IDEAS home Printed from https://ideas.repec.org/p/eie/wpaper/1313.html
   My bibliography  Save this paper

Ranking Scientific Journals via Latent Class Models for Polytomous Item Response

Author

Listed:
  • Francesco Bartolucci

    (University of Perugia)

  • Valentino Dardanoni

    (University of Palermo)

  • Franco Peracchi

    (University of Rome "Tor Vergata" and EIEF)

Abstract

We propose a strategy for ranking scientific journals starting from a set of available quantitative indicators that represent imperfect measures of the unobservable "value" of the journals of interest. After discretizing the available indicators, we estimate a latent class model for polytomous item response data and use the estimated model to classify each journal. We apply the proposed approach to data from the Research Evaluation Exercise (VQR) carried out in Italy with reference to the period 2004-2010, focusing on the sub-area consisting of Statistics and Financial Mathematics. Using four quantitative indicators of the journals' scientific value (IF, IF5, AIS, h-index), some of which not available for all journals, we derive a complete ordering of the journals according to their latent value. We show that the proposed methodology is relatively simple to implement, even when the aim is to classify journals into finite ordered groups of a fixed size. Finally, we analyze the robustness of the obtained ranking with respect to different discretization rules.

Suggested Citation

  • Francesco Bartolucci & Valentino Dardanoni & Franco Peracchi, 2013. "Ranking Scientific Journals via Latent Class Models for Polytomous Item Response," EIEF Working Papers Series 1313, Einaudi Institute for Economics and Finance (EIEF), revised May 2013.
    • Handle: RePEc:eie:wpaper:1313
    as

    Download full text from publisher

    File URL: http://www.eief.it/files/2013/05/wp-13-ranking-scientific-journals-via-latent-class-models-for-polytomous-item-response-data.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Christian Zimmermann, 2013. "Academic Rankings with RePEc," Econometrics, MDPI, vol. 1(3), pages 1-32, December.
    2. Benjamin M. Althouse & Jevin D. West & Carl T. Bergstrom & Theodore Bergstrom, 2009. "Differences in impact factor across fields and over time," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(1), pages 27-34, January.
    3. Bartolucci, Francesco & Bacci, Silvia & Gnaldi, Michela, 2014. "MultiLCIRT: An R package for multidimensional latent class item response models," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 971-985.
    4. Chang, C-L. & McAleer, M.J. & Oxley, L., 2010. "Journal Impect Factor Versus Eigenfactor and Article Influence," Econometric Institute Research Papers EI 2010-67, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    5. Francesco Bartolucci, 2007. "A class of multidimensional IRT models for testing unidimensionality and clustering items," Psychometrika, Springer;The Psychometric Society, vol. 72(2), pages 141-157, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    RePEc Biblio mentions

    As found on the RePEc Biblio, the curated bibliography for Economics:
    1. > Economics Profession > Ranking in Economics > Ranking Methodology

    Citations

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


    Cited by:

    1. Bertocchi, Graziella & Gambardella, Alfonso & Jappelli, Tullio & Nappi, Carmela A. & Peracchi, Franco, 2015. "Bibliometric evaluation vs. informed peer review: Evidence from Italy," Research Policy, Elsevier, vol. 44(2), pages 451-466.

    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. Francesco Bartolucci & Valentino Dardanoni & Franco Peracchi, 2015. "Ranking scientific journals via latent class models for polytomous item response data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(4), pages 1025-1049, October.
    2. Michela Gnaldi & Simone Del Sarto, 2018. "Variable Weighting via Multidimensional IRT Models in Composite Indicators Construction," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 136(3), pages 1139-1156, April.
    3. Genge, Ewa & Bartolucci, Francesco, 2019. "Are attitudes towards immigration changing in Europe? An analysis based on bidimensional latent class IRT models," MPRA Paper 94672, University Library of Munich, Germany.
    4. Del Sarto, Simone & Gnaldi, Michela & Salvini, Niccolò, 2024. "Sustainability and high-level corruption in healthcare procurement: Profiles of Italian contracting authorities," Socio-Economic Planning Sciences, Elsevier, vol. 95(C).
    5. Francesco Bartolucci & Alessio Farcomeni & Luisa Scaccia, 2017. "A Nonparametric Multidimensional Latent Class IRT Model in a Bayesian Framework," Psychometrika, Springer;The Psychometric Society, vol. 82(4), pages 952-978, December.
    6. María Victoria Anauati & Sebastian Galiani & Ramiro H. Gálvez, 2018. "Differences in citation patterns across journal tiers in economics," Documentos de Trabajo 16701, The Latin American and Caribbean Economic Association (LACEA).
    7. María Victoria Anauati & Sebastian Galiani & Ramiro H. Gálvez, 2020. "Differences In Citation Patterns Across Journal Tiers: The Case Of Economics," Economic Inquiry, Western Economic Association International, vol. 58(3), pages 1217-1232, July.
    8. Silvia Bacci & Michela Gnaldi, 2015. "A classification of university courses based on students’ satisfaction: an application of a two-level mixture item response model," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(3), pages 927-940, May.
    9. Michela Gnaldi, 2017. "A multidimensional IRT approach for dimensionality assessment of standardised students’ tests in mathematics," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(3), pages 1167-1182, May.
    10. Ewa Genge & Francesco Bartolucci, 2022. "Are attitudes toward immigration changing in Europe? An analysis based on latent class IRT models," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(2), pages 235-271, June.
    11. Daniele Checchi & Gianni De Fraja & Stefano Verzillo, 2014. "Publish or Perish? Incentives and Careers in Italian Academia," CEIS Research Paper 323, Tor Vergata University, CEIS, revised 07 Aug 2014.
    12. Justus Meyer & Klaus Wohlrabe, 2018. "Standing on the shoulder of giants: the aspect of free-riding in RePEc rankings," Applied Economics Letters, Taylor & Francis Journals, vol. 25(4), pages 223-228, February.
    13. Daniele Checchi & Gianni De Fraja & Stefano Verzillo, 2014. "Publish or Perish: An Analysis of the Academic Job Market in Italy," Discussion Papers 14/04, University of Nottingham, School of Economics.
    14. Ping Chen & Chun Wang, 2021. "Using EM Algorithm for Finite Mixtures and Reformed Supplemented EM for MIRT Calibration," Psychometrika, Springer;The Psychometric Society, vol. 86(1), pages 299-326, March.
    15. Michael Brusco & Hans-Friedrich Köhn & Douglas Steinley, 2015. "An Exact Method for Partitioning Dichotomous Items Within the Framework of the Monotone Homogeneity Model," Psychometrika, Springer;The Psychometric Society, vol. 80(4), pages 949-967, December.
    16. Ewa Genge, 2021. "LC and LC-IRT Models in the Identification of Polish Households with Similar Perception of Financial Position," Sustainability, MDPI, vol. 13(8), pages 1-22, April.
    17. Klaus Wohlrabe, 2018. "Einige Anmerkungen zum FAZ-Ökonomenranking 2018," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 71(20), pages 29-33, October.
    18. Katharina Rath & Klaus Wohlrabe, 2016. "Recent trends in co-authorship in economics: evidence from RePEc," Applied Economics Letters, Taylor & Francis Journals, vol. 23(12), pages 897-902, August.
    19. Padilla, Juan L. & Azevedo, Caio L.N. & Lachos, Victor H., 2018. "Multidimensional multiple group IRT models with skew normal latent trait distributions," Journal of Multivariate Analysis, Elsevier, vol. 167(C), pages 250-268.
    20. Lorenzo Ductor & Sanjeev Goyal & Anja Prummer, 2018. "Gender & Collaboration," Working Papers 856, Queen Mary University of London, School of Economics and Finance.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:eie:wpaper:1313. 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: Facundo Piguillem (email available below). General contact details of provider: https://edirc.repec.org/data/einauit.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.