SUİ252

School of Economics and Administrative Sciences \ Political Science and International Relations

3

6

Compulsory

English

-

The course is compulsory for the undergraduate students in the Political Science and International Relations department. Students of other departments can take it as an elective course, if approved by the department and the students’s academic advisor.

Building upon the SUI 251 – i.e., the introductory course in political science research methods, this class aims to (i) develop a more advanced understanding of variable-oriented research and (ii) introduce students to the use of statistics and quantitative data for inference-making in the study of politics.

Main textbook: Galderisi, P. (2015). Understanding Political Science Statistics: Observations and Expectations in Political Analysis. New York: Routledge.

Supplementary materials include manuals and online video tutorials for the statistical analysis software taught in this class. • Navarro, D.J., & Foxcroft, D.R., (2022). Learning Statistics with jamovi: A tutorial for psychology students and other beginners. (Version 0.75). DOI: 10.24384/hgc3-7p15
• Datacamp jamovi tutorial series accessible as a Youtube playlist: https://www.youtube.com/playlist?list=PLkk92zzyru5OAtc_ItUubaSSq6S_TGfRn

Additionally, the methods covered in this course are examined through a selection of up-to-date research articles. Here are the references for the selection for the Fall 2024:
• Aliyev, H. (2020). Why Are Some Civil Wars More Lethal Than Others? The Effect of Pro-Regime Proxies on Conflict Lethality. Political Studies, 68(3), 749–767.
• Broz, J., Zhang, Z., & Wang, G. (2020). Explaining Foreign Support for China’s Global Economic Leadership. International Organization, 74(3), 417-452.
• Fox, S., & Hoelscher, K. (2012). Political order, development, and social violence. Journal of Peace Research, 49(3), 431–444.
• Park, J. (2013). Forward to the future? The democratic peace after the Cold War. Conflict Management and Peace Science, 30(2), 178-194.

Following an introduction to the essentials of descriptive statistics and the fundamentals of quantitative research, students will familiarize themselves with the inferential statistics, which is the art of estimating population characteristics based on survey data. The class will conclude with an overview of the basic statistical tools for bivariate analysis and multivariate statistical models, which provide some strong hypothesis-testing avenues in contemporary political science and international relations.
As a part of the statistics-in-practice endeavor, students will have bi-weekly assignments that develop their skills in (1) transforming concepts into variables, (2) data collection, and (3) data analysis. They can prepare these assignments individually, or in groups.

1. Students will be able to define, explain and use key statistical concepts, including but not limited to: populations, samples, variables (categorical and continuous), levels of measurement, descriptive statistics (mean, median, mode, standard deviation, variance), probability distributions (normal, t-distribution, chi-square), and inferential statistics.
2. Students will be able to (a) calculate and interpret descriptive statistics to summarize and describe data sets, (b) communicate their findings through tables, graphs, and written summaries.
3. Students will be able to apply inferential statistical methods to estimate population parameters from sample data, including constructing confidence intervals and conducting hypothesis tests.
4. Students will gain a foundational understanding of bivariate multivariate statistical models and their applications in political science and international relations. They will be able to conduct beginner to intermediate level quantitative analysis by using a statistical analysis software.
5. Students will be able to interpret quantitative analysis and draw meaningful conclusions about social and political phenomena. They will be able to communicate these findings to both technical and non-technical audiences.

Week 1: Presentation of the course & some key concepts for the scientific study of politics
Week 2: Translating concepts into numbers for quantitative research: variables, correlation, causation
Week 2: Quantitative data, spreadsheet, and introduction to descriptive statistics Components of Inference, Key Concepts, and Challenges
Week 3: Descriptive Statistics: Measures of dispersion and central tendency & Visualization: Descriptive charts and histograms
Week 4: Measures of Dispersion for Interval Variables: Range, IQR, variance, and standard deviation, Introduction to Excel: How to prepare and edit data for quantitative analysis
Week 5: Pillars of statistical inference: Assumption of normality, standardized scores, and relative observation, How to collect quantitative data in social sciences: surveys and indexes
Week 6: Inferential statistics – Interpreting sample statistics to understand the population
Week 7: Introduction to jamovi: Getting familiar with the interface, menus and using jamovi for descriptive statistics
Week 8: Introduction to hypothesis testing: Bivariate statistics
Week 9: Jamovi session on means comparison and contingency tables, Limits of bivariate statistics: Why one independent variable is not “enough.”
Week 10: Dealing with interval variables in hypothesis testing: Correlation and introduction to regression analysis
Week 11: Linear and logistic regression models, jamovi Session on regression analysis
Week 12: How to make sense of / analyze quantitative research articles

• Midterm 30 %
• Final 40 %
• Assignments 25 %
• Participation 5 %