Although China has a long history of studying specific countries and regions, discussions of research methods — in terms of modern area studies — only began to emerge around 2010. In this article, we will outline the research paths of Chinese scholars, attempting to illustrate the methodology used in this up-and-coming interdisciplinary field.
Investigative research methods
Conducting research and collecting evidence are a prerequisite to problem-solving and the study of causal relationships. In country and area studies, it is also essential to carry out a precise examination of historical events to reconstruct political dynamics behind the scenes and address underlying issues. The most common methods applied in this pursuit are field investigations and historical verification.
Knowledge production involves a series of steps, including collecting, organizing, analyzing, and structuring information within a specific spatiotemporal context. Field investigations are a crucial way to build specialized knowledge about a particular region. In practice, this includes various research approaches such as observation, questionnaires, surveys, in-depth interviews, and field experiments. The primary purpose of field investigations is to gain a deep understanding and insight into the research subject from a range of cultural, social, economic, political, and ecological perspectives, among others. This requires both the rigor of a scientist and the imagination and creativity of an artist. These methods can be further classified into two categories based on differences in investigative approaches.
The first category is interview based. Interviews typically involve the following steps: A researcher selects appropriate interviewees and locations based on research questions and objectives; develop an interview plan, which determines the topics, content, and interview methods; conducts targeted communication with interviewees based on this interview plan; records the interview, content and data is organized and analyzed; finally, conclusions are reached based on an analysis of characteristics, viewpoints, and experiences of interviewees. These steps help ensure that interviews are conducted effectively and yield valuable research insights.
The second investigative category is field observation, where researchers observe subjects’ behavior, dynamics, and environment, recording a detailed description of their observations. The observation method requires a rigorous subject selection process, clearly defined observation methods, a thorough record of all pertinent facts, and finally a summary and analysis. In practice, observation can be divided into participatory and non-participatory observation.
In the field of country and area studies, when studying the history of a specific country, at least three levels of research are conducted. First, historical sources are cross-referenced to verify their authenticity. One of the most common methods to accomplish this is a historical document comparison. Second, scholars need to use multiple types of historical documents. Last, researchers must correctly interpret and corroborate primary sources within each specific historical context.
Causal explanation methods
In contrast to investigative research methods, causal explanations aim to effectively establish causality by following an easily repeatable, standardized process and using existing information. The purpose of causal explanation is to demonstrate cause and effect. Therefore, in specific spatiotemporal contexts, the independent variable X can influence dependent variable Y. Causal explanation methods can vary based on the sample: Process tracing is suitable in individual case studies, while case comparisons are typically used for small samples, Qualitative Comparative Analysis (QCA) for medium-sized samples, and quantitative analysis for large samples.
In country and area studies, language skills often determine understandings of each nation’s history and materials. In Europe, Southeast Asia, and Africa, which are home to a variety of languages, many studies must be case specific. Process tracing is the primary method used in case studies because it tests internal validity. The first step in process tracing is to examine whether the dependent variable Y(t) changes over time as envisioned by the causal mechanism in specific cases.
Process tracing in country and area studies serves at least two purposes. First, when researchers attempt to apply general theories to a specific region, they need to use process tracing to test the effectiveness of each theory in that region. Second, if researchers aim to theorize and generalize the experiences of a region, they should first confirm internal validity via process tracing, and then expand external validity tests through cross-examination.
Case comparisons are the most common research method applied within country and area studies. Given that this field of study can serve policy purposes, researchers often focus on major countries within a region. As there are limited major countries within any given region, research usually relies on small samples. Small sample analysis uses the Mill’s method for analyzing differences: If a case in which a phenomenon occurs, and one in which it does not, differ by only one other feature, that feature is either the cause or a necessary part of the cause, or it is the effect. Because country and area studies emphasize highly specific regional knowledge and the importance of context, there is a natural logical consistency with the Mill’s method for small-sample research.
QCA is also widely used in country and area studies. While some QCA software can handle larger samples, the ideal sample size for QCA is still in the range of 10 to 40 cases. This sample size is too large to conduct individual case studies but not large enough for quantitative analysis. When the research concerns medium-sized countries, the sample size can reach a moderate scale, particularly in regions such as Europe and Africa.
When applying QCA software to research, two issues need to be considered. First, QCA is not designed to accomodate changes over time well, it is more suitable for analyzing short-term, rapid changes. Long-term factors cannot be the outcome variable studied by QCA, such as the growth rates of several countries within a region over several decades or long-term institutional arrangements. Second, since the number of cases is limited, the QCA is a weaker tool than quantitative analysis. Therefore, the sample used in QCA should include the entire population within a region, since including or excluding critical cases can lead to drastic changes in the consistency of analysis.
Despite the relatively small number of countries within a set region, quantitative analysis has become increasingly important in this field as statistical techniques develop and data is more readily available. Several developments in statistical processing have made large-sample analysis possible in this discipline.
First, with the proliferation of cross-sectional unit data, new data sources enable econometricians to construct and test more complex models than those based on a single cross-section or time-series data. With the advent of panel data techniques, which incorporate the dimension of time, the sample size can increase exponentially. Second, the emergence of sub-national comparative analysis allows researchers to go beyond spatial limitations when selecting cases, thereby expanding the number of cases available for analysis.
Quantitative analysis can take two primary approaches. The first is a traditional frequentist approach, which interprets probability as the frequency of events occurring in a large sequence of repeated experiments. This approach is suitable for situations with large sample sizes and relatively stable data. The second approach is the Bayesian school of thought, which emphasizes subjective probability. It argues that subjective experience and knowledge can influence the judgment of event probabilities, and people continuously adjust their beliefs about specific events as they collect evidence.
Big data methods focus more on data correlations than causality. Through big data analysis, many associations that were previously undetectable via causal explanation can be uncovered. Big data can be leveraged to automatically analyze text using machine learning and natural language processing techniques or discover patterns and trends in data using data mining technologies. With improved computer performance and increased data availability, machine learning-based “smart algorithms” have emerged. These intelligent algorithms can rapidly iterate and learn from massive high-dimensional data, allowing them to solve problems that were previously deemed unsolvable. In addition to big data and machine learning, there are several new cutting-edge methods increasingly applied to country and area studies in China.
First, there is Agent-Based Modeling (ABM). ABM is a computational simulation method used to study individual behaviors and how these produce macro-level patterns and structures. It helps establish the connection between micro-level actors and macro-level outcomes.
Second, Geographic Information Systems (GIS) presents an exciting new frontier. Since much of the research in country and area studies involves specific geographical areas, and ABM also deals with agents acting in specific regions, the need for GIS arises naturally. GIS is a software system that manages and stores spatial data, using software packages like ArcGIS to create and analyze spatial data.
Finally, there is the use of mixed methods. Mixed analysis involves aggregating multiple methods to address specific research questions by harnessing the complementary strengths of several different research methods, thus avoiding the limitations of a single method. Mixed methods can involve a variety of research techniques and software systems. During the data collection process, researchers can choose methods such as interviews, observations, historical verification, big data collection, and natural language processing. In the causal explanation phase, two or more methods from process tracing, case comparative analysis, quantitative analysis, and ABM can be applied.
Therefore, the types of mixed methods which exist are growing exponentially, with over a dozen permutations in the causal explanation phase alone, multiplied by the methods used in the data collection phase, resulting in an almost incalculable number of mixed strategies. Among the many combinations of causal explanation methods, the most used mixed method in regional country studies is still “nested analysis,” which involves case studies and quantitative analysis. China’s area and country studies is cumulatively advancing from day-to-day, and certainly merits its own study.
Ye Chengcheng is an associate research fellow from the Institute of International Relations at the Shanghai Academy of Social Sciences.