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Ranking Scientific Journals via Latent Class Models for Polytomous Item Response

  • Francesco Bartolucci

    (University of Perugia)

  • Valentino Dardanoni

    (University of Palermo)

  • Franco Peracchi

    (University of Rome "Tor Vergata" and EIEF)

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.

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File URL: http://www.eief.it/files/2013/05/wp-13-ranking-scientific-journals-via-latent-class-models-for-polytomous-item-response-data.pdf
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Paper provided by Einaudi Institute for Economics and Finance (EIEF) in its series EIEF Working Papers Series with number 1313.

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Length: 26 pages
Date of creation: 2013
Date of revision: May 2013
Handle: RePEc:eie:wpaper:1313
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  1. Chia-Lin Chang & Michael McAleer & Les Oxley, 2010. "Journal Impact Factor Versus Eigenfactor and Article Influence," Working Papers in Economics 10/67, University of Canterbury, Department of Economics and Finance.
  2. Christian Zimmermann, 2007. "Academic Rankings with RePEc," Working papers 2007-36, University of Connecticut, Department of Economics, revised Mar 2009.
  3. 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.
  4. Francesco Bartolucci, 2007. "A class of multidimensional IRT models for testing unidimensionality and clustering items," Psychometrika, Springer, vol. 72(2), pages 141-157, June.
  5. 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.
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