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Bilevel factor analysis models

Thesis (PhD (Applied Statistics))--University of Pretoria, 2007.

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Other Authors: Du Toit, S.H.C.
Format: Thesis
Published: University of Pretoria 2013
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access_status_str Open Access
author2 Du Toit, S.H.C.
author_browse Du Toit, S.H.C.
author_facet Du Toit, S.H.C.
collection Thesis
dc_rights_str_mv © 2000, University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria.
description Thesis (PhD (Applied Statistics))--University of Pretoria, 2007.
format Thesis
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institution University of Pretoria (South Africa)
last_indexed 2026-06-10T12:36:21.928Z
license_str Other — see source repository
provenance_str_mv Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository
publishDate 2013
publishDateRange 2013
publishDateSort 2013
publisher University of Pretoria
publisherStr University of Pretoria
record_format dspace
source_str UPSpace — University of Pretoria Institutional Repository
spelling oai:repository.up.ac.za:2263/30460 Bilevel factor analysis models Du Toit, S.H.C. upetd@up.ac.za Pietersen, Jacobus Johannes Stochastic programming Mathematical optimization UCTD Thesis (PhD (Applied Statistics))--University of Pretoria, 2007. The theory of ordinary factor analysis and its application by means of software packages do not make provision for data sampled from populations with hierarchical structures. Since data are often obtained from such populations - educational data for example ¬the lack of procedures to analyse data of this kind needs to be addressed. A review of the ordinary factor analysis model and maximum likelihood estimation of the parameters in exploratory and confirmatory models is provided, together with practical applications. Subsequently, the concept of hierarchically structured populations and their models, called multilevel models, are introduced. A general framework for the estimation of the unknown parameters in these models is presented. It contains two estimation procedures. The first is the marginal maximum likelihood method in which an iterative expected maximisation approach is used to obtain the maximum likelihood estimates. The second is the Fisher scoring method which also provides estimated standard errors for the maximum likelihood parameter estimates. For both methods, the theory is presented for unconstrained as well as for constrained estimation. A two-stage procedure - combining the mentioned procedures - is proposed for parameter estimation in practice. Multilevel factor analysis models are introduced next, and subsequently a particular two-level factor analysis model is presented. The general estimation theory that was presented earlier is applied to this model - in exploratory and confirmatory analysis. First, the marginal maximum likelihood method is used to obtain the equations for determining the parameter estimates. It is then shown how an iterative expected max¬imisation algorithm is used to obtain these estimates in unconstrained and constrained optimisation. This method is applied to real life data using a FORTRAN program. Secondly, equations are derived by means of the Fisher scoring method to obtain the maximum likelihood estimates of the parameters in the two-level factor analysis model for exploratory and confirmatory analysis. A FORTRAN program was written to apply this method in practice. Real life data are used to illustrate the method. Finally, flowing from this research, some areas for possible further research are pro¬posed. Statistics unrestricted 2013-09-07T19:08:50Z 2007-12-20 2013-09-07T19:08:50Z 2000-09-01 2007-12-20 2007-12-20 Thesis Pietersen, JJ 2000, Bilevel factor analysis models, PhD thesis, University of Pretoria, Pretoria, viewed yymmdd < http://hdl.handle.net/2263/30460 > H732/ag http://hdl.handle.net/2263/30460 http://upetd.up.ac.za/thesis/available/etd-12202007-124957/ © 2000, University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria
spellingShingle Stochastic programming
Mathematical optimization
UCTD
Bilevel factor analysis models
title Bilevel factor analysis models
title_full Bilevel factor analysis models
title_fullStr Bilevel factor analysis models
title_full_unstemmed Bilevel factor analysis models
title_short Bilevel factor analysis models
title_sort bilevel factor analysis models
topic Stochastic programming
Mathematical optimization
UCTD
url http://hdl.handle.net/2263/30460
http://upetd.up.ac.za/thesis/available/etd-12202007-124957/