Course description
The course runs part-time and uses a blended learning format, combining a few campus days with the major part of the course online.
Requirements and Selection
Entry requirements
For admission to the course, the applicant has to be a registered doctoral student in the third cycle or have a doctoral degree. The applicant should also have documented prior knowledge corresponding to the learning goals in the courses QRM1802 Regression Analysis for Educational Research, 7,5 higher education credits and QRM 1806 Structural Equation Modelling for Educational Research, 7,5 higher education credits.
Selection
This is a third-cycle course within the school of Quantitative Research Methods in Education (QRM). Both doctoral students and university lecturers with a PhD are welcome.
The number of participants is limited to 15. Priority will be given to doctoral students with an educational sciences focus, secondly supervisors for doctoral students with a focus on educational science research and thirdly doctoral students and researchers from other fields with an interest in the field. The course is open to Swedish and international participants.
Educational partnership
Collaborating departments
Department of Education and Special Education, University of Gothenburg in collaboration with Department of Applied Educational Science, Umeå University and Department of Education, Uppsala University.
Other information
You need to confirm in your motivation that you fulfil the course requirements.
If you are applying as a post-doc, you still need to use the application form as if you were a doctoral student. Please state in your motivation if you are a post-doc.
Link to website
https://www.gu.se/en/qrm/courses
Course syllabus
QRM1809
Reading and reference list
Reading and reference list for the course
Department
Department of Education and Special Education
Subject
Educational Science
Type of course
Method course
Research School/Graduate School
QRM - Quantitative Research Methods in Education
Keywords
kausal inferens, causal inference