ENVB40370 Data Analyses & Interpretation (On-line)

Academic Year 2022/2023

This module aims to equip you with the skills to professionally synthesize and communicate technical information in the field of biology and environmental science. The module blends online lessons, computer practicals and self-test problem sheets.

Topics covered include the reporting of data, data management, statistical modelling, design and analysis of biological and environmental experiments, hypothesis testing and the use of the R statistical software.

For this module you will require access to a computer that will run the R statistical software (available for Windows, Mac or Linux operating systems at https://www.r-project.org/) and RStudio (freely available at https://www.rstudio.com/products/rstudio/#Desktop)

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Curricular information is subject to change

Learning Outcomes:

Learning Outcomes:
• Design a biological / environmental experiment, taking due account of independence, allocation of replicates and controls;
• Organise and manipulate data on a computer;
• Fit and validate a statistical model to biological data;
• Test a null-hypothesis using a fitted statistical model;
• Accurately communicate data using graphs, tables and written text;
• Answer research questions and draw strong, defendable conclusions using modern statistical data analysis methods.


Skills:
The module will contribute towards the development of the following skills:
• Effective presentation and writing of technical information
• Transparency and collaboration on data analysis projects
• Spreadsheet (Excel), R statistical language and general computer skills

Indicative Module Content:

1. Getting started with R
2. Data import into R
3. Data organisation
4. Exploratory data analysis
5. Data distributions
6. Sampling a population
7. Statistical modelling
8. General linear models (Fitting)
9. General linear models (Hypothesis testing)
10. Model validation
11. Post-hoc testing
12. Models with multiple factors
13. Linear regression using general linear models
14. Modelling qualitative data (Chi-square tests)

Student Effort Hours: 
Student Effort Type Hours
Online Learning

125
Total

125
Approaches to Teaching and Learning:
The module is based around a series of problem sheets which give practical experience of data analysis and interpretation using R. Online videos and online tutorials provide core information to the material covered on the module. 
Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Not applicable to this module.
 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Examination: Data analysis and experimental design exam (open book, online) 2 hour End of Trimester Exam Yes Graded No

50
Continuous Assessment: R scripts to accompany R data analysis test Throughout the Trimester n/a Graded No

10
Continuous Assessment: Test of data analysis skills using R Throughout the Trimester n/a Alternative linear conversion grade scale 40% No

40

Carry forward of passed components
Yes
 
Resit In Terminal Exam
Spring No
Please see Student Jargon Buster for more information about remediation types and timing. 
Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
• Online automated feedback

How will my Feedback be Delivered?

Feedback will be given to posted submitted on the discussion board. Online tests automatically give feedback. Individual feedback to each student on R script best practice

Beckerman, Childs and Petchey (2017) Getting started with R : an introduction for biologists (Oxford University Press, Oxford) [electronic resource] http://library.ucd.ie/iii/encore/record/C__Rb2147070

Crawley (2015) Statistics : an introduction using R (John Wiley & Sons, Ltd, London) [electronic resource] http://library.ucd.ie/iii/encore/record/C__Rb2103637

Raykov and Marcoulides (2013) Basic statistics : an introduction with R (Rowman & Littlefield Publishers, Inc., Plymouth). [electronic resource] http://library.ucd.ie/iii/encore/record/C__Rb1961859

Barnard, Gilbert and McGregor (2011) Asking questions in biology: a guide to hypothesis testing, experimental design and presentation in practical work and research projects (Pearson) [electronic resource] http://library.ucd.ie/iii/encore/record/C__Rb1880444

Ruxton and Colegrave (2016) Experimental design for the life sciences (Oxford University Press, Oxford)

Underwood AJ (1997) Experiments in ecology: their logical design and interpretation using analysis of variance. (Cambridge University Press, Cambridge).



Name Role
Dr Paul Brooks Lecturer / Co-Lecturer