Undergraduate Course: Data Analysis for Linguistics and English Language (LASC10121)
Course Outline
| School | School of Philosophy, Psychology and Language Sciences |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 10 (Year 3 Undergraduate) |
Availability | Available to all students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | This course is an introduction to data analysis with linguistic data, using the statistical software R. |
| Course description |
The course is a gentle introduction to data analysis for linguistics using the R software.
Students will learn how manage linguistic data through hands-on tutorials and how to conduct exploratory data analysis according to practices that are relevant to the field of linguistics. The course will teach the use of the programming software R: students will be able to import common data formats, tidy and transform data, choose and report appropriate summary measures, create compelling graphs, and identify and avoid common interpretation pitfalls.
By the end of the course, students will have gained a sufficient background to independently conduct exploratory data analyses with linguistic data, for example as required for their final dissertation. At the same time, the course will provide students with opportunities to develop transferable computer skills.
While the focus of the course is on the analysis of quantitative data, students interested in qualitative data analysis will also benefit from attending the course by learning how to manage data and create graphs in R.
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Information for Visiting Students
| Pre-requisites | Visiting students should have taken one or more years of introductory linguistics courses (similar to LEL1A/LEL1B/LEL2A). The course expects familiarity with concepts from general linguistics as covered, for example, in Genetti, Carol (2014) ¿How languages work. An introduction to language and linguistics¿ and Pereltsvaig, Asya (2012) ¿Languages of the world: An introduction¿. |
| High Demand Course? |
Yes |
Course Delivery Information
| Not being delivered |
Learning Outcomes
On completion of this course, the student will be able to:
- Master basic computer skills and data management.
- Import common data formats, tidy and transform data in R.
- Choose and report appropriate summary measures in R.
- Create compelling visualisations in R to communicate a specific message about patterns in the data.
- Identify and avoid common interpretation pitfalls in quantitative data analyses.
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Reading List
Winter, Bodo. 2019. Statistics for linguistics with R. 2nd edition.
McElreath, Richard. 2019. Statistical (Re)thinking. 2nd edition.
Wickham, Hadley and Mine Çetinkaya-Rundel and Garrett Grolemund. 2023. R for Data Science. https://r4ds.hadley.nz |
Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | Not entered |
Contacts
| Course organiser | Dr Stefano Coretta
Tel:
Email: s.coretta@ed.ac.uk |
Course secretary | Ms Susan Hermiston
Tel: (0131 6)50 3440
Email: Susan.Hermiston@ed.ac.uk |
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