This book introduces multidimensional scaling (MDS) and unfolding as
data analysis techniques for applied researchers. MDS is used for the
analysis of proximity data on a set of objects, representing the data as
distances between points in a geometric space (usually of two
dimensions). Unfolding is a related method that maps preference data
(typically evaluative ratings of different persons on a set of objects)
as distances between two sets of points (representing the persons and
the objects, resp.).
This second edition has been completely revised to reflect new
developments and the coverage of unfolding has also been substantially
expanded. Intended for applied researchers whose main interests are in
using these methods as tools for building substantive theories, it
discusses numerous applications (classical and recent), highlights
practical issues (such as evaluating model fit), presents ways to
enforce theoretical expectations for the scaling solutions, and
addresses the typical mistakes that MDS/unfolding users tend to make.
Further, it shows how MDS and unfolding can be used in practical
research work, primarily by using the smacof package in the R
environment but also Proxscal in SPSS. It is a valuable resource for
psychologists, social scientists, and market researchers, with a basic
understanding of multivariate statistics (such as multiple regression
and factor analysis).
