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dc.contributor.authorFielding, A.
dc.contributor.authorSpencer, Neil
dc.date.accessioned2008-01-25T10:21:35Z
dc.date.available2008-01-25T10:21:35Z
dc.date.issued2004
dc.identifier.citationFielding , A & Spencer , N 2004 ' Estimation and Comparison of Endogenous Ordered Category Multilevel Models ' Business School Working Papers , vol. UHBS 2004:4 , University of Hertfordshire .
dc.identifier.otherPURE: 83880
dc.identifier.otherPURE UUID: 8b805b2c-0f62-4a03-9f73-f607db4b3155
dc.identifier.otherdspace: 2299/1407
dc.identifier.urihttp://hdl.handle.net/2299/1407
dc.description.abstractData often take the form of ordered categories. For instance, in education, test results are often reported as grades. Where a hierarchical structure exists for--the data, multilevel modelling of such ordered categorisations can be carried out using macros in MLwiN. The ordered categorisation can be seen as the--realisation of an unknown underlying latent variable. A link function is used to relate the two and this determines the scale of the latent variable. This causes a difficulty because whatever model is fitted to the data, the latent variable is rescaled to have the same variance, meaning that developments in the--parameter estimates for different models cannot be followed. A heuristic way of overcoming this difficulty has been used by Fielding (2003), using Conditional--Mean Scoring (CMS), for models where regressors are not related to the random part of the multilevel model (the exogenous case). In this paper the--endogenous case is examined. The use of an instrumental variable approach to overcoming the estimation problems associated with endogenous variables together with the CMS method is shown to be successful in producing a method that allows successive models to be compared. Simulated data and a practical application are used.en
dc.language.isoeng
dc.publisherUniversity of Hertfordshire
dc.relation.ispartofseriesBusiness School Working Papers
dc.rightsOpen
dc.titleEstimation and Comparison of Endogenous Ordered Category Multilevel Modelsen
dc.contributor.institutionDepartment of Marketing and Enterprise
dc.contributor.institutionSocial Sciences, Arts & Humanities Research Institute
dc.contributor.institutionHertfordshire Business School
dc.contributor.institutionStatistical Services Consulting Unit
dc.contributor.institutionHealth Services and Medicine
dc.contributor.institutionHealthcare Management and Policy Research Unit
dc.relation.schoolHertfordshire Business School
dcterms.dateAccepted2004
rioxxterms.typeWorking paper
herts.preservation.rarelyaccessedtrue
herts.rights.accesstypeOpen


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