Module Information

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

Course Delivery



Assessment Type Assessment length / details Proportion
Semester Assessment Scientific writing assignment  related to research project topic 2500 Words  25%
Semester Assessment Critical review  of advanced technique in the biosciences 2500 Words  25%
Semester Assessment Data handling and statistics exercises  based on module tutorials and materials.  50%
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 for different types of data.

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

3. Assess the potential of genomic, biomechanical and spacial analytical approaches to biological/environmental investigations.

4. Generate appropriate research hypotheses and research objectives.

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

6. Clearly communicate research findings.

Brief description

Essential research skills will be developed in this module. These will include:
  • Experimental design, data handling and statistical analysis, and
  • An awareness of advances in technology and of how cutting-edge analytical techniques have transformed bioscience research.
  • This will provide support to the students in designing research projects and in analysing data. Guidance via tutorials and assignments will practice and hone scientific writing skills.


Section One: Data handling and statistics

Formative and summative quizzes built around resources available within the DoLS will cover quantitative and qualitative statistical methods for analysing data. Students will benefit from increased familiarity with applications used to conduct statistical analyses (e.g., R and SPSS).

Initial 'steps' will revisit basic statistical theory and analytical tests namely:
• t-tests, ANOVA, post-hoc significance tests, data handling and presentation, correlation/regression and non-parametric tests.

The coursework materials/ tutorials of each step will then progress onto advanced considerations in experimental design and a range of more advanced statistical procedures including:
• 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.

Section Two: Research Proposal or Funding Application

The theory, principles and practices of the scientific research method will be taught to evaluate existing research published in a range of different sources and to prepare students for independent research. MSc students complete a research proposal (linked with BRM3560) Integrated Masters and MRes students will prepare a grant application. Exercises will be related to student’s dissertation topics.

Section Three: Analytical Techniques and Communication of Scientific Information in the Biosciences

These learning activities, presented by research-active staff in a laboratory or seminar setting, will introduce students to a number of developments in the biosciences in order to help students to understand advances in methods and analytical techniques that have transformed bioscience research. Students will be introduced to a variety of research methods relevant to their PGT or Integrated Masters scheme subject and will select one of the advances for detailed critical review.

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. Workshops will serve to scaffold online learning of data analysis and statistics.
Information Technology Students will be required to source information from a variety of scientific publication databases and be taught to use specialist statistical software.
Personal Development and Career planning Research Project Plan or Grant Application - developed in collaboration with supervisor.
Problem solving Exercises around data handling and statistics 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 and information synthesis skills.
Subject Specific Skills Advanced statistical methods, genomic, spatial analytical and biochemical applications or other techniques relevant to degree scheme.


This module is at CQFW Level 7