Postgraduate Course: Applied Research Skills in Environment and Society (PGGE11268)
|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
|Summary||This course provides a Masters level introduction to the research approaches, skills and methods that underpin modern social and environmental sciences, with an emphasis on collecting and analysing data. Through a mixture of practical┐s, group work, lectures, tutorials and discussion we will explore why and how we do science at the intersection of the social and natural sciences. The course has a focus on mixed methods research by introducing students to quantitative and qualitative skills that can be used independently but that are increasingly used in combination with one another.
This course provides a Masters level introduction to the research approaches, skills and methods that underpin modern social and environmental sciences, with an emphasis on collecting and analysing data. Through a mixture of practical┐s, group work, lectures, tutorials and discussion we will explore why and how we do science at the intersection of the social and natural sciences.
The course will help students prepare for dissertations or projects that involve data collection or analysis. The course has a focus on mixed methods research by introducing students to quantitative and qualitative skills that can be used independently but that are increasingly used in combination with one another.
The course is structured around lectures followed by small group tutorials, a format which caters for students with very varied backgrounds. There is also a focus on getting your hands dirty, collecting and analysing your own data set, as well as working with other larger data sets. 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 Masters Level in both.
Wk 1: Intro to doing environmental and social sciences and How do we do science
Wk 2: Research Skills; Effective group working, Reading, writing, plagiarism, referencing.
Wk 3: Planning a Research Project: Positionality in research; Ethics, risk assessments, research design; Methods and tools for collecting qualitative social science data.
Wk 4: Quantitative Skills; Introduction to RStudio; Thinking about data, how to describe it and how to visualise it; graphs and summary statistics.
Wk 5: Introduction to statistical models; How do we visualise and test if groups of data are different? Boxplots, t-tests, ANOVA.
Wk 6: Reading week
Wk 7: Undertaking Qualitative Research; Group work session to develop and design qualitative research tools.
Wk 8: Collecting Quantitative and Qualitative Data; Implementing the groups qualitative tools designed in earlier weeks.
Wk 9: Analysing Qualitative Data; Once the data has been collected how do we analyse it?
Wk 10: Quantitative Skills 3; how do we analyse relationships amongst variables, with and without causation? Linear regression. Correlation. X-Y scatterplots.
Wk 11: Critical reflection on where the methods work well and where they might not work so well
Entry Requirements (not applicable to Visiting Students)
||Other requirements|| None
Course Delivery Information
|Academic year 2020/21, Not available to visiting students (SS1)
|Learning and Teaching activities (Further Info)
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
|Assessment (Further Info)
|Additional Information (Assessment)
Assessment 1: Research methods workbook ┐ 40%.
For this assignment you will receive a series of short questions that require the students to use quantitative skills to answer (8 in total). Students will be expected to provide plots, statistical outputs and interpret outputs from statistical tests. In addition, 2 questions on qualitative skills requiring more discursive answers. Set in week 5 and due week 7.
A2 Qualitative research report and self-reflection Starts W9 due Monday W12 60% word limit 2500 words.
For this assignment you will design and then execute a set of semi-structured interviews, and then learn how to code and analyse the resulting data. Students will be placed into groups in week 2 and will develop a short survey tool that can be completed within 5-8 minutes and a longer interview. You will be expected to collect 20-30 survey responses and 1-2 interviews. The data will then be analysed individually and this will be written up as a research report, alongside a reflective log of your learning experience. Set in week 2 and due in week 10. Note ┐ Due to COVID-19 it may be that government guidelines prevent students from face-to-face surveys in Edinburgh with members of the public. If this is the case a decision will be made early in the semester and tools will be introduced that can be used to run surveys and interviews online.
||Students will be given formative feedback through small group discussions, tutorials and practical┐s each week. There will be a formative assessment set in week 2 and due in week 3 so students can get some feedback on writing styles, referencing.
Summative feedback will be provided on the two assessments outlined above.
|No Exam Information
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
- Collect, record and organise qualitative and quantitative dat
- Select and then undertake the appropriate type of analysis for a given dataset
- Report your results and analysis in a professional manner appropriate for your audience
The following resources provide an overview of the course and are important references that you will need to consult. There is additional required reading for each week see.
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), 337350.
Flowerdew R. & Martin D. (eds) (1997) Methods in human geography. Pearson Prentice Hall. [e-book, library]
Required reading before each week:
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. 332. Available at: http://www.sciencedirect.com/science/article/pii/B9780125545228500030
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.14357. [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.
Twyman C., Morrison J. & Sporton D. (1999) The final fifth: autobiography, reflexivity and interpretation in cross-cultural research. Area, 31(4), 313-325.
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)
Crang M. (2005) Analysing Qualitative Materials. In Flowerdew & Martin (Eds.) Methods in Human Geography. A guide for students doing a research project (2nd Edition)
|Graduate Attributes and Skills
||The course allows students to develop their skills in undertaking qualitative and quantitative research, relevant to both the social and natural sciences.
You will develop skills in manipulating, summarising, visualising and analysing data, using the statistical software R, as well as developing your conceptual understanding of how statistics can be used to answer research questions.
You will also develop the skills to collect, analyse and visualise spatial data.
You will also develop skills in qualitative research: how to collect, analyse, code, and summarise data from semi-structured interviews, and how to assess data validity.
You will learn how to generalise from your sample / case study to wider relevance, using theory or statistics.
You will also learn how to mix quantitative and qualitative research methods, and how to report and write up research that spans both.
These skills are important for both undertaking research (e.g. as part of your dissertation or project, or a future PhD), and also provide important skills for those who need to utilise, synthesise or evaluate scientific research in a professional context.
|Keywords||applied research skills,environment,social sciences,quantitative,qualitative
|Course organiser||Dr Gary Watmough
Tel: (0131 6)51 4447
|Course secretary||Ms Kathryn Will
Tel: (0131 6)50 2624