EconStor Preprints 104743, ZBW - German National Library of Economics.
    5. 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.
    6. 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.
    7. Richard K. Mansfield, 2015. "Teacher Quality and Student Inequality," Journal of Labor Economics, University of Chicago Press, vol. 33(3), pages 751-788.
    8. 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.
    9. 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.
    10. Wiswall, Matthew, 2013. "The dynamics of teacher quality," Journal of Public Economics, Elsevier, vol. 100(C), pages 61-78.

    More about this item

    Keywords

    student-teacher interactions; logitudinal student achievement data; value-added modeling;

    JEL classification:

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

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