Quantitative and Qualitative Methods for Screening Scientific Grant Projectsand Applications

Zhanna Ixebayeva, Zhenis Bagisov, Dina Abulkassova, Akmaral Khamzina, Aizhan Iskaliyeva

This article explores different methodological approaches to evaluating scientific grant applications and projects,focusing on the combination of quantitative and qualitative methods. Regression analysis, Bayesian networks, and multi-criteria evaluation  are  examined  as  complementary  techniques  within  an  integrated  analytical  framework.  The  studydemonstrates how these methods can be applied to identify relationships, model uncertainty, and support structured decision-making  in  grant  evaluation.  Using both synthetic and empirical data, the  models  are  tested  and  compared  in  terms  ofinterpretability,  predictive  capacity,  and  transparency.  The findings suggest that combining these approaches  has  strongpotential to improve the fairness, consistency, and efficiency of funding allocation when applied under appropriate conditions.Rather  than  claiming  proven  effectiveness,  this  work  illustrates  the  methodological  viability  and  adaptability  of  suchtechniques for future research management and evaluation systems.

Reference to the article: Ixebayeva, Z., Bagisov, Z., Abulkassova, D., Khamzina, A., & Iskaliyeva, A. (2025). Quantitative and Qualitative Methods for Screening Scientific Grant Projects and Applications. Statistics, Optimization & Information Computing, 14(6), 3741-3760. http://www.iapress.org/index.php/soic/article/view/2716/1631