Postgraduate Programme and Module Handbook 2022-2023 (archived)
Module PSYC40130: Applied Statistics
Department: Psychology
PSYC40130: Applied Statistics
| Type | Tied | Level | 4 | Credits | 30 | Availability | Not available in 2022/23 | Module Cap |
|---|
| Tied to | C8K107 |
|---|---|
| Tied to | C8K009 |
| Tied to | C8K109 |
| Tied to | C8K409 |
| Tied to | MA Research Methods |
Prerequisites
- None
Corequisites
- None
Excluded Combination of Modules
- None
Aims
- To introduce students to the theory and application of statistical methods using relevant software
- To develop students' confidence and competence in the use of statistics and the analysis of data relevant to Psychologists
Content
- Data collection and validation
- Data manipulation
- Presentation of data using graphics
- Basic parametric and non-parametric techniques
- Analysis of Variance (Anova) (One way / Two way, Mixed Models, Hierarchical, Covariance)
- Regression (Linear, Non-Linear, Logistic)
- Factor Analysis
- Multidimensional Scaling
- Cluster Analysis
- Analysis of Power
- Meta-analysis
- Additional statistical techniques may be introduced as appropriate
Learning Outcomes
Subject-specific Knowledge:
- range of widely-used statistical tests
- importance of the role of statistics in any successful data analysis
- limitations of the statistical techniques covered
- advantages and limitations of statistical software
Subject-specific Skills:
- use and appllication of a wide variety of statistical techniques
- effective use of statistical applications software
- analysing data and presenting accurage and relevant conclusions
Key Skills:
- implement genral 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 objectives will be met in lecture and practical sessions. Students will be taught a variety of parametric and non-parametric statistical data analysis methods, illustrated by examples. These examples will be in the form of data sets that will be analysed, and in the form of existing research papers that contain results from a statistical analysis, which are discussed to evaluate the results. In practical sessions students will have hands on training in data analysis using SPSS, R and JASP.
Teaching Methods and Learning Hours
| Activity | Number | Frequency | Duration | Total/Hours | |
|---|---|---|---|---|---|
| Lectures | 22 | 1 per week | 2 | 44 | ■ |
| Practicals | 22 | 1 per week | 1 | 22 | ■ |
| Preparation & Reading | 234 | ||||
| Total | 300 |
Summative Assessment
| Component: Examination | Component Weighting: 50% | ||
|---|---|---|---|
| Element | Length / duration | Element Weighting | Resit Opportunity |
| Examination | 2 hours | 100% | |
| Component: Online Test | Component Weighting: 50% | ||
| Element | Length / duration | Element Weighting | Resit Opportunity |
| Online Test | 2 hours | 100% | |
Formative Assessment:
Formative student assessments (both written and oral) will be undertaken throughout the duration of the module. These will be assessed by the tutor to enable students to gauge their own individual rate of progress.
■ Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University