大数据与实证社会科学研究范式的延续与变革

Big Data and Continuation and Transformation of Empirical Research Paradigm in Social Sciences

大数据从两个角度影响实证社会科学。从数据层面看,虽然数据量的增长没有改变传统社会科学研究中从样本推断总体的基本逻辑架构,从而并未对传统社会科学研究范式进行完全重构;但数据形式的急速扩展和数据精度的不断提升拓展了社会科学研究的议题,丰富了社会科学研究的内容。从数据分析技术看,包括计算社会科学在内的一系列大数据算法抛开了理论和解释的必要性,专注于事物之间的相关而非因果关系,并在此基础上寻求对社会现象的预测,从而开拓了实证社会科学的预测新范式;与此同时,大数据算法与传统计量模型的融合进一步改善着因果识别的效果,从而推动着社会科学中传统理论化因果解释范式的发展。

Big data affects empirical social sciences from two perspectives.From a data perspective,the increase in the amount of data does not change the basic logical framework of inference of population from sample in traditional social science research and it does not completely reconstruct traditional research paradigm in social sciences;however,the rapid expansion of data forms and the continuous improvement of data accuracy expand the topic of social science research and enrich the content of social science research.From the perspective of data analysis technology,a series of big data algorithms including computational social science ignore the necessity of theory and explanation while focusing on the correlation among things rather than causal connection and seek for the prediction of social phenomena based on this,thus developing a new paradigm of empirical social science prediction;at the same time,the fusion of big data algorithms and traditional econometric models further improves the effect of causal recognition,thus promoting the development of traditional theorized causal explanation paradigm in social sciences.