Foundations of Quantitative Political Analysis 2102-ANG-L-D2FOQP
During the class the below topics will be discussed:
Course Structure:
The course consists of 12 sessions combining theoretical seminars with practical coding workshops.
Session 1 (2025-02-20): Organizational Issues and Introduction
Seminar: Introduction to quantitative methods in political science, course objectives, and assessment criteria
Workshop: Introduction to programming environment and tools
Session 2 (2025-02-27): Distribution
Seminar: Understanding statistical distributions, frequency distributions, and probability distributions
Workshop: Installing Python, introduction to IPython notebooks, basic Python syntax
Session 3 (2025-03-07): Standard Deviation
Seminar: Measures of central tendency (mean, median, mode) and measures of dispersion (variance, standard deviation)
Workshop: Working with pandas for basic statistical calculations
Session 4 (2025-03-13): Correlation
Seminar: Bivariate analysis, correlation coefficients, scatter plots, and interpretation of relationships between variables
Workshop: Calculating correlations using Python libraries
Session 5 (2025-03-20): Test and Data Visualization
Seminar: First test covering material from sessions 1-4; Introduction to data visualization principles
Workshop: Creating visualizations using Seaborn: histograms, box plots, violin plots, and scatter charts
Session 6 (2025-03-27): Hypothesis
Seminar: Hypothesis testing, null and alternative hypotheses, significance levels, p-values
Workshop: Implementing hypothesis tests in Python
Session 7 (2025-04-10): Sampling
Seminar: Sampling methods, sampling distributions, central limit theorem, confidence intervals
Workshop: Working with samples and statistical inference in Python
Session 8 (2025-04-17): T-test and ANOVA
Seminar: Student's t-test (one-sample, two-sample, paired), Analysis of Variance (ANOVA)
Workshop: Performing t-tests and ANOVA using Python statistical libraries
Session 9 (2025-04-24): Statistical Tests
Seminar: Overview of various statistical tests, choosing appropriate tests for different research questions
Workshop: Coding test - practical assessment of Python programming skills
Session 10 (2025-05-15): Linear Regression
Seminar: Simple and multiple linear regression, interpretation of coefficients, R-squared, model diagnostics
Workshop: Implementing linear regression models in Python
Session 11 (2025-05-22): The FINAL TEST
Seminar: Comprehensive final examination covering all course material
Session 12 (2025-05-29): Recap
Seminar: Review of key concepts, discussion of common challenges in quantitative analysis, Q&A session
AI Usage Level 4: AI Task Completion, Human Evaluation
AI is used to complete certain elements of the task, with students providing discussion or commentary on the AI-generated content. This level requires critical engagement with Al generated content and evaluating its output.
You will use Al to complete specified tasks in your assessment. Any Al created content must be cited.
Prerequisites (description)
Course coordinators
Learning outcomes
Upon completion, students will:
- Understand the specifics of using quantitative methods in political science (K_W01);
- independently find quantitative data on political processes (K_U05), present their own ideas on their analysis (K_U06), and present their own analysis of these data (K_U08);
- critically analyze media messages about political processes on the basis of quantitative data (K_U07);
- be able to use their skills in analyzing quantitative data for the purpose of actively participating in political life, following its processes, and properly exercising the profession of political scientist (K_K01, K_K02, K_K03).
Assessment criteria
20% of the final grade will be based on students' scores from two minor tests during the seminar. 40% of the final grade will be based on the coding test at the end of the workshop. The remaining 40% of the final grade will be based on the final test at the end of the seminar. All tests must be passed by students.
Students are allowed two absences from the seminar and one from the workshop.
Practical placement
Not applicable
Bibliography
Book on research method:
Pyrczak F. & Oh D. M. (2018). Making sense of statistics : a conceptual overview (Seventh). Routledge Taylor & Francis Group.
And selected chapters from:
Rich, R. C., Brians, C. L., Manheim, J. B., & Willnat, L. (2018). Empirical Political Analysis (9th ed.). London, England: Routledge.
Book on statistics:
Witte, R. S., & Witte, J. S. (20216). Statistics (11th ed.). Nashville, TN: John Wiley & Sons.
Book on statistical analysis in Python:
Navarro, D. and Weed, E. (2023). Learning Statistics with Python, https://ethanweed.github.io/pythonbook/landingpage.html
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: