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Data Analysis for Environmental Research - ENV00028C

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  • Department: Environment and Geography
  • Module co-ordinator: Dr. Colin McClean
  • Credit value: 20 credits
  • Credit level: C
  • Academic year of delivery: 2022-23
    • See module specification for other years: 2021-22

Module summary

This module introduces some of the basic data handling and data analysis skills that students will require for studying towards a degree dealing with the Environment. Many of the skills learned will be directly transferable to the world of employment. Lectures will introduce topics which students will then practice in computer practical sessions using real data from staff research. Two simple data collection exercises in the Spring term will allow students to apply skills acquired to data they have collected.

The module will give students an understanding of data types, descriptive statistics, sampling, basic inferential statistics, simple questionnaire design. It will include the use of spreadsheets and statistics software to handle, plot, describe and analyse environmental datasets and will help students in the communication of data analysis using tables, graphs and appropriate statistics

Module will run

Occurrence Teaching period
A Autumn Term 2022-23 to Spring Term 2022-23

Module aims

This module introduces some of the basic data handling and data analysis skills that students will require for studying towards a degree dealing with the Environment. Many of the skills learned will be directly transferable to the world of employment. Lectures will introduce topics which students will then practice in computer practical sessions using real data from staff research. Two simple data collection exercises in the Spring term will allow students to apply skills acquired to data they have collected

Module learning outcomes

Subject content

·Understanding of data types, descriptive statistics, sampling; basic inferential statistics; simple questionnaire design.

Academic and graduate skills

  • Use of spreadsheets and statistics software to handle, plot, describe and analyse environmental datasets

Other learning outcomes (if applicable)

• communication of data analysis using tables, graphs and appropriate statistics

Module content

Autumn Term

Lecture 1 - Module introduction & levels of measurement & tables

Prac. 1 – Intro to excel, simple charts, simple table (in word?)

Lecture 2 – descriptive stats (central tendency, dispersion, shape, skew)

Prac 2 – Descriptive stats in Excel, bar charts with SD error bars

Lecture 3. - Sampling, sampling distributions, central limits theory

Prac 3 – random numbers, random samples in Excel from uniform distribution – sampling distribution of mean becomes normal histograms in Excel.

Lecture 4 – Confidence intervals, hypothesis test, t-tests

Prac 4 – Excel & SPSS confidence intervals & t-tests

Lecture 5 – ANOVA (inc post-hocs)

Prac 5. SPSS ANOVA, making boxplot look good.

Lecture 6. Testing for normality (when and why). Non-parametric alternatives – Mann-Whitney and Kruskal-Wallis

Practical 6-- Testing for normality (when and why). Non-parametric alternatives – Mann-Whitney and Kruskal-Wallis

Lecture 7 – Correlation (Pearson’s & Spearman’s)

Practical 7 – Scatterplots in Excel, SPSS and correlation in Excel and SPSS.

Lecture 8 – OLS regression

Practical 8 – More scatterplots and linear regression in Excel & SPSS

Lecture 9 – Assumptions underlying OLS: checking them and their effects.

Practical 9 – checking assumptions when performing OLS regression.

Spring Term

Lecture 10 – Chi-squared and other tools for looking at questionnaire survey data

Lecture 11. Survey design and launch of survey exercise

Seminar.- Designing the campus questionnaire (3 hours)

Field work to deliver questionnaire

Lecture 12 – Introduction to simple field survey exercise

Field trip: Collection of field data (5 hrs)

Practical 13 – Analysis of field data (2 hrs)

Practical 14 – Analysis of questionnaire survey data (2hrs)

Lecture 13: Transformations: when to use.

Practical 15: Transformations in Excel and SPSS

Assessment

Task Length % of module mark
Essay/coursework
Coursework Portfolio I		 N/A 50
Essay/coursework
Coursework Portfolio II		 N/A 50

Special assessment rules

None

Additional assessment information

Practical exercises form basis of assessment portfolio. Staff and demonstrator support will be available during practicals.

Reassessment

Task Length % of module mark
Essay/coursework
Analysis of Set Dataset		 N/A 100

Module feedback

Standard Environment Department “turnaround time”. Model answers will be posted on VLE for each exercise in portfolio.

Indicative reading

Dytham, Calvin. (2011). Choosing and using statistics : a biologist's guide. Chichester : Wiley-Blackwell


The information on this page is indicative of the module that is currently on offer. The University is constantly exploring ways to enhance and improve its degree programmes and therefore reserves the right to make variations to the content and method of delivery of modules, and to discontinue modules, if such action is reasonably considered to be necessary by the University. Where appropriate, the University will notify and consult with affected students in advance about any changes that are required in line with the University's policy on the Approval of Modifications to Existing Taught Programmes of Study.