Design of experiments and analysing experimental data
Date: 22 June 2018
Instructor: Marzena Nieroda
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. In some instances, such as for unfunded PhD students, we may be able to offer free or bursary places.
Please note: this is not guaranteed and is considered on a case by case basis. Please contact us for more information.
This course is designed for those interested in the design, conduct, and analysis of experiments in the social sciences. The course will examine how to design experiments, carry them out, and analyse the experimental data. Positioned in the context of online market research, the course will also cover the use of experiments in market research.
We will discuss various designs and their respective differences, advantages, and disadvantages. In particular, basic and factorial designs are discussed in greater detail. Basic experiments involve a manipulation of one independent variable. Factorial designs involve a manipulation of two or more independent variables (factors). In factorial designs it is of particular interest to understand how combination (interaction) of the two (or more) factors affects the outcome. Differences between within (paired) and between-groups experiments are explained.
The course includes a review of statistics background that is needed for conducting and analysing experiments. We will start with hypothesis testing and discuss most commonly used techniques for analysing experimental data: t-test, Analysis of Variance (ANOVA) and Analysis of Covariance (ANCOVA). SPSS 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 SPSS software (SPSS environment, be able to use SPSS to run descriptive statistics).
- Mitchell, Mark L. and Janina M. Jolley (2012) Research Design Explained. Elmont, CA; Wadsworth Publishing Co Inc.
- Field, A. (2009) Discovering statistics using SPSS. London: Sage publications.
- 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