2823.13 - Qvantitative methods and statistics

2823.13

Qvantitative methods and statistics

10

Students must have completed the required core courses at the Department of History and Social Sciences, or have equivalent competence.

To provide students with an overview of and insight into the quantitative methods most commonly used in sociology, including descriptive and inferential statistics, as well as to support the development of their practical abilities to use these methods for analysis.

The course is divided into two main components: A) Data collection and organisation, students learn how to collect quantitative data, as well as how to set up data in the simplest possible way while maximising the information value obtained. This component emphasizes descriptive statistics. B) Data analysis, students learn how to use different analytical methods to analyse correlations between variables in a dataset. This component emphasizes inferential statistics.

The total duration of the course is 30 hours. The learning and teaching strategies used are lectures, supervised practical exercises and take-home assignments. Seeing as it is not feasible to simply acquire the skills this course aims to foster from a textbook, students are expected not just to study the required reading for each course session, but also to be present at the sessions and participate actively in all exercises. The course focuses on enabling students to develop their practical skills in the methods described, participation in practical exercises will therefore be an integral part of learning and teaching. Students will also be set take-home assignments, so they can put what they have learnt into practice.

The successful student can demonstrate the ability to: A) Gather quantitative data related to a research problem, organise this data in a simple and informative manner (in tables, graphs, pie charts, bar charts), describe the distribution in a dataset and generate an index. B) Analyse date using regression analysis, carry out a chi-squared test and use these tools to demonstrate possible correlations between variables in a dataset.

Oral examination based on a written assignment, in which methods of data collection, organisation and analysis learnt on the course are used to solve a problem chosen by the student in consultation with the lecturer. In order to sit the examination, students are required to attend 80% of all classes and obtain a pass in all take-home assignments. The lecturer will grade the take-home assignments as pass/fail.

Internal

Maximum 500 pages, in addition to material distributed by the lecturer.

Anna Sofía Veyhe