Course Objectives:

This course covers advanced empirical research design, including developing questions, creating hypotheses, designing research, and analyzing data. Students will gain hands-on experience using statistical software and learn to properly analyze data using appropriate statistical tests. The course will also cover effective communication of experimental findings, helping students develop skills to communicate their research findings to different audiences effectively. By the end of the course, students should be able to design and conduct their experiments and analyze the data they collect using statistical techniques appropriate for their research questions. They should also effectively communicate their experimental findings to scientific audiences. This will allow them to stay up-to-date with the course content and participate in scientific discussions.

In addition to attending lectures and seminars, students will be required to complete a data analysis project, which will be presented as an oral seminar presentation. This project will allow students to apply the data science skills they have learned to a real-world social science research problem. To complete the course, students must accumulate at least 70% of their grade through class activities, including midterm exams and written and orally presented seminar projects. This will ensure that students regularly engage with the course content and actively work towards mastering the skills and concepts covered in the course.

Course Content:

Introduction to Modern data. Introduction to programming language for statistics. Statistics refresher. Exploratory Data Analysis (Principal Component Analysis, Factor Analysis, Cluster Analysis). Confirmatory Data Analysis (Multiple Linear Regression, Survival Analysis, Basics of Machine Learning, Network Analysis, Time Series Analysis, Text Analysis, Basics of Natural Languange Processing). Empirical Project.

Hair Jr., J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate Data Analysis. Pearson.

Stevens, J. P. (2009). Applied Multivariate Statistics for the Social Sciences. Routledge.

 Izenman, A. J. (2013). Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning. Springer.

Sharma, S. (1996). Applied Multivariate Techniques. John Wiley & Sons.

Bartholomew, D. J., & Steele, F. (2008). The Analysis of Multivariate Social Science Data. CRC Press.

Attendance is crucial for success in this course, and students are expected to attend at least 70% of lectures and seminar sessions.

Class activities: Midterm exam (written), seminar presentation (written and oral) and final exam.

Final course grade is based on 100 points earned through student’s continuous involvement in class activities:

Fair (2) – 50 to 64 points

Good (3) – 65 to 79 points

Very good (4) – 80 to 89 points

Excellent (5) – 90 to 100 points

Earning credits:

Class activities contribute to 50% of the grade:

Seminar – maximum 40 points

Seminar presentation – maximum 10 points

Final exam contributes to 50% of the grade:

Final exam – maximum of 50 points (50% of correct answers necessary for passing)