Postgraduate Course: Quantitative Methods for Linguistics and English Language (LASC11177)
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 11 (Postgraduate) |
Availability | Available to all students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | This course is an introduction to study design, statistics and quantitative data analysis as commonly employed in linguistics, using the R software. |
| Course description |
The course will cover the basics of statistics and quantitative data analysis, and how to design studies that effectively address the intended research questions. Students will learn the principles of data visualisation and statistical modelling and develop the practical skills necessary to perform such analyses in R. The course will draw examples from different branches of linguistics and will provide students with hands-on experience in Open Research practices.
The course will teach students how to carry out statistical inference using linear models and how to interpret p-values and Bayesian statistics.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Information for Visiting Students
| Pre-requisites | None |
Course Delivery Information
| Not being delivered |
Learning Outcomes
On completion of this course, the student will be able to:
- Import common data formats, tidy and transform data.
- Choose and report appropriate summary measures.
- Use compelling visualisations to communicate a specific message about patterns in the data.
- Master linear models for different types of data (continuous measures and binary outcomes).
- Use statistical inference to answer research questions and avoid common interpretation pitfalls.
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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 Sasha Wood
Tel:
Email: swood310@ed.ac.uk |
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