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Steven Wilkinson | Archival Methods and Historical Approaches in Political Science

September 16, 2020

Steven I. Wilkinson is a Nilekani Professor of India and South Asian Studies and Professor of Political Science and International Affairs at Yale University, where he is also the Henry R. Luce Director of the Whitney and Betty MacMillan Center for International and Area Studies. He received a Ph.D. in Political Science from the Massachusetts Institute of Technology and an A.M. in History from Duke University and a B.A. in History from University of Edinburgh. He is the author of Army and Nation: The Military and Indian Democracy Since Independence (Harvard University Press, 2015), Votes and Violence: Electoral Competition and Ethnic Riots in India (Cambridge University Press, 2004), and co-editor of the book Patrons, Clients or Politics: Patterns of Political Accountability and Competition (Cambridge University Press, 2007) with Dr. Herbert Kitschelt.


Qualitative Archival Method (QAM) in Increasing Quantification

Professor Wilkinson explained that the drive for quantification and casual identification has accelerated the hunt for big data. Quantitative methods are increasingly adopted not only in social science research but also in the humanities.  The number of articles with keywords ‘natural experiment,’ ‘data,’ and ‘archive’ in Jstor and Google Scholar has continuously increased, and universities are largely producing new courses on data science or data scrapping and taking big data initiative. In the case of South Asia studies, huge amounts of data were gathered during the colonial period and the existence of this big data allowed scholars to examine problems that have been studied in social science within the context of South Asia.

Dr. Wilkinson, whose undergraduate degree was in history, stated that he thinks of himself as a historian and this academic background formed his way of thinking. It also led him to support the Qualitative and Archival Methods (QAM) program in the Political Science department at Yale University. He continues to be involved in the QAM field training program with around 10 other faculty members. This program allows students to take courses related to fieldwork, archival methods, and mixed methods. Dr. Wilkinson also briefly shared his experience of working with a quantitative scholar, who wanted Dr. Wilkinson’s qualitative resources for data generation.


Why Qualitative Methods are Vital for Good Quantitative Methods

Professor Wilkinson emphasized that we need to be clear and confident about what fieldwork and qualitative methods give us that no other methods provide. For example, fieldwork and qualitative methods have unmatched insights in finding motivations, mechanisms, human inter-relationships, and symbols. He specifically made three positive arguments as to why qualitative methods are vital for good quantitative methods.

First, QAM is crucial to understanding whether big data are good data. When Dr. Wilkinson wrote his dissertation, he collected the data on Indian crime and riots; but he discovered that a lot of data are not good data because each state in India had different governance systems which resulted in distinct ways of registration of crimes and riots. Dr. Wilkinson stressed that we should know what is behind all those data, and qualitative methods such as conducting interviews in the field are critical in quantitative data generation.

Second, using QAM to understand what is ‘wrong’ with the numbers generates new questions, new ideas for better data, and new theories. While introducing Paul Brass’s book Theft of an Idol, Dr. Wilkinson explained how some events are never made into official data, so it is important to do qualitative local ethnography work to discover the events and ideas that the official data do not show.

Third, QAM is essential to test the core assumptions being made by the statistical models. Many statistical models assume that some as random processes are at work, which allows us to assess the effect of some treatment of interest on similar territories or units, or that selection into treatment was independent of the characteristics of a unit. However, we need QAM to see if these assumptions are correct.

QAM Examples from Jha and Wilkinson’s Work

A few examples of QAM from Dr. Wilkinson’s work on war and political change with Dr. Jha were presented. That collaborative study tries to explain veterans’ role in ethnic cleansing in the partition of India. However, there were no systematic data on soldiers’ military experience in World War II and their home district. By using documents from the Indian government Dr. Wilkinson and his research assistants checked every document to find the different unit’s war exposure and then matched a unit’s military experience to districts with The Commonwealth War Graves Commission data. In his final remark, Dr. Wilkinson emphasized that QAM is vital to checking the key assumption of the quantitative work: randomness. With QAM, the study found that deployment of soldiers was not ‘as random.’


Dr. Wilkinson's Powerpoint Slides (PDF)