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Assessing The Quality Of Institutions’ Rankings Obtained Through Multilevel Linear Regression Models

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  • Bruno ARPINO
  • Roberta VARRIALE

Abstract

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.

Suggested Citation

  • 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.
  • Handle: RePEc:ush:jaessh:v:5:y:2010:i:5(1)_spring2010:p:88
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    References listed on IDEAS

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    1. Gottard, Anna & Rampichini, Carla, 2007. "Chain graphs for multilevel models," Statistics & Probability Letters, Elsevier, vol. 77(3), pages 312-318, February.
    2. 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.
    3. Aassve, Arnstein & Arpino, Bruno, 2007. "Dynamic multi-level analysis of households' living standards and poverty: evidence from Vietnam," ISER Working Paper Series 2007-10, Institute for Social and Economic Research.
    4. 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.
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    6. 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.
    7. 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 341-358, March.
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    9. Carmen D. Tekwe & Randy L. Carter & Chang-Xing Ma & James Algina & Maurice E. Lucas & Jeffrey Roth & Mario Ariet & Thomas Fisher & Michael B. Resnick, 2004. "An Empirical Comparison of Statistical Models for Value-Added Assessment of School Performance," Journal of Educational and Behavioral Statistics, , vol. 29(1), pages 11-36, March.
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    Cited by:

    1. Leonardo Grilli & Carla Rampichini, 2015. "Specification of random effects in multilevel models: a review," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(3), pages 967-976, May.
    2. Bruno Arpino & Arnstein Aassve, 2014. "The role of villages in households’ poverty exit: evidence from a multilevel model for rural Vietnam," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(4), pages 2175-2189, July.
    3. Mussa, Richard, 2017. "Contextual Effects of Education on Poverty in Malawi," MPRA Paper 75976, University Library of Munich, Germany.
    4. 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.

    More about this item

    Keywords

    effectiveness; multilevel models; ranking of institutions; second-level residuals distribution;

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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