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.

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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 portfolio consisting of univariate, bivariate, and multivariate analysis (regression analysis).

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Semester assignment with a maximum volume of 10 pages (+/- 10 percent), including spaces. Font and font-size: Calibri 12 pt. Line spacing: 1.5.

A student who has completed his or her qualification has the following learning outcomes defined in terms of knowledge, skills and general competence:

Knowledge

The student

• has advanced knowledge of key concepts in the theory and comparative analysis of social welfare policies
• has advanced knowledge of explanatory models, such as the logic of industrialism, institutional and state-centered approaches, and power resource theory
• has advanced knowledge of normative perspectives on social welfare
• has advanced knowledge of concepts that are relevant for assessing the outcomes of social welfare policies

Skills

The student

• can apply theoretical concepts, normative perspectives and explanatory models in the analysis of social policies, in a national as well as international context

General competence

The student

• is able to critically evaluate the use of concepts and models in academic as well as political debates about social policies

The course is organised into a series of lectures. Students are expected to play an active role by participating in discussions of course themes during lectures.

Students must submit a coursework requirement of 10 pages (+/-10%) on a topic relevant to the course, to be approved by the course lecturer. The topic of the work requirement will reflect main areas of the course curriculum and lectures. The coursework requirement should be written in groups of 3-4 students. Students whose papers are not approved after the first submission will be given the chance to resubmit once and they may be required to resubmit papers of about 6 pages individually. Students whose papers are not approved after two submissions will disqualify from sitting the final examination.

The student's learning outcome will be assessed on the basis of an individual six-hour written school examination.