- UCAS course code
- PQ12
- UCAS institution code
- M20
Course unit details:
Quantitative Methods in Language Sciences
Unit code | LELA20231 |
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Credit rating | 20 |
Unit level | Level 2 |
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.
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 Blackboard 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 Blackboard 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
- 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. 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.
- Other
- 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.
Assessment methods
Assessment Task | Formative or Summative | Weighting |
Blackboard Quizzes | Formative | 0% |
Coursework (Analysis Report) | Summative | 75% |
Mid-term Exam | Summative | 25% |
Feedback methods
Feedback Method | Formative or Summative |
Blackboard 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 | |
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Assessment written exam | 1 |
Lectures | 11 |
Tutorials | 22 |
Independent study hours | |
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Independent study | 166 |
Teaching staff
Staff member | Role |
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Andrea Nini | Unit coordinator |
Patrycja Strycharczuk | Unit coordinator |
Additional notes
This course unit is taught jointly with LELA32011.