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Assessing the quality of institutions' rankings obtained through multilevel linear regression models

  • Bruno Arpino
  • Roberta Varriale

The aim of this paper is to assess the quality of the ranking of institutions obtained with multilevel techniques in presence of different model misspecifications and data structures. Through a Monte Carlo simulation study, we find that it is quite hard to obtain a reliable ranking of the whole effectiveness distribution while, under various experimental conditions, it is possible to identify institutions with extreme performances. Ranking quality increases with increasing intra class correlation coefficient and/or overall sample size. Furthermore, multilevel models where the between and within cluster components of first-level covariates are distinguished perform significantly better than both multilevel models where the two effects are set to be equal and the fixed effect models.

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File URL: ftp://ftp.dondena.unibocconi.it/WorkingPapers/Dondena_WP019.pdf
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Paper provided by "Carlo F. Dondena" Centre for Research on Social Dynamics (DONDENA), UniversitĂ  Commerciale Luigi Bocconi in its series Working Papers with number 019.

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Length: 21 pages
Date of creation: Jul 2009
Date of revision:
Handle: RePEc:don:donwpa:019
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  1. Cora J. M. Maas & Joop J. Hox, 2004. "Robustness issues in multilevel regression analysis," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 58(2), pages 127-137.
  2. Ladd, Helen F. & Walsh, Randall P., 2002. "Implementing value-added measures of school effectiveness: getting the incentives right," Economics of Education Review, Elsevier, vol. 21(1), pages 1-17, February.
  3. 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.
  4. Sophia Rabe-Hesketh & Anders Skrondal, 2012. "Multilevel and Longitudinal Modeling Using Stata, 3rd Edition," Stata Press books, StataCorp LP, edition 3, number mimus2, November.
  5. Maddala, G S, 1971. "The Use of Variance Components Models in Pooling Cross Section and Time Series Data," Econometrica, Econometric Society, vol. 39(2), pages 341-58, March.
  6. repec:ese:iserwp:2007-10 is not listed on IDEAS
  7. Anders Skrondal & Sophia Rabe-Hesketh, 2007. "Latent Variable Modelling: A Survey," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 34(4), pages 712-745.
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