Linear Confirmatory Factor Models to Evaluate Multitrait-Multimethod Matrices: The Effects of Number of Indicators and Correlation Among Methods.

Two models for confirmatory factor analysis of multitrait-multimethod data (MTMM) were assessed, the correlated traits-correlated methods (CTCM), and the correlated traits-correlated uniqueness models (CTCU). Two Monte Carlo experiments (100 replications per cell) were performed to study the behavior of these models in terms of magnitude and direction of bias, and accuracy of estimates. Study one included a single indicator per trait-method combination, and it manipulated three independent variables: matrix type, from three traits-three methods to six traits-six methods; correlation among method factors, from zero to .6; and model type (CTCM and CTCU). Study two included simulated MTMM matrices with two or more indicators per trait-method combination. Again, three independent variables were manipulated: number of indicators per trait-method combination, from 2 to 5; correlation among methods; and model type, CTCM and CTCU. The results from study one showed that the CTCU model performed very well for MTMM designs with a single indicator per trait-method combination, and consistently better than the CTCM model. However, the results from study two showed that the CTCM model worked reasonably well and better than the CTCU model when more than two indicators per trait-method combination were available. Despite the CTCM model's allowance for correlation between methods, results pointed to better estimates when methods were orthogonal. The main conclusion of the present article is that the use of CTCU models in the situations described in study one and the use of CTCM models in those represented in study two could be recommended.