Familiarity with basic mathematics, including algebra and geometry, is useful to approach some topics explained in this course.
This course aims to introduce students to statistical methods essential for empirical research in political science. By the end of the course, students should have gained a comprehensive understanding of the research process, including the formulation of theories, hypotheses, and the classification of variables. The objectives include developing proficiency in survey design, ensuring students can effectively formulate and analyse survey questions. Additionally, the course aims to develop students’ skills in error analysis, reliability, and validity, enabling them to critically evaluate the quality of data. Through the exploration of descriptive statistics, probability theory, and statistical inference, students should acquire the ability to interpret and communicate statistical findings. Thanks to the practical sessions, students should also be able to understand the structure of a dataset and acquire the basic knowledge to clean a dataset, recode variables, treat missing cases etc. They should also be able to run simple univariate and bivariate analyses, report statistical results and comment on the output of a statistical software.
This course in Applied Statistics will provide students with a comprehensive understanding of statistical methods and their application to empirical research in the field of Political Science. Beginning with an exploration of the research process and the classification of variables, the course will progress to cover the role of surveys in social research, emphasizing the formulation of effective survey questions. Students will learn about the complexities of error analysis, reliability, and validity, laying the groundwork for robust statistical analysis. Descriptive statistics, including data visualization and measures of central tendency and dispersion, will be introduced to equip students with the tools to summarize and interpret data effectively. The course will then dig into probability theory, probability distributions, and fundamental concepts of statistical inference, introducing the topics of hypothesis testing and confidence intervals. Special attention will be given to practical applications, such as comparing groups, analysing associations between categorical variables, and understanding linear regression and correlation.
