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 …

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

The role of soft skills at school is still debated, but they have emerged as important factors for
students' academic achievement and life satisfaction. This study focuses on the combined …

Item Response Theory (IRT) is widely applied in the human sciences to model persons'
responses on a set of items measuring one or more latent constructs. While several R …

This article explains the foundational concepts of Bayesian data analysis using virtually no
mathematical notation. Bayesian ideas already match your intuitions from everyday …

The last 50 years has seen a dramatic decline in taxes on the rich across the advanced
democracies. There is still fervent debate in both political and academic circles, however …

Little is still known about the neuroanatomical substrates related to changes in specific
cognitive abilities in the course of healthy aging, and the existing evidence is predominantly …

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

Statistical procedures such as Bayes factor model selection and Bayesian model averaging
require the computation of normalizing constants (eg, marginal likelihoods). These …

The year 2022 is the 20th anniversary of Joseph Schafer and John Graham's paper titled
“Missing data: Our view of the state of the art,” currently the most highly cited paper in the …