The research methodology introduces the student to the basics of the quantitative, empirical research process and to the basic statistics required to analyse the research findings. The course has a practical focus on the process of turning broad research questions into an executable research plan and on the process of data analysis.To turn broad research questions into an executable research plan the student will study the process of turning research problems into researchable questions and the execution of data acquisition. Next the student will study the transformation of research data in actionable advice or clear scientific insights.To cover these topics the students will study the design of experiments, statistical descriptive techniques (measures of location, spread, covariation; graphical representation of data) and inferential techniques (t-test and simple linear regression analysis). The statistical topics covered in the lectures will be directly applied in a set of seminars, where students can analyse real experimental data using the R environment for data analysis.
- P. Ghauri and K. Grønhuag (2010), “Research Methods in Business Studies: A Practical Guide”, 4th edition. Financial Times/Prentice Hall.
- I. Diamond and J. Jefferies (2001), “Beginning Statistics: An Introduction for Social Scientists”. Sage Publications.
- W. N. Venables, D. M. Smith et al. (2008), “An Introduction to R – Notes on R: A Programming Environment for Data Analysis and Graphics”. R Development Core Team. Available here
- Reader of selected articles and chapters (provided by lecturer at the first lecture).
Each of the lectures may have one or more assignments that should be handed in. The lecturer of that session will grade the assignment. In addition there will be a written exam. The final grade will be a combination of:
- Assignment grade (3/5 of final result)
- Exam grade (2/5 of final result)
In addition, a bonus point for the exam can be gained by participating in the pop-quizzes (which are optional).