Universität Wien

136079 UE Statistical Modeling for DH with R (2022W)

5.00 ECTS (2.00 SWS), SPL 13 - Europäische und Vergleichende Sprach- und Literaturwissenschaft
Continuous assessment of course work
ON-SITE
Moodle

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 25 participants
Language: German, English

Lecturers

Classes (iCal) - next class is marked with N

  • Monday 03.10. 13:15 - 14:45 Hörsaal 2 Hauptgebäude, Tiefparterre Stiege 5 Hof 3
  • Monday 10.10. 13:15 - 14:45 Hörsaal 2 Hauptgebäude, Tiefparterre Stiege 5 Hof 3
  • Monday 17.10. 13:15 - 14:45 Hörsaal 2 Hauptgebäude, Tiefparterre Stiege 5 Hof 3
  • Monday 24.10. 13:15 - 14:45 Hörsaal 2 Hauptgebäude, Tiefparterre Stiege 5 Hof 3
  • Monday 31.10. 13:15 - 14:45 Hörsaal 2 Hauptgebäude, Tiefparterre Stiege 5 Hof 3
  • Monday 07.11. 13:15 - 14:45 Hörsaal 2 Hauptgebäude, Tiefparterre Stiege 5 Hof 3
  • Monday 14.11. 13:15 - 14:45 Hörsaal 2 Hauptgebäude, Tiefparterre Stiege 5 Hof 3
  • Monday 21.11. 13:15 - 14:45 Hörsaal 2 Hauptgebäude, Tiefparterre Stiege 5 Hof 3
  • Monday 28.11. 13:15 - 14:45 Hörsaal 2 Hauptgebäude, Tiefparterre Stiege 5 Hof 3
  • Monday 05.12. 13:15 - 14:45 Hörsaal 2 Hauptgebäude, Tiefparterre Stiege 5 Hof 3
  • Monday 12.12. 13:15 - 14:45 Hörsaal 2 Hauptgebäude, Tiefparterre Stiege 5 Hof 3
  • Monday 09.01. 13:15 - 14:45 Hörsaal 2 Hauptgebäude, Tiefparterre Stiege 5 Hof 3
  • Monday 16.01. 13:15 - 14:45 Hörsaal 2 Hauptgebäude, Tiefparterre Stiege 5 Hof 3
  • Monday 23.01. 13:15 - 14:45 Hörsaal 2 Hauptgebäude, Tiefparterre Stiege 5 Hof 3

Information

Aims, contents and method of the course

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.).

Assessment and permitted materials

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

Minimum requirements and assessment criteria

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

Examination topics

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

Reading list

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.

Association in the course directory

DH-S II

Last modified: Th 04.07.2024 00:13