What is the effect of country-specific characteristics on the research performance of scientific institutions? Using multi-level statistical models to rank and map universities and research-focused institutions worldwide

Bornmann, Stefaner, de Moya Anegon, and Mutz (in press) have introduced a web application (www.excellencemapping.net) which is linked to both academic ranking lists published hitherto (e.g. the Academic Ranking of World Universities) as well as spatial visualization approaches. The web application visualizes institutional performance within specific subject areas as ranking lists and on custom tile-based maps. The new, substantially enhanced version of the web application and the multilevel logistic regression on which it is based are described in this paper. Scopus data were used which have been collected for the SCImago Institutions Ranking. Only those universities and research-focused institutions are considered that have published at least 500 articles, reviews and conference papers in the period 2006 to 2010 in a certain Scopus subject area. In the enhanced version, the effect of single covariates (such as the per capita GDP of a country in which an institution is located) on two performance metrics (best paper rate and best journal rate) is examined and visualized. A covariate-adjusted ranking and mapping of the institutions is produced in which the single covariates are held constant. The results on the performance of institutions can then be interpreted as if the institutions all had the same value (reference point) for the covariate in question. For example, those institutions can be identified worldwide showing a very good performance despite a bad financial situation in the corresponding country.

💡 Research Summary

The paper presents an enhanced web‑based application (www.excellencemapping.net) that visualizes the research performance of universities and research‑focused institutions worldwide, while explicitly accounting for country‑level characteristics through multilevel logistic regression. Using Scopus data compiled for the SCImago Institutions Ranking, the authors selected institutions that produced at least 500 documents (articles, reviews, conference papers) between 2006 and 2010 in a given subject area. Two performance metrics are examined: the “best paper rate,” defined as the proportion of an institution’s output that falls within the top 10 % most‑cited papers, and the “best journal rate,” the proportion of output published in the top 10 % of journals by impact.

The statistical core is a two‑level logistic model. At level 1, institution‑specific predictors such as total output, field of study, and past performance are included. At level 2, country‑level covariates—most prominently per‑capita GDP, national R&D expenditure as a share of GDP, higher‑education participation rates, and a composite science‑technology infrastructure index—are entered as fixed effects, while random intercepts capture unexplained country‑to‑country variation. By estimating both fixed and random components simultaneously via maximum‑likelihood, the model quantifies how much of the variance in best‑paper and best‑journal rates is attributable to national wealth, research investment, and other macro‑factors.

A key innovation is the production of “covariate‑adjusted” rankings. After fitting the model, the authors fix a selected country covariate at a reference value (e.g., the global mean GDP) and recompute predicted probabilities for each institution. This yields a ranking that reflects how institutions would perform if they all operated under identical country conditions. Consequently, institutions located in low‑GDP nations that still achieve high predicted probabilities can be identified—highlighting “over‑performers” that succeed despite limited national resources.

The web application integrates these analytical results with interactive visualizations. Users can select a subject area, view a traditional rank‑ordered list, and explore a tiled world map where each tile’s colour and size encode the adjusted performance metric. A control panel allows the user to choose which country covariate to hold constant and at what reference level, instantly updating both the map and the adjusted ranking table. This dual representation (list + map) facilitates rapid geographic comparisons and supports policymakers, university administrators, and scholars in assessing institutional strength relative to national context.

The authors acknowledge several limitations. First, reliance on Scopus means that non‑English or regionally indexed journals are under‑represented, potentially biasing performance estimates for institutions that publish heavily in local outlets. Second, the 500‑document threshold excludes many smaller universities and specialized research centres, limiting the generalizability of the findings to larger, more prolific institutions. Third, while multilevel logistic regression captures hierarchical structure, it may not fully model complex non‑linear interactions or network effects among institutions. The paper suggests future extensions such as Bayesian hierarchical models, structural equation modeling, or bibliometric network analyses, as well as the incorporation of additional data sources (e.g., Web of Science, national repositories) to validate and broaden the approach.

In summary, the study demonstrates that country‑specific characteristics significantly shape institutional research performance, but that adjusting for these factors yields a more equitable comparison across the global higher‑education landscape. By coupling rigorous multilevel statistics with an intuitive, map‑based interface, the authors provide a powerful tool for identifying high‑performing institutions that thrive under adverse economic conditions, thereby informing resource allocation, strategic planning, and international benchmarking efforts.