Ranking scientific journals via latent class models for polytomous item response data

We propose a model-based strategy for ranking scientific journals starting from a set of observed bibliometric indicators that represent imperfect measures of the unobserved 'value' of a journal. After discretizing the available indicators, we estimate an extended latent class model for polytomous item response data and use the estimated model to cluster journals. We illustrate our approach by using the data from the Italian research evaluation exercise that was carried out for the period 2004-2010, focusing on the set of journals that are considered relevant for the subarea statistics and financial mathematics. Using four bibliometric indicators (IF, IF5, AIS and the h-index), some of which are not available for all journals, and the information contained in a set of covariates, we derive a complete ordering of these journals. We show that the methodology proposed is relatively simple to implement, even when the aim is to cluster journals into a small number of ordered groups of a fixed size. We also analyse the robustness of the obtained ranking with respect to different discretization rules.