IDEAS home Printed from https://ideas.repec.org/a/ush/jaessh/v5y2010i5(1)_spring2010p88.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.jaes.reprograph.ro/articles/spring2010/ArpinoB_VarrialeR.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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, May.
    3. Anna Gottard & Leonardo Grilli & Carla Rampichini, 2007. "A Multilevel Chain Graph Model for the Analysis of Graduates’ Employment," Springer Books, in: Luigi Fabbris (ed.), Effectiveness of University Education in Italy, pages 169-181, Springer.
    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. 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.
    6. 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.
    7. Stephen W. Raudenbush & JDouglas Willms, 1995. "The Estimation of School Effects," Journal of Educational and Behavioral Statistics, , vol. 20(4), pages 307-335, December.
    8. 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.
    9. Harvey Goldstein & David J. Spiegelhalter, 1996. "League Tables and Their Limitations: Statistical Issues in Comparisons of Institutional Performance," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 159(3), pages 385-409, May.
    10. Carla Rampichini & Leonardo Grilli & Alessandra Petrucci, 2004. "Analysis of university course evaluations: from descriptive measures to multilevel models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 13(3), pages 357-373, December.
    11. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ho-Chul Park & Yang-Jun Joo & Seung-Young Kho & Dong-Kyu Kim & Byung-Jung Park, 2019. "Injury Severity of Bus–Pedestrian Crashes in South Korea Considering the Effects of Regional and Company Factors," Sustainability, MDPI, vol. 11(11), pages 1-17, June.
    2. 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.
    3. 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.
    4. 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.
    5. Mussa, Richard, 2017. "Contextual Effects of Education on Poverty in Malawi," MPRA Paper 75976, University Library of Munich, Germany.
    6. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Arpino, Bruno & Varriale, Roberta, 2009. "Assessing the quality of institutions’ rankings obtained through multilevel linear regression models," MPRA Paper 19873, University Library of Munich, Germany.
    2. Isabella Sulis & Mariano Porcu & Vincenza Capursi, 2019. "On the Use of Student Evaluation of Teaching: A Longitudinal Analysis Combining Measurement Issues and Implications of the Exercise," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 142(3), pages 1305-1331, April.
    3. Lorraine Dearden, 2010. "Administrative Data and Economic Policy Evaluation," The Economic Record, The Economic Society of Australia, vol. 86(s1), pages 18-21, September.
    4. Michele La Rocca & Maria Lucia Parrella & Ilaria Primerano & Isabella Sulis & Maria Prosperina Vitale, 2017. "An integrated strategy for the analysis of student evaluation of teaching: from descriptive measures to explanatory models," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(2), pages 675-691, March.
    5. 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.
    6. Francesca Giambona & Mariano Porcu & Isabella Sulis, 2017. "Students Mobility: Assessing the Determinants of Attractiveness Across Competing Territorial Areas," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 133(3), pages 1105-1132, September.
    7. Pier Ferrari & Laura Pagani & Carlo Fiorio, 2011. "A Two-Step Approach to Analyze Satisfaction Data," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 104(3), pages 545-554, December.
    8. 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.
    9. 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.
    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. Helen F. Ladd & Charles T. Clotfelter & John B. Holbein, 2017. "The Growing Segmentation of the Charter School Sector in North Carolina," Education Finance and Policy, MIT Press, vol. 12(4), pages 536-563, Fall.
    12. 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.
    13. George Leckie, 2022. "A celebration of Harvey Goldstein’s lifetime contributions: Memories of working with Harvey Goldstein on educational research and statistics," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(3), pages 758-762, July.
    14. Nicholas Tibor Longford, 2016. "Decision Theory Applied to Selecting the Winners, Ranking, and Classification," Journal of Educational and Behavioral Statistics, , vol. 41(4), pages 420-442, August.
    15. Rosenthal, Leslie, 2004. "Do school inspections improve school quality? Ofsted inspections and school examination results in the UK," Economics of Education Review, Elsevier, vol. 23(2), pages 143-151, April.
    16. 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.
    17. Columbu, Silvia & Porcu, Mariano & Sulis, Isabella, 2021. "University choice and the attractiveness of the study area: Insights on the differences amongst degree programmes in Italy based on generalised mixed-effect models," Socio-Economic Planning Sciences, Elsevier, vol. 74(C).
    18. Sulis, Isabella & Giambona, Francesca & Porcu, Mariano, 2020. "Adjusted indicators of quality and equity for monitoring the education systems over time. Insights on EU15 countries from PISA surveys," Socio-Economic Planning Sciences, Elsevier, vol. 69(C).
    19. Isabella Sulis & Mariano Porcu, 2012. "Comparing degree programs from students’ assessments: A LCRA-based adjusted composite indicator," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 21(2), pages 193-209, June.
    20. Nicholas T. Longford, 2020. "Performance assessment as an application of causal inference," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1363-1385, October.

    More about this item

    Keywords

    effectiveness; multilevel models; ranking of institutions; second-level residuals distribution;
    All these keywords.

    JEL classification:

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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ush:jaessh:v:5:y:2010:i:5(1)_spring2010:p:88. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Laura Stefanescu (email available below). General contact details of provider: https://edirc.repec.org/data/fmuspro.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.