SPECIAL CONTRIBUTIONS
Bian Yanjie, Miao Xiaolei
Journal of Intelligent Society.
2025, 4(1):
21-42.
Big data provides both rich data resources and analytical dimensions for the social
sciences, promoting a paradigm shift in empirical research from macro to micro, from interpretation
to prediction, and from theory to data. Valuing the five principles of empirical science (exploration
of new knowledge, skepticism, theoretical explanation, verifiability, and evidence-based
approach), this article discusses the respective characteristics and scientific significance of four
data forms: case studies, experiments, surveys, and big data. Furthermore, it proposes four
pathways to integrate traditional data forms with big data: (1) big data analysis of substantive
issues, (2) measurement improvement through big data, (3) theory-guided big data prediction,
and (4) offline random sampling for online data collection. These pathways not only enrich data
sources and enhance analytic depth, but also improve the scientific approach and time-efficiency
of empirical research. Finally, the article proposes data-driven, theory-informed empirical
research as the future direction of social science research, whose functions include theoretical
verification and discovery driven by big data, integration of data mining and measurement,
analysis of group characteristics and overall trends, and combination of real-world and virtualworld samples. These functions will manifest the key role of big data in promoting theoretical
breakthroughs and methodological improvement in the social sciences.