Structural equation models comprise a large class of popular statistical models, including
factor analysis models, certain mixed models, and extensions thereof. Model estimation is …

One major challenge of longitudinal data analysis is to find an appropriate statistical model
that corresponds to the theory of change and the research questions at hand. In the present …

Croon and van Veldhoven discussed a model for analyzing micro–macro multilevel designs
in which a variable measured at the upper level is predicted by an explanatory variable that …

Bayesian MCMC is a widely used model estimation technique, and software from the BUGS
family, such as JAGS, have been popular for over two decades. Recently, Stan entered the …

Continuous-time modeling is gaining in popularity as more and more intensive longitudinal
data need to be analyzed. Current Bayesian software implementations of continuous-time …

The analysis of cross-lagged relationships is a popular approach in prevention research to
explore the dynamics between constructs over time. However, a limitation of commonly used …

Bayesian modeling using Markov chain Monte Carlo (MCMC) estimation requires
researchers to decide not only whether estimation has converged but also whether the …

Small sample structural equation modeling (SEM) may exhibit serious estimation problems,
such as failure to converge, inadmissible solutions, and unstable parameter estimates. A …

With small to modest sample sizes and complex models, maximum likelihood (ML)
estimation of confirmatory factor analysis (CFA) models can show serious estimation …

The APA encourages authors to thoroughly report their results, including confidence
intervals. However, considerable debate exists regarding the computation of confidence …