This course is aimed at introducing students to methodology and research methods, with an emphasis on qualitative and quantitative research methods adapted from social science research and applied to real-world research projects. The course is structured around six interactive lectures and self-study and designed as an intensive two-week 3 EC course (equivalent to 84 hours). Successful completion of this course is a prerequisite for starting work on the Research Seminar and the M.Sc. Thesis Research.
The research methodology course will be launched with two/three lectures – respectively about the case study as one way of doing social science research and then about the secondary or desk research with the aim to understand how to gain background knowledge for a particular subject as well as how to expand understanding of specific research question and to identify plausible investigative questions. The case study lectures will be structured around five main issues: comparing case studies with other research strategies, designing case studies, preparation for data collection and collecting the evidence, analysis of case study evidence, and reporting case studies, whereas the secondary research will cover the basics for doing, analyzing and evaluating secondardy research. Following those lectures, an epistemology lecture will be set to address fundamentals elements of philosophy of science: ontology (how things are?) and epistemology (how can things be known?) and relate them to concrete decisions while developing a research esign as well as typical “ICT in Business” research setups. Specifically, the lecture will first present the dichotomy between ‘being’ and ‘becoming’ ntologies, process and variance epistemologies as well as objectivist, constructivist and subjectivist perspectives. A second interactive phase driven by oncrete cases and examples will enable students to articulate the philosophical and theoretical instruments previously introduced. The last part of the esearch methodology will emphasize on quantitative research and their application to information systems and software engineering through three ectures. In those interactive lectures the basics of experimental design, statistical descriptive techniques (measures of location, spread, covariation; graphical representation of data), statistical inferential techniques (t-test and simple linear regression analysis) and the basics of measurement in IT will be covered. The theory to be covered in the lectures is directly applied in a series of three seminars, where students will analyse real experimental data using the R environment for data analysis.
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: