In small sample contexts, Bayesian estimation is often suggested as a viable alternative to
frequentist estimation, such as maximum likelihood estimation. Our systematic literature …

Survey data in social, behavioral, and health sciences often contain many variables (p).
Structural equation modeling (SEM) is commonly used to analyze such data. With a …

As Bayesian methods continue to grow in accessibility and popularity, more empirical
studies are turning to Bayesian methods to model small sample data. Bayesian methods do …

Bayesian statistical methods are slowly creeping into all fields of science and are becoming
ever more popular in applied research. Although it is very attractive to use Bayesian …

When thinking about sample size in multilevel modeling, it is important to realize that there
are potential sample size issues at several distinct levels. The concern is usually about the …

This series of simulation studies was designed to assess the impact of misspecifications of
the latent variance–covariance matrix (ie,) and residual structure (ie,) on the accuracy of …

Researchers often have difficulties collecting enough data to test their hypotheses, either
because target groups are small or hard to access, or because data collection entails …

Multilevel Structural equation models are most often estimated from a frequentist framework
via maximum likelihood. However, as shown in this article, frequentist results are not always …

Specialized imputation routines for multilevel data are widely available in software
packages, but these methods are generally not equipped to handle a wide range of …

The study examined the performance of maximum likelihood (ML) and multiple imputation
(MI) procedures for missing data in longitudinal research when fitting latent growth models …