Quantitative Methods for Innovation Research


Course Details

Course code: DSV610_1
Credits (ECTS):
7,5

Name of the course: Quantitative Methods for Innovation Research/Kvantitative metoder for innovasjonsforskning

Language of instruction: English
Semester tuition start: 
Autumn
Number of semesters: 1
Exam semester: Spring, Autumn

Who may participate: The course is open to interested PhD candidates at the University of Stavanger and other Norwegian universities.


Prerequisites/Recommended Previous Knowledge: 

The students must satisfy the admissions requirements of the PhD program

Course 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 outcomes:

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.

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 a mix of lectures and computer lab sessions.

Coursework requirements

Active classroom participation.

Exam: 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. 

Literature list: The reading list will be announced before the start of the course.

Course assessment

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.

Offered by: UiS Business School



Course Responsible & Faculty


Course Responsible: Professor Tom Brökel , University of Stavanger Business School 

Participating Faculty: Professor Torben Schubert, CIRCLE, Lund University and Fraunhofer Institute for Systems and Innovation Research ISI


Registration

Registration and registration form: 



Registration deadline: 14th of November

Course Plan

Monday 5th December 

In room: A156 at UiS Stavanger

9.30-10.30: Official opening: Tom Broekel
10.30-12.00: Tom Broekel: A gentle introduction to R, part I
12.00-13.30: Lunch

13.30-14.30: Tom Broekel: A gentle introduction to R, part II
14.30-15.00: Coffee

15.30-16.30: Tom Broekel: A gentle introduction to R, part III
16.30-17.30: Tom Broekel: Working with R, part I

***Optional readings for these lectures****

  • Wickham, H & Grolemund, G., B (2017). R for data science: import, tidy, transform, visualize, and model data, O’Reilly Beijing
  • James, G. et al. (2013): An introduction to statistical learning: with applications in R, Springer, http://faculty.marshall.usc.edu/gareth-james/

Tuesday 6th December 

In room: A156 at UiS Stavanger

09.30-10.30: Tom Broekel: Working with R, part II
11.00-12.30: Tom Broekel: Working with R, part III

12.30-13.30: Lunch
13.30-14.30: Tom Broekel: Working with R, part IV
14.30-15.00: Coffee

15.30-16.30: Tom Broekel: Working with R, part V
16.30-17.30: Tom Broekel: Visualizing data in R, part I

***Optional readings for these lectures****

  • Wickham, H & Grolemund, G., B (2017). R for data science: import, tidy, transform, visualize, and model data, O’Reilly Beijing
  • James, G. et al. (2013): An introduction to statistical learning: with applications in R, Springer, http://faculty.marshall.usc.edu/gareth-james/

Wednesday 1st December

In room: A156 at UiS Stavanger

09.30-10.30: Visualizing data in R, part II
11.00-12.30: Tom Broekel: Introduction to social network analysis

12.30-13.30: Lunch
13.30-14.30: Tom Broekel: Network analysis in R, part I
14.30-15.00: Coffee

15.30-16.30: Tom Broekel: Network analysis in R, part II
16.30-17.30: Tom Broekel: Network analysis in R, part III

***Optional readings for these lectures****

  • Wickham, H & Grolemund, G., B (2017). R for data science: import, tidy, transform, visualize, and model data, O’Reilly Beijing
  • Giuliani, E., & Bell, M. (2005). The micro-determinants of meso-level learning and innovation: evidence from a Chilean wine cluster. Research policy, 34(1), 47-68.
  • Ter Wal, A. L., & Boschma, R. A. (2009). Applying social network analysis in economic geography: framing some key analytic issues. The Annals of Regional Science, 43(3), 739-756.
  • Broekel, T. and Boschma, R. (2011), Aviation, Space or Aerospace? Exploring the knowledge networks of two industries in the Netherlands. European Planning Studies, 19(7): 1205-1227

Thursday 2nd November 

In room: A156 at UiS Stavanger

09.30-10.30: Torben Schubert: Basic techniques for innovation data analysis,

 Part I: Statistical inferences and comparisons of groups 

11.00-12.30: Torben Schubert: Basic techniques for innovation data analysis,

 Part I: Introducing regression analysis

12.30-13.30: Lunch
13.30-14.30: Torben Schubert: Basic statistics and inference in R
14.30-15.00: Coffee

15.30-16.30: Torben Schubert: Linear regression in R
16.30-17.30: Torben Schubert: Specification tests for linear regression in R


Friday 3rd December

In room: A156 at UiS Stavanger

09.15-12.00: Torben Schubert: Introducing marginal effects

12.00-13.15: Lunch
13.15-15.00: Torben Schubert: Limited dependent variables and non-linear regression 

15.00-15.15: Coffee
15.15-17.00: Torben Schubert: Non-linear regression in R

***Readings for these lectures (required) ****

  • Wooldridge JM (2005), Introductory Econometrics: Chapters, 1-4 & 17
    Wooldridge JM (2005), Introductory Econometrics: Chapters 1-4
  • James, G. et al. (2013): An introduction to statistical learning: with applications in R, Springer, http://faculty.marshall.usc.edu/gareth-james/


How to get to Stavanger
The best way to come to The University of Stavanger if you come from abroad is my plane, directely to Sola Airport or via Oslo, Gardermoen Lufthavn to Sola Airport. It is also possible to take a bus from Oslo to Stavanger.

The University of Stavanger is located approximately 25 minutes by buss, from Stavanger city center at Kjell Arholms gate 41, 4021 Stavanger.

Accommodation
NORSI will book hotel for all NORSI student members.

Travel
We ask kindly that all students make their own travel arrangements (economy).

NORSI Travel Reimbursement

As this is a NORSI course all travel expenses etc. will be covered for NORSI students and teaching faculty.

Please see NORSI Travel Policy for more information. 

PhD students registered for the course that are not part of NORSI will have to cover their own expensens.

Date & Location

5 December - 9 December
The University of Stavanger