Contents:
The course is especially useful for students who plan to use interviews and/or questionnaires as part of their MSc. thesis or PhD dissertation. It is targeted at students of all study programs who would like to learn about the design, data collection and analysis of interview and questionnaire data. Using subject-response data, i.e. ‘asking questions to people’, can happen in many modalities, ranging from qualitative, exploratory and relatively unstructured interviews to quantitative, explanatory and tightly structured self-administered questionnaire. The complexity of choosing the right method, designing your data collection plan, pre-testing your instruments, gathering and analyzing your subject-response data is often underestimated.
During this course students will follow interactive sessions where core principles will be explained and then applied in groups to concrete research projects. In-class discussions and (roleplay) exercises will be used to stimulate effective learning. There will be practicals using Atlas.ti and RStudio to analyse the data. With the help of instructor and peer feedback, students will learn about and practice with the complete cycle of research design, pre-testing, data collection, coding and analysis for both semi-structured interviews and structured questionnaires (with the latter building on the outcomes of the former).
Learning outcomes:
After successful completion of this course students are expected to be able to:
work out and justify a conceptual design (or 'blueprint’) for the interview guide and for the questionnaire;
design a practical interview guide based on the blueprint and following taught design principles;
design a practical questionnaire based on the blueprint and following taught design principles;
pre-test the questionnaire using appropriate interviewing techniques;
code and analyze interview data using methods such as inductive and deductive content analysis in Atlas.ti;
construct and test scales for a questionnaire using Cronbach’s alpha and Principal Component Analysis in RStudio;
conduct meaningful statistical analysis of questionnaire using among others general linear models and confirmatory factor analysis (CFA).
