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A novel bootstrap procedure for assessing the relationship between class size and achievement

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  • James R. Carpenter
  • Harvey Goldstein
  • Jon Rasbash

Abstract

Summary. There is on‐going concern about the relationship between class size and achievement for children in their first years of schooling. The Institute of Education's class size project was set up to address this issue and began recruiting in the autumn of 1996. However, because of the non‐normality of achievement measures, especially in mathematics, the results have hitherto been presented by using transformed achievement measures. This makes the interpretation difficult for non‐statisticians. Ideally, the data would be modelled on the original scale and a bootstrap procedure used to ensure that inferences are robust to non‐normality. However, the data are multilevel. In the paper we therefore propose a nonparametric residual bootstrap procedure that is suitable for multilevel models, show that it is consistent and present a simulation study which demonstrates its potential to yield substantial reductions in the difference between nominal and actual confidence interval coverage, compared with a parametric bootstrap, when the underlying distribution of the data is non‐normal. We then apply our approach to estimate the relationship between class size and achievement for children in their reception year, after adjusting for other possible determinants.

Suggested Citation

  • James R. Carpenter & Harvey Goldstein & Jon Rasbash, 2003. "A novel bootstrap procedure for assessing the relationship between class size and achievement," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 52(4), pages 431-443, October.
  • Handle: RePEc:bla:jorssc:v:52:y:2003:i:4:p:431-443
    DOI: 10.1111/1467-9876.00415
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    2. George Leckie, 2018. "Avoiding Bias When Estimating the Consistency and Stability of Value-Added School Effects," Journal of Educational and Behavioral Statistics, , vol. 43(4), pages 440-468, August.
    3. Rebecca C. Steorts & Timo Schmid & Nikos Tzavidis, 2020. "Smoothing and Benchmarking for Small Area Estimation," International Statistical Review, International Statistical Institute, vol. 88(3), pages 580-598, December.
    4. David Afshartous & Michael Wolf, 2007. "Avoiding ‘data snooping’ in multilevel and mixed effects models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(4), pages 1035-1059, October.
    5. Carlo Fezzi & Ian Bateman, 2015. "The Impact of Climate Change on Agriculture: Nonlinear Effects and Aggregation Bias in Ricardian Models of Farmland Values," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 2(1), pages 57-92.
    6. Wen Shi & Xi Chen & Jennifer Shang, 2019. "An Efficient Morris Method-Based Framework for Simulation Factor Screening," INFORMS Journal on Computing, INFORMS, vol. 31(4), pages 745-770, October.
    7. Manuel Gomes & Richard Grieve & Richard Nixon & Edmond S.‐W. Ng & James Carpenter & Simon G. Thompson, 2012. "Methods For Covariate Adjustment In Cost‐Effectiveness Analysis That Use Cluster Randomised Trials," Health Economics, John Wiley & Sons, Ltd., vol. 21(9), pages 1101-1118, September.
    8. L. Bryan, Mark & P. Jenkins, Stephen, 2013. "Regression analysis of country effects using multilevel data: a cautionary tale," ISER Working Paper Series 2013-14, Institute for Social and Economic Research.
    9. Sumonkanti Das & Bappi Kumar & Luthful Alahi Kawsar, 2020. "Disaggregated level child morbidity in Bangladesh: An application of small area estimation method," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-20, May.
    10. Tomasz .Zk{a}d{l}o & Adam Chwila, 2024. "A step towards the integration of machine learning and small area estimation," Papers 2402.07521, arXiv.org.
    11. Weidenhammer, Beate & Schmid, Timo & Salvati, Nicola & Tzavidis, Nikos, 2016. "A unit-level quantile nested error regression model for domain prediction with continuous and discrete outcomes," Discussion Papers 2016/12, Free University Berlin, School of Business & Economics.
    12. Flores-Agreda, Daniel & Cantoni, Eva, 2019. "Bootstrap estimation of uncertainty in prediction for generalized linear mixed models," Computational Statistics & Data Analysis, Elsevier, vol. 130(C), pages 1-17.
    13. Battagliola, Maria Laura & Sørensen, Helle & Tolver, Anders & Staicu, Ana-Maria, 2022. "A bias-adjusted estimator in quantile regression for clustered data," Econometrics and Statistics, Elsevier, vol. 23(C), pages 165-186.
    14. Valéry Dongmo Jiongo & Pierre Nguimkeu, 2018. "Bootstrapping Mean Squared Errors of Robust Small-Area Estimators: Application to the Method-of-Payments Data," Staff Working Papers 18-28, Bank of Canada.
    15. Gomes, M & Grieve, R, 2011. "Estimating the Effects of Friendship Networks on Health Behaviors of Adolescents," Health, Econometrics and Data Group (HEDG) Working Papers 11/14, HEDG, c/o Department of Economics, University of York.

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