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Monday 24 – Friday 28 July 2023
Minimum 2 hours of live teaching per day
10:00 ꟷ 12:00 CEST
This course offers an immersive online learning environment that employs state-of-the-art pedagogical tools. With a maximum of 16 participants, our teaching team can provide personalised attention to each individual, catering to their specific needs. The course is designed for a demanding audience, including researchers, professional analysts, and advanced students, who already have a basic understanding of quantitative methods.
This course aims to provide you with the foundational knowledge and skills required to analyse quantitative data in political science research. You will cover the basics of statistical inference, including probability theory, hypothesis testing, and confidence intervals. The instructor will also focus on the most commonly used statistical techniques, such as regression analysis and experimental designs.
The course will include both lectures and practical exercises, where you will learn how to use software tools such as R or Stata to perform statistical analyses. Additionally, you will discuss the strengths and limitations of different methods, and how to interpret and report statistical results in a meaningful way.
By the end of this course, you will have a solid understanding of the main concepts and methods used in quantitative political science research. You will be able to critically evaluate research findings, design and conduct your own research, and interpret and report statistical results.
4 credits - Engage fully in class activities and complete a post-class assignment
Cristina Mitrea is currently postdoctoral researcher at the Babeș-Bolyai University in Cluj, Romania. She earned her PhD in comparative politics at the Central European University.
Her research interests lie in the fields of political behaviour and political socialisation.
Beginning with building blocks of statistical inference, you will discuss the relationship between populations and samples, issues of measurements and scale, types of variables, basic distributions (with a focus on the normal distribution), as well as measures of central tendency (mean, median, mode) and variability (range, variance and standard deviation).
There will be further discussions on normal distribution and the relationship between samples and populations, delving into the specifics of standardised scores and probability.
You will explore the logic of hypothesis testing. You will learn how to formulate and test simple hypotheses, touching on uncertainty and errors in testing.
In this session, you will progress onto more complex hypothesis testing (t test and ANOVA) and explore comparisons to a given standard, comparisons between groups, and changes over time.
You will learn how to test hypotheses using chi-square tests. you will also discuss the concept of effect size and the importance of power analysis in statistical inference.
The course is delivered using a combination of pre-class activities, live online interaction and post-class activities. Before the course week, you will have access to readings and pre-recorded lectures which explain and clarify course material.
Daily two-hour live sessions will feature class discussions, Q&A, group exercises and tutorials on the application of statistical methods. Freely available software such as web apps and spreadsheets will be used.
Post-class activities include exercises to practice course material. The Instructor and Teaching Assistant will also be available for individual consultations to answer queries, discuss research applications, and to offer additional support.
There is no prerequisite knowledge for this course beyond basic addition, subtraction, division and multiplication.