摘要
大数据为社会科学实证研究提供了丰富的数据资源与分析维度,推动了从宏观到微观、从解释到预测、从理论到数据的研究范式转变。本文从科学实证精神的五大原则(新知探索、怀疑精神、理论解释、可验证性、循证为本)出发,讨论了个案、实验、调查和大数据四种数据形式的各自特征及其对科学实证研究的意义,进而提出传统数据与大数据结合的四种途径:实质问题的大数据分析、大数据测量对传统数据测量的改进、理论导向的大数据预测、线下随机抽样与线上数据收集的有机结合。这些结合途径不仅拓宽了数据来源,增强了分析深度,还提高了研究的科学性与实效性。最后,本文提出数据驱动的理论导向实证研究作为社会科学研究未来的发展方向,包括大数据驱动的理论验证与发现、数据挖掘与测量融合、群体特征与总体趋势分析以及实体样本与模拟样本的结合,强调了大数据资源在推动社会科学理论创新与研究方法发展中的关键作用。
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.
作者
边燕杰
缪晓雷
Bian Yanjie;Miao Xiaolei
关键词
大数据
传统数据
数据结合
社会科学
实证研究
big data
traditional data
data integration
social science
empirical research
