Ranking Scientific Journals via Latent Class Models for Polytomous Item Response
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.
|Date of creation:||2013|
|Date of revision:||May 2013|
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- Chia-Lin Chang & Michael McAleer & Les Oxley, 2010.
"Journal Impact Factor Versus Eigenfactor and Article Influence,"
KIER Working Papers
737, Kyoto University, Institute of Economic Research.
- 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.
- 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.
- 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.
- 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.
- Francesco Bartolucci, 2007. "A class of multidimensional IRT models for testing unidimensionality and clustering items," Psychometrika, The Psychometric Society, vol. 72(2), pages 141-157, June.
- Christian Zimmermann, 2012.
"Academic rankings with RePEc,"
2012-023, Federal Reserve Bank of St. Louis.
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