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Selecting Growth Measures for School and Teacher Evaluations
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Selecting Growth Measures for School and Teacher Evaluations

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Abstract

The specifics of how growth models should be constructed and used to evaluate schools and teachers is a topic of lively policy debate in states and school districts nationwide. In this paper we take up the question of model choice and examine three competing approaches. The first approach, reflected in the popular student growth percentiles (SGPs) framework, eschews all controls for student covariates and schooling environments. The second approach, typically associated with value-added models (VAMs), controls for student background characteristics and aims to identify the causal effects of schools and teachers. The third approach, also VAM-based, fully levels the playing field so that the correlation between school- and teacher-level growth measures and student demographics is essentially zero. We argue that the third approach is the most desirable for use in educational evaluation systems. Our case rests on personnel economics, incentive-design theory, and the potential role that growth measures can play in improving instruction in K-12 schools.

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  • Cory Koedel & Mark Ehlert & Eric Parsons & Michael Podgursky, 2012. "Selecting Growth Measures for School and Teacher Evaluations," Working Papers 1210, Department of Economics, University of Missouri.
    • Handle: RePEc:umc:wpaper:1210
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    2. Cory Koedel & Jiaxi Li, 2016. "The Efficiency Implications Of Using Proportional Evaluations To Shape The Teaching Workforce," Contemporary Economic Policy, Western Economic Association International, vol. 34(1), pages 47-62, January.
    3. Matthew Johnson & Stephen Lipscomb & Brian Gill, 2013. "Sensitivity of Teacher Value-Added Estimates to Student and Peer Control Variables," Mathematica Policy Research Reports 3f875df699534c72b9e57c39d, Mathematica Policy Research.
    4. Cory Koedel & Mark Ehlert & Eric Parsons & Michael Podgursky, 2012. "Selecting Growth Measures for School and Teacher Evaluations," Working Papers 1210, Department of Economics, University of Missouri.
    5. 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.
    6. Eric Parsons & Cory Koedel & Li Tan, 2019. "Accounting for Student Disadvantage in Value-Added Models," Journal of Educational and Behavioral Statistics, , vol. 44(2), pages 144-179, April.
    7. Doris, Aedin & O'Neill, Donal & Sweetman, Olive, 2019. "Good Schools or Good Students? The Importance of Selectivity for School Rankings," IZA Discussion Papers 12459, Institute of Labor Economics (IZA).
    8. repec:mpr:mprres:7941 is not listed on IDEAS
    9. 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.
    10. 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.
    11. Elias Walsh & Eric Isenberg, 2013. "How Does a Value-Added Model Compare to the Colorado Growth Model?," Mathematica Policy Research Reports e703eea3252e43d39fee791e5, Mathematica Policy Research.
    12. Shannon W. Anderson & Amanda Kimball, 2019. "Evidence for the Feedback Role of Performance Measurement Systems," Management Science, INFORMS, vol. 65(9), pages 4385-4406, September.
    13. Andrew McEachin & Allison Atteberry, 2017. "The Impact of Summer Learning Loss on Measures of School Performance," Education Finance and Policy, MIT Press, vol. 12(4), pages 468-491, Fall.
    14. 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.
    15. Steinberg, Matthew P. & MacDonald, John M., 2019. "The effects of closing urban schools on students’ academic and behavioral outcomes: Evidence from Philadelphia," Economics of Education Review, Elsevier, vol. 69(C), pages 25-60.

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    More about this item

    Keywords

    Teacher evaluation; school evaluation; value-added models; value-added versus SGP;
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    JEL classification:

    • I20 - Health, Education, and Welfare - - Education - - - General

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