PS422-5-SP-CO:
Statistical Models for Psychology
2023/24
Psychology
Colchester Campus
Spring
Undergraduate: Level 5
Current
Monday 15 January 2024
Friday 22 March 2024
15
16 November 2023
Requisites for this module
(none)
PS421
(none)
(none)
(none)
Psychology is, in the main, a statistical science. While qualitative analysis and single-case studies are important tools, most findings in psychology rest on the analysis of quantitative data. In a world that is becoming increasingly reliant on data, it is important to develop skills that enable handling and interpreting of such (big) data.
This module will provide a first step in that direction by introducing students to statistical practices that go beyond the standard requirement for the BPS. Moreover, developments in the field (e.g., replication crisis; big data sets) have initiated a move towards "open science" practises such as the sharing of data. The use of script-based analysis packages is a vital component of this movement as it allows researchers to share exactly what analysis was carried out. This module will introduce students to this way of working with data.
The aims of this module are:
- To allow students to develop data science skills that will be of great value beyond their degree.
- To introduce students to the linear modelling approach to statistics. dents will have learnt about in previous modules, as well as more advanced statistical models.
- To enable students to “go beyond p-values” and learn how to assess model fit, interpret effect sizes, and visualise their data.
By the end of this module, students will be expected to be able to:
- Use code (e.g., R, SPSS syntax or similar) to carry out common data processing tasks (e.g., remove outliers, transform variables, select columns).
- Identify a suitable linear model for a research question and fit it to data.
- Use fitted models to further understand and predict human behaviour.
- Understand some key principles in data visualisation and use these to plot their own data and models.
The module will begin with an introduction to data management and data visualisation. Next, students will learn how to implement different statistical tests using the linear models (e.g. t-tests, ANOVA, regression, moderation, mediation). It will also consider the important assumptions around quantitative analysis, and the possible limitations on interpretation drawn from this analysis.
Interpreting and visualising data is an essential skill in many professions, and increasingly this includes analysis of “Big Data”, that require script-based solutions.
The linear modelling approach to statistics is a flexible framework that incorporates many of the traditional tests (t-test, ANOVA, etc.) that stu
This module will be delivered via:
- Lectures.
- Interactive lab classes.
In addition, some material may be presented as workbooks for students to work through in their own time.
This module does not appear to have a published bibliography for this year.
Assessment items, weightings and deadlines
| Coursework / exam |
Description |
Deadline |
Coursework weighting |
| Coursework |
Homework Assignment 1 |
|
5% |
| Coursework |
Homework Assignment 2 |
|
5% |
| Coursework |
Homework Assignment 3 |
|
5% |
| Coursework |
Homework Assignment 4 |
|
5% |
| Coursework |
Class Test |
|
80% |
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 Alasdair Clarke, email: a.clarke@essex.ac.uk.
Dr Alasdair Clarke
a.clarke@essex.ac.uk
No
Yes
Yes
Dr John Patrick Rae
Roehampton University
Reader in Psychology
Available via Moodle
Of 35 hours, 29 (82.9%) hours available to students:
0 hours not recorded due to service coverage or fault;
6 hours not recorded due to opt-out by lecturer(s), module, or event type.
Psychology
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