Undergraduate Course: Quantitative Methods in Linguistics and English Language (Hons) (LASC10125)
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 study design, statistics and quantitative data analysis as commonly employed in linguistics, using the R software.
Students will submit 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.
Accordingly, self-assessment will rely on the following:
- Portfolio (Learn Journal) with self-reflection on class discussions and laboratory exercises.
- Self-evaluations at midterm and end of semester
- Group project
Laboratory exercises will be completed in small groups.
Students give themselves marks at the end of the course, based on their self-assessment. The instructor will moderate these and reserves the right to change them.
|
| 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.
|
Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
|
| Academic year 2024/25, Available to all students (SV1)
|
Quota: 150 |
| Course Start |
Semester 1 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Seminar/Tutorial Hours 33,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
163 )
|
| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
Portfolio worth 100%.
Students will submit 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.
Accordingly, self-assessment will rely on the following:
- Portfolio (Learn Journal) with self-reflection on class discussions and laboratory exercises.
- Self-evaluations at midterm and end of semester
- Group project
Laboratory exercises will be completed in small groups.
Students give themselves marks at the end of the course, based on their self-assessment. The instructor will moderate these and reserves the right to change them.
|
| Feedback |
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. |
| 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.
|
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 |
What does it mean to be a University of Edinburgh graduate? Every student and graduate is unique. They each have their own experiences, learning and way of approaching life. The quality, depth and breadth of their experiences while at the University of Edinburgh develop characteristic attributes that set them apart. University of Edinburgh graduates have: ¿ curiosity for learning that makes a positive difference ¿ courage to expand and fulfil their potential ¿ passion to engage locally and globally University of Edinburgh graduates are: ¿ creative problem solvers and researchers ¿ critical and reflective thinkers ¿ effective and influential contributors ¿ skilled communicators Shaped by our students' experiences, personalities and academic subjects, these graduate attributes evolve over time. They divide into two types: mindsets that influence our students¿ and graduates¿ behaviours, and groups of skills that empower their actions. |
| 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 |
|
|
University Homepage