Relaxing Traditional Assumptions in Structural Equation Modeling

Music City Center 

Structural equation models (SEMs) are typically fit using the maximum likelihood (ML) estimator. However, some ML assumptions might be untenable in particular applications. Of relevance to this presentation, samples might be small leading to issues with the asymptotic properties of ML, and the specified model is quite likely not the true data-generating model, which infuses bias into estimates. These issues can also lead to non-convergence, thus limiting researchers' ability to capitalize on the benefits of SEM. In this presentation, we cover modern developments in parameter estimation. Specifically, we describe the model-implied instrumental variables with two-stage least squares (MIIV-2SLS; Bollen, 1996, 2021) estimator, which affords benefits in the presence of model misspecification and violations of the traditional assumptions required in SEM. We review simulation studies using the MIIV-2SLS estimator, software accessibility, and implications for applied research.

structural equation modeling

two stage least squares

instrumental variables

causal modeling