The Surprising Viability of a Simple Alternate Estimation Procedure for Construction of Large-Scale Structural Equation Measurement Models

As part of developing a comprehensive strategy for structural equation model building and assessment, a large-scale Monte Carlo study of 7,200 covariance matrices sampled from 36 population models was conducted. This study compared maximum likelihood with the much simpler centroid method for the confirmatory factor analysis of multiple-indicator measurement models. Surprisingly, the contribution of maximum likelihood to model analysis is limited to formal evaluation of the model. No statistically discernible differences were obtained for the bias, standard errors, or mean squared error (MSE) of the estimated factor correlations, and empirically obtained maximum likelihood standard errors for the pattern coefficients were only slightly smaller than their centroid counterparts. Further supporting the recommendations of Anderson and Gerbing (1982), the considerably faster centroid method may have a useful role in the analysis of these models, particularly for the analysis of large models with 50 or more input variables. These results encourage the further development of a comprehensive research paradigm that exploits the relative strengths of both centroid and maximum likelihood as complementary estimation procedures along an integrated exploratory-confirmatory continuum of model specification, revision, and formal evaluation.