Clustering, seriation, and subset extraction of confusion data.
The study of confusion data is a well established practice in psychology. Although many types of analytical approaches for confusion data are available, among the most common methods are the extraction of 1 or more subsets of stimuli, the partitioning of the complete stimulus set into distinct groups, and the ordering of the stimulus set. Although standard commercial software packages can sometimes facilitate these types of analyses, they are not guaranteed to produce optimal solutions. The authors present a MATLAB *.m file for preprocessing confusion matrices, which includes fitting of the similarity-choice model. Two additional MATLAB programs are available for optimally clustering stimuli on the basis of confusion data. The authors also developed programs for optimally ordering stimuli and extracting subsets of stimuli using information from confusion matrices. Together, these programs provide several pragmatic alternatives for the applied researcher when analyzing confusion data. Although the programs are described within the context of confusion data, they are also amenable to other types of proximity data.
Brusco
Michael J
MJ
Marketing Department, College of Business, Florida State University, Tallahassee, FL 32306-1110, USA. Mbrusco@cob.fsu.edu
Steinley
Douglas
D
D016000:Cluster Analysis; D006801:Humans; D008960:Models, Psychological; D011584:Psychology / Q000379:methods / Q000706:statistics & numerical data