Module Information

Module Identifier
Module Title
Research Methods
Academic Year
Semester 1 (Taught over 2 semesters)
Other Staff

Course Delivery

Delivery Type Delivery length / details
Workshop 1 x 3 Hour Workshop
Lecture 11 x 1 Hour Lectures
Lecture 2 x 1 Hour Lectures
Miscellaneous 1 x 1 Hour Miscellaneous
Workshop 7 x 1 Hour Workshops


Assessment Type Assessment length / details Proportion
Semester Assessment 10 x summative on line assessments,  related to each statistical online session  50%
Semester Assessment Report  on the application of advanced statistical and molecular techniques (up to 2,500 words)  25%
Semester Assessment Research proposal  (up to 2,500 words)  25%
Supplementary Assessment Students must take elements of assessment that are equivalent to those that led to failure of the module  100%

Learning Outcomes

On successful completion of this module students should be able to:

1. Apply statistical knowledge in the context of postgraduate research using appropriate methods.

2. Analyze and interpret the results from a range of different statistical analyses.

3. Assess the potential of genomic approaches to biological/environmental investigations.

4. Generate appropriate research hypotheses and research objectives.

5. Assimilate and synthesizing of information from a variety of sources.

6. Clearly communicating research findings.

Brief description

This module provides the tools necessary for carrying out independent research. Part 1 will cover quantitative and qualitative statistical methods for analyzing data. Part 2 will build towards the students identifying a suitable dissertation topic and will be taught to evaluate existing research published in a range of different sources. Many elements of the module will be built around online resources already available within the Institute.


The aim of the module is to develop investigation and data handling skills, providing training relevant to other modules, particularly the Masters dissertation. It also aims to familiarize students with advanced statistical procedures related to specific degree schemes. The module will also introduce students to the potential of various –omic technologies for biological/ecological investigations.


In part 1 students undertaking the module will explore a range of quantitative and qualitative statistical techniques suitable for the assessment of biological/environmental data. This aspect of the module will then consider a range of more complex statistical procedures including multi-factorial ANOVA, multivariate analyses, geographical information systems and aspects of experimental design. In detail this will include:
  • T-tests, ANOVA, post-hoc significance tests, data handling and presentation Correlation/Regression and Non-parametric tests.
  • Students will choose a further three sub-units from the following subject areas:
  • Multifactorial ANOVA, repeated measures, split plot ANOVA, Multivariate Analysis of Variance (MANOVA), principal component analysis, canonical variate analysis (CVA), discriminant function analysis (DFA), survey/questionnaire design and analysis, introduction to R and GIS (required for MSc Managing the Environment).
  • Part 1 of the module will utilise a range of online learning resources detailing each statistical test and instructive guides on how to perform them using specialist statistical software. Online formative and summative assessments will test the student's understanding and application of each statistical test. Students will also be introduced to the practice and potential of genomic and post-genomic approaches to biological and environmental investigations and to associated data handling procedures.
Part 2 will build towards the students identifying a suitable dissertation topic. It will include a programme of exercises to develop their research skills considering the research process, assessment of published research and scientific writing, and the production of a postgraduate dissertation.

Part 2 is assessed on a research proposal on their dissertation topic. This will detail the hypothesis that will be tested, highlight some of the key research publications in their chosen area and present the experimental design they intend to implement, including detail on the statistical methods they intend to utilize.

Module Skills

Skills Type Skills details
Application of Number Students will be taught statistical techniques and expected to complete both formative and summative assessment.
Communication Students will be expected to be able to express themselves appropriately in their assignments.
Improving own Learning and Performance Detailed feedback will be given for assignment work.
Information Technology Students will be required to source information from a variety of scientific publication data bases and be taught to use specialist statistical software.
Personal Development and Career planning Research Project Plan - developed in collaboration with employer and supervisor.
Problem solving Online quizzes will be used to help develop and improve students problem solving skills.
Research skills Students will be required to undergo directed self study and so will develop their literature research skills.
Subject Specific Skills Advanced statistical methods and genomic applications.


This module is at CQFW Level 7