Show simple item record

dc.contributor.advisorWallace, James
dc.contributor.authorMohd Jamil, J.B.*
dc.date.accessioned2013-11-28T18:02:01Z
dc.date.available2013-11-28T18:02:01Z
dc.date.issued2013-11-28
dc.identifier.urihttp://hdl.handle.net/10454/5728
dc.description.abstractDespite considerable advances in missing data imputation methods over the last three decades, the problem of missing data remains largely unsolved. Many techniques have emerged in the literature as candidate solutions. These techniques can be categorised into two classes: statistical methods of data imputation and computational intelligence methods of data imputation. Due to the longstanding use of statistical methods in handling missing data problems, it takes quite some time for computational intelligence methods to gain profound attention even though these methods have analogous accuracy, in comparison to other approaches. The merits of both these classes have been discussed at length in the literature, but only limited studies make significant comparison to these classes. This thesis contributes to knowledge by firstly, conducting a comprehensive comparison of standard statistical methods of data imputation, namely, mean substitution (MS), regression imputation (RI), expectation maximization (EM), tree imputation (TI) and multiple imputation (MI) on missing completely at random (MCAR) data sets. Secondly, this study also compares the efficacy of these methods with a computational intelligence method of data imputation, ii namely, a neural network (NN) on missing not at random (MNAR) data sets. The significance difference in performance of the methods is presented. Thirdly, a novel procedure for handling missing data is presented. A hybrid combination of each of these statistical methods with a NN, known here as the post-processing procedure, was adopted to approximate MNAR data sets. Simulation studies for each of these imputation approaches have been conducted to assess the impact of missing values on partial least squares structural equation modelling (PLS-SEM) based on the estimated accuracy of both structural and measurement parameters. The best method to deal with particular missing data mechanisms is highly recognized. Several significant insights were deduced from the simulation results. It was figured that for the problem of MCAR by using statistical methods of data imputation, MI performs better than the other methods for all percentages of missing data. Another unique contribution is found when comparing the results before and after the NN post-processing procedure. This improvement in accuracy may be resulted from the neural network¿s ability to derive meaning from the imputed data set found by the statistical methods. Based on these results, the NN post-processing procedure is capable to assist MS in producing significant improvement in accuracy of the approximated values. This is a promising result, as MS is the weakest method in this study. This evidence is also informative as MS is often used as the default method available to users of PLS-SEM software.en_US
dc.description.sponsorshipMinister of Higher Education Malaysia and University Utara Malaysiaen_US
dc.language.isoenen_US
dc.rights<a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/"><img alt="Creative Commons License" style="border-width:0" src="http://i.creativecommons.org/l/by-nc-nd/3.0/88x31.png" /></a><br />The University of Bradford theses are licenced under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/">Creative Commons Licence</a>.eng
dc.subjectMissing dataen_US
dc.subjectPartial least squaresen_US
dc.subjectStructural equation modellingen_US
dc.subjectNeural networksen_US
dc.subjectImputation methodsen_US
dc.subjectIncomplete dataen_US
dc.titlePartial least squares structural equation modelling with incomplete data. An investigation of the impact of imputation methods.en_US
dc.type.qualificationleveldoctoralen_US
dc.publisher.institutionUniversity of Bradfordeng
dc.publisher.departmentSchool of Managementen_US
dc.typeThesiseng
dc.type.qualificationnamePhDen_US
dc.date.awarded2012
refterms.dateFOA2018-07-19T12:58:57Z


Item file(s)

Thumbnail
Name:
THESIS PHD JB MOHD JAMIL.pdf
Size:
2.092Mb
Format:
PDF

This item appears in the following Collection(s)

Show simple item record