Essec\Faculty\Model\Contribution {#2190`
#_index: "academ_contributions"
#_id: "10080"
#_source: array:26 [``
"id" => "10080"
"slug" => "pls-path-modeling"
"yearMonth" => "2005-01"
"year" => "2005"
"title" => "PLS Path Modeling"
"description" => "TENENHAUS, M., ESPOSITO VINZI, V., CHATELIN, Y.M. et LAURO, C. (2005). PLS Path Modeling. <i>Computational Statistics and Data Analysis</i>, 48(1), pp. 159-205."
"authors" => array:4 [``
0 => array:3 [``
"name" => "ESPOSITO VINZI Vincenzo"
"bid" => "B00067049"
"slug" => "esposito-vinzi-vincenzo"
`]
1 => array:1 [`
"name" => "TENENHAUS Michel"
`]
2 => array:1 [`
"name" => "CHATELIN Yves-Marie"
`]
3 => array:1 [`
"name" => "LAURO Carlo"
`]
]
"ouvrage" => ""
"keywords" => array:3 [`
0 => "Structural Equation Modeling"
1 => "Partial least squaresPLS approach"
2 => "Multiple table analysis"
`]
"updatedAt" => "2021-07-13 14:31:26"
"publicationUrl" => null
"publicationInfo" => array:3 [`
"pages" => "159-205"
"volume" => "48"
"number" => "1"
`]
"type" => array:2 [`
"fr" => "Articles"
"en" => "Journal articles"
`]
"support_type" => array:2 [`
"fr" => "Revue scientifique"
"en" => "Scientific journal"
`]
"countries" => array:2 [`
"fr" => null
"en" => null
`]
"abstract" => array:2 [`
"fr" => """
A presentation of the Partial Least Squares approach to Structural Equation Modeling (or PLS\n
Path Modeling) is given together with a discussion of its extensions. This approach is compared\n
with the estimation of Structural Equation Modeling by means of maximum likelihood (SEM-ML).\n
Notwithstanding, this approach still shows some weaknesses. In this respect, some new\n
improvements are proposed. Furthermore, PLS path modeling can be used for analyzing multiple\n
tables so as to be related to more classical data analysis methods used in this field. Finally, a\n
complete treatment of a real example is shown through the available software.
"""
"en" => """
A presentation of the Partial Least Squares approach to Structural Equation Modeling (or PLS\n
Path Modeling) is given together with a discussion of its extensions. This approach is compared\n
with the estimation of Structural Equation Modeling by means of maximum likelihood (SEM-ML).\n
Notwithstanding, this approach still shows some weaknesses. In this respect, some new\n
improvements are proposed. Furthermore, PLS path modeling can be used for analyzing multiple\n
tables so as to be related to more classical data analysis methods used in this field. Finally, a\n
complete treatment of a real example is shown through the available software.
"""
`]
"authors_fields" => array:2 [`
"fr" => "Systèmes d’Information, Sciences de la Décision et Statistiques"
"en" => "Information Systems, Decision Sciences and Statistics"
`]
"indexedAt" => "2024-04-14T17:21:50.000Z"
"docTitle" => "PLS Path Modeling"
"docSurtitle" => "Journal articles"
"authorNames" => "<a href="/cv/esposito-vinzi-vincenzo">ESPOSITO VINZI Vincenzo</a>, TENENHAUS Michel, CHATELIN Yves-Marie, LAURO Carlo"
"docDescription" => "<span class="document-property-authors">ESPOSITO VINZI Vincenzo, TENENHAUS Michel, CHATELIN Yves-Marie, LAURO Carlo</span><br><span class="document-property-authors_fields">Information Systems, Decision Sciences and Statistics</span> | <span class="document-property-year">2005</span>"
"keywordList" => "<a href="#">Structural Equation Modeling</a>, <a href="#">Partial least squaresPLS approach</a>, <a href="#">Multiple table analysis</a>"
"docPreview" => "<b>PLS Path Modeling</b><br><span>2005-01 | Journal articles </span>"
"docType" => "research"
"publicationLink" => "<a href="#" target="_blank">PLS Path Modeling</a>"
]
+lang: "en"
+"_type": "_doc"
+"_score": 9.119248
+"parent": null
}