Programplaner og emneplaner - Student
SFF4110 Knowledge production in social sciences - quantitative methods Course description
- Course name in Norwegian
- Kunnskapsproduksjon i sosialfag - kvantitativ metode
- Study programme
-
Master Programme in Applied Social Sciences - Programme Option Child Care, part-timeMaster Programme in Applied Social Sciences - Programme Option Child CareMaster Programme in Applied Social Sciences - Programme Option Family Therapy, part timeMaster Programme in Applied Social SciencesMaster Programme in Applied Social Sciences - Programme Option Social Work, part-timeMaster Programme in Applied Social Sciences - Programme Option Social Work
- Weight
- 10.0 ECTS
- Year of study
- 2023/2024
- Curriculum
-
SPRING 2024
- Schedule
- Programme description
-
- Course history
-
Introduction
This course provides in-depth knowledge of quantitative design and statistical analysis that can be used in the students’ master’s theses, the possibilities and limitations of different quantitative designs, knowledge of different sources of data and the social science research tradition’s quality requirements for studies based on statistical analysis.
The course includes in-depth knowledge of cross-sectional design, time design, experiments and the survey method. Students will also acquire in-depth knowledge of the factors that can affect the validity and reliability of a study, as well as skills that are necessary to assess such factors. They will also learn and, not least, practise using univariate, bivariate and multivariate analyses.
The course will pay particular attention to regression analysis, and students will acquire in-depth knowledge of and skills in the use of regression-based analysis through lectures, online resources and seminars. The course aims to enable students who choose a quantitative design to use regression analysis in their master’s theses.
Language of instruction is Norwegian.
Required preliminary courses
None.
Learning outcomes
After completing the course, the student should have the following overall learning outcomes defined in terms of knowledge, skills and general competence:
Knowledge
The students have
- in-depth knowledge of quantitative designs and which types of knowledge they can contribute
- advanced knowledge of different methods used to generate/obtain empirical material, such as surveys and register data
- in-depth knowledge of quality assessment in the quantitative tradition
- insight into the possibility of generalising the results of quantitative studies
- advanced knowledge of the dissemination of results
- in-depth knowledge of regression-based analysis/statistical analysis
Skills
The students can
- engage in methodological reflection on the relationship between research questions and design
- develop a research design for their master's thesis that is appropriate to their research question
- apply statistical analysis techniques and interpret the results they produce
- develop statistical analysis models based on theory and empirical data
- engage in methodological reflection on the relationship between research design and knowledge claims
- acquire in-depth knowledge of the methodological approach chosen for their master’s degree project
- engage in methodological and critical reflection on other research literature
- evaluate research ethics issues in connection with their master's degree project
- evaluate which questions can be empirically studied
General competence
The students
- are familiar with and able to reflect on different consequences of conducting research
- can apply ethical discretion in relation to their own role as researchers and how their participation in research can intervene in people's lives
- are familiar with and are able to comply with ethical guidelines such as correct and complete source information, informant anonymisation, confidentiality and researcher responsibility
Teaching and learning methods
Teaching takes place in the form of lectures, statistics program exercises, e-lectures and assignments that the students work on independently and receive supervision on via e-based resources.
Course requirements
To be eligible to take the exam, the student must have submitted a project outline, which must be approved by the course supervisor.
All required coursework must be completed and approved by the given deadline for the student to take the exam. If the coursework requirements have not been approved, the student will be given one opportunity to submit an improved version by a given deadline.
Assessment
The exam in the course consists of two parts: a multiple-choice exam, and a written course paper.
A 2-hour multiple-choice school exam. Counts for 30 per cent of the final grade.
Semester assignment with a maximum length of 10 pages (+/- 10 percent). Font and font-size: Calibri 12 pt. Line spacing: 1.5. Tables and figures are in addition. Counts for 70 per cent of the final grade.
All parts of the exam must be passed in order to pass the course. If one of the exam components is evaluated as not passed, the failed component may be retaken.
Permitted exam materials and equipment
School exam: No aids are allowed.
Semester assignment: All aids are permitted, as long as source citation rules are complied with.
Grading scale
Grade scale A-F.
Examiners
The exam papers are assessed by one internal and one external examiner.
At least 25% of the exam papers will be assessed by two examiners. The grades awarded for the papers assessed by two examiners form the basis for determining the level for all the exam papers.
Course contact person
Åsmund Hermansen