Schedule, syllabus and examination date

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Course content

Content

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.

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

Organizer: Department of Teacher Education and School Research (ILS).

Learning outcome

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

Admission

This 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.

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.

Registration deadline: March 25, 2022

Prerequisites

Formal prerequisite knowledge

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.

Overlapping courses

Teaching

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

For more information please visit the current semester website.

Examination

Course credits:

Approved participation and paper: 4 credits.

Papers are to be submitted electronically in Canvas.

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).

More information will be published in Canvas.

Evaluation

Evaluation form is sent to participants after each course.

Facts about this course

Credits

4

Level

PhD

Teaching

Every spring

Examination

Every spring

Teaching language

English