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

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

Research output: Contribution to journalArticle

3 Scopus citations

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)798-818
Number of pages21
JournalStructural Equation Modeling
Volume23
Issue number6
DOIs
StatePublished - Nov 1 2016

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)

Fingerprint Dive into the research topics of 'Augmenting the Correlated Trait–Correlated Method Model for Multitrait–Multimethod Data'. Together they form a unique fingerprint.

  • Cite this