Multiclus: A new method for simultaneously performing multidimensional scaling and cluster analysis
dc.contributor.author | Jedidi, Kamel | en_US |
dc.contributor.author | DeSarbo, Wayne S. | en_US |
dc.contributor.author | Howard, Daniel J. | en_US |
dc.date.accessioned | 2006-09-11T16:25:16Z | |
dc.date.available | 2006-09-11T16:25:16Z | |
dc.date.issued | 1991-03 | en_US |
dc.identifier.citation | DeSarbo, Wayne S.; Howard, Daniel J.; Jedidi, Kamel; (1991). "Multiclus: A new method for simultaneously performing multidimensional scaling and cluster analysis." Psychometrika 56(1): 121-136. <http://hdl.handle.net/2027.42/45747> | en_US |
dc.identifier.issn | 1860-0980 | en_US |
dc.identifier.issn | 0033-3123 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/45747 | |
dc.description.abstract | This paper develops a maximum likelihood based method for simultaneously performing multidimensional scaling and cluster analysis on two-way dominance or profile data. This MULTICLUS procedure utilizes mixtures of multivariate conditional normal distributions to estimate a joint space of stimulus coordinates and K vectors, one for each cluster or group, in a T -dimensional space. The conditional mixture, maximum likelihood method is introduced together with an E-M algorithm for parameter estimation. A Monte Carlo analysis is presented to investigate the performance of the algorithm as a number of data, parameter, and error factors are experimentally manipulated. Finally, a consumer psychology application is discussed involving consumer expertise/experience with microcomputers. | en_US |
dc.format.extent | 1103704 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; The Psychometric Society | en_US |
dc.subject.other | Maximum Likelihood Estimation | en_US |
dc.subject.other | Assessment, Testing and Evaluation | en_US |
dc.subject.other | Psychometrics | en_US |
dc.subject.other | Psychology | en_US |
dc.subject.other | Statistical Theory and Methods | en_US |
dc.subject.other | Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law | en_US |
dc.subject.other | Multidimensional Scaling | en_US |
dc.subject.other | Cluster Analysis | en_US |
dc.subject.other | Consumer Psychology | en_US |
dc.title | Multiclus: A new method for simultaneously performing multidimensional scaling and cluster analysis | 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 Depts., School of Business, The University of Michigan, 48109, Ann Arbor, MI | en_US |
dc.contributor.affiliationother | Marketing Department E. L. Cox School of Business, Southern Methodist University, USA | en_US |
dc.contributor.affiliationother | Marketing Department Graduate School of Business, Columbia University, USA | en_US |
dc.contributor.affiliationumcampus | Ann Arbor | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/45747/1/11336_2005_Article_BF02294590.pdf | en_US |
dc.identifier.doi | http://dx.doi.org/10.1007/BF02294590 | en_US |
dc.identifier.source | Psychometrika | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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