Module Catalogue 2024/25

Pre Requisite Comment

N/A

Co Requisite Comment

N/A

Aims

To provide an understanding of advanced statistical methods relating to regression and the design of experiments. To develop an appreciation of the importance of good research designs.

In the era of big data the analysis and interpretation of data, and the ethical issues surrounding data, are vital. The aim of the module is to provide the students with advanced analytical skills which will benefit them in emprical psychology and beyond. It will enable students to plan and analyse their own experiments and critically evaluate the results of others.

This module will be of particular benefit to those students looking to move into further study (e.g. MSc) or a research career. More generally, the advanced statistical techniques covered will enhance student employability outside the field of psychology.

Outline Of Syllabus

Recap of multiple regression

Advanced regression techniques: generalised linear models via logistic regression, Poisson regression and negative binomial regression, linear, mixed effects models, mediation and moderation

Chi-squared tests

Hierarchical clustering

Power calculations

Open science, meta-analysis

Intended Knowledge Outcomes

By the end of the module students will be able to:

Recognise practical situations in which different approaches to regression and design of experiments are appropriate.

Explain the broad principles underlying these statistical methods.

Select the correct method of analysis to suit the circumstances of an investigation.

Discuss relevant ethical considerations, and formulate research questions.

Intended Skill Outcomes

By the end of the module students will be able to:

Perform statistical analyses relating to problems in regression and design of experiments by hand (where appropriate) and using a computer package to carry out the analysis.

Demonstrate skills in the use of SPSS and R software.

Teaching Rationale And Relationship

Lectures lay the foundations of the statistical techniques.

Practical sessions enable students to apply statistical methods and provide an opportunity for students to clarify any misunderstandings about the methods taught in the lectures.

Calculations are carried out on computers using SPSS and R and they are intended to enable students to develop their computing skills.

Assessment Rationale And Relationship

The examination assesses knowledge and understanding of statistical methods and will require an ability to draw on material from throughout the course.

Coursework carried out in the practicals and continued in the students' own time takes the form of a problem solving exercise. This exercise carries 20% of the module marks as an indication of the importance of applying the statistical techniques during the learning process.

The examination will assess the students’ ability to explain the principles underlying statistical methods and perform statistical analyses in regression and design of experiments. The assignment will assess the students’ ability to select the correct method of analysis and allow them to demonstrate skills in SPSS and R.

Formative feedback will be provided on non-assessed questions which are similar in format to the examination questions.

If the module is failed, Stage 3 students may only be offered a resit if an honours degree is not awarded on the first occasion. Failed assessments will be the same format during the August resit period.

General Notes

N/A