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, graphical presentation 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

Having successfully completed this module you will be able to:

Identify statistical procedures appropriate to different types of hypotheses and data.
Independently use freely available software, and know where to search for additional online resources.
Compute statistical analyses and generate 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

Special Features

Reduces the number of hand-marked assessments by using MCQ. Makes better use of modern lecture capture methods, both Panopto recordings of lectures but also pre-recorded snippets of key definitions that do not change and so do not need updating year-on-year. Provides more one-on-one time between educators and students, through large group revision tutorials (trialled on BIOL6052 where students can anonymously submit requests for lecturers to revise old material) and more time for one-on-one practical classes. Keystone in the proposed quantitative stream on MB/OC pathways Builds on: Lectures & prac’s (SOES1001 Func Mar Biol) Lectures & prac’s (SOES1010 Quant Earth Ocean Sci) Should integrate with later analytical needs on (e.g., Falmouth field course, project requirements) through contact time in later modules.

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
Independent Study	110
Lecture	16
Tutorial	6
Practical classes and workshops	18
Total study time	150

Resources & Reading list

Software. Freely distributed R environment through the more intuitive RStudio interface, both of which are already part of the core University packages.

Textbooks. New issue of Getting Started with R, which has a jellyfish on the front. John Fox’s Guide to Applied Regression.

Lectures. All lectures and revision tutorials recorded using Panopto. Use flipped material to augment lectures – film brief videos of definition material that do not require updating each year.

Assessment

Formative

Class practicals

Summative

Method	Percentage contribution
MCQ-applied knowledge assessment	30%
MCQ-applied knowledge assessment	70%