Postgraduate Course: Research Methods and Data Analysis (IAWEL) (VESC11265)
Course Outline
| School | Royal (Dick) School of Veterinary Studies |
College | College of Medicine and Veterinary Medicine |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
| Course type | Online Distance Learning |
Availability | Available to all students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | This course provides an introduction to good scientific practice, data collection, organisation and analysis, scientific communication and common mistakes or misconceptions to avoid in scientific practices. |
| Course description |
This course provides an introduction to research methods and data analysis for students in the animal welfare field. An introduction to the philosophy of science will set the groundwork as to why good scientific practices are important. An overview of different types of data and how to organise and manage the data will provide foundation for an overview of different data analysis and summarisation techniques and project management skills. Students will further explore methods for collecting data, different sources of available data and the potential need for ethical approvals before data collection begins. Specific methods for analysing qualitative and quantitative data will be examined, as well as introductions to different types of data analysis software. Finally, the students will apply what they have learned throughout this course to design a project proposal which could be used to form the basis for their dissertation thesis.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
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| Academic year 2024/25, Available to all students (SV1)
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Quota: None |
| Course Start |
Flexible |
| Course Start Date |
05/08/2024 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
196 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
The major assessment component will be a project proposal that will function as the basis for the dissertation project in the MSc year.
There will also be a few small assessments which will help the students build up to completing the final major assessment, such as a mini-proposal that will feed forward into the final proposal assessment, critical analysis of literature, exploration of different data collection methods and short answer/MCQ¿s regarding course content.
Project proposal (60%): this will form the basis for the dissertation project in the MSc year
Short assessments to aid with preparation of the module proposal may include:
Mini-proposal (10%)
Critical analysis of the literature (10%)
Exploration of data collection methods (10%)
Short answer questions (10%)
Formative assessments will encompass questions and discussion on relevant topics using the discussion board and may include live online sessions for the project proposal assessment. |
| Feedback |
Students will receive feedback on each assessment, with the feedback from the smaller assessments contributing to the major assessment. The feedback from the major assessment will be used to finalise the project proposal into the dissertation topic and methods for the MSc year. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Demonstrate a critical understanding of scientific and statistical concepts. (SCQF 11.1)
- Design and plan a research proposal with consideration to analysis of data (using statistical and data analysis software where appropriate). (SCQF 11.2)
- Undertake critical evaluations of a range of numerical and graphical data and communicate findings. (SCQF 11.3 and 11.4)
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Reading List
Indicative reading. Resources will be provided to the students.
MacKay, J. R. D. & Wu, S. (in press) Veterinary Educational Research in Fogelberg et al Educational Principles and Practice in Veterinary Medicine, Wiley Publications. .
MacKay, J. R. D. (2020). Discipline Based Education Research for Animal Welfare Science. Frontiers in Veterinary Science, 7(7). https://doi.org/10.3389/fvets.2020.00007
Ioannidis, J.P.A. Why most published research findings are false. PLoS Medicine 2:e124
Jones, T., Evans, D. 2000. Conducting a systematic review. Australian Critical Care 13:66-71.
Martin, P.R., Bateson, P.P.G. 2021. Measuring behaviour: an introductory guide. 4th ed. Cambridge University Press
Nosek, B. A., & Errington, T. M. (2020). What is replication? PLOS Biology, 18(3), e3000691. https://doi.org/10.1371/journal.pbio.3000691
Twining, P., Heller, R. S., Nussbaum, M., & Tsai, C.-C. (2017). Some guidance on conducting and reporting qualitative studies. Computers & Education, 106, A1-A9. https://doi.org/10.1016/j.compedu.2016.12.002
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Additional Information
| Graduate Attributes and Skills |
Students will have the opportunity to develop the following
Graduate Attributes and Skills:
analytical thinking, critical thinking, knowledge integration and application, independent research, handling ambiguity and complexity, digital literacy, numeracy
Personal skills:
Planning, organising and time management, ethics and social responsibility in research, independent learning and development, decision making
Professional skills:
written communication, interpersonal skills, cross-cultural communication |
| Keywords | Data Collection,Data Analysis,Qualitative Data,Quantitative Data,Statistical Analysis |
Contacts
| Course organiser | Dr Jill MacKay
Tel: (0131 6)50 6122
Email: jill.mackay@ed.ac.uk |
Course secretary | Mr Stephen Mitchell
Tel: (0131 6)51 7112
Email: stephen.mitchell@ed.ac.uk |
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