Quantitative Research Methods in Innovation Studies (DSV610)

This course is an entry-level course to quantitative analysis in innovation studies for PhD students. It features an introduction to the software R, discusses data reduction techniques, cross-section and panel regressions, as well as social network analyses.


Course description for study year 2024-2025. Please note that changes may occur.

Facts

Course code

DSV610

Version

2

Credits (ECTS)

7.5

Semester tution start

Spring, Autumn

Number of semesters

1

Exam semester

Spring, Autumn

Language of instruction

English

Offered by

Content

This course is part of the Norwegian Research School in Innovation (NORSI) and introduces students to the quantitative analysis part of the common course in research methods under the NORSI program. The course will exclusively be offered as a part of NORSI common courses. The course surveys the most used quantitative analysis techniques in innovation-related research. It features comprehensive teaching in data reduction techniques, cross-sectional and panel regression as well as social network analysis. In addition, the course offers an application-oriented introduction and training to the statistical software R

Learning outcome

Knowledge:

Students will have an overview of quantitative analysis techniques and their application in innovation research.

Students will be able to evaluate the use of methods and the main data sources relevant for innovation research.

Students will be able to develop new knowledge and new theories on innovation using quantitative methods.

Skills:

Students will be able to conduct innovation research at a basic level using quantitative methods, including factor analysis, cross-sectional and panel regressions as well as social network analysis.

Students will be able to formulate new research questions and conduct novel research using quantitative methods.

Students will be able to handle the statistical software R

General competence:

Students will be able to assess when and how to use quantitative research methods.

Students will be able to discuss academic analyses in the field at a basic level.

Students will be able to apply and conduct quantitative methods at a basic to intermediate level.

Required prerequisite knowledge

The students must satisfy the admissions requirements of the PhD programme.

Exam

Form of assessment Weight Duration Marks Aid
Term paper 1/1 Passed / Not Passed

To obtain 7.5 ECTS points requires active participation during the course as well as an accepted paper of 5.000-6.000 words demonstrating competence in using quantitative methods. The paper should be based on the topic of the PhD thesis and reflect the literature used in the course. It has to be centered around a self-selected/self-developed research hypothesis embedded into the contemporary literature, an adequate self-designed research strategy utilizing one or multiple methods taught in the course, and a fully-fledged discussion of the results.Term paper - appr. 5.000-6.000 words, which includes a solid empirical assessment of at least one hypothesis. The paper will be assessed as a pass/fail.

Coursework requirements

The course requires active class room participation.

Course teacher(s)

Course coordinator:

Tom Brökel

Method of work

The course will be delivered as a single-week intensive course at the University of Stavanger, as part of the Norwegian Research School in Innovation. The course will include lectures and computer lab sessions in relation to the methods.

Open for

The course is open to interested PhD candidates at the University of Stavanger and other universities.

Course assessment

There must be an early dialogue between the course supervisor, the student union representative and the students. The purpose is feedback from the students for changes and adjustments in the course for the current semester.In addition, a digital subject evaluation must be carried out at least every three years. Its purpose is to gather the students experiences with the course.

Literature

Search for literature in Leganto