The Research Methods and Project Planning module provides students with a comprehensive Research Toolkit which will enable them to carry out research in the broad field of Biomedical Science. Spanning literature exploration, hypothesis development, study design, and data analysis, the course will provide students with competency in a range of research method and project planning skills. Students master the art of formulating robust hypotheses, designing ethically sound experiments, and implementing good research practices. The module also covers data management and analysis, laboratory record-keeping, and effective communication strategies. By course end, students emerge equipped to conduct comprehensive literature searches, critically appraise scientific literature, analyse data with appropriate statistical tests, interpret their findings and adeptly communicate their research findings to both specialist and non-specialist audiences.
1. Searching and reviewing the literature
- Maximising breadth and depth of searches using relevant databases (e.g. Pubmed, Trip Database)
- Approaches to critical evaluation of the literature
2. Hypothesis development
- Critical evaluation of available knowledge/data
- Identification of knowledge gaps
- Formulation of robust and testable hypothesis
3. Study Design
- Types of research study, including experimental and epidemiological models
- Characteristics of good research studies (e.g. statistical power and sample size, relevant research standards such as STARD)
- Data collection requirements (e.g. sample type, types of replicates, patients, biobanking)
- Ethical, GDPR and HRR requirements (e.g. balancing power imbalances in research, consent and individual agency of research participants)
4. Good research practice
- Data management planning and FAIR principles
- Maintaining the laboratory notebook/record
- Planning the research (e.g. time management, aims and objectives, project planning, milestones, deliverables, risks and setbacks, stratification of test selection)
- How to write and present scientifically (e.g. development of an academic writing plan, effective communication skills) for scientific, professional and lay audiences
5. Data analysis and interpretation
- Application of descriptive and inferential (e.g. Sensitivity, Specificity, ROC curve analysis) statistics to biomedical data
- choosing the correct statistical approach and recognising the underlying assumptions and limitations of such approaches
- Use of statistical software packages for data analysis (e.g. MedCalc, PASW, GraphPad, R)
- Interpretation of the results of statistical analysis
The content of this module addresses UN SDG 9, specifically target 9.5.
This module will be delivered using a blended teaching approach using Brightspace. Online teaching will use a combination of synchronous and asynchronous video sessions and tutorials (e.g. in use of a software package) as well as peer-to-peer discussions. A variety of online teaching and learning activities are used to support the development of competence in the various key skills.
Structured feedback on 'Critical Appraisal of as Scientific paper' assignment will be provided via the marking rubric in Brightspace.
| Module Content & Assessment | |
|---|---|
| Assessment Breakdown | % |
| Other Assessment(s) | 100 |