- UCAS course code
- QT32
- UCAS institution code
- M20
Bachelor of Arts (BA)
BA English Language and Japanese
Duration: 4 years
Year of entry: 2025
UCAS course code: QT32 / Institution code: M20
- Key features:
Study abroad
Study with a language
- Typical A-level offer: ABB
- Typical contextual A-level offer: BBC
- Refugee/care-experienced offer: BBC
- Typical International Baccalaureate offer: 34 points overall with 6,6,5 at HL
Fees and funding
Fees
Tuition fees for home students commencing their studies in September 2025 will be £9,535 per annum (subject to Parliamentary approval). Tuition fees for international students will be £26,500 per annum. For general information please see the undergraduate finance pages.
Policy on additional costs
All students should normally be able to complete their programme of study without incurring additional study costs over and above the tuition fee for that programme. Any unavoidable additional compulsory costs totalling more than 1% of the annual home undergraduate fee per annum, regardless of whether the programme in question is undergraduate or postgraduate taught, will be made clear to you at the point of application. Further information can be found in the University's Policy on additional costs incurred by students on undergraduate and postgraduate taught programmes (PDF document, 91KB).
Scholarships/sponsorships
- Find out more from student finance
- Eligible UK students can apply for bursaries and scholarships
- Funding for EU and international students is on our country-specific pages
- Many students work part-time or complete a student internship
Course unit details:
Quantitative Methods in Language Sciences
| Unit code | LELA32011 |
|---|---|
| Credit rating | 20 |
| Unit level | Level 3 |
| Teaching period(s) | Semester 1 |
| Available as a free choice unit? | Yes |
Overview
This course aims to familiarize students with the basic concepts of statistics through hands-on practice. Topics covered in the course include distributions of data, basic principles of probability, describing and visualizing quantitative data, and interpreting quantitative data through hypothesis testing.
Pre/co-requisites
Aims
The principal aims of the course unit are as follows:
- To familiarize students with basic statistical concepts and terms necessary to understand quantitative research
- To help students understand the rules of describing, visualizing, and interpreting data
- To help students develop computer skills needed to work with quantitative data
- To foster critical thinking skills necessary for conducting quantitative research in the future
Syllabus
Some representative topics covered:
- Variable types
- Descriptive statistics
- Visualising Data
- Probability and Inference
- Correlation and regression
- Multiple regression
- Comparing means
- Experimental design and generalisation
Teaching and learning methods
Weekly 1 hour lecture (online)
Weekly 2 hour computer data-analysis tutorial
E-learning: The Canvas environment will provide: lecture recordings, lecture slides, tutorials on using R, reading assignments (beyond the main textbook), revision quizzes, additional exercises. We will also use the Canvas discussion forum.
Knowledge and understanding
By the end of this course students will be able to:
- Understand fundamentals of quantitative analysis
- Be familiar with basic statistical methods
- Describe, summarize, and visualize data
- Conduct basic statistical tests by hand and using computer statistical software
Intellectual skills
By the end of this course students will be able to:
- Identify appropriate descriptive and data visualization methods for different types of data
- Assess validity and soundness of conclusions drawn from basic statistical tests
Practical skills
By the end of this course students will be able to:
- Form sound statistical hypotheses based on research questions
- Use computer programs to visualize and summarize data and conduct basic statistical tests.
- Write simple computer code (using the R package)
Transferable skills and personal qualities
By the end of this course students will be able to:
- Use a variety of quantitative techniques to explore quantitative data
- Draw conclusions from quantitative data through both descriptive and inferential statistics
- Become comfortable working with quantitative data
- Develop time management skills by working to deadline
Employability skills
- Analytical skills
- By the end of the semester, students are expected to be able to use computer software programs to describe, visualize data, and conduct some basic statistical tests. These skills will be beneficial to students regardless of whether they intend to pursue academic or non-academic careers.
- Research
- For students interested in pursuing postgraduate degrees in linguistics, many subfields of linguists are becoming more and more data-driven, so the quantitative skills acquired in this class will provide a good foundation, making it easier for students to start their own research.
- Other
- For students intending to pursue non-academic careers, quantitative skills will be a valuable asset as many employers increasingly look for job candidates that have both domain-specific knowledge (e.g., linguistic knowledge) and general quantitative skills.
Assessment methods
| Assessment Task | Formative or Summative | Weighting |
| Canvas Quizzes | Formative | 0% |
| Coursework (Analysis Report) | Summative | 60% |
| Mid-term Exam | Summative | 40% |
Feedback methods
| Canvas quizzes: individual scores | Formative |
| Coursework mark and individual feedback | Formative and summative |
| Exam mark | Summative |
Recommended reading
Winter, B. (2019). Statistics for linguists: An introduction using R. Routledge
Field, A. and Hole, G., 2002. How to design and report experiments. Sage.
Crawley, Michael J. (2005). Statistics: An Introduction Using R.
Johnson, Keith (2008). Quantitative Methods in Linguistics.
Study hours
| Scheduled activity hours | |
|---|---|
| Assessment written exam | 1.5 |
| Lectures | 11 |
| Tutorials | 22 |
| Independent study hours | |
|---|---|
| Independent study | 165.5 |
Teaching staff
| Staff member | Role |
|---|---|
| Andrea Nini | Unit coordinator |