Regression Analysis

Learning Objectives of the Course:

• gain competence in conducting standard regression analyses
• understand the benefits and limitations of regression as a data analysis approach
• become critical consumers of research involving regressions

The course has been developed for PhD candidates affiliated with the Faculty of Educational Sciences (UV), but others may also apply. As a minimum requirement, all participants must hold at least a Master`s degree.

Registration for autumn semesters opens June 4st.

PhD candidates affiliated with the Faculty of Educational Sciences will be given priority and are to register through Studentweb.

Others may apply through the application form autumn 2026 (opens June 4st).

Registration deadline: 20th September.

Wondering about available spots? Contact our department PhD administration: [email protected]

The course combines lectures, data demonstrations, and practical computer exercises.

For information on dates and location please visit the current semester website.

Approved participation, course activities and final paper: 4 credits.

Formal Requirements:

• 80% attendance
• Completion of theory-based problem set. The problem set will include a set of exploratory and regression analyses. You will be expected to draw implications from exploratory analysis for future regression models and make critical inferences and draw substantive conclusions from the regression analyses. Most problems will require short paragraphs to be written.
• Short research paper that includes a multiple regression analysis (7-10 pages).

Papers are to be submitted electronically in Canvas.

More information will be published in Canvas.