Advanced Quantitative Methods in the Social Sciences

This course is an advanced-level methods course within the Master’s Programme in Political Science. It is also offered as a freestanding course.

Admission to the course requires a pass in at least 5 credits in quantitative methods or statistics at first-cycle level, or equivalent, as well as at least 7.5 credits from a core course at second-cycle level in a social science subject, or equivalent knowledge. In addition, language proficiency equivalent to English 6 or English Level 2 is required.

This is a course in advanced quantitative data analysis for the social sciences. The course provides practical tools for data analysis, statistics, and programming, with a focus on real-world data from social science research.

The course covers concepts such as causality, operationalization, and prediction, as well as probability theory and specific statistical methods.

After successful completion of the course, the student will be able to:

Knowledge and understanding

• Account for central principles in quantitative social science research, including operationalization, measurement, sampling, causality, probability, and inference, and relate these to specific research questions.
• Describe and compare basic quantitative analysis strategies and state reasonable assumptions for their use.
• Explain basic principles of programming and data analysis, including how code, data management, and visualization fit together in a reproducible analysis workflow.

Skills and abilities

• Conduct quantitative data analysis: import, structure, clean, and transform data in a transparent and efficient manner, and document the workflow.
• Interpret and evaluate output from common statistical analyses and draw correct conclusions within the scope of the method’s assumptions.
• Select and produce appropriate visualizations and summaries to answer given questions about data, and identify misleading or unsuitable presentations.

Judgement and approach

• Critically assess quantitative studies by evaluating research design, data quality, assumptions, and interpretations, and identify reasonable alternative explanations and limitations.
• Identify and evaluate common risks of incorrect conclusions in quantitative analyses and propose relevant measures.
• Apply principles of transparent quantitative analysis in given scenarios.

The course is taught through lectures, exercises, seminars, and a written exam.

Language of instruction: English

The course is assessed through participation in the mandatory seminars and through a written in-person exam.

The introductory seminar corresponds to 1 credit; the remaining six seminars correspond to 1.5 credits each; and the in-person exam corresponds to 5 credits. The in-person exam is graded Pass with Distinction (VG), Pass (G), or Fail (U), while the seminars are graded Pass (G) or Fail (U).

A student who does not participate in a mandatory component of the course may complete an alternative assignment. The assignment is described in the course guide.

Supplementary completion of an assessed task is permitted in the event of a failing grade (U) for the mandatory seminars, and is specified in the course guide. If the student does not complete the task within the stipulated time, the component is failed. In the event of a failing result on the in-person exam, a re-exam must be taken.

Restrictions on the use of generative AI are stated in the course guide. Students are obliged to inform themselves about the current rules for examining elements such as assignments, seminar papers and other forms of exams in the course in question.

If a student who has been failed twice for the same examination element wishes to change examiner before the next examination session, such a request is to be granted unless there are specific reasons to the contrary (Chapter 6 Section 22 HF).

If a student has received a certificate of disability study support from the University of Gothenburg with a recommendation of adapted examination and/or adapted forms of assessment, an examiner may decide, if this is consistent with the course’s intended learning outcomes and provided that no unreasonable resources would be needed, to grant the student adapted examination and/or adapted forms of assessment.

If a course has been discontinued or undergone major changes, the student must be offered at least two examination sessions in addition to ordinary examination sessions. These sessions are to be spread over a period of at least one year but no more than two years after the course has been discontinued/changed.

For the course, one of the following grades is awarded: Pass with Distinction (VG), Pass (G), or Fail (U).

To receive the grade Pass (G) for the course as a whole, the student must obtain a passing grade on all assessment components. To receive the grade Pass with Distinction (VG), the student must obtain the grade Pass with Distinction (VG) on the written exam and Pass (G) on all other assessments.

The students will be given the opportunity to do a course evaluation. The results of and possible changes to the course will be shared with students who participated in the evaluation and students who are starting the course.