Social sciences Photo: Artur/TUCHONG

There has always been the tension between humanities and sciences within the field of social sciences. Regardless of one’s personal interests or chosen area of study, our analytical methods and forms largely determine how we are evaluated (including by public opinion) and influence the fate of a discipline.

Descriptive turn

In today’s context, the knock-on effect of the data explosion induced by big data may be greater than initially anticipated.

The empirical tradition of the social sciences is closely related to the formation and development of modern sociology. As early as the first half of the 19th century, Auguste Comte sought to establish a new discipline—sociology—based on scientific methods for studying society, while at the same time introducing research methods from natural sciences into sociology. Scientificity is an inherent characteristic of sociology.

Since the reconstruction of sociology in China, scientificity has played a crucial role. During the market-based transition, China’s social structure has undergone drastic changes, which have given rise to many new social phenomena.

Comparing the “localization” process of sociology in China and the United States, the production and ownership of data have strengthened the scientificity of the discipline. Sociology may seem “complacent” with the methodological tools accumulated in the second half of the 20th century, but the challenge we face of “Knowing Capitalism” is irreversible. It is an indisputable fact that big data challenges the original data-based sources of legitimacy of empirical social sciences such as sociology, and even begins to weaken its scientific advantages. This challenge is evident not only in data collection and analysis, but also in the conceptualization of the data itself. So, how can we face the crisis of the empirical tradition? While there has been much discussion regarding data collection and analysis, discussions on the concept of backtracking data have been relatively weak, and the situation is urgent.

British sociologist Mike Savage began contemplating these issues as early as 2007. He believes that in the face of the explosive influence of data, sociology has had to undergo a transformation from expert-driven data production to embracing data from multiple sources. At the same time, he explicitly calls for a reflection on the mode of causal analysis and knowledge production, advocating for a strengthening of descriptive analysis, that is, the “Descriptive Turn.” Because descriptive analysis opens up new worlds for academics and non-academics, causal analysis has to identify relative causality based on various hypotheses, lacking the capacity for world-making.

Since then, several scholars have begun a series of criticisms that continue to this day. They argue that sociology should defend its authority in data production, and emphasize that sociology should not only describe social phenomena, but also explore causal explanations such as structures and mechanisms behind phenomena. In response, Savage recently re-emphasized that descriptive analysis resonated deeply with both the public and academia at the beginning of the 21st century. If descriptive analysis were to be removed from the sociological toolbox, we would lose an important and crucial contribution to the voice of the times. When we take a closer look at this academic dialogue, we can find that the two sides agree far more than they differ. This “consensus” can be expressed as: at a  time when sociology is losing the authority in data, how to reconcile the relationship between descriptive analysis and causal inference is crucial to resolving the discipline’s greatest crisis since World War II.

Methodological ecosystem

Undoubtedly, when quantitative research methods based on statistical techniques became the accelerators for the scientification of sociology, descriptive analysis experienced a process of marginalization.

We believe that although big data possesses characteristics such as being unstructured and non-pre-designed, and may have some congenital deficiencies in establishing causality, its data mining potential is more conducive to the discovery of new knowledge and is more likely to break through the original theoretical framework. Descriptive analysis and causal identification are not inherently contradictory. In fact, the latter is precisely based on a large number of regularities (i.e., abundant descriptions). Furthermore, descriptive analysis plays an increasingly important role in narrowing the gap between academic research and application scenarios. 

Whether based on qualitative “deep description” or quantitative descriptive statistics, it can more directly translate into business planning and policy making. Ultimately, descriptive analysis holds a unique charm in our understanding of society.

To reiterate that descriptive analysis is the cornerstone of empirical social sciences is to make full use of advanced statistical methods and analytical strategies to increase the breadth, depth, and profoundness of descriptions with the help of new data, greater computing power, and better algorithms. At the same time, it does not rely excessively on a single theoretical framework and methodology paradigm, and excels at processing data and information from various sources to create a diversified and three-dimensional methodological ecosystem. More importantly, we need to build a more open expert system rooted in China’s social foundation.

Finally, it should be emphasized that the discussions of the descriptive analytic turn in sociology, led by Savage and others, are also instructive for other disciplines. In the social sciences, academic ideas and fields have waxed and waned due to research methods. It is now more urgent than ever to contemplate the role and significance of descriptive analysis.

Fan Xiaoguang is a research fellow from the School of Public Affairs at Zhejiang University.

Edited by ZHAO YUAN