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Research Skills & Statistical Methods - ENV00030H

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  • Department: Environment and Geography
  • Module co-ordinator: Dr. Dean Waters
  • Credit value: 20 credits
  • Credit level: H
  • Academic year of delivery: 2022-23
    • See module specification for other years: 2021-22

Module summary

This module provides a training in the use of R and SPSS for the analysis of basic through to complex datasets. No prior experience of R is assumed.

Module will run

Occurrence Teaching period
A Autumn Term 2022-23 to Spring Term 2022-23

Module aims

The course aims to equip all students with skills and knowledge for (1) developing a successful research career including applying for jobs, ethical considerations and writing, and (2) designing, executing and analysing environmental and social surveys.
The statistical element of the course does not aim to provide a comprehensive guide to all statistical techniques, rather to build confidence and skills such that students are familiar with the common principles and methods and competent in the use of statistical software.

Module learning outcomes

On completion of the module, successful students will:
- have an understanding of techniques for improving their employability in job applications
- be aware of the ethical considerations for environmental and social research
- be able to design and execute robust environmental and social sampling methods
- have developed skills for formatting, storing and editing a dataset
- be familiar with the procedure for exploring and analysing data
- be aware of the basic statistical techniques used for analysing data
- have sufficient knowledge of the R environment in order to begin to develop statistical skills beyond the scope of the course
- have awareness of the basic principles of scientific writing and publication

Assessment

Task Length % of module mark
Essay/coursework
Report 2000 words		 N/A 100

Special assessment rules

None

Reassessment

Task Length % of module mark
Essay/coursework
Report 2000 words		 N/A 100

Module feedback

Feedback will be provided within the University standard four weeks.

Indicative reading

Field, Miles & Field (2012) Discovering Statistics Using R. Sage.

Field (2013) Discovering Statistics Using IBM SPSS Statistics. Sage 

Quinn & Keough (2002) Experimental Design and Analysis for Biologists. Cambridge University Press.
Zuur et al (2012) Beginner's Guide to GLM and GLMM with R: a Frequentist and Bayesian Approach for Ecologists. Highland Statistics Ltd.

Crawley (2005) Statistics: An Introduction Using R. Wiley.


The information on this page is indicative of the module that is currently on offer. The University is constantly exploring ways to enhance and improve its degree programmes and therefore reserves the right to make variations to the content and method of delivery of modules, and to discontinue modules, if such action is reasonably considered to be necessary by the University. Where appropriate, the University will notify and consult with affected students in advance about any changes that are required in line with the University's policy on the Approval of Modifications to Existing Taught Programmes of Study.