There is still an ongoing debate on whether quantitative and/or qualitative methods are more appropriate to answer different research questions. Yet, both methodological approaches offer valuable contributions depending on the aim of research. While qualitative studies are suited for theory creation and require primarily exploratory designs (constructivist or phenomenological approaches), quantitative research seeks more to test theories by quantifying structured data.

The course covers special topics in both, qualitative and quantitative, methods.

The quantitative part of the course relates to agent-based modelling and simulation. Models are developed with heterogeneous interacting agents. Their cognition, behavior and organization gets grounded via qualitative empirical research (mixed methods). Eventually it is intended to explore socioeconmomic complexity, test models on robustness as well as ontological correspondence through (in silico) simulation experiments and conduct statistical analysis thereafter. Modelling and simulation will be taught so as to prepare students to conduct research in the area of demographic studies, environmental research, social policy and regional development.

In the qualitative part the aim is to develop mixed methods research designs which fit to the topics and questions of the students’ projects. With a special focus on the premises and basic ideas of empirical research, we look at ways of how these are implemented adequately in the designs. Furthermore, this part will focus on the qualitative method of expert interviews. Students will generate and analyze qualitative data.

After successful completion of this course, students will be able to:

- develop or deepen a research question (based on the state of the art and on theories)

- specify a mixed methods research strategy in more depth (empirical design)

- design an agent-based model appropriate to the research question

- to analyse, interpret and try to link the varying results from quantitative and qualitative methods

- to present research results (including a quality assessment)

- to write a research report

Students are expected to:

- prepare the literature for discussions in class

- actively participate in discussions and assignments in class

- submit a research design

- to focus on a research question with bottom-up modelling and simulation techniques

- to use Netlogo for modelling and simulation

- to analyse and interpret computaionally generated data

- to submit a research report

80% attendance of the class is required! If you miss a class, please inform us in advance!

Notice of Special Regulation for Covid-19: 

If a student is required to quarantine, or is otherwise prevented from attending class, due to a certified case of Covid-19 infection or a federally mandated Covid-19 lockdown, and this affects either attendance or the completion of an exam or other required course assignment, the course instructor is empowered to provide an alternative means for said student to meet the attendance/assessment requirement as necessary. The same means will be required of any student in the same situation in the same course.

Design of Teaching

drafting and applying a research design, presentation, discussion, readings followed by a question-answer session to the obligatory texts and practical exercises.

Exigencies

Active participation in class, group assignments, presentation in class, final seminar paper on own project

Practical exercises in the framework of a group work dealing with a special topic will enhance the students’ understanding and ability to critically assess empiricalstudies and to apply those methods which are adequate for answering specificquestions.

- in-class participation and readings (10%)

- individual Tasks (quantitative and qualitative assignments) (40%)

- Group Tasks  (Research Report and Poster presentation) (50%)

SEEP courses do not allow creation of assignments, exam answers or other assessed work using generative AI (e.g. ChatGPT).  All such work is expected to be the original work by the student concerned and is assessed as such.  Work copied from a generative AI source is equivalent to plagiarism and will be treated as such.

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