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Member rate £492.50
Non-Member rate £985.00
Save £45 Loyalty discount applied automatically*
Save 5% on each additional course booked
*If you attended our Methods School in the last calendar year, you qualify for £45 off your course fee.
Monday 6 – Friday 10 February 2023
Minimum 2 hours of live teaching per day
09:30 – 12:00 CET
This course provides a highly interactive teaching and learning environment, using state-of-the-art pedagogical tools. It is designed for a demanding audience (researchers, professional analysts, advanced students) and capped at a maximum of 16 participants so that the teaching team can cater to the specific needs of each individual.
Quantitative data is becoming increasingly available and publications are making the use of it more prevalent in political science. This course gives you the tools to engage in and become an informed consumer of quantitative research. It will teach you the basic inferential statistical techniques used in political science research, allowing you to progress to more advanced techniques.
3 credits Engage fully with class activities
4 credits 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.
We begin with building blocks of statistical inference. We 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).
Discussions continue around normal distribution and the relationship between samples and populations, delving into the specifics of standardised scores and probability.
We explore the logic of hypothesis testing. You will learn how to formulate and test simple hypotheses, touching on uncertainty and errors in testing.
We progress to more complex hypothesis testing (t test and ANOVA) and you will explore comparisons to a given standard, comparisons between groups, and changes over time.
You will test relationships between variables using correlations, bivariate regression and the Chi-Square test. We discuss the principles behind these statistical techniques, the interpretation of results, and assumptions. This session gives you the foundation for multiple regression analysis.
Before the course week, you can access online pre-course materials at your own pace. 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.