How do you effectively analyse data as a conservation scientist? How can hypothesis testing guide your analysis? You will be introduced to research and survey design drawing upon different scientific approaches. You will learn the principles of experimental design and how these can be applied to field projects, together with the nature of both quantitative and qualitative data. You’ll then be introduced to sampling strategies and the role of probability in inferential statistics, which will then lead into the role of descriptive statistics and measures of variability in data exploration. You will gain experience in parametric and nonparametric statistics in data analysis (t-tests, ANOVA, regression, correlation, their nonparametric equivalents), including multivariate tests and the rules underlying the appropriate presentation of statistical data in research reports. By the end of the module, you’ll have developed your statistical expertise to an extent that you’ll be able to confidently apply quantitative analytical approaches to research data and know how to present results effectively—skills that are vital for any research project as a conservation scientist.
Lectures 12, Seminars/PC workshops 20
The module is compulsory for the following courses
MSc Conservation Science
This module is not available as an optional module
Report. Assessment Details: Empirical Report 2,200 words worth 50%.
Report. Assessment Details: Statistical Analysis 1000 words worth 50%.
Reassessment Method: Like-for-like (different topic choice where specified).
The University is committed to ensuring that core reading materials are in accessible electronic format in line with the Kent Inclusive Practices. The most up to date reading list for each module can be found on the university's reading list pages.
On successfully completing the module, students will be able to:
1. Critically evaluate advanced methodologies and be able to align inferential statistical tests with different types of variables and different research designs
2. Formulate and develop innovative ways to analyse quantitative data with univariate and multivariate inferential statistical tests using appropriate software (R).
3. Systematically present results suitable for a scientific report and interpret interrelationships with other relevant disciplines.
4. Present analysed data within the framework of a formal report and be able to define and critically evaluate conclusions from the analyses.
University of Kent makes every effort to ensure that module information is accurate for the relevant academic session and to provide educational services as described. However, courses, services and other matters may be subject to change. Please read our full disclaimer.