Quantitative research methods, i.e. those methods rooted in statistical, numerical, or otherwise quantified data, are focused on revealing or testing where and to what extent a causal relationship exists between two things: an independent variable (the thing that causes something) and a dependent variable (the thing being caused). Generally speaking, all the methods we have looked at up to this point explore these connections, but quantitative methods are doing so numerically, often with a large number of cases. Rather than make hard and fast arguments, this type of analysis allows the researcher to speak in terms of probabilistic causal relationships.

Quantitative methods have a sometimes uncomfortable relationship with area studies, because for many years quantitative methods were largely used in the social sciences for investigating across different countries and, more often than not, across different regions. In most quantitatively-focused research designs, the goal is to have many cases, more or less randomized.  It is clear that this can be antithetical to the area studies enterprise, which values deep knowledge about specific places or groups of related places. However, this need not be the case. Statistical analysis can be and often is applied to distinctly area-specific research projects. Census data from India, for example, can be compiled to track demographic changes over time, perhaps correlating these findings to political movements. Or, if you were interested in authoritarian governments and economic development, you might compare growth rates and other variables throughout many of the countries of Southeast Asia.

The important thing to consider here, of course, is the difference between correlation and causation. Put simply, causation implies a cause and effect relationship: X causes Y. Correlation simply means that in a given period of time, two variables change alongside one another, but may or may not be causally related. It can also be difficult to determine whether a single variable actual causes something when intervening variables, i.e. variables also related to the phenomenon, might be the actual cause. These issues can be effectively dealt with statistically, by controlling for intervening variables and running various types of analysis, especially regressions, to test hypothetical relationships between the things you are measuring.  

What your research plans, quantitative methods, already dominant in many social science disciplines, are increasingly important in South and Southeast Asian Studies. Even those committed to qualitative methodologies need to be able to understand and interact with quantitative work.

Luckily for you, Yale University Library has many resources to assist with training in quantitative methods. I recommend contacting Gwyneth Crowly (gwyneth.crowly@yale.edu) over at Marx Library for information about upcoming workshops and classes on statistics and data analysis.