Research Skills & Statistical Methods - ENV00049M
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
Indicative assessment
| Task | % of module mark |
|---|---|
| Essay/coursework | 100.0 |
Special assessment rules
None
Indicative reassessment
| Task | % of module mark |
|---|---|
| Essay/coursework | 100.0 |
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.