LG533-7-SP-CO:
Experimental Analysis
2020/21
Linguistics
Colchester Campus
Spring
Postgraduate: Level 7
Current
Sunday 17 January 2021
Friday 26 March 2021
15
01 April 2019
Requisites for this module
(none)
(none)
(none)
(none)
(none)
MRESQ14512 Analysing Language Use,
MRESQ10412 Experimental Linguistics,
MA Q10012 Linguistics,
MA Q15012 Psycholinguistics,
MPHDQ15048 Psycholinguistics,
PHD Q15048 Psycholinguistics
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).
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.
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)
No previous experience with statistics required.
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 |
Coursework 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% |
Exam format definitions
- Remote, open book: Your exam will take place remotely via an online learning platform. You may refer to any physical or electronic materials during the exam.
- In-person, open book: Your exam will take place on campus under invigilation. You may refer to any physical materials such as paper study notes or a textbook during the exam. Electronic devices may not be used in the exam.
- In-person, open book (restricted): The exam will take place on campus under invigilation. You may refer only to specific physical materials such as a named textbook during the exam. Permitted materials will be specified by your department. Electronic devices may not be used in the exam.
- In-person, closed book: The exam will take place on campus under invigilation. You may not refer to any physical materials or electronic devices during the exam. There may be times when a paper dictionary,
for example, may be permitted in an otherwise closed book exam. Any exceptions will be specified by your department.
Your department will provide further guidance before your exams.
Overall assessment
Reassessment
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
No
No
No
Dr Sarah Ann Liszka
University of Greenwich
Senior Lecturer
Available via Moodle
Of 562 hours, 0 (0%) hours available to students:
562 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s).
Linguistics
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