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|Title:||The analysis of free-sorting data: Beyond pairwise cooccurrences|
|Author(s):||Daws, John Thomas|
|Doctoral Committee Chair(s):||Hubert, Lawrence J.|
|Department / Program:||Psychology|
|Degree Granting Institution:||University of Illinois at Urbana-Champaign|
|Abstract:||Free-sorting data are obtained when subjects are given a set of objects and are asked to divide them into subsets. Such data are usually reduced by counting, for each pair of objects, how many subjects placed the two into the same subset. The more frequently two objects are sorted together, the more similar the two are inferred to be. These pair cooccurrences, however, do not inform the researcher about the higher-order relationships among the objects.
The present study examines the utility of a group of additional statistics, the cooccurrences of sets of three objects. The triple cooccurrence is taken as a measure of how similar the three objects are to each other. Because there are dependencies among the pair and triple cooccurrences, adjusted triple similarity statistics were developed. These statistics are independent of the pair cooccurrences under the null assumption that the subjects are sorting the objects randomly. The characteristics of the adjusted triple statistics under various null and non-null models is explored.
Multidimensional scaling and cluster analysis--which usually use pair similarities (or dissimilarities) as their input data--can be modified to operate on triple similarities to create representations of the set of objects. Such methods are applied to two sets of empirical sorting data: Rosenberg and Kim's (1975) fifteen kinship terms and Fitzgerald's (1991) forty rape myths. In both cases, the triple similarities do provide interesting and interpretable information about the subjects' perceptions of the objects they sorted.
|Rights Information:||Copyright 1993 Daws, John Thomas|
|Date Available in IDEALS:||2011-05-07|
|Identifier in Online Catalog:||AAI9411601|