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Quantitative Methods: Multivariate Analysis - SIMM32

7,5 credits

Overview

Using advanced quantitative analysis

This course introduces the most widespread methods of multivariate analysis in social sciences. The course aims to give you knowledge of the multivariate statistical techniques most commonly used within the social sciences,
as well as an understanding of the kind of research questions that each technique can be used to address.

Teaching includes lectures and teacher-assisted hands-on exercises with data drawn from different social science databases. The course will be examined on the basis of these exercises and the writing of an individual course paper (a research overview). The focus is on the relationship between complex research questions and different multivariate statistical techniques. As a student, you will have the opportunity to deepen your knowledge in one or two methods of your choice.

Prior knowledge needed

Participants in this course are expected to have good knowledge of descriptive statistics and basic knowledge of inductive statistics (statistical inference) and multiple regression analysis. If you are interested in a more basic level course in quantitative methods, we recommend SIMM16 Introduction to Quantitative Methods.

PhD students

This course is also offered as a PhD course. For more information on this option, please see the Social Sciences Faculty website.

Online course platform

This course uses Canvas as the online course platform. The course platform will be opened one week before the course begins to all students who were accepted. Here you will be able to access literature, assignments, announcements, as well as participate in discussions and communicate with teachers. 

Schedule

The course schedule can be found under the course information on the right. Please note that the final version of the schedule will be made available four weeks before the course begins, and changes may occur until that point. A more detailed schedule will be available on the course platform on Canvas.

About the Course

Course period:
2021-05-03 to 2021-06-06

Course schedule:
List view | Calendar view

Course syllabus 

Course literature

Course guide