Essec\Faculty\Model\Contribution {#2233
  #_index: "academ_contributions"
  #_id: "6895"
  #_source: array:26 [
    "id" => "6895"
    "slug" => "pls-partial-least-squares-path-modeling-methodological-foundations-and-the-xlstat-plspm-software"
    "yearMonth" => "2007-06"
    "year" => "2007"
    "title" => "PLS (Partial Least Squares) Path Modeling: Methodological Foundations and the XLSTAT-PLSPM Software"
    "description" => "ESPOSITO VINZI, V. (2007). PLS (Partial Least Squares) Path Modeling: Methodological Foundations and the XLSTAT-PLSPM Software."
    "authors" => array:1 [
      0 => array:3 [
        "name" => "ESPOSITO VINZI Vincenzo"
        "bid" => "B00067049"
        "slug" => "esposito-vinzi-vincenzo"
      ]
    ]
    "ouvrage" => ""
    "keywords" => array:1 [
      0 => "Structural Equation Modeling"
    ]
    "updatedAt" => "2021-04-19 17:57:25"
    "publicationUrl" => null
    "publicationInfo" => array:3 [
      "pages" => null
      "volume" => null
      "number" => null
    ]
    "type" => array:2 [
      "fr" => "Communications dans une conférence"
      "en" => "Presentations at an Academic or Professional conference"
    ]
    "support_type" => array:2 [
      "fr" => null
      "en" => null
    ]
    "countries" => array:2 [
      "fr" => null
      "en" => null
    ]
    "abstract" => array:2 [
      "fr" => "PLS (Partial Least Squares) Path Modeling (PLS-PM) is a statistical modeling technique with data analysis features linking several blocks of variables by means of a causality network. This general approach studies a system of linear relationships between latent (non observable) variables. Each latent variable is described by a set of manifest (observable) indicators. The nature of this approach is rather exploratory and data-driven than confirmatory. Therefore, it represents an alternative to the classical maximum likelihood-based approach to structural equation modeling, commonly known as LISREL. The features of PLS-PM methods make them very interesting for applications and developments in several domains of application. This presentation aims at providing the audience with an expository review of the PLS Path Modeling methodology, a presentation and a critical assessment of some of the most recent developments, a guide to applications run by means of the PLSPM module in XLSTAT."
      "en" => "PLS (Partial Least Squares) Path Modeling (PLS-PM) is a statistical modeling technique with data analysis features linking several blocks of variables by means of a causality network. This general approach studies a system of linear relationships between latent (non observable) variables. Each latent variable is described by a set of manifest (observable) indicators. The nature of this approach is rather exploratory and data-driven than confirmatory. Therefore, it represents an alternative to the classical maximum likelihood-based approach to structural equation modeling, commonly known as LISREL. The features of PLS-PM methods make them very interesting for applications and developments in several domains of application. This presentation aims at providing the audience with an expository review of the PLS Path Modeling methodology, a presentation and a critical assessment of some of the most recent developments, a guide to applications run by means of the PLSPM module in XLSTAT."
    ]
    "authors_fields" => array:2 [
      "fr" => "Systèmes d'Information, Data Analytics et Opérations"
      "en" => "Information Systems, Data Analytics and Operations"
    ]
    "indexedAt" => "2024-11-21T12:21:46.000Z"
    "docTitle" => "PLS (Partial Least Squares) Path Modeling: Methodological Foundations and the XLSTAT-PLSPM Software"
    "docSurtitle" => "Communications dans une conférence"
    "authorNames" => "<a href="/cv/esposito-vinzi-vincenzo">ESPOSITO VINZI Vincenzo</a>"
    "docDescription" => "<span class="document-property-authors">ESPOSITO VINZI Vincenzo</span><br><span class="document-property-authors_fields">Systèmes d'Information, Data Analytics et Opérations</span> | <span class="document-property-year">2007</span>"
    "keywordList" => "<a href="#">Structural Equation Modeling</a>"
    "docPreview" => "<b>PLS (Partial Least Squares) Path Modeling: Methodological Foundations and the XLSTAT-PLSPM Software</b><br><span>2007-06 | Communications dans une conférence </span>"
    "docType" => "research"
    "publicationLink" => "<a href="#" target="_blank">PLS (Partial Least Squares) Path Modeling: Methodological Foundations and the XLSTAT-PLSPM Software</a>"
  ]
  +lang: "fr"
  +"_type": "_doc"
  +"_score": 8.816764
  +"parent": null
}