Jed Stephens 31 March 2020
ExpertChoice has two vignettes to help you get started. The Theoretical Introduction to ExpertChoice focusses on the theory for designing efficiently for experiments, conjoint and discrete choice. The Practical Introduction to ExpertChoice aims to explain how to use this R package. It also gives two worked examples. The documents reflect each other. For more detail keep reading.
The need for the ExpertChoice R package emerged from the methodological desire to implement a discrete choice experiment in my research. There exists a lack of comprehensive open source software to assist in the design of discrete choice experiments. Currently there are three R packages on CRAN that have some overlap with ExpertChoice: choiceDes, idefix and support.CEs. Two of these packages are no longer under active development and some of the functions have not been maintained and consequently no longer work. Two packages also lack documentation making it difficult for all but experts in this field to use. ExpertChoice provides a unified framework suitable for a first time learner to understand how to design an experiment and convert this experiment into a discrete choice. Its scope is also wider and more current than the above alternate packages.
Theoretical introduction to ExpertChoice is the first vignette: its objective is to explain the theory of experimental design and discrete choice design. It focusses on explaining how efficiently measure tests play an important role in the designing process. The silver object choice experiment, analysed in my dissertation, is one of the two examples in this vignette. A hypothetical choice experiment on a restaurant is another.
The second vignette, Practical introduction to ExpertChoice, provides a worked example of both experimental designs. The worked examples make it clear how this procedure could be adapted for the reader’s own experiment. Some of the more advanced functionality of the package is explored in particular with the restaurant example.
ExpertChoice now provides a unified open source alternative to many routines previously only available in SAS and Ngene.