A Reformulated Correlated Trait-Correlated Method Model for Multitrait-Multimethod Data Effectively Increases Convergence and Admissibility Rates.

Abstract

The correlated trait-correlated method (CTCM) model for the analysis of multitrait-multimethod (MTMM) data is known to suffer convergence and admissibility (C&A) problems. We describe a little known and seldom applied reparameterized version of this model (CTCM-R) based on Rindskopf's reparameterization of the simpler confirmatory factor analysis model. In a Monte Carlo study, we compare the CTCM, CTCM-R, and the correlated trait-correlated uniqueness (CTCU) models in terms of C&A, model fit, and parameter estimation bias. The CTCM-R model largely avoided C&A problems associated with the more traditional CTCM model, producing C&A solutions nearly as often as the CTCU model, but also avoiding parameter estimation biases known to plague the CTCU model. As such, the CTCM-R model is an attractive alternative for the analysis of MTMM data.