Clustering South African households based on their asset status using latent variable models

Files in This Item:
File Description SizeFormat 
McParlandEtAl.pdf6 MBAdobe PDFDownload
Title: Clustering South African households based on their asset status using latent variable models
Authors: McParland, Damien
Gormley, Isobel Claire
McCormick, Tyler H.
et al.
Permanent link: http://hdl.handle.net/10197/7094
Date: Jun-2014
Abstract: The Agincourt Health and Demographic Surveillance System has since 2001 conducted a biannual household asset survey in order to quantify household socio-economic status (SES) in a rural population living in northeast South Africa. The survey contains binary, ordinal and nominal items. In the absence of income or expenditure data, the SES landscape in the study population is explored and described by clustering the households into homogeneous groups based on their asset status. A model-based approach to clustering the Agincourt households, based on latent variable models, is proposed. In the case of modeling binary or ordinal items, item response theory models are employed. For nominal survey items, a factor analysis model, similar in nature to a multinomial probit model, is used. Both model types have an underlying latent variable structure—this similarity is exploited and the models are combined to produce a hybrid model capable of handling mixed data types. Further, a mixture of the hybrid models is considered to provide clustering capabilities within the context of mixed binary, ordinal and nominal response data. The proposed model is termed a mixture of factor analyzers for mixed data (MFA-MD). The MFA-MD model is applied to the survey data to cluster the Agincourt households into homogeneous groups. The model is estimated within the Bayesian paradigm, using a Markov chain Monte Carlo algorithm. Intuitive groupings result, providing insight to the different socio-economic strata within the Agincourt region.
Funding Details: Science Foundation Ireland
Type of material: Journal Article
Publisher: Institute of Mathematical Statistics (IMS)
Keywords: Clustering;Mixed data;Item response theory;Metropolis-within-Gibbs
DOI: 10.1214/14-AOAS726
Language: en
Status of Item: Peer reviewed
Appears in Collections:Mathematics and Statistics Research Collection

Show full item record

SCOPUSTM   
Citations 20

9
Last Week
1
Last month
checked on Jun 22, 2018

Download(s) 50

99
checked on May 25, 2018

Google ScholarTM

Check

Altmetric


This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. For other possible restrictions on use please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.