conjoint analysis random coefficient regression model full factorial design fractional factorial design design matrix
Issue Date:
2005
Publisher:
Institute of Mathematics and Informatics Bulgarian Academy of Sciences
Citation:
Pliska Studia Mathematica Bulgarica, Vol. 17, No 1, (2005), 71p-84p
Abstract:
Since late 1960s conjoint analysis has been applied in estimating consumer preferences in marketing research. This article discusses how to model the data coming from a full or a fractional factorial design within a unique regression model, as an alternative to the estimation done by n independent multiple linear regression models, one for each subject. The advantage of the method presented here resides in the possibility of computing correct standard errors for the conjoint analysis utility values based on a particular group of subjects. The model assumes that the utility values within subjects could be correlated.
