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Quantitative Methods - LAN00033M

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  • Department: Language and Linguistic Science
  • Credit value: 10 credits
  • Credit level: M
  • Academic year of delivery: 2022-23

Module summary

This module provides a firm grounding in the theory and practice of quantitative data analysis, and its application across all areas of linguistics. Training in the R statistical software environment is a key component of the module.

Module will run

Occurrence Teaching period
A Spring Term 2022-23 to Summer Term 2022-23

Module aims

This module provides a firm grounding in the theory and practice of quantitative data analysis. It focuses on developing skills and knowledge in data management, visualisation and statistical modelling through the analysis of linguistic data sets. A key element of the module is training in the R statistical software environment, providing the tools for students to develop the skills to use R independently for quantitative analysis in dissertation research. Further, the module aims to foster quantitative literacy in general, helping students become critical consumers of arguments based on numbers, both in linguistics and beyond.

Module learning outcomes

  • understand and critically evaluate quantitative arguments and statistical analyses in linguistics and elsewhere;
  • perform a wide variety of data-related tasks in the R statistical software environment;
  • create, manage and manipulate data sets;
  • design and produce professional and informative visualisations;
  • select appropriate statistical tests and models for making predictions and evaluating hypotheses, and apply these to linguistic data;
  • present quantitative results following established conventions in the field of linguistics.

Indicative assessment

Task % of module mark
Essay/coursework 40
Essay/coursework 60

Special assessment rules

None

Indicative reassessment

Task % of module mark
Essay/coursework 40
Essay/coursework 60

Module feedback

Marks and feedback for summative work provided within four weeks of submission

Indicative reading

  • Baayen, R. H. (2006). Analyzing linguistic data: A practical introduction to statistics using R. Cambridge: Cambridge University Press
  • Langdridge, D. (2004). Research methods and data analysis in Psychology. Harlow, England: Pearson Education Limited.
  • Levshina, N. (2015). How to do linguistics with R: Data exploration and statistical analysis. Amsterdam/Philadelphia: John Benjamins.
  • Wickham, H. and Grolemund, G. (2017). R for Data Science. O'Reilly, Sebastopol, CA.


The information on this page is indicative of the module that is currently on offer. The University constantly explores ways to enhance and improve its degree programmes and therefore reserves the right to make variations to the content and method of delivery of modules, and to discontinue modules, if such action is reasonably considered to be necessary. In some instances it may be appropriate for the University to notify and consult with affected students about module changes in accordance with the University's policy on the Approval of Modifications to Existing Taught Programmes of Study.