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Article: Factor analysis for ranked data with application to a job selection attitude survey

TitleFactor analysis for ranked data with application to a job selection attitude survey
Authors
KeywordsFactor analysis
Factor score
Gibbs sampler
Monte Carlo expectation- maximization algorithm
Ranked data
Issue Date2005
PublisherWiley-Blackwell Publishing Ltd. The Journal's web site is located at http://www.blackwellpublishing.com/journals/RSSA
Citation
Journal Of The Royal Statistical Society. Series A: Statistics In Society, 2005, v. 168 n. 3, p. 583-597 How to Cite?
AbstractFactor analysis is a powerful tool to identify the common characteristics among a set of variables that are measured on a continuous scale. In the context of factor analysis for non-continuous-type data, most applications are restricted to item response data only. We extend the factor model to accommodate ranked data. The Monte Carlo expectation-maximization algorithm is used for parameter estimation at which the E-step is implemented via the Gibbs sampler. An analysis based on both complete and incomplete ranked data (e.g. rank the top q out of k items) is considered. Estimation of the factor scores is also discussed. The method proposed is applied to analyse a set of incomplete ranked data that were obtained from a survey that was carried out in GuangZhou, a major city in mainland China, to investigate the factors affecting people's attitude towards choosing jobs. © 2005 Royal Statistical Society.
Persistent Identifierhttp://hdl.handle.net/10722/82696
ISSN
2015 Impact Factor: 1.702
2015 SCImago Journal Rankings: 0.987
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorYu, PLHen_HK
dc.contributor.authorLam, KFen_HK
dc.contributor.authorLo, SMen_HK
dc.date.accessioned2010-09-06T08:32:21Z-
dc.date.available2010-09-06T08:32:21Z-
dc.date.issued2005en_HK
dc.identifier.citationJournal Of The Royal Statistical Society. Series A: Statistics In Society, 2005, v. 168 n. 3, p. 583-597en_HK
dc.identifier.issn0964-1998en_HK
dc.identifier.urihttp://hdl.handle.net/10722/82696-
dc.description.abstractFactor analysis is a powerful tool to identify the common characteristics among a set of variables that are measured on a continuous scale. In the context of factor analysis for non-continuous-type data, most applications are restricted to item response data only. We extend the factor model to accommodate ranked data. The Monte Carlo expectation-maximization algorithm is used for parameter estimation at which the E-step is implemented via the Gibbs sampler. An analysis based on both complete and incomplete ranked data (e.g. rank the top q out of k items) is considered. Estimation of the factor scores is also discussed. The method proposed is applied to analyse a set of incomplete ranked data that were obtained from a survey that was carried out in GuangZhou, a major city in mainland China, to investigate the factors affecting people's attitude towards choosing jobs. © 2005 Royal Statistical Society.en_HK
dc.languageengen_HK
dc.publisherWiley-Blackwell Publishing Ltd. The Journal's web site is located at http://www.blackwellpublishing.com/journals/RSSAen_HK
dc.relation.ispartofJournal of the Royal Statistical Society. Series A: Statistics in Societyen_HK
dc.subjectFactor analysisen_HK
dc.subjectFactor scoreen_HK
dc.subjectGibbs sampleren_HK
dc.subjectMonte Carlo expectation- maximization algorithmen_HK
dc.subjectRanked dataen_HK
dc.titleFactor analysis for ranked data with application to a job selection attitude surveyen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0964-1998&volume=168&issue=3&spage=583&epage=597&date=2005&atitle=Factor+analysis+for+ranked+data+with+application+to+a+job+selection+attitude+surveyen_HK
dc.identifier.emailYu, PLH: plhyu@hkucc.hku.hken_HK
dc.identifier.emailLam, KF: hrntlkf@hkucc.hku.hken_HK
dc.identifier.authorityYu, PLH=rp00835en_HK
dc.identifier.authorityLam, KF=rp00718en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1111/j.1467-985X.2005.00363.xen_HK
dc.identifier.scopuseid_2-s2.0-21244443877en_HK
dc.identifier.hkuros104401en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-21244443877&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume168en_HK
dc.identifier.issue3en_HK
dc.identifier.spage583en_HK
dc.identifier.epage597en_HK
dc.identifier.isiWOS:000230307700006-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridYu, PLH=7403599794en_HK
dc.identifier.scopusauthoridLam, KF=8948421200en_HK
dc.identifier.scopusauthoridLo, SM=36828557600en_HK
dc.identifier.citeulike216582-

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