UV9217 – Regression Analysis
Course description
Schedule, syllabus and examination date
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 to the course
Please note a change in frequency for this course from spring to every other autumn: Next course will be offered autumn 2024, then autumn 2026.
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 2024 (opens June 4st).
Registration deadline: 20th September.
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
- 3 credits overlap with UV9214 – Regression Analysis (discontinued).
- 3 credits overlap with UV9218 – Linear Models.
- 1 credits overlap with UV9217U.
- 1 credits overlap with UV9214U.
- 1 credits overlap with UV9218U.
Teaching
The course combines lectures, data demonstrations, and practical computer exercises.
For information on dates and location please visit the current semester website.
Examination
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.
Grading scale
Grades are awarded on a pass/fail scale. Read more about the grading system.
More about examinations at UiO
- Use of sources and citations
- Special exam arrangements due to individual needs
- Withdrawal from an exam
- Illness at exams / postponed exams
- Explanation of grades and appeals
- Resitting an exam
- Cheating/attempted cheating
You will find further guides and resources at the web page on examinations at UiO.