The NORSI course Quantitative Methods for Innovation Research will be held Monday, 29 November to Friday, 3 December 2021.
Course Details
Norsi Institutions: The course is part of Nordic Research School in Innovation and Entreprenurship (NORSI). This NORSI course is organized in collaboration between University of Stavanger, Blekinge Institute of Technology and Lund University (CIRCLE). The Course gives 5 ECTS, and is administered by UiS but held at Circle at Lund University in Sweden
ECTS credits: 5
UiS Course code: DSV610
UiS course page: Link
Level of course: Ph.D. course
Type of course: Elective for students in business or other social science disciplines studying entrepreneurship and innovation for their PhD.
Language: English
Course delivery:
We are planning for an in-person class at Circle at Lund University in Sweden. The course will include lectures and computer lab sessions in relation to the methods.
If the situation at the time does not permit any campus teaching, the course will be fully online (Zoom).
Registration deadline: 1 November 2021
Course Responsible & Faculty
Professor Tom Brökel , University of Stavanger Business School
Professor Martin Andersson, CIRCLE, Lund University and Blekinge Technical College
Professor Torben Schubert, CIRCLE, Lund University and Fraunhofer Institute for Systems and Innovation Research ISI
Course Content
This course is part of the Nordic Research School in Innovation and entreprenurship (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 will survey the most commonly used quantitative analysis techniques in the social sciences such as an introduction to R, data reduction techniques, regression and correlation analysis, and social network analysis. Students will learn about these techniques as applied in the field of innovation studies.
Learning outcomes
Knowledge:
Students will have an basic 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, regression and correlation analysis, and social network analysis.
Students will be able to formulate new research questions and conduct innovation 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 assess research using quantitative methods.
Registration
Registration and registration form: To registered for the course please send an email to Nadya Sandsmark <nadya.sandsmark@uis.no> together with UiS application form for PhD courses. Download UiS course registration form here.
Registration deadline: 1 November 2021
Program
Monday 29th November
In conference room: XX at CIRCLE, Sölvegatan 16, Lund
9.30-10.30: Official opening: Martin Andersson
10.30-12.00: Martin Andersson: Overview of Quantitative Research Methods in Innovation Studies
12.00-13.30: Lunch
13.30-14.30: Tom Broekel: A gentle introduction to R, part I
14.30-15.00: Coffee
15.30-16.30: Tom Broekel: A gentle introduction to R, part II
16.30-17.30: Tom Broekel: A gentle introduction to 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
James, G. et al.(2013): An introduction to statistical learning: with applications in R, Springer, http://faculty.marshall.usc.edu/gareth-james/
Tuesday 30th November
In conference room: XX at CIRCLE, Sölvegatan 16, Lund
09.30-10.30: Tom Broekel: Working with R, part I
11.00-12.30: Tom Broekel: Working with R, part II
12.30-13.30: Lunch
13.30-14.30: Tom Broekel: Working with R, part III
14.30-15.00: Coffee
15.30-16.30: Tom Broekel: Working with R, part IV
16.30-17.30: Tom Broekel: Working with R, part V
***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 conference room: XX at CIRCLE, Sölvegatan 16, Lund
9.30-10.30: Tom Broekel: Visualizing data in R, part I
11.00-12.30: Tom Broekel: Visualizing data in R, part II
12.30-13.30: Lunch
13.30-15.00: Martin Andersson: Basic techniques for innovation data analysis, Part I: Statistical inferences and comparisons of groups
15.00-15.15: Coffee
15.15-18.00: Martin Andersson: Basic techniques for innovation data analysis, Part II: Introducing regression analysis
***Optional readings for these lectures****
Kennedy, P. (2008). A Guide to Econometrics (6 ed.): Chapters 1-4 & 22
Wooldridge JM (2005), Introductory Econometrics: Chapters 1-4
Dean, M. A., Shook, C. L., & Payne, G. T. (2007). The Past, Present, and Future of Entrepreneurship Research: Data Analytic Trends and Training1. Entrepreneurship Theory and Practice, 31(4), 601-618
Shook, C. L., Ketchen, D. J., Cycyota, C. S., & Crockett, D. (2003). Data analytic trends and training in strategic management. Strategic Management Journal, 24(12), 1231-1237.
James, G. et al.(2013): An introduction to statistical learning: with applications in R, Springer, http://faculty.marshall.usc.edu/gareth-james/
Wickham, H & Grolemund, G., B (2017). R for data science: import, tidy, transform, visualize, and model data, O’Reilly Beijing
Thursday 2nd December
In conference room: XX at CIRCLE, Sölvegatan 16, Lund
09.30-10.30: Tom Broekel: Introduction to social network analysis I
11.00-12.30: Tom Broekel: Introduction to social network analysis II
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 ****
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
James, G. et al.(2013): An introduction to statistical learning: with applications in R, Springer, http://faculty.marshall.usc.edu/gareth-james/
Wickham, H & Grolemund, G., B (2017). R for data science: import, tidy, transform, visualize, and model data, O’Reilly Beijing
Friday 3rd December
In conference room: XX at CIRCLE, Sölvegatan 16, Lund
09.15-12.00: Torben Schubert: Survey design, including cluster and factor analysis
12.00-13.15: Lunch
13.15-15.00: Torben Schubert: Hypothesis testing using Community Innovation Survey data
15.00-15.15: Coffee
15.15-17.00: Torben Schubert: Lab session
***Readings for these lectures (required) ****
Wooldridge JM (2005), Introductory Econometrics: Chapter 17
Course assessment and requirement
Assessment: To obtain 5 ECTS point requires active participation during the course as well as an accepted paper of 3.000-4.000 words demonstrating competence in using quantitative methods. The paper should be based on the topic of the PhD thesis and reflect literature used in the course. If quantitative methods will not be used in the thesis a paper answering given tasks could substitute a normal paper. However, the concrete form of the written delivery can be further discussed during the course.
Exam: Term paper – appr. 3.000-4.000 words. The paper will be assessed as a pass/fail. Active class room participation required.
Practical information
Preparations
All students have to bring their own PC with access to R and STATA (software).
How to get to Lund
The closest international airport to Lund is Copenhagen Airport (also known as “Kastrup”), in Denmark. The train connection from Copenhagen Airport to Lund is fast (35 minutes) and frequent (every 20 minutes). Train tickets can be purchased from the “Skånetrafiken” ticket machines in the arrivals hall, near the escalators going down to the train platform. You can pay in Euros, Swedish kronor or Danish kronor, and you can use a debit/credit card with a chip. The journey will take you over the Öresund Bridge connecting Denmark to Sweden and via Malmö central station to Lund central station.
The closest local airport to Lund is Malmö Airport (also known as “Sturup”). It has flights from other Swedish airports including Stockholm and some international flights from destinations within Europe. There is an airport shuttle bus to Malmö airport (called “Flygbussarna”) or you can get a taxi to Lund.
Accommodation
We ask kindly that all students make their own travel arrangements (economy). We can recommend “StayAt Lund” with reasonable prices and the more expensive “Hotel Concordia”, both located in the city centre. You can find, compare and book hotels at Lund’s tourist office.
How to get to CIRCLE in Lund, Sweden
The workshop will take place at CIRCLE, located within 15 walking minutes from the train station and the city centre. Street address is Sölvegatan 16, Lund.
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.