Content
This course is a continuation of our introduction to Statistics for the Humanities in May 2020. In the first part of the course we covered basic statistical concepts and theories, introduced descriptive measures and inspected data graphically by plotting it in “R”.
The second part builds on the first one and introduces basic inferential models and the theory behind them, covering those that came up most frequently during the individual discussions with course participants: (a) correlation, (b) ordinary least square regression and (c) logistic regression. We will also have a look at non-parametric tests such as Chi-Square tests and at inductive approaches such as cluster analyses.
This course will offer a practical introduction to the different models. We will in particular:
This course is a continuation of our introduction to Statistics for the Humanities in May 2020. In the first part of the course we covered basic statistical concepts and theories, introduced descriptive measures and inspected data graphically by plotting it in “R”.
The second part builds on the first one and introduces basic inferential models and the theory behind them, covering those that came up most frequently during the individual discussions with course participants: (a) correlation, (b) ordinary least square regression and (c) logistic regression. We will also have a look at non-parametric tests such as Chi-Square tests and at inductive approaches such as cluster analyses.
This course will offer a practical introduction to the different models. We will in particular:
- Explore the most common statistical assumptions behind the models
- Check if our data fulfils the criteria
- Discuss ways to transform it, if it doesn’t
- Choose the appropriate model for our analyses
- Conduct an analysis in “R”
- Learn how to interpret the output
- Learn how to report the results and produce publication-quality plots
- Learn how to test the accuracy of a regression model
- Learn what to do if things go wrong
The course will again be based on an html script that will be made available to participants beforehand. In this second part of the course we will work with data sets as part of the exercises that accompany the script. The course also introduces the coding steps in “R” to read, transform, analyse and plot the results.
Those interested in the course without any prior experience in statistics or “R” and have not participated in May should work through the script of the first part and the accompanying podcast, which are available on ILIAS. This course is supported by DataCamp (www.datacamp.com).
Registration
Until August 15, 2020 to: toggweiler@wbkolleg.unibe.ch as on KSL: https://www.ksl.unibe.ch/ (Login with UniBe-Account, search with title)
Thomas Häussler studied lingustics, communication studies and political science. He is currently a post-doctoral researcher at the Institute of Communication and Media Studies at the University of Bern and works in the area of political communication. His research interests include social movements and digital mobilization in the networked public sphere, the fragmentation and polarization of the political space, and methodological aspects of computational approaches to digital political communication.
