CATSCALE: A stochastic multidimensional scaling methodology for the spatial analysis of sorting data and the study of stimulus categorization
dc.contributor.author | DeSarbo, Wayne S. | en_US |
dc.contributor.author | Libby, Robert | en_US |
dc.contributor.author | Jedidi, Kamel | en_US |
dc.date.accessioned | 2006-04-10T17:58:10Z | |
dc.date.available | 2006-04-10T17:58:10Z | |
dc.date.issued | 1994-08 | en_US |
dc.identifier.citation | DeSarbo, Wayne S., Libby, Robert, Jedidi, Kamel (1994/08)."CATSCALE: A stochastic multidimensional scaling methodology for the spatial analysis of sorting data and the study of stimulus categorization." Computational Statistics & Data Analysis 18(1): 165-184. <http://hdl.handle.net/2027.42/31400> | en_US |
dc.identifier.uri | http://www.sciencedirect.com/science/article/B6V8V-45GN701-B/2/419d405fc1ff8ea843b7bb0b7afd0027 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/31400 | |
dc.description.abstract | Sorting tasks have provided researchers with valuable alternatives to traditional paired-comparison similarity judgments. They are particularly well-suited to studies involving large stimulus sets where exhaustive paired-comparison judgments become infeasible, especially in psychological studies investigating stimulus categorization. This paper presents a new stochastic multidimensional scaling procedure called CATSCALE for the analysis of three-way sorting data (as typically collected in categorization studies). We briefly present a review of the role of sorting tasks, especially in categorization studies, as well as a description of several traditional modes of analysis. The CATSCALE model and maximum likelihood based estimation procedure are described. An application of CATSCALE is presented with respect to a behavioral accounting study examining auditor's categorization processes with respect to various types of errors found in typical financial statements. | en_US |
dc.format.extent | 1399312 bytes | |
dc.format.extent | 3118 bytes | |
dc.format.mimetype | application/pdf | |
dc.format.mimetype | text/plain | |
dc.language.iso | en_US | |
dc.publisher | Elsevier | en_US |
dc.title | CATSCALE: A stochastic multidimensional scaling methodology for the spatial analysis of sorting data and the study of stimulus categorization | en_US |
dc.type | Article | en_US |
dc.rights.robots | IndexNoFollow | en_US |
dc.subject.hlbsecondlevel | Statistics and Numeric Data | en_US |
dc.subject.hlbsecondlevel | Mathematics | en_US |
dc.subject.hlbtoplevel | Social Sciences | en_US |
dc.subject.hlbtoplevel | Science | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | School of Business Administration, University of Michigan, Ann Arbor, MI, USA | en_US |
dc.contributor.affiliationother | Johnson Graduate School of Management, Cornell University, Ithaca, NY, USA | en_US |
dc.contributor.affiliationother | Graduate School of Business, Columbia University, New York, NY USA | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/31400/1/0000315.pdf | en_US |
dc.identifier.doi | http://dx.doi.org/10.1016/0167-9473(94)90137-6 | en_US |
dc.identifier.source | Computational Statistics & Data Analysis | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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