Augmenting the Correlated Trait-Correlated Method Model for Multitrait-Multimethod Data
We introduce an approach for ensuring empirical identification of the correlated trait-correlated method (CT-CM) model under a variety of conditions. A set of models are referred to as augmented correlated trait-correlated method (ACT-CM) models because they are based on systematically augmenting th...
Ausführliche Beschreibung
Autor*in: |
Castro-Schilo, Laura [verfasserIn] |
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Format: |
Artikel |
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Sprache: |
Englisch |
Erschienen: |
2016 |
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Rechteinformationen: |
Nutzungsrecht: Copyright © Taylor & Francis Group, LLC |
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Schlagwörter: |
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Übergeordnetes Werk: |
Enthalten in: Structural equation modeling - Philadelphia, Pa. : Psychology Press, Taylor & Francis Group, 1994, 23(2016), 6, Seite 798 |
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Übergeordnetes Werk: |
volume:23 ; year:2016 ; number:6 ; pages:798 |
Links: |
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DOI / URN: |
10.1080/10705511.2016.1214919 |
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OLC1986057542 |
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520 | |a We introduce an approach for ensuring empirical identification of the correlated trait-correlated method (CT-CM) model under a variety of conditions. A set of models are referred to as augmented correlated trait-correlated method (ACT-CM) models because they are based on systematically augmenting the multitrait-multimethod matrix put forth by Campbell and Fiske (1959). We show results from a Monte Carlo simulation study in which data characteristics lead to an empirically underidentified standard CT-CM model, but a well-identified fully augmented correlated trait-correlated method (FACT-CM) model. This improved identification occurs even for a model in which equality constraints are imposed on loadings on each trait factor and loadings on each method factor-a specific case shown to lead to an empirically underidentified CT-CM model. | ||
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10.1080/10705511.2016.1214919 doi PQ20161201 (DE-627)OLC1986057542 (DE-599)GBVOLC1986057542 (PRQ)i1443-3497908e07841ced838373adb9cdce20af9910de3452e49e1ea1023b423256800 (KEY)0238167220160000023000600798augmentingthecorrelatedtraitcorrelatedmethodmodelf DE-627 ger DE-627 rakwb eng 300 DE-600 31.00 bkl Castro-Schilo, Laura verfasserin aut Augmenting the Correlated Trait-Correlated Method Model for Multitrait-Multimethod Data 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier We introduce an approach for ensuring empirical identification of the correlated trait-correlated method (CT-CM) model under a variety of conditions. A set of models are referred to as augmented correlated trait-correlated method (ACT-CM) models because they are based on systematically augmenting the multitrait-multimethod matrix put forth by Campbell and Fiske (1959). We show results from a Monte Carlo simulation study in which data characteristics lead to an empirically underidentified standard CT-CM model, but a well-identified fully augmented correlated trait-correlated method (FACT-CM) model. This improved identification occurs even for a model in which equality constraints are imposed on loadings on each trait factor and loadings on each method factor-a specific case shown to lead to an empirically underidentified CT-CM model. Nutzungsrecht: Copyright © Taylor & Francis Group, LLC construct validity multitrait-multimethod models correlated trait-correlated method model method variance method effects Monte Carlo simulation Mathematical models Grimm, Kevin J oth Widaman, Keith F oth Enthalten in Structural equation modeling Philadelphia, Pa. : Psychology Press, Taylor & Francis Group, 1994 23(2016), 6, Seite 798 (DE-627)188644075 (DE-600)1285122-X (DE-576)049955675 1070-5511 nnns volume:23 year:2016 number:6 pages:798 http://dx.doi.org/10.1080/10705511.2016.1214919 Volltext http://www.tandfonline.com/doi/abs/10.1080/10705511.2016.1214919 http://search.proquest.com/docview/1829807528 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_4012 31.00 AVZ AR 23 2016 6 798 |
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10.1080/10705511.2016.1214919 doi PQ20161201 (DE-627)OLC1986057542 (DE-599)GBVOLC1986057542 (PRQ)i1443-3497908e07841ced838373adb9cdce20af9910de3452e49e1ea1023b423256800 (KEY)0238167220160000023000600798augmentingthecorrelatedtraitcorrelatedmethodmodelf DE-627 ger DE-627 rakwb eng 300 DE-600 31.00 bkl Castro-Schilo, Laura verfasserin aut Augmenting the Correlated Trait-Correlated Method Model for Multitrait-Multimethod Data 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier We introduce an approach for ensuring empirical identification of the correlated trait-correlated method (CT-CM) model under a variety of conditions. A set of models are referred to as augmented correlated trait-correlated method (ACT-CM) models because they are based on systematically augmenting the multitrait-multimethod matrix put forth by Campbell and Fiske (1959). We show results from a Monte Carlo simulation study in which data characteristics lead to an empirically underidentified standard CT-CM model, but a well-identified fully augmented correlated trait-correlated method (FACT-CM) model. This improved identification occurs even for a model in which equality constraints are imposed on loadings on each trait factor and loadings on each method factor-a specific case shown to lead to an empirically underidentified CT-CM model. Nutzungsrecht: Copyright © Taylor & Francis Group, LLC construct validity multitrait-multimethod models correlated trait-correlated method model method variance method effects Monte Carlo simulation Mathematical models Grimm, Kevin J oth Widaman, Keith F oth Enthalten in Structural equation modeling Philadelphia, Pa. : Psychology Press, Taylor & Francis Group, 1994 23(2016), 6, Seite 798 (DE-627)188644075 (DE-600)1285122-X (DE-576)049955675 1070-5511 nnns volume:23 year:2016 number:6 pages:798 http://dx.doi.org/10.1080/10705511.2016.1214919 Volltext http://www.tandfonline.com/doi/abs/10.1080/10705511.2016.1214919 http://search.proquest.com/docview/1829807528 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_4012 31.00 AVZ AR 23 2016 6 798 |
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10.1080/10705511.2016.1214919 doi PQ20161201 (DE-627)OLC1986057542 (DE-599)GBVOLC1986057542 (PRQ)i1443-3497908e07841ced838373adb9cdce20af9910de3452e49e1ea1023b423256800 (KEY)0238167220160000023000600798augmentingthecorrelatedtraitcorrelatedmethodmodelf DE-627 ger DE-627 rakwb eng 300 DE-600 31.00 bkl Castro-Schilo, Laura verfasserin aut Augmenting the Correlated Trait-Correlated Method Model for Multitrait-Multimethod Data 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier We introduce an approach for ensuring empirical identification of the correlated trait-correlated method (CT-CM) model under a variety of conditions. A set of models are referred to as augmented correlated trait-correlated method (ACT-CM) models because they are based on systematically augmenting the multitrait-multimethod matrix put forth by Campbell and Fiske (1959). We show results from a Monte Carlo simulation study in which data characteristics lead to an empirically underidentified standard CT-CM model, but a well-identified fully augmented correlated trait-correlated method (FACT-CM) model. This improved identification occurs even for a model in which equality constraints are imposed on loadings on each trait factor and loadings on each method factor-a specific case shown to lead to an empirically underidentified CT-CM model. Nutzungsrecht: Copyright © Taylor & Francis Group, LLC construct validity multitrait-multimethod models correlated trait-correlated method model method variance method effects Monte Carlo simulation Mathematical models Grimm, Kevin J oth Widaman, Keith F oth Enthalten in Structural equation modeling Philadelphia, Pa. : Psychology Press, Taylor & Francis Group, 1994 23(2016), 6, Seite 798 (DE-627)188644075 (DE-600)1285122-X (DE-576)049955675 1070-5511 nnns volume:23 year:2016 number:6 pages:798 http://dx.doi.org/10.1080/10705511.2016.1214919 Volltext http://www.tandfonline.com/doi/abs/10.1080/10705511.2016.1214919 http://search.proquest.com/docview/1829807528 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_4012 31.00 AVZ AR 23 2016 6 798 |
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10.1080/10705511.2016.1214919 doi PQ20161201 (DE-627)OLC1986057542 (DE-599)GBVOLC1986057542 (PRQ)i1443-3497908e07841ced838373adb9cdce20af9910de3452e49e1ea1023b423256800 (KEY)0238167220160000023000600798augmentingthecorrelatedtraitcorrelatedmethodmodelf DE-627 ger DE-627 rakwb eng 300 DE-600 31.00 bkl Castro-Schilo, Laura verfasserin aut Augmenting the Correlated Trait-Correlated Method Model for Multitrait-Multimethod Data 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier We introduce an approach for ensuring empirical identification of the correlated trait-correlated method (CT-CM) model under a variety of conditions. A set of models are referred to as augmented correlated trait-correlated method (ACT-CM) models because they are based on systematically augmenting the multitrait-multimethod matrix put forth by Campbell and Fiske (1959). We show results from a Monte Carlo simulation study in which data characteristics lead to an empirically underidentified standard CT-CM model, but a well-identified fully augmented correlated trait-correlated method (FACT-CM) model. This improved identification occurs even for a model in which equality constraints are imposed on loadings on each trait factor and loadings on each method factor-a specific case shown to lead to an empirically underidentified CT-CM model. Nutzungsrecht: Copyright © Taylor & Francis Group, LLC construct validity multitrait-multimethod models correlated trait-correlated method model method variance method effects Monte Carlo simulation Mathematical models Grimm, Kevin J oth Widaman, Keith F oth Enthalten in Structural equation modeling Philadelphia, Pa. : Psychology Press, Taylor & Francis Group, 1994 23(2016), 6, Seite 798 (DE-627)188644075 (DE-600)1285122-X (DE-576)049955675 1070-5511 nnns volume:23 year:2016 number:6 pages:798 http://dx.doi.org/10.1080/10705511.2016.1214919 Volltext http://www.tandfonline.com/doi/abs/10.1080/10705511.2016.1214919 http://search.proquest.com/docview/1829807528 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_4012 31.00 AVZ AR 23 2016 6 798 |
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doi_str_mv |
10.1080/10705511.2016.1214919 |
dewey-full |
300 |
title_sort |
augmenting the correlated trait-correlated method model for multitrait-multimethod data |
title_auth |
Augmenting the Correlated Trait-Correlated Method Model for Multitrait-Multimethod Data |
abstract |
We introduce an approach for ensuring empirical identification of the correlated trait-correlated method (CT-CM) model under a variety of conditions. A set of models are referred to as augmented correlated trait-correlated method (ACT-CM) models because they are based on systematically augmenting the multitrait-multimethod matrix put forth by Campbell and Fiske (1959). We show results from a Monte Carlo simulation study in which data characteristics lead to an empirically underidentified standard CT-CM model, but a well-identified fully augmented correlated trait-correlated method (FACT-CM) model. This improved identification occurs even for a model in which equality constraints are imposed on loadings on each trait factor and loadings on each method factor-a specific case shown to lead to an empirically underidentified CT-CM model. |
abstractGer |
We introduce an approach for ensuring empirical identification of the correlated trait-correlated method (CT-CM) model under a variety of conditions. A set of models are referred to as augmented correlated trait-correlated method (ACT-CM) models because they are based on systematically augmenting the multitrait-multimethod matrix put forth by Campbell and Fiske (1959). We show results from a Monte Carlo simulation study in which data characteristics lead to an empirically underidentified standard CT-CM model, but a well-identified fully augmented correlated trait-correlated method (FACT-CM) model. This improved identification occurs even for a model in which equality constraints are imposed on loadings on each trait factor and loadings on each method factor-a specific case shown to lead to an empirically underidentified CT-CM model. |
abstract_unstemmed |
We introduce an approach for ensuring empirical identification of the correlated trait-correlated method (CT-CM) model under a variety of conditions. A set of models are referred to as augmented correlated trait-correlated method (ACT-CM) models because they are based on systematically augmenting the multitrait-multimethod matrix put forth by Campbell and Fiske (1959). We show results from a Monte Carlo simulation study in which data characteristics lead to an empirically underidentified standard CT-CM model, but a well-identified fully augmented correlated trait-correlated method (FACT-CM) model. This improved identification occurs even for a model in which equality constraints are imposed on loadings on each trait factor and loadings on each method factor-a specific case shown to lead to an empirically underidentified CT-CM model. |
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container_issue |
6 |
title_short |
Augmenting the Correlated Trait-Correlated Method Model for Multitrait-Multimethod Data |
url |
http://dx.doi.org/10.1080/10705511.2016.1214919 http://www.tandfonline.com/doi/abs/10.1080/10705511.2016.1214919 http://search.proquest.com/docview/1829807528 |
remote_bool |
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author2 |
Grimm, Kevin J Widaman, Keith F |
author2Str |
Grimm, Kevin J Widaman, Keith F |
ppnlink |
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mediatype_str_mv |
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isOA_txt |
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hochschulschrift_bool |
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author2_role |
oth oth |
doi_str |
10.1080/10705511.2016.1214919 |
up_date |
2024-07-04T04:00:15.377Z |
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