Syllabus for Advanced Quantitative Methods

Avancerad kurs i kvantitativ metod

A revised version of the syllabus is available.


  • 7.5 credits
  • Course code: 2FK055
  • Education cycle: Second cycle
  • Main field(s) of study and in-depth level: Peace and Conflict Studies A1F
  • Grading system: Fail (U), Pass (G), Pass with distinction (VG)
  • Established: 2016-06-02
  • Established by:
  • Revised: 2019-05-09
  • Revised by: The Department Board
  • Applies from: Autumn 2019
  • Entry requirements:

    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.

  • Responsible department: Department of Peace and Conflict Research

Learning outcomes

After completion, the students are expected to:

  • 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
    • count 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.

Other directives

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.

Reading list

Reading list

Applies from: Spring 2022

Some titles may be available electronically through the University library.

  • Carsey, Thomas M.; Harden, Jeffrey J. Monte Carlo simulation and resampling : methods for social science

    Thousand Oaks, California: Sage Publications, [2014]

    Find in the library


  • Gelman, Andrew; Hill, Jennifer Data analysis using regression and multilevel/hierarchical models


    Find in the library


Articles, e-books, and book chapters available through electronic services of the library will also be included in the reading list. Detailed and up-to-date information about the reading list for this year will be made available in the Course Guide.

Reading list revisions

Last modified: 2022-04-26