Quantitative research methods 1600-SZD-N-MBi-BPA

https://classroom.google.com/c/ODQyMDY5NTUwODg4?cjc=ic2ue7gf

Course outline
Session 1
What does it mean to “do quantitative political science”?
Quantification as abstraction. Numbers as arguments. Why political science counts. Strengths and limits of statistical reasoning.
Session 2
From theory to research question
What makes a question empirically testable? The difference between normative, descriptive, and explanatory questions. Hypotheses and expectations.
Session 3
Concepts, variables, and measurement
Operationalization. Validity and reliability. Measurement error. Examples from democracy, gender, inequality, participation.
Session 4
Data in political science
Surveys, administrative data, experiments, and observational data. Cross-national and longitudinal designs. What data can and cannot tell us.
Session 5
Description as analysis
Distributions, central tendency, variation. Tables and graphs as theoretical statements. When description is analytically meaningful.
Session 6
Association and correlation
Covariation, correlation coefficients, and their interpretation. Spurious relationships. Why correlation is not causation (and why we still use it).
Session 7
Introduction to causal thinking
Causal inference in observational research. Counterfactual logic. Confounding, controls, and selection bias (conceptually, not technically).
Session 8
Regression as a modeling strategy
What regression does conceptually. Dependent and independent variables, controls, coefficients. Reading regression tables without fear.
(Advanced track: model assumptions, functional form, interaction terms.)
Session 9
Interpreting results and uncertainty
Statistical significance vs. substantive significance. Confidence intervals. Effect sizes. Common misinterpretations and abuses.
Session 10
Evaluating quantitative research and designing your own
How to read quantitative articles critically. Typical design failures. Aligning theory, data, and methods. Discussion of students’ research ideas.

Term 2024L:

Objective of the Course
The course, Quantitative Research Methods in Political Science, is designed to equip students with the necessary skills and knowledge relevant in the field, which forms an important methodological approach in the analysis of phenomena within social and political contexts. The course ensures that students are consistent in examining and interpreting quantitative data globally and comparatively by combining it with relevant sociological and political theories. With this in focus, the course will help students to become proficient in using quantitative methods for analyzing complex, global social and political problems by application of both classical and contemporary methods.
Fundamental Themes Addressed
1. Introduction to Quantitative Research in Political Science
• Scientific methods in political and social inquiry.
• Key concepts in the inquiry: variables, hypotheses, causality, and research design.
2. Data Collection and Management
• Categories of data, such as surveys, secondary data, administrative/official statistics, and big data.
• Ethical issues in data gathering and management.
3. Descriptive Statistics and Data Visualization
• Summarizing and presenting data using tables, charts, and other forms of data visualization.
• Investigating the patterns and trends within political and social phenomena.
4. Inferential Statistics
• Probability, sampling methods, and hypothesis testing.
• Methods of data interpolation and extrapolation to broader populations.
5. Regression Analysis
• Understanding relationships between variables.
• Linear, logistic, multivariate regression as tools for political analysis.
6. Quantitative Topics in Political Science
• Exploring such themes as voting behavior, public opinion, and policy evaluation using quantitative methods.
7. Structuralist and Institutionalist Ties
• Linking quantitative analysis to theoretical frameworks, including structuralist and institutionalist approaches.
8. Working with Statistical Software
• Hands-on training with statistical software, e.g., SPSS, Stata, or R.
9. Interpretation of the Results and Scientific Communication
• Writing clear, evidence-based reports and presenting findings effectively.

By the end of the course, students will have the knowledge, skills, and tools to critically evaluate empirical research, conduct their own quantitative studies, and apply sociological and political theories to explain political and social phenomena.

Type of course

obligatory courses

Course coordinators

Olga Lavrinenko

Mode

Classroom

Learning outcomes

Learning objectives
By the end of the course, students will be able to:
• understand the epistemological assumptions of quantitative research in political science;
• formulate empirically testable research questions;
• translate political concepts into measurable variables;
• distinguish between description, association, and causal inference;
• critically assess quantitative research designs and statistical claims;
• read and interpret quantitative political science articles with methodological awareness;
• design a feasible quantitative component of a research project.

Assessment criteria

Final project
A short quantitative research design memo (5–7 pages), including:
• research question;
• theoretical motivation;
• proposed data;
• key variables and expected relationships;
• discussion of potential limitations.
Given the heterogeneous methodological background of the group, the empirical component of final project is flexible and depends on students’ prior methodological training. Students with limited quantitative training may complete final project without conducting statistical analyses, provided that methodological reasoning is explicit and well developed. Students with prior quantitative training are expected to include an empirical data analysis component consistent with their methodological skills.

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.

Bibliography

Readings
Core textbook
Kellstedt, P. M., & Whitten, G. D. (2018). The Fundamentals of Political Science Research (3rd ed.). Cambridge University Press.
Sessions 1–2: Quantitative logic and research questions
• Kellstedt & Whitten, ch. 1–2
• King, G., Keohane, R. O., & Verba, S. (1994). Designing Social Inquiry, ch. 1
Session 3: Concepts, variables, measurement
• Kellstedt & Whitten, ch. 5
• Adcock, R., & Collier, D. (2001). Measurement validity: A shared standard for qualitative and quantitative research. American Political Science Review, 95(3), 529–546.
Session 4: Data in political science
• Kellstedt & Whitten, ch. 4
Sessions 5–6: Data description and correlation
• Kellstedt & Whitten, ch. 6–7
• Kastellec, J. P., & Leoni, E. L. (2007). Using graphs instead of tables in political science. Perspectives on Politics, 5(4), 755–771.
Sessions 7–8: Causal thinking and regression
• Kellstedt & Whitten, ch. 3, 8–9
• Angrist, J. D., & Pischke, J.-S. (2015). Mastering ‘Metrics: The Path from Cause to Effect, ch. 1–2. Princeton University Press.
Session 9: Interpretation and uncertainty
• Rainey, C. (2014). Arguing for a negligible effect. American Journal of Political Science, 58(4), 1083–1091.
• Wasserstein, R. L., & Lazar, N. A. (2016). The ASA statement on p-values: Context, process, and purpose. The American Statistician, 70(2), 129–133. (optional)
Session 10: Evaluating research and design
• Gerring, J. (2012). Social Science Methodology: A Unified Framework (2nd ed.), ch. 6. Cambridge University Press.

Term 2024L:

Islam, M. R., Khan, N. A., & Baikady, R. (Eds.). (2022). Principles of social research methodology. Springer.
Mukherjee, S. P., Sinha, B. K., & Chattopadhyay, A. K. (2018). Statistical methods in social science research. Springer.
Stockemer, D. (2019). Quantitative methods for the social sciences: A practical introduction with examples in SPSS and Stata. Springer.
Stockemer, D. (2023). Quantitative methods for the social sciences: A practical introduction with examples in R. Springer.

Term 2025L:

Additional information

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

Description of 1600-SZD-N-MBi-BPA in USOSweb