Quantitative methods (BPS310)
Course description for study year 2025-2026. Please note that changes may occur.
Course code
BPS310
Version
2
Credits (ECTS)
10
Semester tution start
Autumn
Number of semesters
1
Exam semester
Autumn
Language of instruction
Norwegian
Content
The course provides an introduction to quantitative methods for analyzing psychological data. The course gives students an introduction to statistical procedures for analyzing quantitative research data, such as data from questionnaire responses and psychological tests.
The students will be introduced to basic statistical inference, i.e. drawing conclusions about unknown quantities, parameters, related to the population and the use of statistics programs, e.g. SPSS. The course will step by step review descriptive statistics of one variable (univariate analysis), relationship between two variables (bivariate analysis), and statistical modeling of relationships (regression analysis). The course will review traditional statistical tests (including chi-square test, T-test, ANOVA and correlation), and put these in context with linear and logistic regression. The course will also cover mixed methods approaches.
The course will provide a broad introduction to quantitative methods and knowledge of when different methods are used. The students will learn to apply the various methods and discuss the results from these. Furthermore, students will learn to document procedures, as well as to discuss the importance of being able to reproduce results at a later stage.
Learning outcome
Knowledge
After completing the course, the student will be able to:
- basic knowledge of various quantitative methods, their assumptions and challenges
- basic knowledge of statistical analysis of psychologically relevant data
- knowledge of statistical inference
- knowledge of the use of statistical software
- knowledge of research ethics and principles of transparency in scientific activity
Skils
After completing the course, the student will be able to:
- import data from common formats (open text format, CSV and similar)
- perform common statistical analyzes in one or more common statistical programs (SPSS)
- communicate results from statistical analyzes in text, similar to how it is done in relevant journals
- document the above-mentioned process (from import to results section) in a transparent and verifiable manner so that all results, tables and figures can be reproduced by others without other documentation
- be able to critically assess statements that are wholly or partly based on statistics
General competence
After completing the course, the student will be able to:
- have a basic understanding of key quantitative methods in psychology
- be able to apply quantitative methods within a sound and satisfactory scientific standard
- be able to use statistics programs to analyze various forms of quantitative data
Required prerequisite knowledge
Recommended prerequisites
Exam
Multiple choice and home exam
| Form of assessment | Weight | Duration | Marks | Aid |
|---|---|---|---|---|
| Multiple choice | 1/2 | Letter grades | ||
| Home exam | 1/2 | 7 Days | Letter grades | All |
Students who have not passed either the ordinary or the new and postponed exam, are referred to the next ordinary exam. Students must take the exam according to any new and revised course description for the course but may choose not to attend seminars and lectures.
Coursework requirements
All of the compulsory activities must be approved in order for the student to be eligible for examination. Approval/non-approval of compulsory activities is announced on Studentweb, normally no later than 7 days before the examination. Students who lack approval of one or more compulsory activities will be withdrawn from the examination.
Compulsary activities:
Multiplechoice assessement
Course teacher(s)
Course coordinator:
Aleksandra SevicStudy Program Director:
Muriel Marisa Katharina BruchhageMethod of work
Various teaching and learning methods are used in the subject, including lectures, seminars, demonstration videos and individual work.
The student is expected to prepare for the lectures through literature studies. Recommended literature is indicated in the lecture plan.
At the seminars, the different theoretical directions and methods are treated via different learning activities, including hands-on practical methods in statistics. The seminars are structured around practical problem solving and guided skills training, including data collection, data analysis, interpretation and dissemination of results.