Finite mixture and genetic algorithm segmentation in partial least aquares path modeling: Identification of multiple segments in complex path models

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dc.contributor.author Ringle, C
dc.contributor.author Sarstedt, M
dc.contributor.author Schlittgen, R
dc.contributor.editor Fink, A
dc.contributor.editor Lausen, B
dc.contributor.editor Seidel, W
dc.contributor.editor Ultsch, A
dc.date.accessioned 2012-02-02T02:02:44Z
dc.date.issued 2010-01
dc.identifier.citation Advances in Data Analysis, Data Handling and Business Intelligence, 2010, 1, pp. 167 - 176
dc.identifier.other B1 en_US
dc.identifier.uri http://hdl.handle.net/10453/14247
dc.description.abstract When applying structural equation modeling methods, such as partial least squares (PLS) path modeling, in empirical studies, the assumption that the data have been collected from a single homogeneous population is often unrealistic. Unobserved heterogeneity in the PLS estimates on the aggregate data level may result in misleading interpretations. Finite mixture partial least squares (FIMIX-PLS) and PLS genetic algorithm segmentation (PLS-GAS) allow the classification of data in variance-based structural equation modeling. This research presents an initial application and comparison of these two methods in a computational experiment in respect of a path model which includes multiple endogenous latent variables. The results of this analysis reveal particular advantages and disadvantages of the approaches. This study further substantiates the effectiveness of FIMIX-PLS and PLS-GAS and provides researchers and practitioners with additional information they need to proficiently evaluate their PLS path modeling results by applying a systematic means of analysis. If significant heterogeneity were to be uncovered by the procedures, the analysis may result in group-specific path modeling outcomes, thus allowing further differentiated and more precise conclusions to be formed.
dc.publisher Springer
dc.relation.isbasedon 10.1007/978-3-642-01044-6_15
dc.title Finite mixture and genetic algorithm segmentation in partial least aquares path modeling: Identification of multiple segments in complex path models
dc.type Chapter
dc.parent Advances in Data Analysis, Data Handling and Business Intelligence
dc.journal.number en_US
dc.publocation Berlin, Germany en_US
dc.identifier.startpage 167 en_US
dc.identifier.endpage 176 en_US
dc.cauo.name BUS.School of Marketing en_US
dc.conference Verified OK en_US
dc.for 0104 Statistics
dc.personcode 104474
dc.percentage 100 en_US
dc.classification.name Statistics en_US
dc.classification.type FOR-08 en_US
dc.edition 1 en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity en_US
dc.description.keywords Finite mixture - Genetic algorithm - Heterogeneity - PLS path modeling - Segmentation en_US
pubs.embargo.period Not known
pubs.organisational-group /University of Technology Sydney
pubs.organisational-group /University of Technology Sydney/Faculty of Business
pubs.organisational-group /University of Technology Sydney/Faculty of Business/School of Marketing
utslib.copyright.status Closed Access
utslib.copyright.date 2015-04-15 12:17:09.805752+10
utslib.collection.history School of Marketing (ID: 330)
utslib.collection.history Closed (ID: 3)


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