Alternative measures of fit for the Schönemann-carroll matrix fitting algorithm
dc.contributor.author | Schönemann, Peter H. | en_US |
dc.contributor.author | Lingoes, James C. | en_US |
dc.date.accessioned | 2006-09-11T16:24:09Z | |
dc.date.available | 2006-09-11T16:24:09Z | |
dc.date.issued | 1974-12 | en_US |
dc.identifier.citation | Lingoes, James C.; Schönemann, Peter H.; (1974). "Alternative measures of fit for the Schönemann-carroll matrix fitting algorithm." Psychometrika 39(4): 423-427. <http://hdl.handle.net/2027.42/45731> | en_US |
dc.identifier.issn | 1860-0980 | en_US |
dc.identifier.issn | 0033-3123 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/45731 | |
dc.description.abstract | In connection with a least-squares solution for fitting one matrix, A , to another, B , under optimal choice of a rigid motion and a dilation, Schönemann and Carroll suggested two measures of fit: a raw measure, e , and a refined similarity measure, e s , which is symmetric. Both measures share the weakness of depending upon the norm of the target matrix, B , e.g. , e ( A , kB ) ≠ e ( A , B ) for k ≠ 1. Therefore, both measures are useless for answering questions of the type: “Does A fit B better than A fits C ?”. In this note two new measures of fit are suggested which do not depend upon the norms of A and B , which are (0, 1)-bounded, and which, therefore, provide meaningful answers for comparative analyses. | en_US |
dc.format.extent | 260169 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 | 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.title | Alternative measures of fit for the Schönemann-carroll matrix fitting algorithm | 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 | The University of Michigan, USA | en_US |
dc.contributor.affiliationother | Purdue University, USA | en_US |
dc.contributor.affiliationumcampus | Ann Arbor | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/45731/1/11336_2005_Article_BF02291666.pdf | en_US |
dc.identifier.doi | http://dx.doi.org/10.1007/BF02291666 | en_US |
dc.identifier.source | Psychometrika | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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