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.

Sustainable development implies inter-, multi- and transdisciplinary encounters. The course will introduce the candidates to different research methodologies especially suitable to illuminate complex phenomena and expose the candidates to opportunities and challenges of inter- and transdisciplinary research collaboration. The candidates will get comprehensive insight into Responsible Research and Innovation and acquire skills in analysis and reflection on ethical dilemmas in research. The candidates will learn to develop a research design appropriate for their PhD project. The syllabus may be abbreviated and adapted to fit the interest of the participants of the course in cooperation with the supervisors.

Completed Master’s degree (120 ECTS credits) or equivalent education level.

Upon completing the course, the candidates are expected to have gained the following learning outcomes (knowledge, skills, and general competence).

Knowledge

The candidate:

• has advanced knowledge about opportunities and challenges of inter- and transdisciplinary research
• has comprehensive knowledge about research ethics
• has a good understanding of Responsible Research and Innovation (RRI) and how to translate this into new, responsible practices.

Skills

The candidate:

• can reflect critically on strengths and weaknesses of various methods for production of knowledge
• can make a valid interdisciplinary, transdisciplinary or multidisciplinary research design
• has advanced skills in co-creation of knowledge
• can contribute to advanced collaboration in inter- and transdisciplinary disciplinary projects
• can analyse and reflect on ethical dilemmas in data collection

General competence

The candidate:

• can communicate in inter- and transdisciplinary teams
• can identify transfer value from empirical studies to other areas
• can translate the principles of Responsible Research and Innovation (RRI) into practice for socially and environmentally robust science and innovation

Learning activities may include lectures, workshops, fieldwork, group work, and individual work.

Active participation in the seminars is necessary to adequately understand the course material and themes. Participation is therefore mandatory, and candidates are expected to attend all days of teaching and required to attend at least 80 percent of teaching days. In special cases of documented illness, the course leader may accept exceptions to this requirement. In these cases, lack of participation can be substituted with alternative arrangements such as writing a reflection note.

Course requirements are assessed as confirmed or not confirmed. The course requirement must be completed and confirmed within the given deadline in order to have the right to submit a final essay.

The course requirements are:

• A plenary presentation on a subject decided in collaboration with the course lecturer.
• A prepared opposition to at least one other presentation.
• 80 % attendance is required

Individual essay (4000-5000 words). The essay will discuss the most important theoretical aspects from the syllabus, with relevance for the candidate’s PhD project. Cover page, illustrations, and list of references come in addition.

If an essay is graded fail, the candidate has one opportunity to resubmit a revised essay within a given time-period.

All examination support material is allowed as long as source reference and quotation technique requirements are applied.