SOES2036 Quantitative Methods in Marine Science
Module Overview
This module develops analytical skills required for evidence-based interpretation of data generated in the Marine Sciences. The material will focus initially on both the philosophical background of statistical analysis, illustrating how applications in public documents often abuse the basic principles of graphical and statistical analysis. The module will then develop skills in computer literacy and hypothesis testing using marine examples drawn from the spectrum of activities routinely used in final year Honours projects.
Aims and Objectives
Module Aims
To appreciate how the fundamental relationships of experimental design apply to all statistical analyses, not just formal experiments. To develop analytical skills to a level sufficient to understand the principles of marine data collection and the statistical modelling thereof To introduce and use the R environment for statistical analysis, graphical figure generation, result presentation and conclusion evaluation.
Learning Outcomes
Learning Outcomes
Having successfully completed this module you will be able to:
- Identify statistical procedures appropriate to different types of hypotheses and data.
- Independently use the R environment and know where to search for additional online resources.
- Compute statistical analyses in integrated workflows (reading data in, exporting report and publication-quality figures) using the R environment.
- Present and interpret the results of statistical tests on given data sets
- Contrast your empirical data with the theoretical assumptions to differentiate between the relative support for different conclusions.
Syllabus
This module develops analytical skills required for evidence-based interpretation of data generated in the Marine Sciences. The material will focus initially on both the philosophical background of statistical analysis, illustrating how applications in public documents often abuse the basic principles of graphical and statistical analysis. The module will then develop skills in computer literacy, graphical presentation and hypothesis testing using marine examples drawn from the spectrum of activities routinely used in final year Honours projects.
Special Features
This module is a keystone in the proposed quantitative stream on MB/OC pathways and integrates with later analytical needs on (e.g., Plymouth field course, project requirements). It introduces students to the philosophy of why we need statistics in an uncertain world, and provides an education in a complete workflow from importing data from local files or web resources, through the actual statistical analysis to exporting high-quality report- and publication-ready figures. The syllabus provides more one-on-one time between educators and students, through large group revision tutorials where students can anonymously submit requests for lecturers to revise old material and substantial time for one-on-one discussions in practical classes. Makes better use of modern lecture capture methods, both Panopto recordings of lectures. Pre-recorded snippets of key definitions that do not change year-on-year will be added. Contemporary modes of assessment include an applied knowledge assessment when students will run analyses in the R environment during the second piece of summative work. This mode further increases the diversity of assessments within our degree programmes.
Learning and Teaching
Teaching and learning methods
The overarching goal is to provide more one-on-one time between educators and students to overcome the initial steep learning curve presenting by statistics and statistical software. • Traditional lectures • Large-group tutorials led by educator based on anonymized student requests for material to revise. • Student-led practical sessions giving hands-on experience of the R environment, • Studenet-led workshops to discuss experimental design in different circumstances. • Short videos of key definitions and concepts via Blackboard • Panopto lecture capture.
| Type | Hours |
|---|---|
| Tutorial | 6 |
| Practical classes and workshops | 24 |
| Lecture | 10 |
| Independent Study | 110 |
| Total study time | 150 |
Resources & Reading list
Textbooks. • Getting Started with R (2017) Beckerman, Childs, Petchy. Oxford University Press • The New Statistics: An Introduction for Biologists (2015) Hector. Oxford University Press
Software. Freely distributed R environment through the more intuitive RStudio interface, both of which are already part of the core University packages.
Assessment
Formative
Class practicals
Summative
| Method | Percentage contribution |
|---|---|
| MCQ-applied knowledge assessment | 70% |
| MCQ-applied knowledge assessment | 30% |
Costs
Costs associated with this module
Students are responsible for meeting the cost of essential textbooks, and of producing such essays, assignments, laboratory reports and dissertations as are required to fulfil the academic requirements for each programme of study.
In addition to this, students registered for this module typically also have to pay for:
Other
There are no additional costs associated with this module.
Please also ensure you read the section on additional costs in the University’s Fees, Charges and Expenses Regulations in the University Calendar available at www.calendar.soton.ac.uk.