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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
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    References listed on IDEAS

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    1. Christian Zimmermann, 2013. "Academic Rankings with RePEc," Econometrics, MDPI, Open Access Journal, vol. 1(3), pages 1-32, December.
    2. 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.
    3. 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.
    4. 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.
    5. Althouse, Benjamin M. & West, Jevin D. & Bergstrom, Ted C & Bergstrom, Carl T., 2008. "Differences in Impact Factor Across Fields and Over Time," University of California at Santa Barbara, Economics Working Paper Series qt76h442pg, Department of Economics, UC Santa Barbara.
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    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.

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