2823.14 - Quantitative Methods and Statistics

2823.14

Quantitative Methods and Statistics

10

The student must have completed the basic course (first two years) at the Department for History and Social Sciences, or have similar competences.

The course will give the student an overview of, and insight into, the most commonly used quantitative methods, hereunder descriptive and inferential statistics, in social sciences. It will also develop the student’s practical skills in the use of these methods for analysis.

The course is divided into two main parts: A) Collection and organisation of data. Specifically, how to gather quantitative data, and how these data can be organised and presented so that the most informational value can be achieved in the clearest and most economical way. In this part, the emphasis is on descriptive statistics. B) Analysis of data. Here the student will be taught how one can use the different analytical tools to investigate connections between the different variables in the data set. In this part, the emphasis is on inferential statistics.

The course (30 hours in total) consists of lectures, practical in-class exercises with instruction, and homework. Since reading alone is not sufficient to develop the skills and competences that are aimed to develop during this course, emphasis is put on not only reading and study of the material before each class, but also on attendance and active participation during class and all the exersices. The emphasis is on developing practical competences in these methods. The students are therefore given practical exercises as a part of the teaching. There is also assigned homework, allowing the students to practice what they have learned.

By the end of the course the student shall be able to: A) Collect quantitative data in connection with a research question, to organize these data in a simple and informative way (tables, graphs, diagrams, histogram), to describe the distribution in a dataset, and to make an index. B) Analyze a set of data with a regression analysis, to do a chi square test, and with these tools to be able to argue for a possible causal connection between variables in a dataset.

Oral exam based on a written essay, where methods learned for data collection, organisation, and analyses are used to answer a research question that they, in conjunction with the lecturer, have developed. To be able to take the exam, the student has to have been present at 80% of the lectures, and have passed all the assignments. The lecturer evaluates the assignments as passed/failed.

External

7-

Max. 500 pages and hand out material provided by the lecturer.

Jens Christian Svabo Justinussen