Methodological innovations have allowed researchers to consider increasingly
sophisticated statistical models that are better in line with the complexities of real-world …

Data in social and behavioral sciences typically contain measurement errors and also do not
have predefined metrics. Structural equation modeling (SEM) is commonly used to analyze …

In the structural equation modeling literature, the normal-distribution-based maximum
likelihood (ML) method is most widely used, partly because the resulting estimator is …

We argue for separate analyses of the measurement and structural portions of latent or
mixed manifest and latent variable models. We present limited information (single equation) …

Studies on small sample properties of multilevel models have become increasingly
prominent in the methodological literature in response to the frequency with which small …

Despite the widespread and rising popularity of structural equation modeling (SEM) in
psychology, there is still much confusion surrounding how to choose an appropriate sample …

The increasing availability of software with which to estimate multivariate multilevel models
(also called multilevel structural equation models) makes it easier than ever before to …

Little, Bovaird and Widaman (2006) proposed an unconstrained approach with residual
centering for estimating latent interaction effects as an alternative to the mean-centered …

The purpose of this simulation study was to assess the performance of latent variable
models that take into account the complex sampling mechanism that often underlies data …

Research in regularization, as applied to structural equation modeling (SEM), remains in its
infancy. Specifically, very little work has compared regularization approaches across both …