Predicting Research Productivity in International Evaluation Journals across Countries

Main Article Content

Christoph E. Mueller Hansjoerg Gaus Ingo Konradt


Background: Progress in evaluation research depends on the continuous generation of scholarly knowledge and its dissemination in the community. One way of disseminating findings is to publish in scientific journals and researchers, institutions, and even whole countries are assessed by their output in these journals. Particularly with regard to countries, there is an uneven distribution of research productivity in evaluation journals. A viable model for predicting countries’ research output in international evaluation journals, however, has not yet been developed.


Purpose: The purpose of this study was to develop and test a model for the prediction of countries’ research output in international evaluation journals by predictors from the research, economic, and social/political system.


Setting: NA


Intervention: NA


Research Design: A cross-sectional design was used for predicting research output in evaluation journals across countries.


Data Collection and Analysis: Our sample consists of 65 countries that made contributions to ten international peer-reviewed evaluation journals. We collected data for the period from 2009 to 2013 and predicted the number of authorships across countries by using boosted regression trees, a machine learning procedure.


Findings: Our model provided accurate predictions of countries’ research output. Research productivity in the social sciences had the strongest effect, followed by economic prosperity, control of corruption, and age of evaluation society. The model was generalizable to another period of time with only marginal loss in predictive accuracy.


Download data is not yet available.

Article Details

How to Cite
MUELLER, Christoph E.; GAUS, Hansjoerg; KONRADT, Ingo. Predicting Research Productivity in International Evaluation Journals across Countries. Journal of MultiDisciplinary Evaluation, [S.l.], v. 12, n. 27, p. 79-92, oct. 2016. ISSN 1556-8180. Available at: <>. Date accessed: 28 oct. 2021.