Research methods

Brief description of aims and content

This module presents an in depth knowledge of statistics, biometry and research methodology The module is designed to introduce learners to the fields of statistical research in Agricultural Sciences. Concepts on ethics and philosophy of science, and scientific writing skills will be introduced. Advanced statistical methods, experimental design, data collection, data exploration and analyses will form part of the modules.

Learning Outcomes

1. A.     Knowledge and Understanding
2. Having successfully completed the module, learners should be able to demonstrate a thorough understanding of: 

• Ethical considerations in research
• Philosophy of science
• How to choose and develop proper research projects

1. Learners should demonstrate a comprehensive understanding of relevant techniques and approaches applicable to the research
2. Learners should demonstrate a clear understanding of how established techniques of research and enquiry are used in the discipline
3. How to formulate hypotheses and to design tests of hypotheses
4. Experimental design
5. Data collection
6. Data exploration and handling of data
7. Interpretation and reporting of results

1. B.     Cognitive/Intellectual skills/Application of Knowledge

Having successfully completed the module, learners should be able to:

• Use a significant range of the principle skills, techniques, practices appropriate for the research in their discipline
• Apply a range of standards and specialised research techniques to execute their research project
• Demonstrate originality in the application of knowledge
• Use a significant range of the principle skills, techniques, practices and/or materials, including some at the forefront of developments, associated with their discipline
• Apply a range of standard and specialised research techniques of enquiry
• Plan and carry out a research or development project.
• Demonstrate originality in the application of knowledge

1. C.     Generic cognitive skills

Having successfully completed the module, students must be able to demonstrate the following skills:

1. Deal with complex issues and make informed judgement in the absence of complete data,

2. Analyse, evaluate and synthesise issues, in complex which are at the forefront of knowledge,

3. Demonstrate original responses to problems and issues

1. D.    Communication/ICT/Numeracy/Analytic Techniques/Practical Skills

Having successfully completed the course, learners should be able to:

• Communicate their research to wide range of audience with some levels of expertise
• Communicate with peers, more senior colleagues and specialists
• Use a wide range of appropriate software for presentation/communication to the audience
• Evaluate a wide range of numerical and graphical information
• Synthesise and critically analysing the content of a scientific paper
• Use an appropriate experimental design and sampling schedule.
• Use an appropriate statistical method to analyse data, evaluate and report the results.
• Communicate research findings to a range of audience using appropriate statistical methods
• Correctly interpret and numerical and graphical information

1. E.     Autonomy, responsibility and working with others

1. Exercise initiative and personal responsibility

2. Demonstrate self-direction and originality in tackling and solving problems,

3. Act autonomously in planning and implementing decisions at a professional level,

4. Demonstrate the skills of life-long learning in his/her own discipline,

5. Demonstrate the skills of leadership and the management of resources

Indicative Content

• Ethics in research
• Philosophy of science
• Research methods
• The scientific writing process
• Preparation of scientific presentations
• Presentation and communication of scientific research results
• Principles of experimental design and census techniques
• Design of field experiments – characteristics, merits and limitations
• Statistical tools – tests and report of results.
• Data exploration
• Distributions - (Normal vs. other and data transformation)
• Regression analysis and analysis of variance
• Analysis of categorical data
• Missing data
• Principles of experimentation
• Generalized Linear Models
•  Mixed Linear Models
• Restricted Maximum Livelihood (REML)
• Multivariate analysis

- Principal Components Analysis (PCA)

- Discriminant analysis

- Cluster analysis

• Genotype × environment Interaction Analysis