Ranking Scientific Journals via Latent Class Models for Polytomous Item Response
AbstractWe 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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Bibliographic InfoPaper provided by Einaudi Institute for Economics and Finance (EIEF) in its series EIEF Working Papers Series with number 1313.
Length: 26 pages
Date of creation: 2013
Date of revision: May 2013
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-06-09 (All new papers)
- NEP-DCM-2013-06-09 (Discrete Choice Models)
- NEP-SOG-2013-06-09 (Sociology of Economics)
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