Vorlesungs­verzeichnis

Statistical Modeling for DH with R

136079 UE 2023W

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Moodle

Vortragende der Germanistik:

• Andreas Baumann

Ziele, Inhalte und Methode der Lehrveranstaltung

This course provides an introduction to multivariate statistical modeling (more specifically, regression models) with a particular focus on the analysis of data coming from various subareas of humanities research, such as linguistics, musicology, or cultural science.

The first part of the course introduces descriptive as well as inferential statistics, i.e., measures of central tendency and variability, parameter estimation and confidence intervals (as well as a quick introduction to hypothesis testing). In the second part of the course, we will consider various regression-model families (linear models, logistic regression, generalized linear models, mixed effects models) which can be used to analyze the impact of multiple predictor (independent) variables on a single outcome (dependent) variable. Finally, we will discuss more advanced topics such as model optimization and multimodel-inference techniques.

For calculating purposes, the statistical software package R together with the frontend RStudio will be used. I will use RStudio Cloud to distribute RStudio projects, but I highly recommend installing RStudio locally as well. Students need to bring their own laptops with them.

No previous knowledge of statistics or statistical software is required but a solid knowledge of high school mathematics (at least Unterstufe) will prove useful (linear functions, basic arithmetical operations, fractions, percentages etc.).

 

Art der Leistungskontrolle und erlaubte Hilfsmittel

Warm-up exercise, four home assignments, and participation in class

 

Literatur

Nakagawa, S., & Cuthill, I. C. (2007). Effect size, confidence interval and statistical significance: a practical guide for biologists. Biological reviews, 82(4), 591-605.
Butler, C. (1985). Statistics in Linguistics. Oxford: Blackwell.
Burnham, K. P., Anderson, D. R., & Huyvaert, K. P. (2011). AIC model selection and multimodel inference in behavioral ecology: some background, observations, and comparisons. Behavioral ecology and sociobiology, 65(1), 23-35.

 

Prüfungsstoff

Statistical knowledge and coding skills in R as covered by the course.

 

Mindestanforderungen und Beurteilungsmaßstab

The ability to analyze, present and describe quantitative linguistic data as well as the ability to understand and interpret statistical analyses and relevant statistical parameters.

Assessment:
Warm-up exercise: 5%
Four home assignments: 20% each
Participation in class: 15%
Minimum pass grade: 60% in total