Computer Assisted Qualitative Data Analysis 2400-OG-EN-CAQDA

What is qualitative analysis in science? Can it be structured? Is it based solely on the instinct of researchers, or can it be refuted? Can the computer be useful to us in qualitative analysis only for playing sapper or as a typewriter? In this course, we will learn the basics of computer-assisted qualitative data analysis. Using Maxqda (qdalab) software, which is widespread among analysts, we will learn procedures for coding data, mapping relationships, linking processes, visualising relationships, adjusting and building theoretical models based on data sets. During the course, we will work on textual material, images, videos, websites, social networks and maps.

Total student workload

Hours carried out with teachers ( hours): - participation in exercises - 30h Time devoted to individual student work ( hours): - preparation for laboratory - 30 - Total: 60 hrs (.... ECTS)

Learning outcomes - knowledge

W1: has advanced knowledge of qualitative data analysis - in areas such as coding, mapping, modelling. W2: has knowledge of advanced algorithms for qualitative data analysis (types of coding, qualitative models and relationships, case-based model, topic-based models, visual relationships, correlation matrices).

Learning outcomes - skills

U1: can/will select an appropriate way of analysing qualitative data in areas such as mapping, building a theoretical model based on qualitative data. U2: is able to select an appropriate qualitative analytical approach to the problem under study. U3: is able to analyse advanced data sets (text, video, images, maps, web pages)

Learning outcomes - social competencies

K1: understands the importance of working groups in qualitative data analysis K2: understands own limitations and strengths and is able to add value to team work. K3: is well prepared to work in teams in advanced analytical projects.

Teaching methods

Laboratory, practical method, participatory lecture

Expository teaching methods

- problem-based lecture
- participatory lecture

Exploratory teaching methods

- laboratory
- seminar

Online teaching methods

- methods developing reflexive thinking

Course coordinators

Wojciech Goszczyński

Assessment criteria

Assessment methods:
Research project, participation

Grading criteria:

ndst - work does not meet requirements
dst/dst+ - basic analytical techniques, basic coding
db/db+ - extensive database, advanced analytical techniques
bdb- additionally data visualisation techniques

Additional information

Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system:

Description of 2400-OG-EN-CAQDA in USOSweb