Lessons Learned About Social Science Integration
Social science integration poses many challenges. First, there is a steep learning curve regarding terminology and methods for interdisciplinary research teams. For instance, “risk” may be expressed in monetary terms by an environmental economist, as probability by a statistician, or as a more qualitative and multi-dimensional construct by a decision theorist. These differences of course have important implications for choices about the measures collected and analyzed. Our team meets this challenge by holding frequent (often biweekly) meetings throughout proposal development and project implementation to identify and learn differences in our understandings and approaches.
Another challenge for social science integration is the need to measure complex phenomena such as “modernization,” which often occur on very large scales. Our approach is to recognize the subjectivity and the value-laden judgments that scientists make about the validity of alternative measures. Accordingly, we recommend selecting variables directly from relevant theories and statistically testing (e.g., through factor analysis) the extent to which sets of variables combine in an internally consistent manner. The higher the consistency, the more confidence we have that the measures capture an underlying construct relevant to our model.
To illustrate, our primary goal for measuring the complex construct of “modernization” is to create a “degree of modernization” national map based on the smallest administrative unit as the unit of analysis and use secondary data sources to do so. We start by identifying three latent concepts for modernization—urbanization, agricultural intensification and land use changes. For each of these concepts, we identify theories and metrics from diverse fields and then select those variables which seem to be valid for the type of transition happening in Vietnam. This list is shortened simply by eliminating variables for which high quality secondary data do not exist. The next step in the process is to realize that we are creating a comparative rather than an absolute metric of modernization. Even though the metric helps in the comparative assessment of the level of modernization, there is still a need to validate it based on ground-truthing. So then we use a multi-disciplinary approach to ground truthing—walk through field surveys, random ground-level photographs, satellite imagery, commune data archives—to ensure that our classification was at least accurate on an ordinal scale.
A final key element facilitating the integration of diverse social sciences relates to the disciplines represented by the research team. Our core team is comprised of researchers from fields that already reflect an interdisciplinary approach (geography, urban planning, environmental science, and decision science). Also, each team member has experience working at different scales. We are fortunate to work in an institutional setting that encourages interdisciplinary work and multiply authored papers. Support for publishing in cross disciplinary journals helps generate recognition for the value of integrated work.
