Avoiding “Drawing Conclusions That Shouldn’t Be Drawn”: Identification and Credibility in Social Science Research

China Social Science Review

No.3, 2021

Avoiding “Drawing Conclusions That Shouldn’t Be Drawn”: Identification and Credibility in Social Science Research

(Abstract)

Pang Xun

As a unique mapping of empirical information and research topics based on theoretical assumptions, identification is the fundamental task and central work of empirical research in the social sciences. Social sciences are undergoing two major changes: the data revolution and the identification revolution. The data revolution makes “possible” almost everything that was “impossible,” while the identification revolution casts doubt on the credibility of these “possibilities,” questions the cost of making every piece of research into a “possibility,” and emphasizes the cleanness and transparency of theoretical assumptions. This calls for the establishment of a “design-driven” empirical research paradigm with strict credibility standards. In some areas not yet reached by the identification revolution, the opportunities brought by big data are accompanied by the emergence of such problems as a lack of credibility in research, a disconnect between theory and empirical evidence, excessive quantification, etc. In the era of big data, raising identification awareness and strengthening identification strategy design in social science development; improving the credibility of empirical research, bridge theory and empirical evidence; and using data and technology in an appropriate manner are tasks of pressing and far-reaching significance.