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, 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. (15.09.2024)
Cíl předmětu - angličtina
The specific objectives of the course are:
to familiarize students with research design principles
to help students appreciate the variety of quantitative data available and learn to find, or create, data suitable for own research tasks
to help students understand and use the key tools of descriptive statistics
to help students understand and use basic tools of inferential statistics, including multiple regression
to help students appreciate the possibilities stats give them for their own future careers and academic work
Poslední úprava: Parízek Michal, doc., Ph.D. (15.09.2024)
Podmínky zakončení předmětu - angličtina
Successful completion of this course requires first and foremost active interest in the subject matter. On the formal level, this means you need to:
read carefully the required textbook readings and do the quizzes based on these readings (always available on the course Moodle site, altogether 20%)
do all the problem sets that implement the material covered in class in MS Excel or in R (always available on the course Moodle site, altogether 20%)
take the mid-term test (10% of grade); no specific threshold for passing is set
take the final test (50% of the grade) and obtain 51% of the points available or more
actively participate in the discussion fora on Moodle (up to 5% bonification)
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 following grading scheme is applied:
100-91: A
90-81: B
80-71: C
70-61: D
60-51: E
50 or less: F (fail)
Note that as much as 40% of the grade is based on your regular assignments during the semester. This means your final grade will build up over the entire course in a very cumulative manner. Having said that, doing well on the mid-term test and especially on the final test is equally important.
The grading scheme is designed so that everyone who regularly prepares himself/herself for the classes will have no problem passing. The relatively benevolent grading scheme notwithstanding, please note that this course does require continuous work. If one loses track of what is happening in the course, it may be extremely difficult to catch up. So students should understand that continuous work on the assignments and the readings is a necessary condition for the success in this course.
Mid-term review takes place after class 8.
Poslední úprava: Parízek Michal, doc., Ph.D. (15.09.2024)
Literatura - angličtina
Core textbook:
Kenneth J. Meier, Jeffrey L. Brudney, and John Bohte, Applied Statistics for Public and Nonprofit Administration, 8th ed. (Wadsworth, 2010)
Useful alternative textbooks, providing slightly different frameworks and explanations:
Imai, Kosuke. Quantitative Social Science: An Introduction. Illustrated edition. Princeton: Princeton University Press, 2018.
Gerring, John, and Dino Christenson. Applied Social Science Methodology: An Introductory Guide. Cambridge, United Kingdom ; New York: Cambridge University Press, 2017.
Additional readings:
Alan Bryman, Social Research Methods (Oxford: Oxford University Press, 2012).
Andy Field, Jeremy Miles, and Zoë Field, Discovering Statistics Using R (London: Sage, 2012).
James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. An Introduction to Statistical Learning: With Applications in R (New York: Springer, 2013).
Gary King, Robert O. Keohane, and Sidney Verba, Designing Social Inquiry: Scientific Inference in Qualitative Research (Princeton: Princeton University Press, 1994).
Michael S. Lewis-Beck, Applied Regression: An Introduction (SAGE Publications, Inc, 1980).
Rein Taagepera, Making Social Sciences More Scientific: The Need for Predictive Models (Oxford University Press, USA, 2008).
Poslední úprava: Parízek Michal, doc., Ph.D. (02.09.2024)
Metody výuky - angličtina
The course consists of weekly lectures. Attendance is highly recommended, although strictly speaking this is not a formal requirement. From week 3, 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 Tereza Plistilova, 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. (24.09.2024)
Sylabus - angličtina
Introduction and motivation; (quantitative) research as seeking answers to the right questions
Research design, inference, and causality
Data: measurement theory, levels of measurement, and difficult-to-measure phenomena
Getting quantitative data, including from political text
Key descriptive statistics: measures of central tendency and measures of dispersion
Probability; standard normal distribution, binomial distribution
Statistical inference and hypothesis testing
T-test (testing the difference between two groups) and experiments
Categorical and ordinal variables analysis: cross-tabs and chi-square; measures of association
Bivariate regression, principles, assumptions, and fit
Multiple regression
Model specification, interactions, and what's next
The troubleshooting tutorials will be scheduled later.
Poslední úprava: Parízek Michal, doc., Ph.D. (29.09.2024)
