PT  - JOURNAL ARTICLE
AU  - Bárbara S. Lancho-Barrantes
AU  - Héctor G. Ceballos
AU  - Francisco J. Cantú-Ortiz
TI  - Factors that influence scientific productivity from different countries: A causal approach through multiple regression using panel data
AID  - 10.1101/558254
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 558254
4099  - http://biorxiv.org/content/early/2019/02/25/558254.short
4100  - http://biorxiv.org/content/early/2019/02/25/558254.full
AB  - The main purpose of the economic expenditure of countries in research and development is to achieve higher levels of scientific findings within research ecosystems, which in turn could generate better living standards for society. Therefore, the collection of scientific production constitutes a faithful image of the capacity, trajectory and scientific depth assignable to each country. The intention of this article is to contribute to the understanding of the factors that certainly influence in the scientific production and how could be improved. In order to achieve this challenge, we select a sample of 19 countries considered partners in science and technology. On the one hand we download social and economic variables (gross domestic expenditure on R&D (GERD) as a percentage of gross domestic product (GDP) and researchers in full-time equivalent (FTE)) and on the other hand variables related to scientific results (total scientific production, scientific production by subject areas and by different institutions, without overlook the citations received as an impact measure) all this data within a 17-year time window. Through a causal model with multiple linear regression using panel data, the experiment confirms that two independent (or explanatory) variables of five selected explain the amount of scientific production by 98% for the countries analyzed. An important conclusion that we highlight stays the importance of checking for compliance of statistical assumptions when using multiple regression in research studies. As a result, we built a reliable predictive model to analyze scenarios in which the increase in any of the independent variables causes a positive effect on scientific production. This model allows decision maker to make comparison among countries and helps in the formulation of future plans on national scientific policies.