Hi fellow researchers,
As part of my master's degree I'm performing a stated choice experiment about the most effective communication of sustainability initiatives for steering consumers preference for a product. Since the full set consists of 480 choice sets, I am using a fractional factorial design. Since my university doesn't provide us with any budget, I was wondering if there is any free or affordable software to help me create the factorial design or any software which helps with the process of performing this experiment.
All tools and tips are welcome.
Thanks in advance.
Kind regards,
Michael
Dear Michael,
You may consult this article " van Cranenburgh, S., & Collins, A. T. (2019). New software tools for creating stated choice experimental designs efficient for regret minimisation and utility maximisation decision rules.Journal of choice modelling, 31, 104-123", available at: https://www.sciencedirect.com/science/article/abs/pii/S1755534518300940
Another interesting article relative to your research topic is:
Article Acceptance of Life-Threatening Hazards among Young Tourists:...
Hope that you will find the sources valuable.
Hi Michael,
not sure which software you are using for analysing your data, but data analysis software such as Stata is also capable of doing a fractional factorial design:
https://www.stata.com/meeting/italy15/abstracts/materials/it15_pisati.pdf
However, Ngene is tailored to survey design for discrete choice experiments and might allow you to explore a few more design options. If I were you, I would ask around in your department is there is an Ngene license available. There should be a researcher with a licence to help you out.
Cheers,
Chris
I guess the straightforward answer is ngene for experimental design and nlogit for data analysis, which are user friendly and specifically designed for discrete choice experiments. Be aware that an open source software like r can do everything you need for free as it has complete packages for choice models like mlogit, gmnl and apollo. You just need a little extra effort in coding
