Multivariate Analysis for Social Scientists

The objective of the course is to introduce students to the logic of quantitative statistical reasoning in social and political sciences. Students will be presented with the main statistical techniques for data analysis used in social sciences, so that they will be able to independently assess and conduct quantitative research.
By the end of the course, students will be equipped with the necessary skills to:
- understand the assumptions underlying the main statistical techniques used in the analysis of social and political phenomena;
- perform descriptive and inferential multivariate analyses on R;
- elaborate a research design for answering a research question with empirical data, applying the statistical techniques learned in this course.
The program is designed to be flexible and can be adjusted if needed, based on the starting level of the students.
In the first part of the course students will be introduced to key concepts and processes involved in designing a research project in social sciences, including formulating a research question and developing hypotheses, and selecting an appropriate data set. This will be followed by a review of univariate and bivariate analyses, OLS regression and its assumptions, and how to deal with deviations from such assumptions. The students will then be introduced to other analytical tools for quantitative analysis, such as non-linear regression functions, logit and probit regressions, and limited dependent variable models. More advanced topics such as time-series and panel-data analyses can be covered upon collective agreement. Students will also learn how to use the statistical software R to organize and analyze data. Lectures are coordinated with computer lab instruction in data analysis.
In the second part of the course, students will be required to elaborate a research design using a real political or social science dataset, and apply the concepts they have learnt to address a research question. Great emphasis will be placed on the formulation of hypotheses and on the use of data to test such hypotheses. Lectures will be based on hands-on material and will provide interactive learning experiences.

The course will prepare students to:
- perform descriptive and inferential multivariate analyses using R, including multiple regression models, non-linear regression functions, limited dependent variables, and time-series analysis;
- understand and discuss the underlying assumptions of common statistical techniques in social and political science;
- develop a research design to study social and political phenomena, including defining an original research question, creating a research design, formulating hypotheses, selecting a suitable dataset, conducting statistical analysis, interpreting results, and discussing limitations;
- present their research findings through a public presentation.