This review summarizes the current state of the art of statistical and (survey) methodological
research on measurement (non) invariance, which is considered a core challenge for the …

Bayesian statistics is an approach to data analysis based on Bayes' theorem, where
available knowledge about parameters in a statistical model is updated with the information …

Previous surveys of the literature have shown that reports of statistical analyses often lack
important information, causing lack of transparency and failure of reproducibility. Editors and …

Assessing and analysing individual differences in change over time is of central scientific
importance to developmental neuroscience. However, the literature is based largely on …

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

In linear regression problems with many predictors, penalized regression techniques are
often used to guard against overfitting and to select variables relevant for predicting an …

The current paper highlights a new, interactive Shiny App that can be used to aid in
understanding and teaching the important task of conducting a prior sensitivity analysis …

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 …

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

This article demonstrates the usefulness of Bayesian estimation with small samples. In
Bayesian estimation, prior information can be included, which increases the precision of the …