Design of experiments and analysing experimental data
Date: 17 June 2019
Instructor: Rosa Parisi and Christos Grigoroglou
Fee: £195 (£140 for those from educational, government and charitable institutions).
CMI offers up to five subsidised places at a reduced rate of £60 per course day to research staff and students within Humanities at The University of Manchester. These places are awarded in order of application.
Humanities PGR students at The University of Manchester can apply for a methods@manchester bursary to help cover their costs. All applications will be considered on a case-by-case basis and applicants will be required to provide a supporting statement from their supervisor.
Please contact Joshua Edgar (email: firstname.lastname@example.org) for an application form and further information.
Please note: this is not guaranteed and is considered on a case by case basis. Please contact us for more information.
For any general course enquires please contact email@example.com.
This course is designed for those interested in the design, conduct, and analysis of experiments. The course will examine how to design and analyse the experimental data in the context of medical statistics.
We will discuss various designs and their respective differences, advantages, and disadvantages. The course includes a review of statistics background that is needed for conducting and analysing experiments. We will start discussing how to summarise data, confidence intervals and hypothesis testing and illustrate analysis techniques such as t-test, Analysis of Variance (ANOVA) and Analysis of Covariance (ANCOVA).
In addition, basic models with one independent variable and more complex models with two or more independent variables will be explained in greater detail. Stata and/or R software will be used to analyse the data.
After attending the course you should be able to:
- Understand the principles of experimental design
- Understand different types of experimental design
- Know how to design experiments that are likely to yield valid results
- Understand the logic of hypothesis testing
- Have a basic understanding of the most common techniques used to analyse experimental data
- Be able to select an appropriate statistics to test the proposed hypothesis
- Gain understanding of how experiments can be used in business settings and help to increase profits and build competitive advantage.
Course attendees should be familiar with statisical software.
- Mitchell, Mark L. and Janina M. Jolley (2012) Research Design Explained. Elmont, CA; Wadsworth Publishing Co Inc.
- Labrecque, Lauren I. and George R. Milne (2012) Exciting red and competent blue: the importance of color in marketing. Journal of the Academy of Marketing Science, 40(5), pp 711-727