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Quantitative Methods - LAN00098M

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  • Department: Language and Linguistic Science
  • Module co-ordinator: Dr. Simon Roessig
  • Credit value: 20 credits
  • Credit level: M
  • Academic year of delivery: 2023-24
    • See module specification for other years: 2024-25

Module summary

In this module, you will learn how to do statistical analysis for linguistic research and use the R programming language.

Module will run

Occurrence Teaching period
A Semester 2 2023-24

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

By the end of the module, you will be able to:

  • 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.

Module content

Throughout the module, you will attend one lecture per week and one practical per week. The lecture focuses on developing statistical thinking and learning the concepts behind statistical analysis. The practical is devoted to learning the R programming language and applying statistical analysis to data. In your private study time, you should read the assigned readings, prepare and practice practical exercises, finish any tasks that you do not manage to complete in class during practicals, complete formative assignments and work on summative assignments.


Task Length % of module mark
Quantitative Methods data analysis task
N/A 40
Quantitative Methods data analysis task
N/A 60

Special assessment rules



Task Length % of module mark
Quantitative Methods Data analysis reassessment report
N/A 60
Quantitative Methods data analysis reassessment task
N/A 40

Module feedback

For formative assessments, students will receive group-level feedback and an approximate mark. For summative assessments, students will receive individual feedback.

Indicative reading

Langdridge, D. and Hagger-Johnson, G. (2013). Introduction to Research Methods and Data Analysis in Psychology. Pearson Education, Harlow, UK, 3rd edition (you may use the 2nd edition).

Navarro, D. (2019). Learning statistics with R: A tutorial for psychology students and other beginners. (Version 0.6.1).

The information on this page is indicative of the module that is currently on offer. The University is constantly exploring 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 by the University. Where appropriate, the University will notify and consult with affected students in advance about any changes that are required in line with the University's policy on the Approval of Modifications to Existing Taught Programmes of Study.