Module Information
Course Delivery
| Delivery Type | Delivery length / details |
|---|---|
| Lecture | 1 x 1 hour lecture per week (both semesters) |
| Lecture | 1 x 2 hour lecture per week (Semester 1 only) |
| Practical | 11 x 3 hour workshop sessions (Semester 1) 3 x 3 hour computer support sessions (Semester 2) |
Assessment
| Assessment Type | Assessment length / details | Proportion |
|---|---|---|
| Semester Assessment | BlackBoard E-Exercises | 20% |
| Semester Exam | 3 Hours Online exam | 30% |
| Semester Assessment | Literature Review | 25% |
| Semester Assessment | Research Proposal | 25% |
| Supplementary Assessment | 3 Hours Resit elements that led to failure | 100% |
Learning Outcomes
On successful completion of this module students should be able to:
1. Search and review the scientific literature to identify valid research questions
2. Identify appropriate methods of analysis for different types of research
3. Design statistically valid experiments
4. Identify and mitigate against confounding factors in research design
5. Demonstrate an understanding of the ethical issues involved in research
6. Analyze data using a range of quantitative and qualitative techniques
7. Interpret the results of data analyses and apply statistical knowledge in the evaluation of research investigations
Brief description
The course will cover the principles and practice of a range of basic quantitative and qualitative procedures of data analysis, coupled with an understanding of good research design and planning. Delivery will rely heavily on student-centered e-learning, supported by computer workshops. Through e-learning, students will receive training in the use of statistical software packages, literature searching and research design. Use of subject-specific tutorial videos through Abercast will ensure that students receive explanatory examples directly relevant to their particular subject area. Formative assessment will be through the use of e-exercises in Blackboard using adaptive release, prior to the submission of a fully developed research plan and an exam to test statistical skills learnt.
Content
Following a core foundation in basic procedures of data analysis, students will have the opportunity to select a limited number of techniques relevant to specific schemes of study. The subject material will include:
The nature of variability
Populations, samples and sampling strategies
Types of data
Probability
Standard deviation
Normal distribution
Sampling from the normal distribution, standard error
Students t distribution, t tests
Analysis of variance
Multifactorial analyses, blocking
Correlation and regression
Chi-square analysis, contingency tables
Non-parametric tests
Questionnaire technique and structure
Interviewing techniques, focus groups
Content analysis (textual, discourse, conversation, visual)
Research planning
Running concurrently with the training in data analysis will be lectures on the basics of experimental design and research planning. These will include:
Literature searching and interpretation
Strategies and approaches to reviewing literature
Identifying hypotheses and research questions
Sampling strategies and size
Identification and mitigation of confounding factors
Appropriate research design
Ethical issues in research design
Protocol development
Identification of resources
These issues will be developed through a number of formative exercises delivered via e-learning. The final summative assessment will require students to produce, in consultation with individual members of academic staff, a comprehensive research plan in an unique area relevant to their scheme of study. If successfully completed, this plan will form the basis of the student’s final year dissertation when progressing to the final year of the BSc.
Notes
This module is at CQFW Level 5