Module Information

Module Identifier
BR27500
Module Title
Research Methods
Academic Year
2023/2024
Co-ordinator
Semester
Semester 1 (Taught over 2 semesters)
Exclusive (Any Acad Year)

Course Delivery

 

Assessment

Assessment Type Assessment length / details Proportion
Semester Assessment Statistics assignment  50%
Semester Assessment Research Plan  50%
Supplementary Assessment Supplementary research plan  Student to complete assessment(s) equivalent to that (those) failed.  50%
Supplementary Assessment Supplementary statistics assignment  Assessment (2000 words). Student to complete assessment(s) equivalent to that (those) failed.  50%

Learning Outcomes

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

1. Demonstrate an understanding of the importance of intra/inter-disciplinary research to a wide range of careers and enterprises.

2. Search and review the scientific literature to identify valid research questions and/or novel IP.

3. Convert valid research questions into testable hypotheses

4. Identify appropriate methods of analysis for different types of research.

5. Design statistically valid experiments to test the hypothesis/hypotheses posed.

6. Identify and mitigate against confounding factors in research design.

7. Demonstrate an understanding of the ethical issues involved in research, and other societal impacts.

8. Analyze data using a range of quantitative and qualitative techniques.

9. Interpret the results of data analyses and apply statistical knowledge in the evaluation of research investigations.

Brief description

This module seeks to provide deeper understanding of the processes involved in planning good scientific research. The 20-credit module will be run as a long-thin module over two semesters. This structure is intended to allow for reflected learning for the numeric aspects of the module, whilst also providing students with timely understanding of research planning immediately prior to selection of their final year dissertations. Content in the first semester will focus on data handling and statistics. This will provide essential grounding for the second part of the module, and for subject-specific modules within part 2 of each degree program. In the second semester, students will be introduced to the key principles underpinning experimental science. Information flow will be timed to help the students to select tractable titles for their final year dissertations that have most relevance for their preferred career aspirations. Students will then be guided through the process of planning their research project, and will compose a robust research plan for their chosen dissertation topic. Towards the end of the semester, the students will be prepared for the execution phase of their dissertation and for composing and writing of their dissertation.

Content

Semester 1. Data handling and statistical analysis. This part of the course will provide students with an understanding of the different kinds of data generated by experimental science and of the most widely used statistical techniques. Emphasis will be placed on providing students with the knowledge base necessary to select the most appropriate statistical tests to differentiate between chance events and those more likely to be attributable to the factor being manipulated in the study. The range of tests covered will reflect the broad range of degree disciplines represented on the module, but will include, among others: T-tests (independent and paired), ANOVA (one-way and two-way), linear regression, various non-parametric tests, tests for normality, and selected multivariate analyses. For each test type, students will be given practical experience using mock data applied to different statistical software platforms.

Semester 2. Composing a tractable research plan. Students will be guided from the initial concept of an idea through to the assembly of a detailed and valid research plan. This process requires creative identification of a pertinent gap in scientific knowledge or a short-coming in methodological capability. Students will be guided on how to encapsulate their idea into the form of a tractable research question and then on how to convert this into a testable alternative hypothesis and associated null hypothesis. The importance of specifying both the dependent and independent variables during hypothesis formulation will be particularly emphasised. Students will be encouraged to consider how to measure the dependent variable and how to control (or group) the independent variable when scoping their research strategy. Help will be provided over the creative process of identifying possible constraints, sources of potential bias, assumptions and confounding variables/factors that could compromise the ability to test the hypothesis set. This then leads to specifying measures that mitigate for the effect of these factors when shaping the structure and scale of the experimental design. Consideration will be given to the number and genetic diversity of organism under study, the type of data generated (quantitative or qualitative), of the controls and (technical and biological) replicates, and of the statistical approach best suited to effectively differentiate between null and alternative hypotheses. Thus, an understanding of the various statistical analyses available (part 1) is shown to be as important in the design of the experiment as it is in the interpretation of the results. A reiterative approach will be encouraged for the entire planning process. Hypothesis creation according to formal criteria will be encouraged to enable better differentiation between good and vague research topics. This will aide selection of dissertation topics for their final year and will foster a more sceptical perspective of research findings. The importance of selecting a dissertation topic that matches their career aspirations will be emphasised, along with the scope for exploitation of the results generated in a non-scientific context (public good/commercial/IP).

Module Skills

Skills Type Skills details
Application of Number Most aspects of the module will require manipulation of data and application of statistics. Feedback on this will be given in on-line exercises.
Communication Students will be expected to be able to express themselves appropriately in all assessments. Feedback will be given in the assignments.
Improving own Learning and Performance Outside the formal contact hours, students will be expected to research materials, manage time and meet deadlines for the assignments
Information Technology Students will be required to source information from a variety of scientific publication databases. The use of various software packages will be required for the correct presentation of the assignments.
Personal Development and Career planning Students will gain confidence in their ability to evaluate and use research findings in their chosen career.
Problem solving Students will need to determine the most appropriate research design and methods of analysis to use with different types of data. Feedback will be given in the assignments.
Research skills Students will be required to source and summarize a substantial amount of information without staff direction in order to complete the assessments. Feedback will be given in the assignments.
Subject Specific Skills By allowing students to focus on an area of research of interest to them this module will help students develop subject specific skills and knowledge.
Team work Not a significant component of this module

Notes

This module is at CQFW Level 5