Clustering Ordinal Data via Latent Variable Models

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dc.contributor.authorMcParland, Damien
dc.contributor.authorGormley, Isobel Claire, Springer International Publishing Switzerland
dc.descriptionIFCS 2011 Symposium of the International Federation of Classification Societies (IFCS), August 30, 2011, Frankfurten
dc.description.abstractItem response modelling is a well established method for analysing ordinal response data. Ordinal data are typically collected as responses to a number of questions or items. The observed data can be viewed as discrete versions of an underlying latent Gaussian variable. Item response models assume that this latent variable (and therefore the observed ordinal response) is a function of both respondent specific and item specific parameters. However, item response models assume a homogeneous population in that the item specific parameters are assumed to be the same for all respondents. Often a population is heterogeneous and clusters of respondents exist; members of different clusters may view the items differently. A mixture of item response models is developed to provide clustering capabilities in the context of ordinal response data. The model is estimated within the Bayesian paradigm and is illustrated through an application to an ordinal response data set resulting from a clinical trial involving self-assessment of arthritis.en
dc.relation.ispartofBerthold Lausen, Dirk Van den Poel, Alfred Ultsch (eds.). Algorithms from and for Nature and Life : Classification and Data Analysis
dc.rightsThe final publication is available at
dc.subjectStatistics and Computing/Statistics Programs
dc.subjectComputer Applications in Social and Behavioral Sciences
dc.titleClustering Ordinal Data via Latent Variable Modelsen
dc.typeConference Publicationen
dc.internal.availabilityFull text availableen
dc.statusPeer revieweden
dc.neeo.contributorGormley|Isobel Claire|aut|-
dc.description.othersponsorshipScience Foundation Irelanden
dc.description.adminAuthor's name is listed as "ISOBEL Claire Gormley" on the actual paper. ASen
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