Quantitative and Qualitative Methods for Screening Scientific Grant Projects and Applications

Abstract

This article aims to investigate different methods of evaluating scientific grant applications and projects, including quantitative and qualitative approaches to their analysis. Regression analysis, Bayesian networks and multi-criteria evaluation were used in the study. Quantitative analyses included statistical methods to compare the performance of different projects to identify patterns and trends affecting the success of research initiatives. The study provided unique insights into how quantitative and qualitative methods can help improve the objectivity of science project evaluation. Specific numerical measures of the methods’ effectiveness were collected and analysed, identifying the key benefits of each approach. Results showed that regression analysis is effective for predicting dependent variables based on linear relationships, Bayesian networks are useful for modelling complex relationships and accounting for a priori knowledge, especially when dealing with incomplete data, and multi-criteria evaluation provides a structured and transparent decision-making process based on multiple criteria.