Many modern estimators require bootstrapping to calculate confidence intervals because
either no analytic standard error is available or the distribution of the parameter of interest is …

Many texts are excellent sources of knowledge about individual statistical tools, but the art of
data analysis is about choosing and using multiple tools. Instead of presenting isolated …

Epidemiologic studies are frequently susceptible to missing information. Omitting
observations with missing variables remains a common strategy in epidemiologic studies …

Missing values are a major problem in medical research. As the complete case analysis
discards useful information, estimation and inference may suffer strongly. Multiple imputation …

The present research examines processes involved in how people believe and share news
posts on social media. We tested whether the relation between individuals' previous political …

Abstract Model selection and model averaging are two important techniques to obtain
practical and useful models in applied research. However, it is now well-known that many …

When analyzing incomplete data, is it better to use multiple imputation (MI) or full information
maximum likelihood (ML)? In large samples ML is clearly better, but in small samples ML's …

What makes followers act collectively when called upon by their leaders? To answer this
question, participants were randomly allocated to leader–follower relationships embedded …

Phylogenetic comparative methods (PCMs), especially ones based on linear models, have
played a central role in understanding species' trait evolution. These methods, however …

Abstract Purpose of Review Incomplete data are a common problem in statistical analysis of
environmental epidemiological research. However, many researchers still ignore this …