SOCI 269 - Social Science Inquiry

Social scientists have many methodological approaches to choose from in their research toolkit ranging from qualitative to quantitative to comparative historical. Each of these approaches allows us to see and understand society from different perspectives, sometimes offering us complementary snapshots and other times presenting us with contradictory information. How, then, can we engage evidence to make claims about the social world? What even counts as evidence and who gets to speak for whom? Social Science Inquiry is part of a methods training sequence that emphasizes quantitative social science inquiry-that is, the approach to social science research that entails statistically analyzing relatively large datasets and emphasizes concerns such as measurement, replicability, objectivity, generalizability, and causal inference. However, this methodological approach is not without its critics. Therefore, this course begins by being in conversation with quantitative social science’s critics, examining the abuse of quantitative knowledge in social science research and how quantitative social scientists have themselves been complicit in building many of the tools now at the center of contemporary ethical debates over online privacy, digitization, facial recognition, AI, and their use in policing, surveillance, and border enforcement. Taking the stance that quantitative methods are tools with the capacity to either build up or break down social structures, we will work with municipal data from the City of Minneapolis to explore how quantitative methods may be, in sociologist Ruha Benjamin’s words, “retooled” to serve emancipatory and abolitionist purposes. Readings will focus on case studies from social science research and critical texts from science and technology studies. Methods-training content will specifically entail the use of R and will cover inferring population characteristics from survey samples, estimating causality and predicting outcomes using linear regression, the principles of probability and estimating statistical uncertainty, and how to critically read quantitative information presented via figures and tables. Every year. (4 Credits)