SIW4210 Knowledge Production in Social Sciences - Quantitative Methods Course description

The course provides in-depth knowledge of quantitative design and statistical analysis for use in the student's own master's thesis. The course focus on the possibilities and limitations of different quantitative designs, understanding various data sources, and quality requirements for studies based on statistical analysis within a social science research tradition.

The course includes in-depth knowledge of cross-sectional design, longitudinal design, experiment, and the survey method. Students will also gain in-depth knowledge of conditions that may affect the validity and reliability of a survey and skills in assessing such conditions. Furthermore, students will both learn and not least practice univariate, bivariate, and multivariate analyses.

There will be a particular focus on regression analysis. Through lectures, e-resources and seminars, students will gain in-depth knowledge and skills in the use of regression-based analysis. 

Language of instruction is English.

None.

A student who has completed the course has the following learning outcomes defined in terms of knowledge and skills:

Knowledge

The student has

• thorough knowledge of the research process 
• advanced knowledge of quantitative research designs: their appropriate use as well as strengths and limitations
• thorough knowledge of important concepts such as measurement, reliability, validity, and generalization.

Skills

The student can

• relate research processes to scientific theory and ethical principles
• apply research methods in a reflective manner and appropriately choose methods that are relevant for different kinds of research questions
• use relevant methods for producing, analyzing and interpreting diverse types of empirical data (e.g. surveys or register data)
• use univariate, bivariate and multivariate analysis techniques  
• understand inferential statistics 
• evaluate research based on quantitative methods in terms of methodological quality and appropriateness

General Competence

The students are

• familiar with, and critically reflect on various consequences of research
• able to use ethical judgement regarding one’s own position as a researcher and how participation in research can intervene in people’s lives.
• familiar with and can adhere to ethical regulations such as: correct and complete source information, informant anonymisation, confidentiality, researcher responsibility.

The course consists of lectures, exercises with statistical software, e-lectures and assignments that the students work with on their own and receive guidance via e-based resources.

Students will work with an analysis project consisting of univariate, bivariate, and multivariate analysis (regression analysis).

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. 

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.  

School exam: No aids are allowed.  

Semester assignment: All aids are permitted, as long as source citation rules are complied with.  

Grade scale A - F. 

The exam papers are assessed by an internal and an external examiner. A random selection consisting of at least 25% of the exam papers will be graded by two examiners which will form the basis for determining the level for all the exam papers.

Mats Eirik Lillehagen