IPSA – HSE Summer School for Methods of Political & Social Research

This course offers an introduction to popular statistical tests and project reporting in R and R Markdown. Participants will learn the techniques of data wrangling, parametric and non-parametric tests and linear regression in R. In addition, the course will introduce creating interactive reports in RStudio. By the end of this course, participants will have completed a full-cycle analysis in R, from reading in the data, performing basic pre-processing, to performing statistical tests and reporting the results. Previous knowledge of statistical tests and any statistical software is not required but will be an asset.

Basic Statistics and Reporting in R with RStudio Syllabus (IPSA-HSE 2021) (PDF, 128 Кб)

This course introduces students to experiments as a tool for social science research and provides the tools needed to integrate them into their research agendas. Contemporary social science faces a number of fundamental challenges – omitted variables, selection effects, and reverse causality – that complicate our ability to study the world. This course will cover the basic logic of experimental methods, illustrating how and when they best address these problems. The course will cover a variety of experimental methodologies, as well as best practices for designing, analyzing, and presenting them. We will also discuss practical issues such as ethical review, implementation, and integration with other methods.   

Experimental Research in the Social Sciences Syllabus (IPSA-HSE 2021) (PDF, 129 Кб)

Many research questions require “triangulation”. This procedure does not only mean “looking at the same data” from different perspectives, i.e. along the concepts of various research guiding theories. It also means using different data sources to find out the “how” and “why” of a phenomenon under study, and it also may mean the parallel or sequential use of different methods of data collection. This is what mixed methods research is about.

The course will start with an overview of the qualitative and quantitative methods used in the social sciences, and with an introduction into the basic ideas of mixed methods research. Next, some often discussed “background theories” on the nature of social reality and on fitting research approaches will be presented. Afterwards, the different research designs for integrating qualitative and quantitative methods will be described and discussed.

After completing this course, participants should be able to develop, and to work through, convincing mixed methods research designs of their own. Basic knowledge of social science methodology would be very helpful for this course. A detailed power-point slide show on mixed methods approaches and on case study research by the instructor will be used throughout the course, and it will by downloadable by participants two weeks before the class starts.

Morning sessions will be devoted to lectures and classroom discussions, afternoon sessions to individual exercises and to seminar presentations by participants.

Mixed Methods Research (IPSA-HSE 2021) (PDF, 171 Кб)

The purpose of this course is to acquaint the student with basic regression methods, including linear regression, logistic regression, and Poisson regression. The course assumes knowledge of basic statistics. Some prior experience with the R programming language will be very helpful but is not required. Each session has a lecture and a lab component. Labs review examples of problems discussed in the lecture.

Applied Regression Syllabus (IPSA-HSE 2021) 

Bayesian data analysis is a rapidly developing field of statistics, which has many useful applications in various areas of political science, sociology, and international relations. The goal of this course is to provide a brief and “mostly harmless” (that is, as informal as possible) introduction to the theory and application of Bayesian statistical methods. The course begins with the basic concepts of Bayesian statistics (e.g., conditional probability, Bayes’ theorem, prior and posterior distribution). Then we consider various approaches to the estimation and assessment of Bayesian models (with the focus on MCMC methods) in the context of generalized linear models. Next, we learn about the main Bayesian approaches to model selection, including Bayes factors, DIC, and cross-validation methods. We conclude by discussing Bayesian model averaging (BMA), a powerful Bayesian approach to reducing model specification uncertainty.

Students are assumed to have basic knowledge of statistics and be familiar with several conventional statistical methods, most importantly regression analysis. Knowledge of advanced topics, such as multilevel regression analysis and maximum-likelihood estimation, is helpful, but not critical. In addition, for practical exercises we will use R programming environment, so another major prerequisite is the basic knowledge of R.

Bayesian Statistics Syllabus (IPSA-HSE 2021)