Module PSYC42315: Advanced Statistics for Psychology and the Behavioural Sciences

Department: Psychology

PSYC42315: Advanced Statistics for Psychology and the Behavioural Sciences

Type	Level	Credits	Availability	Module Cap	Location
Tied	4	15	Available in 2026/2027	None.	Durham

Tied To

C6K009: Physical Activity, Health and Society
C8K009: Developmental Psychopathology (see Postgraduate Handbook 2025-2026 for details)
C8K107: Research Methods (Developmental Psychology)
C8K109: Cognitive Neuroscience
C8K409: Behavioural Science
C8K507: Research Methods (Behavioural Science)
C8K709: Neurodiversity and Development
L3KB07: Social Research Methods
L3KE14: Research Methods
L3KG07: Interdisciplinary Research Methods
N2R201: Management (with Integrated Studies)
N5R201: Marketing (with Integrated Studies)
T6K109: Arab World Studies (see Postgraduate Handbook 2025-2026 for details)
V1KB07: Social and Economic History (Research Methods)

Pre-requisites

• PSYC42415 Statistics for Psychology and The Behavioural Sciences, OR SGIA49915 Quantitative Research Methods, OR alternative prior knowledge and understanding (i.e. firm understanding of how to explore data using appropriate descriptive statistics and graphs; confidence using R to undertake data management and statistical analyses, e.g. linear regression and visualisation; firm understanding of multiple linear regression through substantive analysis and the ability to implement, interpret and report, e.g. having passed the equivalent of one term of quantitative methods, up to and including linear regression; firm understanding of statistical inference, and the basis for this).

Corequisites

None

Excluded Combinations

None

Aims

To teach students a set of advanced statistical methods used across psychology and the behavioural sciences
To develop students' capacity to confidently identify appropriate statistical techniques and analyse data using suitable statistical software across a range of research contexts

Content

The module develops advanced approaches to statistical modelling and inference for psychology and the behavioural sciences. The specific sequence, emphasis, examples, datasets, readings and software used may vary from year to year to reflect developments in the field, staff expertise, student learning needs, and timetabling constraints. The following list is indicative and not exhaustive:
Logistic and non-parametric (robust) regression
Multi-level modelling
Mediation, moderation and conditional process analysis
Structural equation modelling
Confirmatory factor analysis
Multidimensional scaling and cluster analysis
Meta-analysis

Learning Outcomes

Subject-specific Knowledge

On completion of this module, students will be able to demonstrate knowledge and understanding of:
A range of advanced statistical techniques used in psychology and the behavioural sciences
Assumptions, limitations, and appropriate application of the techniques covered
The advantages and limitations of using different statistical software packages that may be used to implement these techniques (e.g., SPSS, R, JASP)

Subject-specific Skills

By the end of the module students should be able to:
Select and apply appropriate advanced statistical techniques used to address research questions
Use statistical software to conduct advanced analyses (using one or more packages as specified for module activities and assessment)
Analyse data accurately and transparently
Interpret and report results appropriately for a psychological/behavioural science audience.

Key Skills

Students will also develop transferable skills, including the ability to::
General IT and research skills relevant to advanced data analysis
Manage their own time and resources
Work to deadlines and within defined parameters

Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

The weekly workshops will normally be taught in a computer laboratory and will typically include a 1-2 hour lecture which will outline the key statistical methods and concepts, how they can be used in psychology and the behavioural sciences, and their appropriate interpretation followed by practical activities in which students will undertake practical data analysis exercises using statistical software.
The summative assessment consisting of statistical analyses and theory questions this will require students to demonstrate their knowledge and understanding of advanced statistics by carrying out analyses of pre-specified data-sets, writing up results and interpretation.
Formative assessment provides students with an opportunity to perform, write up and obtain feedback on a series of analyses of pre-specified secondary data sets.

Teaching Methods and Learning Hours

Activity	Number	Frequency	Duration	Total Hours
Lectures	10	1 per week	2 hours	20
Workshops	10	1 per week	2 hours	20
Preparation and Reading	1			110
Total				150

Summative Assessment

Component	Component Weighting	Element	Length/Duration	Element Weighting	Resit Opportunity
Summative Assessment	100%	On Campus Written Examination	2 hours	100%

Formative Assessment

Formative assessment will be undertaken in class and feedback will be provided. Students will be set short-answer questions which might include being provided with data sets to analyse and interpret. The questions set for formative assessment will follow a similar format as the examination and is designed to familiarise students with the requirements and expectations.

Students who do not attend monitored activities shown under Teaching Methods and Learning Hours, or who fail to complete the summative or formative assessment(s) specified above, may be subject to the Academic Progress procedures defined in the University's General Regulation V, and may be required to leave the University.