Conjoint Use of Variables Clustering and PLS Structural Equations Modeling
In PLS approach, it is frequently assumed that the blocks of variables satisfy the assumption of unidimensionality. In order to fulfill at best this hypothesis, we use clustering methods of variables. We illustrate the conjoint use of variables clustering and PLS structural equations modeling on dat...
Gespeichert in:
Hauptverfasser: | , |
---|---|
Format: | Buchkapitel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 246 |
---|---|
container_issue | |
container_start_page | 235 |
container_title | |
container_volume | |
creator | Stan, Valentina Saporta, Gilbert |
description | In PLS approach, it is frequently assumed that the blocks of variables satisfy the assumption of unidimensionality. In order to fulfill at best this hypothesis, we use clustering methods of variables. We illustrate the conjoint use of variables clustering and PLS structural equations modeling on data provided by PSA Company (Peugeot Citroën) on customers’ satisfaction. The data are satisfaction scores on 32 manifest variables given by 2,922 customers. |
doi_str_mv | 10.1007/978-3-540-32827-8_11 |
format | Book Chapter |
fullrecord | <record><control><sourceid>hal_sprin</sourceid><recordid>TN_cdi_hal_primary_oai_HAL_hal_01125749v1</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>oai_HAL_hal_01125749v1</sourcerecordid><originalsourceid>FETCH-LOGICAL-c1591-be53e782599e12a659573c07a51eae053b56e9760039cf3cf30127cd3a24b9e13</originalsourceid><addsrcrecordid>eNo1kMFOwzAMhoMQEjD6Bhxy5RBI4qZpjlM1GKgIpDGuUdpm0FGakbRIvD3pBpYly79-W_aH0CWj14xSeaNkToCIlBLgOZck14wdoSTKEMW9lh-j8_9GpKcoCWFLY6Qg0yw_Qw-F67eu7Qe8Dha7DX41vjVVZwMuujEM1rf9GzZ9g5_LFV4NfqyH0ZsOL75GM7SuD_jRNbaLrgt0sjFdsMlfnaH17eKlWJLy6e6-mJekZkIxUlkBVsZrlLKMm0woIaGm0ghmjaUCKpFZJTNKQdUbiEkZl3UDhqdVHIEZujrsfTed3vn20_gf7Uyrl_NSTxpljAuZqu_Jyw_esJsesV5Xzn0EzaieCOqISoOOfPSelp4Iwi9SU2FQ</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>book_chapter</recordtype></control><display><type>book_chapter</type><title>Conjoint Use of Variables Clustering and PLS Structural Equations Modeling</title><source>Springer Books</source><creator>Stan, Valentina ; Saporta, Gilbert</creator><contributor>Esposito Vinzi, Vincenzo ; Chin, Wynne W. ; Henseler, Jörg ; Wang, Huiwen</contributor><creatorcontrib>Stan, Valentina ; Saporta, Gilbert ; Esposito Vinzi, Vincenzo ; Chin, Wynne W. ; Henseler, Jörg ; Wang, Huiwen</creatorcontrib><description>In PLS approach, it is frequently assumed that the blocks of variables satisfy the assumption of unidimensionality. In order to fulfill at best this hypothesis, we use clustering methods of variables. We illustrate the conjoint use of variables clustering and PLS structural equations modeling on data provided by PSA Company (Peugeot Citroën) on customers’ satisfaction. The data are satisfaction scores on 32 manifest variables given by 2,922 customers.</description><identifier>ISBN: 3540328254</identifier><identifier>ISBN: 9783540328254</identifier><identifier>EISBN: 9783540328278</identifier><identifier>EISBN: 3540328270</identifier><identifier>DOI: 10.1007/978-3-540-32827-8_11</identifier><language>eng</language><publisher>Berlin, Heidelberg: Springer Berlin Heidelberg</publisher><subject>Complete Linkage ; General Satisfaction ; Latent Variable ; Manifest Variable ; Mathematics ; Statistics ; Variable Cluster</subject><ispartof>Handbook of Partial Least Squares, 2010, p.235-246</ispartof><rights>Springer-Verlag Berlin Heidelberg 2010</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c1591-be53e782599e12a659573c07a51eae053b56e9760039cf3cf30127cd3a24b9e13</citedby><orcidid>0000-0002-3406-5887</orcidid><relation>Springer Handbooks of Computational Statistics</relation></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/978-3-540-32827-8_11$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/978-3-540-32827-8_11$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>230,779,780,784,793,885,27925,38255,41442,42511</link.rule.ids><backlink>$$Uhttps://hal.science/hal-01125749$$DView record in HAL$$Hfree_for_read</backlink></links><search><contributor>Esposito Vinzi, Vincenzo</contributor><contributor>Chin, Wynne W.</contributor><contributor>Henseler, Jörg</contributor><contributor>Wang, Huiwen</contributor><creatorcontrib>Stan, Valentina</creatorcontrib><creatorcontrib>Saporta, Gilbert</creatorcontrib><title>Conjoint Use of Variables Clustering and PLS Structural Equations Modeling</title><title>Handbook of Partial Least Squares</title><description>In PLS approach, it is frequently assumed that the blocks of variables satisfy the assumption of unidimensionality. In order to fulfill at best this hypothesis, we use clustering methods of variables. We illustrate the conjoint use of variables clustering and PLS structural equations modeling on data provided by PSA Company (Peugeot Citroën) on customers’ satisfaction. The data are satisfaction scores on 32 manifest variables given by 2,922 customers.</description><subject>Complete Linkage</subject><subject>General Satisfaction</subject><subject>Latent Variable</subject><subject>Manifest Variable</subject><subject>Mathematics</subject><subject>Statistics</subject><subject>Variable Cluster</subject><isbn>3540328254</isbn><isbn>9783540328254</isbn><isbn>9783540328278</isbn><isbn>3540328270</isbn><fulltext>true</fulltext><rsrctype>book_chapter</rsrctype><creationdate>2010</creationdate><recordtype>book_chapter</recordtype><recordid>eNo1kMFOwzAMhoMQEjD6Bhxy5RBI4qZpjlM1GKgIpDGuUdpm0FGakbRIvD3pBpYly79-W_aH0CWj14xSeaNkToCIlBLgOZck14wdoSTKEMW9lh-j8_9GpKcoCWFLY6Qg0yw_Qw-F67eu7Qe8Dha7DX41vjVVZwMuujEM1rf9GzZ9g5_LFV4NfqyH0ZsOL75GM7SuD_jRNbaLrgt0sjFdsMlfnaH17eKlWJLy6e6-mJekZkIxUlkBVsZrlLKMm0woIaGm0ghmjaUCKpFZJTNKQdUbiEkZl3UDhqdVHIEZujrsfTed3vn20_gf7Uyrl_NSTxpljAuZqu_Jyw_esJsesV5Xzn0EzaieCOqISoOOfPSelp4Iwi9SU2FQ</recordid><startdate>2010</startdate><enddate>2010</enddate><creator>Stan, Valentina</creator><creator>Saporta, Gilbert</creator><general>Springer Berlin Heidelberg</general><scope>1XC</scope><orcidid>https://orcid.org/0000-0002-3406-5887</orcidid></search><sort><creationdate>2010</creationdate><title>Conjoint Use of Variables Clustering and PLS Structural Equations Modeling</title><author>Stan, Valentina ; Saporta, Gilbert</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c1591-be53e782599e12a659573c07a51eae053b56e9760039cf3cf30127cd3a24b9e13</frbrgroupid><rsrctype>book_chapters</rsrctype><prefilter>book_chapters</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Complete Linkage</topic><topic>General Satisfaction</topic><topic>Latent Variable</topic><topic>Manifest Variable</topic><topic>Mathematics</topic><topic>Statistics</topic><topic>Variable Cluster</topic><toplevel>online_resources</toplevel><creatorcontrib>Stan, Valentina</creatorcontrib><creatorcontrib>Saporta, Gilbert</creatorcontrib><collection>Hyper Article en Ligne (HAL)</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Stan, Valentina</au><au>Saporta, Gilbert</au><au>Esposito Vinzi, Vincenzo</au><au>Chin, Wynne W.</au><au>Henseler, Jörg</au><au>Wang, Huiwen</au><format>book</format><genre>bookitem</genre><ristype>CHAP</ristype><atitle>Conjoint Use of Variables Clustering and PLS Structural Equations Modeling</atitle><btitle>Handbook of Partial Least Squares</btitle><seriestitle>Springer Handbooks of Computational Statistics</seriestitle><date>2010</date><risdate>2010</risdate><spage>235</spage><epage>246</epage><pages>235-246</pages><isbn>3540328254</isbn><isbn>9783540328254</isbn><eisbn>9783540328278</eisbn><eisbn>3540328270</eisbn><abstract>In PLS approach, it is frequently assumed that the blocks of variables satisfy the assumption of unidimensionality. In order to fulfill at best this hypothesis, we use clustering methods of variables. We illustrate the conjoint use of variables clustering and PLS structural equations modeling on data provided by PSA Company (Peugeot Citroën) on customers’ satisfaction. The data are satisfaction scores on 32 manifest variables given by 2,922 customers.</abstract><cop>Berlin, Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/978-3-540-32827-8_11</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0002-3406-5887</orcidid></addata></record> |
fulltext | fulltext |
identifier | ISBN: 3540328254 |
ispartof | Handbook of Partial Least Squares, 2010, p.235-246 |
issn | |
language | eng |
recordid | cdi_hal_primary_oai_HAL_hal_01125749v1 |
source | Springer Books |
subjects | Complete Linkage General Satisfaction Latent Variable Manifest Variable Mathematics Statistics Variable Cluster |
title | Conjoint Use of Variables Clustering and PLS Structural Equations Modeling |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-02T05%3A27%3A40IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-hal_sprin&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=bookitem&rft.atitle=Conjoint%20Use%20of%20Variables%20Clustering%20and%20PLS%20Structural%20Equations%20Modeling&rft.btitle=Handbook%20of%20Partial%20Least%20Squares&rft.au=Stan,%20Valentina&rft.date=2010&rft.spage=235&rft.epage=246&rft.pages=235-246&rft.isbn=3540328254&rft.isbn_list=9783540328254&rft_id=info:doi/10.1007/978-3-540-32827-8_11&rft_dat=%3Chal_sprin%3Eoai_HAL_hal_01125749v1%3C/hal_sprin%3E%3Curl%3E%3C/url%3E&rft.eisbn=9783540328278&rft.eisbn_list=3540328270&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |