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Row and column matrices in multiple correspondence analysis with ordered categorical and dichotomous variables

In multiple correspondence analysis, whenever the number of variables exceeds the number of observations, row matrix should be used, but if the number of variables is less than the number of observations column matrix is the suitable procedure to follow. One of the following matrices (rows, columns)...

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Main Authors: Thanoon, Thanoon Y., Adnan, Robiah
Format: Article
Language:English
Published: Penerbit UTM Press 2016
Subjects:
QA Mathematics
Online Access:http://eprints.utm.my/id/eprint/73885/1/RobiahAdnan2016_RowAndColumnMatricesInMultiple.pdf
http://eprints.utm.my/id/eprint/73885/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84957878975&doi=10.11113%2fjt.v78.4077&partnerID=40&md5=641b024c1e2f20a966b48e81475fc967
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spelling my.utm.738852017-11-21T08:17:13Z http://eprints.utm.my/id/eprint/73885/ Row and column matrices in multiple correspondence analysis with ordered categorical and dichotomous variables Thanoon, Thanoon Y. Adnan, Robiah QA Mathematics In multiple correspondence analysis, whenever the number of variables exceeds the number of observations, row matrix should be used, but if the number of variables is less than the number of observations column matrix is the suitable procedure to follow. One of the following matrices (rows, columns) leads to loss of information that can be found by the other method, therefore, this paper developed a proposal to overcome this problem, which is: to find a shortcut method allowing the use of the results of one matrix to obtain the results of the other matrix. Taking advantage of all information available, the phenomenon was studied. Some of these results are: Eigenvectors, factor loadings and factor scores based on ordered categorical and dichotomous data. This method is illustrated by using a real data set. Results were obtained by using Minitab program. As a result, it is possible to shortcut transformation between the results of row and column matrices depending on factor loadings and factor scores of the row and column matrices. Penerbit UTM Press 2016 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/73885/1/RobiahAdnan2016_RowAndColumnMatricesInMultiple.pdf Thanoon, Thanoon Y. and Adnan, Robiah (2016) Row and column matrices in multiple correspondence analysis with ordered categorical and dichotomous variables. Jurnal Teknologi, 78 (2). pp. 149-156. ISSN 0127-9696 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84957878975&doi=10.11113%2fjt.v78.4077&partnerID=40&md5=641b024c1e2f20a966b48e81475fc967
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic QA Mathematics
spellingShingle QA Mathematics
Thanoon, Thanoon Y.
Adnan, Robiah
Row and column matrices in multiple correspondence analysis with ordered categorical and dichotomous variables
description In multiple correspondence analysis, whenever the number of variables exceeds the number of observations, row matrix should be used, but if the number of variables is less than the number of observations column matrix is the suitable procedure to follow. One of the following matrices (rows, columns) leads to loss of information that can be found by the other method, therefore, this paper developed a proposal to overcome this problem, which is: to find a shortcut method allowing the use of the results of one matrix to obtain the results of the other matrix. Taking advantage of all information available, the phenomenon was studied. Some of these results are: Eigenvectors, factor loadings and factor scores based on ordered categorical and dichotomous data. This method is illustrated by using a real data set. Results were obtained by using Minitab program. As a result, it is possible to shortcut transformation between the results of row and column matrices depending on factor loadings and factor scores of the row and column matrices.
format Article
author Thanoon, Thanoon Y.
Adnan, Robiah
author_facet Thanoon, Thanoon Y.
Adnan, Robiah
author_sort Thanoon, Thanoon Y.
title Row and column matrices in multiple correspondence analysis with ordered categorical and dichotomous variables
title_short Row and column matrices in multiple correspondence analysis with ordered categorical and dichotomous variables
title_full Row and column matrices in multiple correspondence analysis with ordered categorical and dichotomous variables
title_fullStr Row and column matrices in multiple correspondence analysis with ordered categorical and dichotomous variables
title_full_unstemmed Row and column matrices in multiple correspondence analysis with ordered categorical and dichotomous variables
title_sort row and column matrices in multiple correspondence analysis with ordered categorical and dichotomous variables
publisher Penerbit UTM Press
publishDate 2016
url http://eprints.utm.my/id/eprint/73885/1/RobiahAdnan2016_RowAndColumnMatricesInMultiple.pdf
http://eprints.utm.my/id/eprint/73885/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84957878975&doi=10.11113%2fjt.v78.4077&partnerID=40&md5=641b024c1e2f20a966b48e81475fc967
_version_ 1643656775227932672
score 13.211869

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