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Design-based Approach in Social Science Research

In their famous book on econometric research methods, Angrist and Pischke (2009) urge the reader to imagine their research questions as a randomized experiment. According to the authors, if you cannot think of your hypothesis in terms of a randomized experiment, however unrealistic or expensive, you may not have a causal research question at hand. By imagining the hypothesis as a randomized experiment, the researcher is forced to clearly identify the ‘treatment’, or the independent variable of interest. This is important because, as researchers we are interested not just in describing a phenomenon, but also in explaining the cause(s) of the phenomenon; it is difficult to make a causal argument without having a clear idea about what is being ‘manipulated’ or ‘changed’. Other researchers have also echoed this thought process by saying, ‘To find out what happens when you change something, it is necessary to change it’ (Box et al., 1978, p. 495). Behind every causal statement, there is an implicit assumption of manipulation of some independent variable that seems to be the ‘cause’ (Holland, 1986). This idea that there is no causation without manipulation is at the heart of current shifts in social science research, especially the fields of economics and political science that have placed greater emphasis on causal analysis that clearly identifies the cause of a particular outcome. Randomized control trials (RCTs)—experiments where researchers use random assignment to create well-defined treatment and control groups—have acquired the status of ‘gold standard’, and have become synonymous with high-quality research.2 However, RCTs are expensive and resource intensive to implement, and many times provide answers to narrowly defined questions. Further, there are many questions of interest that are beyond the realm of RCTs. For example, if one is interested in ‘causally’ estimating the effect of exposure to violence on a given set of outcomes