View in EDS

The Analysis of Multivariate Group Differences Using Common Principal Components.

Saved in:
Bibliographic Details
Title: The Analysis of Multivariate Group Differences Using Common Principal Components.
Authors: Bechger, Timo M.1, Blanca, María J.2, Maris, Gunter3
Source: Structural Equation Modeling. Oct2014, Vol. 21 Issue 4, p577-587. 11p.
Subjects: Multivariate analysis, Principal components analysis, Demographic characteristics, Structural equation modeling, Statistics methodology
Abstract: Although it is simple to determine whether multivariate group differences are statistically significant or not, such differences are often difficult to interpret. This article is about common principal components analysis as a tool for the exploratory investigation of multivariate group differences and its relation to traditional statistical methods and structural equation modeling. Real data are used for illustration. [ABSTRACT FROM AUTHOR]
Copyright of Structural Equation Modeling is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 98645434
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: The Analysis of Multivariate Group Differences Using Common Principal Components.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Bechger%2C+Timo+M%2E%22">Bechger, Timo M.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Blanca%2C+María+J%2E%22">Blanca, María J.</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Maris%2C+Gunter%22">Maris, Gunter</searchLink><relatesTo>3</relatesTo>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Structural+Equation+Modeling%22">Structural Equation Modeling</searchLink>. Oct2014, Vol. 21 Issue 4, p577-587. 11p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Multivariate+analysis%22">Multivariate analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Principal+components+analysis%22">Principal components analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Demographic+characteristics%22">Demographic characteristics</searchLink><br /><searchLink fieldCode="DE" term="%22Structural+equation+modeling%22">Structural equation modeling</searchLink><br /><searchLink fieldCode="DE" term="%22Statistics+methodology%22">Statistics methodology</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Although it is simple to determine whether multivariate group differences are statistically significant or not, such differences are often difficult to interpret. This article is about common principal components analysis as a tool for the exploratory investigation of multivariate group differences and its relation to traditional statistical methods and structural equation modeling. Real data are used for illustration. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Structural Equation Modeling is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=98645434
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1080/10705511.2014.919827
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 11
        StartPage: 577
    Subjects:
      – SubjectFull: Multivariate analysis
        Type: general
      – SubjectFull: Principal components analysis
        Type: general
      – SubjectFull: Demographic characteristics
        Type: general
      – SubjectFull: Structural equation modeling
        Type: general
      – SubjectFull: Statistics methodology
        Type: general
    Titles:
      – TitleFull: The Analysis of Multivariate Group Differences Using Common Principal Components.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Bechger, Timo M.
      – PersonEntity:
          Name:
            NameFull: Blanca, María J.
      – PersonEntity:
          Name:
            NameFull: Maris, Gunter
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 10
              Text: Oct2014
              Type: published
              Y: 2014
          Identifiers:
            – Type: issn-print
              Value: 10705511
          Numbering:
            – Type: volume
              Value: 21
            – Type: issue
              Value: 4
          Titles:
            – TitleFull: Structural Equation Modeling
              Type: main
ResultId 1