UV9217 – Regression Analysis

Schedule, syllabus and examination date

Choose semester

Course content

Spring 2020: As a precautionary measure, to help prevent the Corona virus from spreading, this PhD course will be held with a digital solution. All participants will be notified.


In the repertoire of quantitative methods, regression analysis is a key approach to analyzing relations between variables—moreover, it forms the basis for more advanced analytic techniques, such as structural equation modeling and multilevel analysis.

The course provides an introduction into the basic principles of regression analysis, including its assumptions, interpretation, and implementation in the statistical software package SPSS. Different regression models are introduced (e.g., multiple linear regression, logistic regression), and their application in educational and psychological contexts is illustrated. Furthermore, participants learn how to deal with certain data issues (e.g., categorical predictors and outcomes, multicollinearity) and select appropriate regression models. Finally, the course introduces some more advanced regression techniques, such as moderation and mediation models.

Prior to the course, participants should familiarize themselves with basic statistical concepts, including statistical significance, confidence intervals, and correlations. Prior knowledge of using SPSS for data management (including the use of the SPSS syntax) is desired yet not required.

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

Organizer: Department of Teacher Education and School Research (ILS) in collaboration with the Department of Special Needs Education (ISP).


PhD candidates affiliated with the Faculty of Educational Sciences register through Studentweb.

Others may apply through the application form.

PhD candidates at The Faculty of Educational Sciences will be given priority. As a minimum requirement, all participants must hold at least a Master's degree.

Registration deadline: May 4 2020

Overlapping courses

3 credits overlap with UV9214 – Regression Analysis (discontinued)


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

For more information please visit the current semester website.


Course credits: 

Approved participation without paper (at least 80% participation required): 1 credit. Approved participation and paper: 4 credits.

Papers are to be submitted electronically in Canvas.

More information can be found on current semester site and Canvas.

Facts about this course






Every spring


Every spring

Teaching language