Knowledge Production in Social Sciences - Quantitative Methods

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.

After completing the course, the student should have the following overall learning outcomes defined in terms of knowledge, skills and general competence:

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 student is

• 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 on campus, 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).

The following coursework requirements must have been approved for the student to take the exam:

• Coursework 1: A test of basic knowledge. At the start of the course period, the student will be required to watch an online pre-course that presents basic statistical concepts and methods. The student must pass an online quiz covering the contents of the pre-course. The quiz must be passed within two weeks after the start of the course. The student may make as many attempts as needed within this two-week period The purpose is to make sure that all students have a basic foundation before regression analysis is taught.

• Coursework 2: The student must submit answers to a set of practical exercises. These tasks involve working with a specified data set using specified statistical software to produce and comment on simple statistical analyses according to instructions provided by the teacher(s). The student will be asked to explain basic software commands. The purpose is to make sure that students have basic competency in the statistical software that will be used for coursework as well as the semester assignment.

• Coursework 3: An individual project sketch. The purpose of the project sketch is to prepare the student for work on the semester assignment.

The courseworks must be completed and approved by the given deadline for the student to take the exam.

If one or more of coursework requirements 1–3 has not been approved within the relevant deadlines, the student will be given one more attempt with submission before a final specified deadline.

The exam has two parts: 1) an individual semester assignment and 2) an individual on-campus exam.

1. Semester assignment with a length of up to 10 pages. Font: Calibri, 12 pt. Line spacing: 1.5. Tables and figures are not included in the page count. The semester assignment will be in the form of a research paper, where the student presents a research problem and carries out a quantitative analysis that is relevant for that problem. The findings are then reported, followed by the student's statistical and substantive reflections.

The semester assignment accounts for 40 per cent of the final grade.

2. A four-hour on-campus exam. The on-campus exam will consist of multiple-choice questions as well as questions that require brief and precise answers.

The on-campus exam accounts for 60 per cent of the final grade.

Students who do not obtain a pass grade for their semester assignment may submit a reworked version once.

To pass the course, both parts of the exam must be passed.

School examination: No aids are permitted.

Semester assignment: All aids are permitted, as long as the student follows the rules for examinations, source use and referencing, and OsloMet's guidelines for using artificial intelligence (AI) in student assignments.

Grade scale A - F.

At least 25% of the exam papers will be assessed by two examiners. The remaining papers are assessed by one examiner. The grades awarded for the papers assessed by two examiners form the basis for determining the level for all the exam papers.

Mats Eirik Lillehagen