SGO9009 – Advanced statistical methods
Schedule, syllabus and examination date
This course cover selected topics in statistical methods and research workflow related to statistical analysis. The topics covered are typically not included in statistical methods courses at the Master’s level,
The course will consist of a set of modules, each taught through a two-day workshop. The modules included may change each semester the course is taught. Students’ preferences is accommodated as far as possible in module selection.
We will use R and/or Stata as our main tools. This decision is made independently for each module. Language of instruction is english, unless all students have a sufficient command of Norwegian.
Potential course modules:
- Research designs for causal inference: instruments and discontinuities
- Introduction to reproducible research using R and RMarkdown
- Event history analysis: durations and intensities as dependent variables
- Is signifiance testing significant (p<0.05)? A discussion module.
- Panel data: fixed and random effects
- Quantile regression
- Multivariate data redux: Factor analysis and principal component analysis
The learning outcome of the course is mastery of a suite of methods and workflow styles that will enable the student to produce several new statistical analyses and correctly and efficiently present the results from those analyses.
The course is available for Ph.D. candidates at the Department of Sociology and Human Geography. In case there are open places, Ph.D. candidates in sociology and geography from other universities and advanced Master’s level students may be admitted. Maximum enrollment is 20 students.
Ph.D.-students at the Department of Sociology and Human Geography register for the course in Studentweb.
Participants outside the Department of Sociology and Human Geography shall fill out this application form.
Master students register for the course's equivalent: SOS4022 - Advanced Statistical Methods.
The application deadline is four weeks prior the course.
Formal prerequisite knowledge
Participants must have a good working knowledge of basic statistics and linear regression analysis. Having completed SOS4020 or equivalent is sufficient preparation for the course.
5 credits overlap with SOS9009 in the same semester.
Credit overlap otherwise will be determined semester by semester, as the content of the course varies over semesters.
SOS9009/SGO9009 overlaps with SOS4022 taught Spring 2020.
Each module is taught separately as a two-day workshop. Readings associated with each module will be distributed several weeks ahead of the module teaching dates. Some modules will be taught by external teachers.
For dates and modules for the next Spring semester, see the semester page.
This course has jointly taught classes with SOS4022 - Advanced Statistical Methods (master-level).
Each module requires active class participation, readings, and submission of a short assignment. To obtain 5 ECTS credits, students must participate and complete in minimum three modules.
Master students find detailed information about course requirements on the webpage of the master-variant of this course - SOS4022 Please go to this page for further information as requirements for master-students are slightly different from those for Ph.D.-students.
Grades are awarded on a pass/fail scale. Read more about the grading system.