The course provides a gentle introduction to the logic of quantitative statistical analysis in social and political sciences. By the end of the course, students who will attend classes will be able to:
· Develop a sound research design on social and political phenomena (i.e., a great emphasis will be placed on the formulation of hypotheses and on the use of data to test such hypotheses); · Perform descriptive and inferential multivariate analyses on R; · Master debates on the assumptions underlying the main statistical techniques social and political scientists use.
The programme is pondered and, if needed, adjusted depending on students' starting level. It aims at providing students with several analytical tools for making good empirical inferences in the realm of social sciences and training them in recognizing strengths and limitations of the evidences provided by others.
The first part of the course covers the basic logic of quantitative research design in social and political sciences and then refreshes univariate and bivariate analyses, Ordinary Least Squares (OLS) and its assumptions, as well as how to deal with violations of the basic linear model. Upon collective agreement, the second part will be dedicated to more advanced techniques for quantitative analysis (such as logit and probit regression models, time-series and panel-data analyses). Lectures will be based on hands-on material and will provide interactive learning experiences.