Postgraduate Course: Analysing the Environment (PGGE11198)
Course Outline
| School | School of Geosciences |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | This course provides a Master¿s level introduction to the research approaches and methods that underpin modern social and environmental sciences, with an emphasis on collecting and analysing data. Through a mixture of practicals, group work, lectures and discussion we will explore why and how we do science. The course provides a core set of skills that will be useful in many other courses dealing with empirical science, and in particular is recommended for anyone proposing to conduct a dissertation that involves data collection or analysis. As well as developing skills in qualitative and quantitative data analysis, the associated field course provides an opportunity to build up your group work skills and capacity for professional self-reflection.
The course is actually formed from two 10 credit course (Analysing the Environment and Analysing the Environment Study Tour): in most cases you will have to take both, but for students outwith the MSc Ecosystem Services it may be possible to only take the first course.
In semester one a weekly series of seminars, lectures and practicals introduces the key concepts and methods. This course is assessed through a test and report. The following Easter, a programme-specific fieldtrip provides the opportunity to put the skills you have learned into practice. As part of the field trip you will conduct a group research project, which you will have to present and reflect on.
The course is aimed at those with an undergraduate in either social or natural science. It is introductory in the sense that it assumes no prior experience of either, but quickly moves to Master¿s level.
|
| Course description |
Week 1. Intro to doing environmental & social sciences: what and why?
Week 2. How we do science: 2 perspectives
Week 3. Core concepts for quantitative research
Week 4. describing and visualising data
Week 5. Introducing statistical models
Week 6. Choosing a good model and avoiding common pitfalls.
Week 7. Planning your research
Week 8. Putting your plan into action
Week 9. Fieldwork (for assignment 2)
Week 10. Analysis + Writing
Week 11. Course wrap-up
|
Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
Students MUST have passed:
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | Students MUST be studying on the following programmes to be accepted onto the course: MSc in Ecosystem Services, MSc in Environmental Protection & Management, MSc in Food Security, MSc in Soils & Sustainability.
Students on other programmes may be accepted but MUST request this via the course secretary. |
Course Delivery Information
|
| Academic year 2017/18, Not available to visiting students (SS1)
|
Quota: 56 |
| Course Start |
Semester 1 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 18,
Seminar/Tutorial Hours 5,
Supervised Practical/Workshop/Studio Hours 5,
Fieldwork Hours 2,
Feedback/Feedforward Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
66 )
|
| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
Assessment details:
A1: Testing skills in statistics and data visualisation. Due Fri W7 (40%)
A2: Research report and self-reflection Starts W9 due Monday W12 (60%)
All deadlines are 12:00 unless stated. All assignments must be submitted on Learn only. You work will be marked on Learn and released to you within three weeks (excluding university holidays). |
| Feedback |
Not entered |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Understand and appreciate that science is not value neutral, and that it is conducted for a variety of reasons and with different beliefs about reality
- Plan for, collect, record and organise qualitative and quantitative data
- Select and then undertake the appropriate type of analysis for a given data set
- Report your results and analysis in a professional manner appropriate for your audience
|
Reading List
Overview reading:
These resources provide an overview of the course and are important references that you will need to consult. There will be additional required reading for each week - see below - and further references will be provided during lectures.
Quantitative statistics:
- Dalgaard, P. (2008). Introductory Statistics with R (2nd ed.). Springer New York. [Available electronically and in hard copy from the University library]
- Greenland, S., Senn, S. J., Rothman, K. J., Carlin, J. B., Poole, C., Goodman, S. N., & Altman, D. G. (2016). Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations. European Journal of Epidemiology, 31(4), 337-350.
Qualitative approaches:
- Flowerdew R. & Martin D. (eds) (1997) Methods in human geography. Pearson Prentice Hall. [e-book, library]
Required reading before each week:
Week 1
Evely A, et al. (2008) The Influence of Philosophical Perspectives in Integrative Research: a Conservation Case Study in the Cairngorms National Park. Ecology & Society, 13(2), 52.
Pickett, S.T.A., Kolasa, J. & Jones, C.G., 2007. Integration in Ecology. In Ecological Understanding:The Nature of Theory and the Theory of Nature. Elsevier, pp. 3-32. Available at: http://www.sciencedirect.com/science/article/pii/B9780125545228500030
Week 2
Turner M.D. (2004) Political Ecology and the moral dimensions of 'resource conflicts': the case of farmer-herder conflicts in the Sahel. Political Geography, 23(7), 863-889.
Huxham, M. (2000). Science and the search for truth. Chapter 1 in, Huxham, M., and Sumner, D. (eds), Science and Environmental Decision-making, Pearson Educational Ltd., Harlow.
Rodrigues, A.S.L. et al., 2009. Boom-and-bust development patterns across the Amazon deforestation frontier. Science, 324(5933), pp.1435-7. [this is used in the class exercise, and reading this in advance will make life easier]
Week 3 - Core concepts for quantitative research
Dalgaard (2008) Ch1-3.
Very important: please make sure you can use RStudio when you log in to the University managed desktop machines, or on your own laptop. For instruction on how to install and use Rstuido see: https://www.rstudio.com
Week 4 - Describing and visualising data
Dalgaard (2008) Ch4.
Week 5 - Introducing statistical models.
Dalgaard (2008) Ch5-6.
Week 6 - Choosing a good model and avoiding common pitfalls.
Greenland, S., Senn, S. J., Rothman, K. J., Carlin, J. B., Poole, C., Goodman, S. N., & Altman, D. G. (2016). Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations. European Journal of Epidemiology, 31(4), 337-350.
Week 7
Twyman C., Morrison J. & Sporton D. (1999) The final fifth: autobiography, reflexivity and interpretation in cross-cultural research. Area, 31(4), 313-325.
Week 8
Rocheleau D. (1995) Maps, Numbers, Text and Context: Mixing Methods in Feminist Political Ecology. Professional Geographer, 47(4), 458-466.
Week 9 (This week will be spent conducting fieldwork i.e. no class)
Week 10
Crang M. (2005) Analysing Qualitative Materials. In Flowerdew & Martin (Eds.) Methods in Human Geography. A guide for students doing a research project (2nd Edition)
Week 11
tbc
|
Additional Information
| Graduate Attributes and Skills |
1. Organisational skills to plan, execute and report on scientific investigation
2. Use of appropriate computer software (R or Excel) to organise and analyse data
3. Practical experience of collecting data including the use of interviews
4. Interpersonal skills - participating in team activities toward the completion of assignments and goals.
|
| Special Arrangements |
Students MUST be studying on the following programme to be accepted onto the course: MSc in Ecosystem Services
Students on other programmes may be accepted but MUST request this via the course secretary.
Please note that students are not permitted to audit this course. This is because much of the learning takes place during small discussions and lab sessions, and space and resources for these are limited to those enrolled on the course for credit. |
| Additional Class Delivery Information |
each 2 hr session is a mixture of lectures and discussion or lab practicals |
| Keywords | Data capture,handling,analysis and reporting,dissertation delivery,field skills,statistics.,Epistemo |
Contacts
| Course organiser | Dr Casey Ryan
Tel: (0131 6)50 7722
Email: |
Course secretary | Miss Susie Crocker
Tel: (0131 6)51 7126
Email: |
|
|
University Homepage