Postgraduate Course: Quantitative Methods in Linguistics and English Language (MSc) (LASC11187)
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.
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| Course description |
The course will cover the basics of statistics and quantitative data analysis, how to design studies that effectively address the intended research questions and how to identify and avoid common pitfalls and questionable research practices. Students will learn the principles of visualising, summarising and modelling data 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 Scholarship and Research practices.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | 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". |
Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
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| Academic year 2025/26, Available to all students (SV1)
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Quota: 0 |
| Course Start |
Semester 1 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Seminar/Tutorial Hours 33,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
65 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Portfolio worth 100%. Students will submit self-reflections after each class and a group project at the end of the term, including a final reflection. The portfolio is composed of the weekly and final self-reflections and the group project.
Assessment will be based on the portfolio. |
| Feedback |
Feedback (feed-forward) will be provided weekly in response to the self-reflections and in class. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Understand general principles of data analysis, including summarising, visualising and modelling data.
- Develop state-of-the-art Open Scholarship practices for a more egalitarian, diverse, and inclusive scholarship.
- Conduct data analyses with the open software R.
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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
Bruno Nicenboim, Daniel Schad, and Shravan Vasishth. 2023. An Introduction to Bayesian Data Analysis for Cognitive Science. https://vasishth.github.io/bayescogsci/book/ |
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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