Course Catalogue

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This course provides students with a grounding in the most common quantitative, computational, and statistical methods that are used to analyse linguistic data. This course covers probability, descriptive and inferential statistics, and computational methods for cleaning, visualising, and analysing data.

10 x 1hr lectures, 10 x 1hr practical workshops over 10 weeks as scheduled in MyCampus.

This is one of the Honours options in English Language & Linguistics, and may not run every year. The options that are running this session are available on MyCampus.

Available to all students fulfilling requirements for Honours entry in English Language & Linguistics, and by arrangement to visiting students or students of other Honours programmes who qualify under the University's 25% regulation.

ENGLANG5092 Experimental Design and Data Analysis

The standard assessments for this course are: 

■ Examination (90 minutes duration) - 50% 

■ Two set exercises - 25% each (each exercise will comprise 20-40 lines of R Code, plus 200 words of discussion) 

If required, for instance where a disability prevents a student from undertaking a specific method of assessment, the following alternatives are available: 

■ 24-hour open exam can be offered as alternative to exam. 

■ There are no alternatives to the Two set exercises, but flexible deadlines can be offered as reasonable adjustment (Students should follow the usual process for extensions). 

Further reasonable adjustments may be provided where necessary. Students are encouraged to consult with the course convenor.  

This course will provide the opportunity to:

■ Become familiar with the free, open-source software package R for data analysis and visualisation

■ Acquire a core understanding of probability and inferential statistics

■ Carry out a variety of statistical tests on different types of data

■ Analyse independently-collected data to answer a research question

■ Learn the common pitfalls and misconceptions in carrying out inferential statistical analyses

By the end of this course students will be able to:

■ Clean and manipulate raw data sets so they are ready for analysis

■ Visualise linguistic data to illustrate key patterns

■ Determine and carry out the appropriate statistical test for a variety of experimental questions about different data sets

■ Correctly interpret the result of inferential statistics tests

■ Draw conclusions about whether research hypotheses have been supported by empirical data