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BIOL6052 Data Management and Generalised Linear Modelling for Biologists

Module Overview

Evidence-based ecology, evolution and conservation require quantitative analyses of field data typically collected under imperfectly controlled conditions and across heterogeneous habitats. This module will develop generic skills in (1) the design of data collection protocols, particularly for field experiments and observational studies, and (2) the testing of hypotheses with appropriate statistical models. Although basic statistical awareness will be assumed to undergraduate level, the first third of the module will review core principles of experimental design and analysis that underpin all quantitative methods using the freely distributed environment R (www.r-project.org). Thereafter, the module will develop and apply statistical models for data types commonly encountered in fieldwork using the generalised linear model framework. The importance of scripting for transparent data management will be of central importance. Life science questions commonly seek to explain response variables in terms of predictor variables that co-vary with each other or with nuisance variables or cannot be measured in balanced designs. Techniques to resolve these issues will be introduced in a practical approach designed to pre-empt common issues of later independent research projects. The final part of the module will treat these issues in multifactorial and multivariate analysis.

Aims and Objectives

Module Aims

1) To teach the principles of experimental design. 2) To develop analytical skills to a level sufficient to understand the principles of field-based data collection and the statistical modelling thereof. 3) To introduce and use the R environment for statistical analysis, graphical figure generation ad result presentation.

Learning Outcomes

Learning Outcomes

Having successfully completed this module you will be able to:

  • Design a logical and suitable data collection protocol for statistical analysis of a test hypothesis;
  • Identify statistical procedures appropriate to different types of hypotheses and data;
  • Interpret the results of statistical tests on given data sets, and use that interpretation to justify the conclusions you draw;
  • Compute statistical analyses in integrated workflows (importing and cleaning data using transparent, repeatable scripts, exporting report and publication-quality figures) using the R environment;
  • Independently use the freely available R environment.

Syllabus

The course will start by introducing study design. Various types of statistical analysis will be covered, including: Regression, ANOVA (using the General Linear Model), ANCOVA, the use of Linear Mixed Models, Generalized Linear Models and Multivariate Techniques. Students will be taught how to analyse data using the R Environment for Statistical and Graphical analysis in student-led workshops that facilitate peer-to-peer learning. These lessons will be reinforced in “no agenda” interactive feedback sessions and through one-to-one surgeries to assist with the particulars of data sets for programme research projects.

Special Features

The module is not subject-specific, and all skills developed on it are transferrable across scientific disciplines.

Learning and Teaching

Teaching and learning methods

- Computer workshops will introduce and train in the use of statistical packages for hypothesis testing and presentation of results. - Large-group tutorials led by educator based on anonymized student requests for material to revise. - Panopto Lecture capture. - Formal lectures will provide the framework of core concepts and issues. - Discussion workshops will be interactive sessions.

TypeHours
Tutorial4
Lecture8
Independent Study122
Workshops24
Project supervision4
Total study time162

Resources & Reading list

Beckerman, A.P. & Petchey, O.L (2012). Getting Started with R. 

Doncaster, C.P. & Davey, A.J.H. (2007). Analysis of Variance and Covariance: How to Choose and Construct Models of the Life Sciences. 

Fox, J. & Weisberg, S. (2011). An R Companion to Applied Regression. 

Hector. The New Statistics: An Introduction for Biologists. 

Assessment

Formative

Exercise

Summative

MethodPercentage contribution
Exercise 30%
Multiple choice question 70%

Referral

MethodPercentage contribution
Assignment Marks carried forward 70%
Exercise 30%

Repeat Information

Repeat type: Internal & External

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