Quantitative Methods in PSaPA (upper-intermediate level) 1600-SZD-SPEC-MI-PA

Classes devoted to the use of statistical analysis methods in the interpretation of quantitative results of social studies, preparing participants to directly use the results of quantitative studies in their scientific work. During the course, students will have the opportunity to conduct their own research project using data (to choose from): publicly available research conducted in Poland (such as ESS, EVS, WVS, ISSP and others) or results of quantitative research induced by the PhD candidate (e. g. as part of a doctoral dissertation). Classes are aimed at practical use of knowledge and skills in the field of quantitative research methodology and fundamentals of statistics. As part of the classes it is planned to develop knowledge and skills in the field of: statistical inference, hypothesis testing, factor analysis, linear and logistic regression analysis, using R program dedicated to statistical data analysis.

Term 2025Z:

Course Program

Block I: Upper-Intermediate to Advanced Visualization and Reproducible Research
Section A: Study Setting
Setting up R/RStudio for sophisticated analysis
• Reproducible research workflow principles
• Important packages: tidyverse, here, renv, targets
Section B: Complex Data Visualization
• Advanced ggplot2 techniques and graphic grammar;
• Plotly-based interactive visualizations;
• Multidimensional and network visualizations
• Exercise: Using ESS/WVS data to create a dashboard

Block II: Analysis of Panel Data
Section A: Fundamental Models
• Hausman test and model selection;
• Fixed effects versus random effects;
• Panel data structure in political research;
• Important packages: plm, fixest, and broom
Section B: Causal Inference and Dynamic Models
• Dynamic models and lag variables
• Practice: Examining how electoral reforms affect turnout (international data); • Difference-in-differences (DiD);
• Instrumental variables in panel data

Block III: Latent Class Analysis (LCA) and Multilevel Modeling
Part A: Latent Class Analysis
• Identifying unobservable heterogeneity
• Selecting the number of classes and assessing model fit
• Interpreting LCA results
• Key packages: poLCA, tidyLPA
• Practice: Identifying voter typologies in Central European countries
Part B: Multilevel Modeling
• Hierarchical data structures in political research
• Models with random intercepts and slopes
• Cross-level interactions
• Key packages: lme4, brms

Block IV: Advanced Categorical Methods and Causal Inference
Part A: Advanced Categorical Models
• Multinomial logit models for party choice analysis
• Nested logit models
• Zero-inflated models
• Key packages: nnet, mlogit, pscl
Part B: Causal Inference Methods
• Propensity Score Matching
• Regression Discontinuity Design
• Instrumental variables
• Key packages: MatchIt, rdrobust, estimatr
• Practice: Evaluating the impact of electoral campaigns on party choice

Databases and datasets
During the course, we will work with the following datasets:
• European Social Survey (ESS) – attitudes and behaviors in Europe
• World Values Survey (WVS) – global cultural values
• Polish Panel Survey (POLPAN) – social and economic changes in Polish society since 1988
• Comparative Study of Electoral Systems (CSES) – electoral behavior across countries
• Varieties of Democracy (V-Dem) – democratic institutions
• Quality of Government (QoG) – institutional quality and governance
• Manifesto Project Database – political party platform positions
• Fragmentation of Women’s Organizations and Women’s Empowerment, 1980-2024 (FEMPOWER) - ideological fragmentation of women's organizations

Tools and Software
Core software:
• R (latest version) with RStudio

Key R packages
# Core packages
library(tidyverse) # Data manipulation and visualization
library(broom) # Tidy statistical results
library(modelsummary) # Professional tables and plots

# Specialized packages
library(plm) # Panel data
library(lme4) # Multilevel models
library(poLCA) # Latent class analysis
library(MatchIt) # Causal inference
library(fixest) # Fast FE models
library(marginaleffects) # Marginal effects

Prerequisites (description)

Prerequisites: Basic statistics and introductory R programming.

Course coordinators

Term 2025Z:

Olga Lavrinenko

Term 2024Z:

Marta Bożewicz

Type of course

elective courses

Mode

Classroom

Learning outcomes

Knowledge | The graduate knows and understands:
WG_01 - to the extent necessary for existing paradigms to be revised - a worldwide body of work, covering theoretical foundations as well as general and selected specific issues - relevant to a particular discipline
within the social sciences
WG_02 - the main development trends in the disciplines of the social sciences in which the education is provided
WG_03 - scientific research methodology in the field of the social sciences
WK_01 - fundamental dilemmas of modern civilisation from the perspective of the social sciences
Skills | The graduate is able to:
UK_05 - speaking a foreign language at B2 level of the Common European Framework of Reference for Languages using the professional terminology specific to the discipline within the social sciences, to the extent enabling participation in an international scientific and professional environment
Social competences | The graduate is ready to
KO_01 - fulfilling the social obligations of researchers and creators
KO_02 - fulfilling social obligations and taking actions in the public interest, in particular in initiating actions in the public interest
KO_03 - think and acting in an entrepreneurial manner

Assessment criteria

Description of requirements related to participation in classes, including the
permitted number of explained absences: The prerequisite for passing the course is attendance at classes (1 absence is allowed) and conducting a research project, the progress of which will be monitored from week to week.
Principles for passing the classes and the subject (including resit session): The assessment of the subject consists of activity in the classes and a research note - a report from the own quantitative study (submitting it in term I or resit session)

Methods for the verification of learning outcomes: Final project - research note

Final project:
• Submission deadline: two weeks after the end of classes
• Format: Research paper (6000-8000 words with bibliography) + R code + data

Evaluation criteria: Substantive content of the quantitative research report: correctness of statistical methods used, selection of research questions, formulated hypotheses, analyses and conclusions (75%), and formal side: correctness and legibility of graphical forms of data presentation, preparation of footnotes, formatting of the report text (25%).

AI Usage Policy: AIAS Level 3 - AI Assisted Editing
AI can be used to make improvements to the clarity or quality of student created work to improve the final output, but no new content can be created using AI. AI can be used, but your original work with no AI content must be provided in an appendix.
Examples of permitted AI use:
• Improve R code readability and formatting
• Debug syntax errors in existing code
• Enhance code comments and documentation
• Optimize performance of student-written code
• Improve clarity of data visualizations
Submission Requirements:
Two versions must be submitted:
1. Final version: Your work after AI-assisted improvements
2. Appendix: Your original work with no AI assistance
Documentation: Brief statement describing what AI improvements were made.
Example Documentation:
AI Usage: Used ChatGPT to improve code formatting and add clearer comments to my original statistical analysis. Original unedited work included in Appendix A.

Practical placement

Bibliography

G. Wieczorkowska, J. Wierzbiński, Statystyka: analiza badań społecznych, Warszawa: Wydawnictwo Naukowe Scholar, 2010.
J. Jóźwiak, J. Podgórski, Statystyka od podstaw, Warszawa: Polskie Wydawnictwo Ekonomiczne, 2012.
R. Szwed, Metody statystyczne w naukach społecznych: elementy teorii i zadania, Lublin : Wydawnictwo KUL, 2009.
M. Nawojczyk, Przewodnik po statystyce dla socjologów, SPSS Polska, Kraków 2004.
J. Górniak, J. Wachnicki, Pierwsze kroki w analizie danych. SPSS for Winows.

A. Field, Discovering Statistics Using SPSS (and sex and drugs and rock'n'roll) SAGE, 2004, rozdz. 8.
M. Sobczyk, Statystyka opisowa, Wydawnictwo C.H. Beck, Warszawa 2010.

Term 2025Z:

Primary Literature

Methodological Textbooks:
• Kellstedt, P. M., & Whitten, G. D. (2018). The Fundamentals of Political Science Research. 3rd ed. Cambridge University Press.
• Imai, K. (2018). Quantitative Social Science: An Introduction. Princeton University Press.
• Angrist, J. D., & Pischke, J. S. (2014). Mastering 'Metrics: The Path from Cause to Effect. Princeton University Press.

Advanced Methods:
• Baltagi, B. H. (2021). Econometric Analysis of Panel Data. 6th ed. Springer.
• McCutcheon, A. L., & Hagenaars, J. A. (2002). Applied Latent Class Analysis. Cambridge University Press.
• Snijders, T. A., & Bosker, R. J. (2011). Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling. Sage.

Practical R Guides:
• Wickham, H., Çetinkaya-Rundel, M., & Grolemund, G. (2023). R for Data Science: Import, Tidy, Transform, Visualize, and Model Data (2nd ed.). O'Reilly Media.
• Long, J. A. (2024). Interactions: Comprehensive, User-Friendly Toolkit for Probing Interactions (R package version 1.2.0). https://cran.r-project.org/package=interactions. https://doi.org/10.32614/CRAN.package.interactions

Notes

Term 2025Z:

Laptops will be required for the classes. Please bring laptops to every class.

Additional information

Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system:

Description of 1600-SZD-SPEC-MI-PA in USOSweb