A nonspatial methodology for the analysis of two-way proximity data incorporating the distance-density hypothesis
dc.contributor.author | DeSarbo, Wayne S. | en_US |
dc.contributor.author | Manrai, Ajay K. | en_US |
dc.contributor.author | Burke, Raymond R. | en_US |
dc.date.accessioned | 2006-09-11T16:25:12Z | |
dc.date.available | 2006-09-11T16:25:12Z | |
dc.date.issued | 1990-06 | en_US |
dc.identifier.citation | DeSarbo, Wayne S.; Manrai, Ajay K.; Burke, Raymond R.; (1990). "A nonspatial methodology for the analysis of two-way proximity data incorporating the distance-density hypothesis." Psychometrika 55(2): 229-253. <http://hdl.handle.net/2027.42/45746> | en_US |
dc.identifier.issn | 0033-3123 | en_US |
dc.identifier.issn | 1860-0980 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/45746 | |
dc.description.abstract | This paper presents a nonspatial operationalization of the Krumhansl (1978, 1982) distancedensity model of similarity. This model assumes that the similarity between two objects i and j is a function of both the interpoint distance between i and j and the density of other stimulus points in the regions surrounding i and j . We review this conceptual model and associated empirical evidence for such a specification. A nonspatial, tree-fitting methodology is described which is sufficiently flexible to fit a number of competing hypotheses of similarity formation. A sequential, unconstrained minimization algorithm is technically presented together with various program options. Three applications are provided which demonstrate the flexibility of the methodology. Finally, extensions to spatial models, three-way analyses, and hybrid models are discussed. | en_US |
dc.format.extent | 1653484 bytes | |
dc.format.extent | 3115 bytes | |
dc.format.mimetype | application/pdf | |
dc.format.mimetype | text/plain | |
dc.language.iso | en_US | |
dc.publisher | Springer-Verlag; Psychometric Society | en_US |
dc.subject.other | Statistical Theory and Methods | en_US |
dc.subject.other | Psychology | en_US |
dc.subject.other | Ultrametric Trees | en_US |
dc.subject.other | Krumbansl's Distance-density Model | en_US |
dc.subject.other | Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law | en_US |
dc.subject.other | Psychometrics | en_US |
dc.subject.other | Assessment, Testing and Evaluation | en_US |
dc.subject.other | Asymmetric Similarity | en_US |
dc.subject.other | Hierarchical Clustering | en_US |
dc.title | A nonspatial methodology for the analysis of two-way proximity data incorporating the distance-density hypothesis | en_US |
dc.type | Article | en_US |
dc.subject.hlbsecondlevel | Psychology | en_US |
dc.subject.hlbtoplevel | Social Sciences | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | Marketing and Statistics Departments, graduate School of Business, University of Michigan, 48109-1234, Ann Arbor, MI | en_US |
dc.contributor.affiliationother | Marketing Department Wharton School, University of Pennsylvania, USA | en_US |
dc.contributor.affiliationother | Marketing Department Wharton School, University of Pennsylvania, USA | en_US |
dc.contributor.affiliationumcampus | Ann Arbor | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/45746/1/11336_2005_Article_BF02295285.pdf | en_US |
dc.identifier.doi | http://dx.doi.org/10.1007/BF02295285 | en_US |
dc.identifier.source | Psychometrika | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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