This is an archived handbook for a previous year. The current undergraduate and postgraduate handbooks can be viewed using the links above.
Postgraduate Handbook 2024-2025 (archived)
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 2024/2025 | None. | Durham |
Tied To
- C8K009: Developmental Psychopathology
- C8K107: Research Methods (Developmental Psychology)
- C8K109: Cognitive Neuroscience
- C8K409: Behavioural Science
- C8K509: (programme not found)
- L2KA07: Research Methods (Politics, International Relations, Security)
- L3KB07: Social Research Methods
- L3KE14: Research Methods
- L6K307: Research Methods (Anthropology)
- L8K507: Geography (Research Methods)
- T6K109: Arab World Studies
- V1KB07: Social and Economic History (Research Methods)
- X1K107: Research Methods (Education)
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 that are used across psychology and the behavioural sciences
- To provide students with the capacity to confidently identify appropriate statistical techniques and analyse data using relevant software across a range of different types of research
Content
- Indicative content as follows:
- 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 acquire knowledge and understanding of:
- A range of advanced statistical tests used in psychology and the behavioural sciences
- The assumptions and limitations of the statistical techniques covered
- The advantages and limitations of using different statistical software (e.g., SPSS, R, JASP)
Subject-specific Skills
- By the end of the module students should be able to:
- Use and apply a range of advanced statistical techniques used in psychology and the behavioural sciences
- Effectively use statistical applications software (e.g., SPSS, R, JASP)
- Analyse data accurately
- Interpret data appropriately
Key Skills
- Students will also develop some important key skills, suitable for underpinning study at this and subsequent levels, such as:
- Implement general IT and research skills
- 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 be taught in a computer laboratory and will usually 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 | Attendance Monitored |
|---|---|---|---|---|---|
| Workshops | 11 | 1 per week | 3 hours | 33 | |
| Preparation and Reading | 117 | ||||
| Total | 150 | ||||
Summative Assessment
| Component | Component Weighting | Element | Length/Duration | Element Weighting | Resit Opportunity |
|---|---|---|---|---|---|
| Summative Assessment | 100% | Take-home assignment consisting of statistical analyses and theory questions | the word count should not exceed 5000 words | 100% | Yes |
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 assignment 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.