Embarking on sociological quantitative research path

By GAO YONG / 04-18-2024 / Chinese Social Sciences Today

Exploring methodologies may generate new insights. Photo: TUCHONG

The so-called “sociology-based quantitative research path” refers to a research approach that is based on data and statistical procedures while closely aligning with the subjects of sociological inquiry. Although our tools are taken from statistics, the craft of using them to illuminate the underlying mechanisms of the social world is unique to sociology.

Three processes of quantitative research

Properly using data to understand society necessarily involves three distinct processes: the statistical mathematical logic process, the generation of the data itself, and the actual process of social action. The core craft of sociology is to integrate these three different processes from a holistic perspective, ultimately serving the goal of understanding social entities, social actions, and social processes.

Expanding theories with data analysis

There has long been an unsubstantiated bias that quantitative research is used only to test existing interpretations but cannot generate new interpretations or extend them, while qualitative research generates new interpretations or extends them without the need for testing. In reality, comprehensive and self-consistent qualitative and quantitative research should include both generating and testing interpretations.

When we obtain statistically significant results to validate our hypotheses, the research is not concluded. In fact, this is just the beginning. In sociological research, we need to return from the expression of variable relations to our understanding of social contexts and social agents: Who is driving the presentation of such statistical results? How is it being driven? In what social context does it appear? Are the details of the data consistent with the above understanding? A wealth of empirical details is thus derived from a single empirical hypothesis, which is then tested in the data. Of course, it’s impossible to verify all the details perfectly, but the process holds the potential for new insights into the social world. Research is no longer a linear hypothesis testing process, but a cyclic process of approaching the truth between empirical data and interpretational logic.


Elaborating social processes of data generation

Moreover, data generation itself must be taken seriously as a “social process.” All data is generated by human means, following certain procedures, within a specific “social” space and a “social” time. Therefore, it is necessary to reflect on the potential consequences of how data is collected.

Society not only operates according to its own organizational principles (such as structure, temporality, space, networks, interaction, subjective meaning, etc.), but also necessarily generates and outputs data based on these organizational principles. This is precisely what makes social data unique. Where statistics may see distractions, sociology sees opportunities.

Finally, it should be emphasized that quantitative research in sociology should not only keep looking forward to trace the latest technological advancements, but also look back to re-understand and reflect on certain classic works of quantitative research in light of new problem awareness. In particular, we need to pay attention to the interface between “data statistics” and “real social processes” in these classic works, which often contain some important ideas that are overlooked, and where subtle misinterpretations and ambiguities may be concealed. To explore the quantitative research path based on sociology, this serious work of critical reasoning is a necessary prerequisite to conducting research.

Gao Yong is a professor from the School of Sociology at China University of Political Science and Law.

Edited by ZHAO YUAN