## BIMWA12-Quantitative Methods

Module Provider: School of Biological Sciences
Number of credits: 20 [10 ECTS credits]
Level:7
Terms in which taught: Autumn / Spring term module
Pre-requisites:
Non-modular pre-requisites:
Co-requisites:
Modules excluded:
Current from: 2018/9

Module Convenor: Dr Tom Oliver

Type of module:

Summary module description:

This module aims to introduce students to a range of statistical procedures commonly used in the analysis of data in the life sciences. In addition to indicating which tests to use in which circumstances, the module will also focus on the underlying assumptions associated with different procedures and how these should be verified. Students will also be instructed on the appropriate approaches to summarising and presenting the results of these statistical methods, including the use of graphs. The module will use the ‘R’ statistical software which is commonly used in the natural and social sciences.

Aims:

This module aims to introduce students to a range of statistical procedures commonly used in the analysis of data in the life sciences. In addition to indicating which tests to use in which circumstances, the module will also focus on the underlying assumptions associated with different procedures and how these should be verified. Students will also be instructed on the appropriate approaches to summarising and presenting the results of these statistical methods, including the use of graphs. The module will use the ‘R’ statistical software which is commonly used in the natural and social sciences.

Assessable learning outcomes:

On completion of this module it is expected that the students will have acquired an understanding of:

• the role of statistics in wildlife management and conservation

• the use a range of common univariate statistical tests with a single predictor variable but which vary in relation to: (i) the number of groups of data being analysed; (ii) whether the data are normally distributed or not; and (iii) whether the data are independent or not

• the critical assumptions associated with parametric tests, and how to test to ensure that data conform to these assumptions

• the use of transformations to manipulate data so that parametric tests can be used preferentially

• A range of common statistical tests which are essential knowledge for analysing quantitative data in ecology and the wider life sciences (e.g. Chi-squared test, ANOVA, regression, ANCOVA).

• testing multiple predictors including interaction terms

• use of general linear models for analysing data where the response variable is non normal (e.g. poisson and binomial errors for count and proportion data respectively)

• understanding appropriate experimental design and the use of mixed effects models to analyse account for random effects (e.g. blocking factors)

• the correct approach for presenting and summarising the statistical methodologies discussed, including data visualisation approaches

• the use of the R statistical software and RStudio interface.

• An appreciation of the importance of good data management practices

• Data manipulation skills (e.g. importing data, re-ordering tables, taking subsets of data) and running automated analyses using R-scripts.

Outline content:

The first lectures of the module will introduce the R statistical software and Rstudio interface which is commonly used in the natural and social sciences and is an important skill set for improving employability. We will focus initially on basic statistical procedures where there is a single independent (predictor) variable. Tests to be discussed will include: one-sample t test, independent-sample t test; paired-sample t test; Mann-Whitney test; Wilcoxon matched-pairs test; one-way ANOVA; repeated-measures ANOVA; Kruskal-Wallis test; and Friedman test. These tests will be discussed in the context of: the number of groups of data being analysed; whether the data are normally distributed or not; and whether the data are independent or not. Additional sessions will cover: (i) the key assumptions associated with these tests (e.g. normality, homogeneity of variances); (ii) how these assumptions can be verified; and (iii) how data transformations can be used to manipulate non-normally distributed data so that parametric tests can be used. Consideration will be given to the use of degrees of freedom as a method to identify how statistical methods have been applied.

Students will then be introduced to a range of common statistical tests which are commonly used in ecology and the wider sciences (e.g. Chi-squared test, ANOVA, regression, ANCOVA). This includes tests with multiple predictors including interaction terms. Each session will involve a 1hr taught lecture followed by hands-on 2hr practical classes working on example datasets.

At the end of the autumn term, students will be given a continuous assessment exercise consisting of a data analysis and presentation exercise: students will be given a set of data which they will be asked to analyse using several different techniques and to present the results of these analyses in an appropriate format.

In the spring term, students will be introduced to a range of further statistical techniques commonly used in wildlife management and conservation. These include generalised linear models for the analysis of count, proportion and presence/absence data and mixed effects models.

The last two weeks of the spring term will be allocated to the continuous assessment component. This will be in the form of an open book assessment.

Throughout the module students will be introduced to the most appropriate way to present results of these procedures as they would be expected to in e.g. a scientific paper or their research project thesis. There will also be a range of reading material and R coding resources provided for self-guided learning to complement the classroom component.

Brief description of teaching and learning methods:

Each session will typically be broken down into a 1h lecture and a 2h computer session. Each lecture will use a combination of an oral presentation and small-group exercises to illustrate one or more topics. The computer session will then provide students with the opportunity to apply and practice the techniques discussed in the lecture.

In the last few weeks of the autumn term, formal contact hours will be set aside to allow students to complete their continuous assessment work in the presence of the module convenor.

In the last few weeks of the spring term, formal contact hours will be set aside for the formal open-book assessment.

Contact hours:
 Autumn Spring Summer Lectures 5 11 Project Supervision 2 2 Practicals classes and workshops 10 22 Guided independent study 50 98 Total hours by term 67.00 133.00 Total hours for module 200.00

Summative Assessment Methods:
 Method Percentage Written assignment including essay 100

Summative assessment- Examinations:

Summative assessment- Coursework and in-class tests:

Coursework: Students are expected to submit two pieces of coursework, one in each term. In the autumn term, this will be a data analysis and presentation exercise where students are given a set of data which they have to analyse using different techniques and to present the results of these analyses in an appropriate format.

In the spring term, this will be a formal open-book examination in which students will be given a set of data which they will be asked to analyse using several different techniques and to present the results of these analyses in an appropriate format. The last two weeks of the module will be set aside for students to complete this assessment:

Formative assessment methods:

During the module students will undertake a series of formal and informal exercises designed to test and reaffirm their understanding of the material covered. These will consist of within-class individual & group exercises, computer based worksheet sessions and a mock examination.

Penalties for late submission:
Penalties for late submission on this module are in accordance with the University policy. Please refer to page 5 of the Postgraduate Guide to Assessment for further information: http://www.reading.ac.uk/internal/exams/student/exa-guidePG.aspx

Assessment requirements for a pass:
A mark of at least 50%

Reassessment arrangements:
Re-examination in August/September