Clustering Ordinal Data via Latent Variable Models

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Title: Clustering Ordinal Data via Latent Variable Models
Authors: McParland, Damien
Gormley, Isobel Claire
Permanent link: http://hdl.handle.net/10197/4284
Date: Aug-2011
Abstract: Item 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.
Type of material: Conference Publication
Publisher: Springer
Copyright (published version): 2013, Springer International Publishing Switzerland
Keywords: Statistics and Computing/Statistics Programs;Computer Applications in Social and Behavioral Sciences
DOI: 10.1007/978-3-319-00035-0_12
Language: en
Status of Item: Peer reviewed
Is part of: Berthold Lausen, Dirk Van den Poel, Alfred Ultsch (eds.). Algorithms from and for Nature and Life : Classification and Data Analysis
Conference Details: IFCS 2011 Symposium of the International Federation of Classification Societies (IFCS), August 30, 2011, Frankfurt
Appears in Collections:Mathematics and Statistics Research Collection

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