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The Estimation of School Effects

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  • Stephen W. Raudenbush
  • JDouglas Willms

Abstract

The increasing public demand to hold schools accountable for their effects on student outcomes lends urgency to the task of clarifying statistical issues pertaining to studies of school effects. This article considers the specification and estimation of school effects, the variability of effects across schools, and the proportion of variation in student outcomes attributable to differences in school context and practice. We present a statistical model that defines two different types of school effect: one appropriate for parents choosing schools for their children, the second for agencies evaluating school practice. Studies of both types of effect are viewed as quasi-experiments posing formidable obstacles to valid causal inference. A multilevel decomposition of variance within and between schools has important and perhaps counterintuitive implications for school evaluation. The potential for unbiased estimation depends on the type of effect under consideration because the two types of school effect have markedly different data requirements. Commonly used estimators of each effect are shown to be biased and, in some cases, inconsistent. Analyses of survey data from Scotland illustrate the recommended techniques. We conclude with a brief discussion of the role of school evaluation in a broader agenda of research in support of school improvement.

Suggested Citation

  • Stephen W. Raudenbush & JDouglas Willms, 1995. "The Estimation of School Effects," Journal of Educational and Behavioral Statistics, , vol. 20(4), pages 307-335, December.
  • Handle: RePEc:sae:jedbes:v:20:y:1995:i:4:p:307-335
    DOI: 10.3102/10769986020004307
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    Citations

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    Cited by:

    1. Daniela R. Urbina, 2018. "Intergenerational Educational Mobility During Expansion Reform: Evidence from Mexico," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 37(3), pages 367-417, June.
    2. Corak, Miles & Lauzon, Darren, 2009. "Differences in the distribution of high school achievement: The role of class-size and time-in-term," Economics of Education Review, Elsevier, vol. 28(2), pages 189-198, April.
    3. 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.
    4. George Leckie & Harvey Goldstein, 2009. "The limitations of using school league tables to inform school choice," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(4), pages 835-851, October.
    5. Andrew Bell & Malcolm Fairbrother & Kelvyn Jones, 2019. "Fixed and random effects models: making an informed choice," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(2), pages 1051-1074, March.
    6. Bruno ARPINO & Roberta VARRIALE, 2010. "Assessing The Quality Of Institutions’ Rankings Obtained Through Multilevel Linear Regression Models," Journal of Applied Economic Sciences, Spiru Haret University, Faculty of Financial Management and Accounting Craiova, vol. 5(1(11)_Spr), pages 7-22.
    7. Mika Kortelainen & Kalle Manninen, 2019. "Effectiveness of Private and Public High Schools: Evidence from Finland," CESifo Economic Studies, CESifo, vol. 65(4), pages 424-445.
    8. 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.
    9. Luis Alejandro Lopez-Agudo & Oscar David Marcenaro Gutierrez, 2016. "Identifying effective teachers: The case study of Spain," Investigaciones de Economía de la Educación volume 11, in: José Manuel Cordero Ferrera & Rosa Simancas Rodríguez (ed.), Investigaciones de Economía de la Educación 11, edition 1, volume 11, chapter 18, pages 349-366, Asociación de Economía de la Educación.
    10. Anders Skrondal & Sophia Rabe‐Hesketh, 2009. "Prediction in multilevel generalized linear models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(3), pages 659-687, June.
    11. Konstantopoulos, Spyros, 2007. "How Long Do Teacher Effects Persist?," IZA Discussion Papers 2893, Institute of Labor Economics (IZA).
    12. Garritt L. Page & Ernesto San Martín & Javiera Orellana & Jorge González, 2017. "Exploring complete school effectiveness via quantile value added," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(1), pages 315-340, January.
    13. Cruz, Rebecca A. & Rodl, Janelle E., 2018. "Crime and punishment: An examination of school context and student characteristics that predict out-of-school suspension," Children and Youth Services Review, Elsevier, vol. 95(C), pages 226-234.
    14. Darmody, Merike & Smyth, Emer & McCoy, Selina, 2012. "School Sector Variation among Primary Schools in Ireland," Research Series, Economic and Social Research Institute (ESRI), number BKMNEXT221, June.
    15. Harvey Goldstein & Simon Burgess & Brendon McConnell, 2007. "Modelling the effect of pupil mobility on school differences in educational achievement," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(4), pages 941-954, October.
    16. Burger, Kaspar, 2019. "The socio-spatial dimension of educational inequality: A comparative European analysis," MPRA Paper 95309, University Library of Munich, Germany, revised 2019.

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