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…

The "Kobayashi Maru" is a training simulation that has its roots in the Star Trek series notable for its defining characteristic as a no-win scenario with no "correct" resolution and where the solution actually involves redefining the problem. Drawing upon these characteristics, we designed a board meeting simulation for an…

Measurement invariance (MI) is a property of measurement that is often implicitly assumed, but in many cases, not tested. When the assumption of MI is tested, it generally involves determining if the measurement holds longitudinally or cross-culturally. A growing literature shows that other groupings can, and should, be considered as well.…

Estimation problems associated with the correlated-trait correlated-method (CTCM) parameterization of a multitrait-multimethod (MTMM) matrix are widely documented: the model often fails to converge; even when convergence is achieved, one or more of the parameter estimates are outside the admissible parameter space. In this study, the authors…

A simulation study compared the performance of robust normal theory maximum likelihood (ML) and robust categorical least squares (cat-LS) methodology for estimating confirmatory factor analysis models with ordinal variables. Data were generated from 2 models with 2-7 categories, 4 sample sizes, 2 latent distributions, and 5 patterns of category…

Studied planned incomplete data designs for the purpose of substantially reducing the amount of data required for multitrait-multimethod models. Simulations studied the effectiveness of Listwise Deletion, Pairwise Deletion, and the expectation maximization (EM) algorithm. Results indicate that EM is generally precise and efficient. (SLD)

Applications of the confirmatory factor analysis block-diagonal model to published data on 18 multitrait-multimethod matrices were reviewed to show widespread estimation problems. Possible causes of estimation difficulties were explored using computer simulations. These problems make the block-diagonal approach less useful than has generally been…

This simulation investigates bias in trait factor loadings and intercorrelations when analyzing multitrait-multimethod (MTMM) data using the correlated uniqueness (CU) confirmatory factor analysis (CFA) model. A theoretical weakness of the CU model is the assumption of uncorrelated methods. However, previous simulation studies have shown little…