Digital Methods

 

Winter Term 2022/2023

Lecturer

Course Objectives and Content

This course will guide students through a framework for creating conceptually congruent research designs to answer meaningful questions about what is happening in online conversations (Paulus & Wise, 2019). Participants will learn how to use qualitative data analysis software as the primary tool for their digital research workflow. Using their own data or a provided dataset, participants will identify an object of interest for investigation; recognize philosophical and theoretical assumptions that impact research design; create focused and relevant research questions; ensure methodological alignment across aspects of the study design; resolve ethical dilemmas; extract and transform “big data” into a coherent dataset for closer, qualitative analysis; analyze data using thematic, narrative and discursive techniques; and establish the quality of the findings.

 

Learning Philosophy and Format: The course will include a combination of mini-lecture, large- and small-group discussion activities as well as hands-on analytic work. Students will be able to tailor activities to their own research interests and/or a dataset will be provided.

 

The full information and a schedule can be found in the Syllabus.

 

Winter Term 2021/2022

Course Objectives and Content:

The course will provide an introduction to linear mixed-effects models (LMMs) in R. It will start by discussing the linear model. An important topic in LMMs are contrasts, which provide the way to encode hypotheses about factors in linear (mixed effects) models. Therefore, the course will provide a detailed discussion of contrast coding, and will introduce a powerful way to encode any linear hypotheses about factors into contrasts by using the generalised matrix inverse, which can be easily implemented using the R package hypr. The course will also cover the coding of covariates (i.e., continuous predictor variables). Based on the knowledge about contrasts, the second day will provide an introduction to the LMM, it will discuss fixed effects and variance components, and how they can be estimated in R using the lmer function. Moreover, we will treat the important question of how variance components and correlation parameters can be selected to achieve parsimonious LMMs. In case there is interest and enough time, we can moreover discuss power analyses for LMMs using the design R package.

The full information can be found in the Syllabus.

Lecturer:

 

 

Winter Term 2020/2021

Course Objectives and Content:

The course aims to provide participants with a theoretical and methodological toolkit enabling them to understand our digital consumer societies. The course contains a social-sciences informed discussion of algorithms, datafication & platformization as well as epistemological discussions on the use of digital data for doing consumer research. Furthermore, it will link the theoretical part with practical work with creative digital methods. This includes data collection and analysis using a combination of quantitative (network analysis with Gephi, automated content analysis with KH Coder) and qualitative interpretive methods (visual content analysis, semiotic, and discourse analysis). It will be a online combination of Lecture, Discussion, hands-on exercises.

The reading list can be found in the Syllabus. 

 

 

Lecturer:

 

Syllabus 2020/2021

Winter Term 2019/2020

Course Objectives and Content:

This course is designed to enable students to situate Digital Methods historically and epistemologically. Furthermore, students will acquire the ability to practically use digital methods and to critically evaluate its ethical implications.

The course opens with a discussion of how to repurpose digital "methods of the medium" for social and cultural scholarly research, including its limitations, critiques and ethics. Subsequently participants are trained in using digital methods in hands-on sessions. How to use crawlers for dynamic URL sampling and issue network mapping? How to employ scrapers to create a bias or partisanship diagnostic instrument? We also consider how to deploy online platforms for social research. How to transform Wikipedia from an online encyclopedia to a device for cross-cultural memory studies? How to make use of social media so as to profile the preferences and tastes of politicians’ friends, and also locate most engaged with content? How to make use of Twitter analytics to debanalize tweets, and provide compelling accounts of events on the ground? Finally, the course turns to the question of employing web data and metrics as societal indices more generally.

Lecturer:

Reading List:

 

Syllabus 2019/2020