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This course introduces students of international relations and security studies to political research methods and specifically to their quantitative branch. Given the key role data analysis enjoys in political research and practice, and given how prominently data-analytical skills are demanded in jobs in politics and beyond, the course serves an important role in the development of students’ competences. Students will learn how to create or collect quantitative political data and how to use them to solve practical and theoretical political problems. The classes cover a range of topics from research design principles to data collection and visualization, probability and inference, descriptive statistics, and a series of inferential statistical techniques. Each class discusses the core tenets of the issues covered, but it also introduces more advanced material and insights from the practice of applied political research. Some specialty topics in most recent quantitative methods advances, such as natural language processing and large langauge models, are also touched upon. The class does not assume any prior knowledge of statistics but it does assume a good deal of motivation on the part of students, as the learning curve may be somewhat steeper for some. The powerful free statistical package R will be used in the class, in combination with RStudio. Students thus also acquire practical transferable coding (programming) skills. Poslední úprava: Parízek Michal, doc., Ph.D. (15.09.2025)
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The specific objectives of the course are:
Poslední úprava: Parízek Michal, doc., Ph.D. (15.09.2025)
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Successful completion of this course requires first and foremost active interest in the subject matter. On the formal level, this means you need to:
I recommend that students attend all classes, though this is not formally required. Students are also well advised to keep up with the assigned readings as the material covered is highly cumulative. The standard Faculty grading scheme is applied:
Poslední úprava: Parízek Michal, doc., Ph.D. (13.11.2025)
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Core textbook:
Useful alternative textbooks, providing slightly different frameworks and explanations:
Additional readings:
Poslední úprava: Parízek Michal, doc., Ph.D. (15.09.2025)
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The course consists of weekly lectures. Attendance is highly recommended, although strictly speaking this is not a formal requirement. A significant element of individual work on data analysis is present, whereby students perform at home, on their computers, statistical analysis in MS Excel and in R. This is time-consuming and, for many, a demanding part of the course, but ultimately this individual work is a key part of the learning process. In addition to the weekly classes, across the semester there will be two tutorial sessions with Stepan Jaburek, mostly for troubleshooting purposes. These are meant especially for those who might struggle with some technical aspect of the course or with specific parts of the material covered. The use of AI-powered tools, including generative AI, is permitted in the course for all programming- and analysis-related tasks. However, note that even powerful generative AI models are language models only, as we will learn, there is no guarantee that what they "say" has a close connection to reality. Note that the use of generative AI tools is not permitted for the non-computational, verbal parts of the mid-term and final tests (the open questions there oriented at theory). Poslední úprava: Parízek Michal, doc., Ph.D. (15.09.2025)
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Mid-term The mid-term test will take place after the 7th class (week 9 of the semester). The mid-term will be available online on Moodle (class 7) as a take-home exam, so students are free to use any online or offline material they deem suitable. It will take place over a span of 4 days, on November 25-28. Within that period, students pick freely a time window within which to do the mid-term. Students can take up to 4 hours to do the mid-term, but in practice most will need far less. Note that the mid-term is a strictly individual work. The mid-term consists of two parts focused 1) on data and calculations in R and Excel, and 2) on theory, based primarily on lectures and readings.
Final exam The exam consists of two parts focused 1) on data and calculations in R and Excel, and 2) on theory, based primarily on lectures and readings. To pass the exam, students need to obtain 51% or more from each part of the exam. The exam will take place in PC labs in Jinonice. For the exam, there will be 5 dates which students will be able to sign up for. Typically, on an exam date, there will be two or three slots of 2-2.5 hours each. Students will not be able to use any online materials during the exam (the PCs will be disconnected from the internet), but they are allowed to use any offline material they bring on a USB stick/flash drive. Poslední úprava: Parízek Michal, doc., Ph.D. (13.11.2025)
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The troubleshooting tutorials will be scheduled later. Poslední úprava: Parízek Michal, doc., Ph.D. (12.09.2025)
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