Establishing a Science and Technology Evaluation Indicator System Using Cluster-Factor Analysis

Abstract

This paper proposes a novel approach to establish a science and technology evaluation indicator system based on cluster-factor analysis. Effective evaluation of science and technology initiatives requires a comprehensive set of indicators that capture various dimensions of performance, impact, and relevance. Cluster-factor analysis offers a data-driven method for identifying clusters of related indicators and extracting latent factors underlying complex evaluation criteria. Through a step-by-step process, this study demonstrates how cluster-factor analysis can be applied to identify clusters of science and technology evaluation indicators and derive latent factors representing different aspects of performance and impact. The research explores the interrelationships among these factors and their implications for science and technology policy, investment, and decision-making. By providing a systematic and rigorous approach to indicator system development, this paper aims to enhance the effectiveness, transparency, and accountability of science and technology evaluation processes.