Testing for measurement invariance and latent mean differences across methods: interesting incremental information from multitrait-mult method studies

Models of confirmatory factor analysis (CFA) are frequently applied to examine the convergent validity of scores obtained from multiple raters or methods in so-called multrtrait-multimethod (MTMM) investigations. We show that interesting incremental information about method effects can be gained fro...

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Detalles Bibliográficos
Autores: Geiser, Christian, Burns, G. Leonard, Servera, Mateu
Tipo de recurso: artículo
Fecha de publicación:2014
País:España
Institución:Instituto de Salud Carlos III (ISCIII)
Repositorio:Repisalud
Idioma:inglés
OAI Identifier:oai:repisalud.isciii.es:20.500.12105/20019
Acceso en línea:http://hdl.handle.net/20.500.12105/20019
Access Level:acceso abierto
Palabra clave:Multitrait-multimethod (MTMM) analysis
measurement invariance
Measurement equivalence
Mean and covariance structures
Mean differences across raters
Random vs. fixed methods
Rater agreement
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spelling Testing for measurement invariance and latent mean differences across methods: interesting incremental information from multitrait-mult method studiesGeiser, ChristianBurns, G. LeonardServera, MateuMultitrait-multimethod (MTMM) analysismeasurement invarianceMeasurement equivalenceMean and covariance structuresMean differences across ratersRandom vs. fixed methodsRater agreementModels of confirmatory factor analysis (CFA) are frequently applied to examine the convergent validity of scores obtained from multiple raters or methods in so-called multrtrait-multimethod (MTMM) investigations. We show that interesting incremental information about method effects can be gained from including mean structures and tests of MI across methods in MTMM models. We present a modeling framework for testing MI in the first step of a CFA-MTMM analysis. We also discuss the relevance of MI in the context of four more complex CFA-MTMM models with method factors. We focus on three recently developed multiple-indicator CFA-MTMM models for structurally different methods [the correlated traits-correlated (methods 1), latent difference, and latent means models; Geiser et al., 2014a; Pohl and Steyer, 2010; Pohl et al., 2008] and one model for interchangeable methods (Eid et al., 2008). We demonstrate that some of these models require or imply MI by definition for a proper interpretation of trait or method factors, whereas others do not, and explain why MI may or may not be required in each model. We show that in the model for interchangeable methods, testing for MI is critical for determining whether methods can truly be seen as interchangeable. We illustrate the theoretical issues in an empirical application to an MTMM study of attention deficit and hyperactivity disorder (ADHD) with mother, father, and teacher ratings as methods.Frontiers Media20242024-07-0320142014-10-3020142014-10-30research articlehttp://purl.org/coar/resource_type/c_2df8fbb1info:eu-repo/semantics/articlehttp://hdl.handle.net/20.500.12105/20019reponame:Repisaludinstname:Instituto de Salud Carlos III (ISCIII)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:repisalud.isciii.es:20.500.12105/200192026-06-12T12:43:37Z
dc.title.none.fl_str_mv Testing for measurement invariance and latent mean differences across methods: interesting incremental information from multitrait-mult method studies
title Testing for measurement invariance and latent mean differences across methods: interesting incremental information from multitrait-mult method studies
spellingShingle Testing for measurement invariance and latent mean differences across methods: interesting incremental information from multitrait-mult method studies
Geiser, Christian
Multitrait-multimethod (MTMM) analysis
measurement invariance
Measurement equivalence
Mean and covariance structures
Mean differences across raters
Random vs. fixed methods
Rater agreement
title_short Testing for measurement invariance and latent mean differences across methods: interesting incremental information from multitrait-mult method studies
title_full Testing for measurement invariance and latent mean differences across methods: interesting incremental information from multitrait-mult method studies
title_fullStr Testing for measurement invariance and latent mean differences across methods: interesting incremental information from multitrait-mult method studies
title_full_unstemmed Testing for measurement invariance and latent mean differences across methods: interesting incremental information from multitrait-mult method studies
title_sort Testing for measurement invariance and latent mean differences across methods: interesting incremental information from multitrait-mult method studies
dc.creator.none.fl_str_mv Geiser, Christian
Burns, G. Leonard
Servera, Mateu
author Geiser, Christian
author_facet Geiser, Christian
Burns, G. Leonard
Servera, Mateu
author_role author
author2 Burns, G. Leonard
Servera, Mateu
author2_role author
author
dc.contributor.none.fl_str_mv
dc.subject.none.fl_str_mv Multitrait-multimethod (MTMM) analysis
measurement invariance
Measurement equivalence
Mean and covariance structures
Mean differences across raters
Random vs. fixed methods
Rater agreement
topic Multitrait-multimethod (MTMM) analysis
measurement invariance
Measurement equivalence
Mean and covariance structures
Mean differences across raters
Random vs. fixed methods
Rater agreement
description Models of confirmatory factor analysis (CFA) are frequently applied to examine the convergent validity of scores obtained from multiple raters or methods in so-called multrtrait-multimethod (MTMM) investigations. We show that interesting incremental information about method effects can be gained from including mean structures and tests of MI across methods in MTMM models. We present a modeling framework for testing MI in the first step of a CFA-MTMM analysis. We also discuss the relevance of MI in the context of four more complex CFA-MTMM models with method factors. We focus on three recently developed multiple-indicator CFA-MTMM models for structurally different methods [the correlated traits-correlated (methods 1), latent difference, and latent means models; Geiser et al., 2014a; Pohl and Steyer, 2010; Pohl et al., 2008] and one model for interchangeable methods (Eid et al., 2008). We demonstrate that some of these models require or imply MI by definition for a proper interpretation of trait or method factors, whereas others do not, and explain why MI may or may not be required in each model. We show that in the model for interchangeable methods, testing for MI is critical for determining whether methods can truly be seen as interchangeable. We illustrate the theoretical issues in an empirical application to an MTMM study of attention deficit and hyperactivity disorder (ADHD) with mother, father, and teacher ratings as methods.
publishDate 2014
dc.date.none.fl_str_mv 2014
2014-10-30
2014
2014-10-30
2024
2024-07-03
dc.type.none.fl_str_mv research article
http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.12105/20019
url http://hdl.handle.net/20.500.12105/20019
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Frontiers Media
publisher.none.fl_str_mv Frontiers Media
dc.source.none.fl_str_mv reponame:Repisalud
instname:Instituto de Salud Carlos III (ISCIII)
instname_str Instituto de Salud Carlos III (ISCIII)
reponame_str Repisalud
collection Repisalud
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.81155

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