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Comparing Least-Squares Value-Added Analysis and Student Growth Percentile Analysis for Evaluating Student Progress and Estimating School Effects

Author

Listed:
  • Brendan Houng

    () (Melbourne Institute of Applied Economic and Social Research, The University of Melbourne)

  • Moshe Justman

    () (Melbourne Institute of Applied Economic and Social Research, The University of Melbourne; and Department of Economics, Ben Gurion University, Israel)

Abstract

This paper compares two functionally different approaches to analyzing standardized test data: least-squares based value-added analysis, geared principally to supporting teacher and school accountability; and Betebenner’s (2009) student growth percentiles, which focuses primarily on tracking individual student progress in a normative context and projecting probable trajectories of future performance. Applying the two methods to Australian standardized numeracy and reading test scores (NAPLAN) in grades 3 to 5 and 7 to 9, we find that although they are used differently, the two methods share key structural elements, and produce similar quantitative indicators of both individual student progress and estimated school effects.

Suggested Citation

  • 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.
  • Handle: RePEc:iae:iaewps:wp2013n07
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    File URL: http://melbourneinstitute.unimelb.edu.au/downloads/working_paper_series/wp2013n07.pdf
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    References listed on IDEAS

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    1. Lorraine Dearden & John Micklewright & Anna Vignoles, 2011. "The Effectiveness of English Secondary Schools for Pupils of Different Ability Levels," Fiscal Studies, Institute for Fiscal Studies, vol. 32(2), pages 225-244, June.
    2. 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.
    3. Timothy N. Bond & Kevin Lang, 2013. "The Evolution of the Black-White Test Score Gap in Grades K–3: The Fragility of Results," The Review of Economics and Statistics, MIT Press, vol. 95(5), pages 1468-1479, December.
    4. Rebecca Allen & Simon Burgess, 2011. "Can School League Tables Help Parents Choose Schools?," Fiscal Studies, Institute for Fiscal Studies, vol. 32(2), pages 245-261, June.
    5. 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.
    6. Dale Ballou & William Sanders & Paul Wright, 2004. "Controlling for Student Background in Value-Added Assessment of Teachers," Journal of Educational and Behavioral Statistics, , vol. 29(1), pages 37-65, March.
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    Cited by:

    1. Kevin Pugh & Gigi Foster, 2014. "Australia's National School Data and the ‘Big Data’ Revolution in Education Economics," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 47(2), pages 258-268, June.

    More about this item

    Keywords

    Value-added analysis; student growth percentiles; NAPLAN;

    JEL classification:

    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • I28 - Health, Education, and Welfare - - Education - - - Government Policy

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