This paper describes the development of an unfolding
methodology designed to analyze "pick any" or
"pick any/n" binary choice data (e.g., decisions to
buy or not to buy various products). Maximum likelihood
estimation procedures are used to obtain a joint
space representation of both persons and objects. A
review of the relevant literature concerning the spatial
treatment of such binary choice data is presented. The
nonlinear logistic model type is described, as well as
the alternating maximum likelihood algorithm used to
estimate the parameter values. The results of an application
of the spatial choice model to a synthetic data
set in a monte carlo analysis are presented. An application
concerning consumer (intended) choices for
nine competitive brands of sports cars is discussed.
Future research may provide a means of generalizing
the model to accommodate three-way choice data.
DeSarbo, Wayne S & Hoffman, Donna L. (1986). Simple and weighted unfolding threshold models for the spatial representation of binary choice data. Applied Psychological Measurement, 10, 247-264. doi:10.1177/014662168601000304
DeSarbo, Wayne S.; Hoffman, Donna L..
Simple and weighted unfolding threshold models for the spatial representation of binary choice data.
Retrieved from the University of Minnesota Digital Conservancy,
Content distributed via the University of Minnesota's Digital Conservancy may be subject to additional license and use restrictions applied by the depositor.