Main field(s) of study and in-depth level:
Peace and Conflict Studies A1F
Explanation of codes
The code indicates the education cycle and in-depth level of the course in relation to other courses within the same main field of study according to the requirements for general degrees:
G1N: has only upper-secondary level entry requirements
G1F: has less than 60 credits in first-cycle course/s as entry requirements
G1E: contains specially designed degree project for Higher Education Diploma
G2F: has at least 60 credits in first-cycle course/s as entry requirements
G2E: has at least 60 credits in first-cycle course/s as entry requirements, contains degree project for Bachelor of Arts/Bachelor of Science
GXX: in-depth level of the course cannot be classified.
A1N: has only first-cycle course/s as entry requirements
A1F: has second-cycle course/s as entry requirements
A1E: contains degree project for Master of Arts/Master of Science (60 credits)
A2E: contains degree project for Master of Arts/Master of Science (120 credits)
AXX: in-depth level of the course cannot be classified.
Fail (U), Pass (G), Pass with distinction (VG)
The Department Board
A Bachelor's degree, equivalent to a Swedish Kandidatexamen, from an internationally recognised university. Also required is 90 credits in peace and conflict studies, or 90 credits in a related relevant discipline and at least 30 credits in peace and conflict studies or the equivalent. A social science methods course at the Master's level of at least 15 credits. Familiarity with R or similar statistic software is essential.
have expanded their familiarity with quantitative methods in peace and conflict research
know how to specify complex Monte Carlo simulation models and use to evaluate specification problems
have attained basic knowledge of programming and data-management techniques
have attained comprehensive knowledge of the R statistical software package
know how to specify, estimate, interpret generalised linear regression models such as:
time-series models and panel models
binary, multinomial, and ordinal logit models
be familiar with techniques for imputing missing data and simulating predictions, first differences, and other quantities of interests based on estimated models
independently write assignments within a given time frame
Focus will be on practical use in the form of specifying, estimating, interpreting, and evaluating models, and be able to identify what types of models are appropriate for different types of data-generating processes. The theoretical introduction to the models will involve basic mathematical notation. The introduction to R will place considerable emphasis on R's scripting language, and also introduce basic programming techniques required for efficient and transparent research procedures as well as for the application of Monte Carlo techniques.
There will be 10 lectures. Four assignments will be given and responded to throughout the course (approximately one every week). PhD students will have a more extensive reading list and be required to submit a longer course paper in addition to the assignments.
Assessment for master students will be based on the four assignments (80%) and active participation during lectures (20%). Assessment for PhD students will be based on the four assignments (50%), the course paper (30%), and active participation during lectures (20%). Each assignment will consist of a short course paper and a working R script that produces the results in the paper. All assignments must be handed in.
Grades: Pass with distinction (VG), Pass (G), Fail (U). Two dates to resubmit course papers are offered per year.
If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the University's disability coordinator.
The course will be given jointly to PhD and master students. The course aims at preparing the student for writing a quantitative thesis or research paper. Upon completion, the student will also have strengthened ability to read, evaluate critically, and replicate the majority of the published studies within quantitative peace and conflict research.