Biclustering Models for Two-Mode Ordinal Data

The work in this paper introduces finite mixture models that can be used to simultaneously cluster the rows and columns of two-mode ordinal categorical response data, such as those resulting from Likert scale responses. We use the popular proportional odds parameterisation and propose models which p...

Descripción completa

Detalles Bibliográficos
Autores principales: Matechou, Eleni, Liu, Ivy, Fernández, Daniel, Farias, Miguel, Gjelsvik, Bergljot
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2016
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4978779/
https://www.ncbi.nlm.nih.gov/pubmed/27329648
http://dx.doi.org/10.1007/s11336-016-9503-3
_version_ 1782447218317328384
author Matechou, Eleni
Liu, Ivy
Fernández, Daniel
Farias, Miguel
Gjelsvik, Bergljot
author_facet Matechou, Eleni
Liu, Ivy
Fernández, Daniel
Farias, Miguel
Gjelsvik, Bergljot
author_sort Matechou, Eleni
collection PubMed
description The work in this paper introduces finite mixture models that can be used to simultaneously cluster the rows and columns of two-mode ordinal categorical response data, such as those resulting from Likert scale responses. We use the popular proportional odds parameterisation and propose models which provide insights into major patterns in the data. Model-fitting is performed using the EM algorithm, and a fuzzy allocation of rows and columns to corresponding clusters is obtained. The clustering ability of the models is evaluated in a simulation study and demonstrated using two real data sets. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11336-016-9503-3) contains supplementary material, which is available to authorized users.
format Online
Article
Text
id pubmed-4978779
institution National Center for Biotechnology Information
language English
publishDate 2016
publisher Springer US
record_format MEDLINE/PubMed
spelling pubmed-49787792016-08-19 Biclustering Models for Two-Mode Ordinal Data Matechou, Eleni Liu, Ivy Fernández, Daniel Farias, Miguel Gjelsvik, Bergljot Psychometrika Article The work in this paper introduces finite mixture models that can be used to simultaneously cluster the rows and columns of two-mode ordinal categorical response data, such as those resulting from Likert scale responses. We use the popular proportional odds parameterisation and propose models which provide insights into major patterns in the data. Model-fitting is performed using the EM algorithm, and a fuzzy allocation of rows and columns to corresponding clusters is obtained. The clustering ability of the models is evaluated in a simulation study and demonstrated using two real data sets. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11336-016-9503-3) contains supplementary material, which is available to authorized users. Springer US 2016-06-21 2016 /pmc/articles/PMC4978779/ /pubmed/27329648 http://dx.doi.org/10.1007/s11336-016-9503-3 Text en © The Author(s) 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Article
Matechou, Eleni
Liu, Ivy
Fernández, Daniel
Farias, Miguel
Gjelsvik, Bergljot
Biclustering Models for Two-Mode Ordinal Data
title Biclustering Models for Two-Mode Ordinal Data
title_full Biclustering Models for Two-Mode Ordinal Data
title_fullStr Biclustering Models for Two-Mode Ordinal Data
title_full_unstemmed Biclustering Models for Two-Mode Ordinal Data
title_short Biclustering Models for Two-Mode Ordinal Data
title_sort biclustering models for two-mode ordinal data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4978779/
https://www.ncbi.nlm.nih.gov/pubmed/27329648
http://dx.doi.org/10.1007/s11336-016-9503-3
work_keys_str_mv AT matechoueleni biclusteringmodelsfortwomodeordinaldata
AT liuivy biclusteringmodelsfortwomodeordinaldata
AT fernandezdaniel biclusteringmodelsfortwomodeordinaldata
AT fariasmiguel biclusteringmodelsfortwomodeordinaldata
AT gjelsvikbergljot biclusteringmodelsfortwomodeordinaldata

Ejemplares similares