Postgraduate Course: Quantitative Methods in Linguistics MSc (LASC11182)
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, 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 | None |
Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
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| Academic year 2024/25, 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 (100%)«
Students will submit exercises and self-reflections after each class, a mid-semester self-evaluation at the mid-semester point, a group project towards the end of the semester, and a final self-evaluation.
We want to evaluate:
- What we've learned and how we've grown (portfolio: reflections)
- How well we've learned the skills (portfolio: exercises)
- How we can apply the new knowledge (project)
Accordingly, self-assessment will rely on the following:
- Portfolio (Learn Journal) with self-reflection and answers to exercises
- Self-evaluations at midterm and end of semester
- Group project
- Working in pairs or small groups on the exercises will be permitted (and encouraged).
Students give themselves marks at the end of the course, based on their self assessment. The instructor reserves the right to change these.
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| Feedback |
Not entered |
| 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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