A1327
Title: A maximum likelihood estimator for composite models
Authors: Tamara Schamberger - Bielefeld University (Germany) [presenting]
Florian Schuberth - University of Twente (Netherlands)
Yves Rosseel - Ghent University (Belgium)
Joerg Henseler - University of Twente (Netherlands)
Abstract: Structural equation modeling (SEM) is a popular and widely applied method that predominantly models latent variables by means of common factor models. Yet, in recent years, the composite model has gained increasing research attention. In contrast to common factor models, approaches to estimate composite models are limited. The contribution is a full-information maximum likelihood (ML) estimator for composite models. The general composite model is presented, a closed form of the variance-covariance matrix implied by this model, and a full information ML estimator is used to obtain the parameter estimates of composite models. Moreover, a test is provided to assess the overall fit of composite models. To demonstrate the performance of the ML estimator and to compare it to its closest contender, i.e., partial least squares path modeling (PLS-PM), in finite samples, a Monte Carlo simulation is conducted. The Monte Carlo simulation reveals that, overall, the ML estimator performs well and is similar to PLS-PM in finite samples. Hence, under the considered conditions, the proposed estimator is a valid alternative with known superior statistical properties.
