Factor Screening Design of Experiment with Nominal Response?Hi all,
I was wondering if there is a good strategy for designing a set of experiments to find the factors with the most effect on the response, which is nominal (yes/no, pass/fail type of response) instead of the typical continuous response? While for nominal response a logistic regression can be performed on available data, I doubt the usual factorial/fractional factorial design still works in this case (since they are meant for continuous response). What would be a suitable approach in this case? Kindly point me to any relevant terms/theories if anything comes to mind.
Thanks in advance.
DOERe: Factor Screening Design of Experiment with Nominal Response?There is absolutely no apriori reason why a nominal response precludes or renders the sound practice of experimental design as an unsound path to discovery. All the principles of sound DOE methods still apply. As a JMP senior systems engineer (now retired) I called on numerous consumer product companies that use DOE as the lifeblood of their product design efforts. Very often they would use sensory panels of product evaluators and almost universally the individauls who sit on these panels provide nominal type response data for the specific experimental designs that have been executed. In fact depending on the specific problem at hand you might find one of the more specialized designs that presume nominal responses supported by JMP such as Choice, Max Dif, or Covering Arrays. But there's no reason a good old fashioned fractional factorial or more modern design such as a d-optimal design couldn't be or shouldn't be used for screening purposes.
