Augmenting the Correlated Trait–Correlated Method Model for Multitrait–Multimethod Data

Laura Castro-Schilo, Kevin Grimm, Keith F. Widaman

Research output: Contribution to journalArticle

2 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)1-21
Number of pages21
JournalStructural Equation Modeling
DOIs
StateAccepted/In press - Aug 19 2016

Fingerprint

Model
Identification (control systems)
Equality Constraints
equality
Monte Carlo Simulation
Simulation Study
simulation
Standards
Monte Carlo simulation
Factors
Simulation study
Equality

Keywords

  • construct validity
  • correlated trait–correlated method model
  • method effects
  • method variance
  • multitrait–multimethod models

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Modeling and Simulation
  • Sociology and Political Science
  • Economics, Econometrics and Finance(all)

Cite this

Augmenting the Correlated Trait–Correlated Method Model for Multitrait–Multimethod Data. / Castro-Schilo, Laura; Grimm, Kevin; Widaman, Keith F.

In: Structural Equation Modeling, 19.08.2016, p. 1-21.

Research output: Contribution to journalArticle

@article{e9af7bdda0a44fa997d925186080b3e8,
title = "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.",
keywords = "construct validity, correlated trait–correlated method model, method effects, method variance, multitrait–multimethod models",
author = "Laura Castro-Schilo and Kevin Grimm and Widaman, {Keith F.}",
year = "2016",
month = "8",
day = "19",
doi = "10.1080/10705511.2016.1214919",
language = "English (US)",
pages = "1--21",
journal = "Structural Equation Modeling",
issn = "1070-5511",
publisher = "Psychology Press Ltd",

}

TY - JOUR

T1 - Augmenting the Correlated Trait–Correlated Method Model for Multitrait–Multimethod Data

AU - Castro-Schilo, Laura

AU - Grimm, Kevin

AU - Widaman, Keith F.

PY - 2016/8/19

Y1 - 2016/8/19

N2 - 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.

AB - 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.

KW - construct validity

KW - correlated trait–correlated method model

KW - method effects

KW - method variance

KW - multitrait–multimethod models

UR - http://www.scopus.com/inward/record.url?scp=84982296236&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84982296236&partnerID=8YFLogxK

U2 - 10.1080/10705511.2016.1214919

DO - 10.1080/10705511.2016.1214919

M3 - Article

AN - SCOPUS:84982296236

SP - 1

EP - 21

JO - Structural Equation Modeling

JF - Structural Equation Modeling

SN - 1070-5511

ER -