Credits: 6

Schedule: 24.09.2018 - 09.11.2018

Teacher in charge (valid 01.08.2018-31.07.2020): 

Teacher: Professor Andrew Delios, Professor and Head, Department of Strategy & Policy, NUS Business School, National University of Singapore

Responsible professor at BIZ: Henrikki Tikkanen, Director of Doctoral Programme

Teaching Period (valid 01.08.2018-31.07.2020): 

Intensive course, lectures on September 25 - 29, on each day of that week, and from November 6 to 9, for four consecutive days, at Töölö campus

Learning Outcomes (valid 01.08.2018-31.07.2020): 

The objective of the course is to enable the students to use quantitative data analysis techniques in business and economic research. The course will provide the students with a set of tools useful in empirical research.

 Upon completion of the module, students should be conversant with standard statistical methodologies that underlay much of the work undertaken for research on management and organizations. Being conversant means being able to understand and critically evaluate the methods used in papers published in leading journals in their field. Being conversant also means that students will be able to apply this same understanding to the development of their own research from the preparation of data for analysis, the development of descriptive statistics, the use of data reduction techniques, the application of multivariate models for standard hypothesis testing, and then effective and accurate interpretation of results and the clear presentation of the same.

Content (valid 01.08.2018-31.07.2020): 

The module begins with an online data analysis primer of basic statistical techniques. After this primer has been completed by the students, they can begin attending the lecture and tutorial sessions. We will first cover the basics of data analysis, including the foundational work required to undertake data analysis. This includes screening data missing value analysis and visualizing multivariate observation.

 Next, the course will define and introduce an extensive set of statistical multivariate methods and explain when their use is appropriate and how they are related to each other. This begins with standard OLS analysis before moving to multiple types of maximum likelihood analysis.

 We will work on data visualization, understanding how to effectively present data both for accuracy and for clarity of interpretation. Given the recent advances in data visualization, and the trends to present data incorporating both effect sizes and estimates of error around any data plot, the module will give coverage to these important areas of data visualization and presentation.


The methods covered during the course range from commonly applied dimension reduction tools (e.g., principal components, factor analysis) and dependence techniques (e.g. regression analysis, ANOVA) to basics of categorical data analysis. As time permits, we will consider moving to advanced topics, such an introductions to structural equation modelling (SEM) and event history analysis.

 Methodological aspects and interpretation of analysis are also explained. After completing the course, the students have an understanding of how and where the methods can be applied to solve a variety of research/business problems. The students will also be able to evaluate the results critically and summarize key findings in a concise manner while focusing on the actionable information.

 SPSS and STATA will be used in exercises and demonstrations during the course.

Assessment Methods and Criteria (valid 01.08.2018-31.07.2020): 

In-class assignments 20%

First project 30%

Second project 40%

Study Material (valid 01.08.2018-31.07.2020): 

In-class assignments 20%

First project 30%

Second project 40%

Course Homepage (valid 01.08.2018-31.07.2020):

Grading Scale (valid 01.08.2018-31.07.2020): 


Registration for Courses (valid 01.08.2018-31.07.2020): 

Via Weboodi.

Further Information (valid 01.08.2018-31.07.2020): 

Professor Henrikki Tikkanen

Professor Carl Fey

Professor Andrew Delios

Coordinator Ritva Laaksovirta


Registration and further information