UGİ328

School of Economics and Administrative Sciences \ International Entrepreneurship

3

6

Compulsory

Turkish

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All Programs

Artificial Intelligence comparative Economic Analysis" is an introductory course in quantitative research methods. The course covers widely used data analysis techniques in the social sciences, delivered through both theoretical instruction and hands-on applications. Topics include descriptive statistics, inferental analysis, and regression models (e.g., oLS, Logit). Data analysis will be conducted using appropriate statistical software. In addition, AI-powered tools will be used as supportive resources for interpreting analysis results and preparing written reports or presentations.

• Pollock III, P. H., & Edwards, B. C. (2019). The Essentials of Political Analysis. CQ Press.
• Pollock III, P. H., & Edwards, B. C. (2018). A Stata® Companion to Political Analysis. CQ Press.
• Gujarati, D. N., & Porter, D. C. (2009). Basic Econometrics (Turkish Translation Eds.: Şenesen, Ü., & Şenesen, G. G.). Literatür Yayıncılık.
• Lisa Daniels & Nicholas Minot (2023). An Introduction to Statistics and Data Analysis Using Stata. SAGE Publications.
• Nancy Whittier, Tina Wildhagen, & Howard J. Gold (2023). Statistics for Social Understanding: With Stata and SPSS. Oxford University Press.
• Wooldridge, J. M. Introductory Econometrics: A Modern Approach, 5th ed.
• Stock, J. H., & Watson, M. W. Introduction to Econometrics, 4th ed.
• Studenmund, A. H. A Practical Guide to Econometrics.
• Schwabish, J. A. (2021). Better Data Visualizations: A Guide for Scholars, Researchers, and Wonks. Columbia University Press.
• Additional course materials will be posted on UZAK: https://uzak.etu.edu.tr/login/index.php

1. To introduce students to the fundamental quantitative research methods used in the social sciences.
2. To develop students’ skills in collecting, organizing, and analyzing data.
3. To teach descriptive and inferential statistics as well as regression analysis through practical applications.
4. To demonstrate how AI-powered tools can be used in data analysis, interpretation of results, and presentation.
5. To equip students with the ability to interpret findings and present them effectively within a scientific framework.

1. Can collect data on sustainability from different databases and official statistical sources.
2. Analyse data using basic statistical methods and Stata software.
3. Apply quantitative research methods such as regression analysis and hypothesis testing using Stata software.
4. Visualise the data using Stata software.
5. Interpret and present the findings obtained by quantitative methods in a scientific framework.

Week 1: Introduction to the course
Week 2: Introduction to Research Design and Econometrics
Week 3: Measuring, defining and transforming variables, Methods for summarising and/or describing data
Week 4: Organisation of quantitative data (Use of tables, Presentation of data with graphs), Formulation of hypotheses, Making controlled comparisons
Week 5: Making controlled comparisons, Hypothesis testing, Organisation of quantitative data continued (Use of tables, Presentation of data with graphs)
Week 6: Midterm exam
Week 7: Econometrics, Regression
Week 8: Econometrics, Regression,
Week 9: Econometrics, Regression with Dummy Variables, Planning projects with students
Week 10: Models used to explain categorical dependent variables, Article presentation
Week 11: Models used to explain categorical dependent variables, Introduction to Panel Data Analysis
Week 12: Introduction to Panel Data Analysis and Project Presentations

• Midterm
• Final
• Project