Experimental Analysis

The details
Colchester Campus
Postgraduate: Level 7
Sunday 17 January 2021
Friday 26 March 2021
01 April 2019


Requisites for this module



Key module for

MRESQ14512 Analysing Language Use,
MRESQ10412 Experimental Linguistics,
MA Q15012 Psycholinguistics,
MPHDQ15048 Psycholinguistics,
PHD Q15048 Psycholinguistics and Neurolinguistics

Module description

The module provides an introduction to Experimental Analysis. In lectures, students will be introduced to statistical techniques, whereas practical classes will offer students practice with implementing these techniques using freely-available R software. The module will cover basic principles such as sampling, distributions, and hypothesis testing, as well as how to select the appropriate statistics to analyse collected data. We will also discuss descriptive statistics and how best to report results in written work such as journal submissions and dissertations.

Students will be familiarised with how to test for relationships between variables (i.e., correlation and regression), and how to test for differences in behaviour between participants or groups (using, for instance, t-tests, ANOVAs, multiple regression, mixed effects modelling).

Module aims

This course aims to:
• Prepare PG students for the analysis of original data
• Provide preparation for reporting statistics in MA dissertations and published articles
• Promote the acquisition of ‘transferable skills’ (e.g., data manipulation and analysis, report writing, computer skills, etc.), which will be useful both inside and outside of academic contexts.

Module learning outcomes

On completion of the module, students will be able to:
1. Define key terminology related to experimental design and analysis
2. Summarise experimental studies in terms of hypotheses, design, variables, and analyses
3. Use basic descriptive and inferential statistics to analyse data sets (using statistical analysis software such as SPSS or R)

Module information

No previous experience with statistics required.

Learning and teaching methods

This course consists of 10 weekly 2-hour lectures. Students are expected to attend regularly, and to actively contribute to class discussions. Reading will be expected in advance of lectures.


  • Field, Andy P.; Miles, Jeremy; Field, Zoë. (2012) Discovering statistics using R, London: Sage.

The above list is indicative of the essential reading for the course. The library makes provision for all reading list items, with digital provision where possible, and these resources are shared between students. Further reading can be obtained from this module's reading list.

Assessment items, weightings and deadlines

Coursework / exam Description Deadline Weighting
Coursework   Analysis Exercise 1     10% 
Coursework   Analysis Exercise 2    10% 
Coursework   Analysis Exercise 3    10% 
Coursework   Analysis Exercise 4    10% 
Coursework   Analysis Exercise 5    10% 
Coursework   Analysis Exercise 6    10% 
Coursework   Analysis Exercise 7    10% 
Coursework   Analysis Exercise 8    10% 
Coursework   Analysis Exercise 9    10% 
Coursework   Analysis Exercise 10    10% 

Overall assessment

Coursework Exam
100% 0%


Coursework Exam
100% 0%
Module supervisor and teaching staff
Dr Laurel Lawyer, email: l.lawyer@essex.ac.uk.
Dr Laurel Lawyer
Laurel Lawyer, 4.340, 1206 872 087, l.lawyer@essex.ac.uk



External examiner

Dr Sarah Ann Liszka
University of Greenwich
Senior Lecturer
Available via Moodle
Of 20 hours, 10 (50%) hours available to students:
10 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s).


Further information

* Please note: due to differing publication schedules, items marked with an asterisk (*) base their information upon the previous academic year.

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