Update 23 March 2020
Due to the COVID-19 pandemic, the course will be online, with lectures and exercise sessions in Teams. The format of the course is otherwise unchanged. The course will start Monday 1 June 10.30am and end Friday 5 June 12am. We will do what it takes to deliver the course also this year.
Additional information and updates will be published on the course page in Moodle. To access the course in Moodle, you will first have to register for the course.
Best regards and see you all in Teams.
Time & place: 1.6.-5.6.2020, Helsinki, Hanken School of Economics
Learning goal and objectives
The aim of the course is to introduce doctoral students in Management and Business Studies to statistical methods for analysing multivariate data. The methods covered in the course are multiple linear regression, logistic regression, Poisson regression, multilevel models, panel data models and factor analysis. Best practice concerning the presentation of empirical results is covered in the course.
After completing the course, the student is able to undertake high-quality quantitative research in Management and Business Studies, including Accounting, International Business, Logistics, Management, Marketing and Strategy. The student is able to present empirical results in a way that meets the technical requirements of scientific journals in Management and Business Studies.
Instruction and examination
The course consists of 20 hours of lectures and practical exercise sessions. The first two lectures on basic concepts from matrix algebra and statistical inference will be delivered by video before the course starts.
The emphasis is on the practical use of statistical models in quantitative research. To meet this goal, the course includes 10 hours of supervised project work when the student works on his or her term paper under the supervision of the teachers in the course. The student will receive a short report in the form of a referee report on his or her term paper in the course.
The statistical programs SPSS and R will be used in the course.
Credits: 6 Credits
Grading: 20% article analysis and 80% term paper. The course will be graded on the scale Sufficient (50-59)/Satisfactory (60-69)/Good (70-79)/Very Good (80-89)/Excellent (90-100).
Prerequisites: Basic knowledge of statistics including regression at the master’s level in Management and Business Studies. No previous knowledge of matrix algebra is required. Because the term paper in the course is based on the student’s own data, it is advisable that the student has collected data before coming to the course.
Admittance: The course is targeted towards (but not limited to) doctoral students in Accounting, International Business, Logistics, Management, Marketing, Strategy and other fields of Management and Business Studies. The maximum number of students admitted to the course is 25. Should applications exceed the limit, one criterion for selecting students is collection of data that can be used in the term paper.
Deadline for application is 23 April 2020. After the deadline, students will be accepted to the course if there is still space.
Fill in the form below and send it to the course coordinator ( firstname.lastname@example.org )
University, faculty and department :
Officially accepted as Ph.D. student (when and where?):
Are you a member of a graduate school, where?:
Completed methodology studies:
Subject or title of dissertation:
Methodological approach of the study:
Phase of the dissertation (do you have own empirical data, have you analyzed it and if yes, how?):
Summary of the objectives, research questions and methodologies (approx. 500 words):
Your own objectives for participating the course:
Associate Professor Niklas Ahlgren, Hanken School of Economics, https://www.hanken.fi/en/person/niklas-ahlgren
Professor Emeritus Gunnar Rosenqvist, Hanken School of Economics, https://www.hanken.fi/en/person/gunnar-rosenqvist
Dr. Niclas Meyer, Hanken School of Economics, https://www.hanken.fi/en/person/niclas-meyer
Course coordinator and contact information: Associate Professor Niklas Ahlgren, Hanken School of Economics, Department of Finance and Statistics, PO Box 479 (Arkadiagatan 22), 00101 Helsingfors, Finland. E-mail: email@example.com.