Qualitative methods part of the course is focusing on understanding thoroughly the complex research design and triangulation with focus on qualitative approach. Students will learn how to design qualitative research via acquiring the theory and doing practical tasks/ projects. Course will focus on sample selections, research problems, and project scenarios and how it sits with approaching complex issues. Students will learn about tools connected with qualitative approach so, e.g. QCA or ethnographic research and why triangulation approach is important factor that we should take into consideration and how do we create it.

The second part of the course will focus on an overview of various quantitative methods of complexity research. We will begin with the mathematical framework of stochastic processes and dynamical systems, commonly used to represent complex systems in mathematical terms. Then we will proceed to more directly applied subjects: Monte Carlo methods, used to simulate processes too complex to be mathematically tractable; graph theory and network analysis, used to describe and analyze systems decomposable into discrete interacting components; and machine learning. The latter will be discussed both as a tool for analysis of complex systems, in particular for classification and pattern detection, and as a tool for processing of data unsuitable for direct statistical analysis (such as natural language data). The focus of the course will be on which method to choose for a particular analysis task (what kind of questions the method is designed to answer, what kinds of data it requires, what are its assumptions, etc.) rather than how to apply it.

Lecturers: Magdalena Kossowska-Lai, PhD, Dariusz Stolicki, PhD, Jagiellonian University (Poland)