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

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)

Reassessments are normally available for all courses, except those which contribute to the Honours classification. For non-Honours courses, students are offered reassessment in all or any of the components of assessment if the satisfactory (threshold) grade for the overall course is not achieved at the first attempt. This is normally grade D3 for undergraduate students and grade C3 for postgraduate students. Exceptionally it may not be possible to offer reassessment of some coursework items, in which case the mark achieved at the first attempt will be counted towards the final course grade. Any such exceptions for this course are described below. 

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

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.