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 …

Missing data pose challenges to real-life data analysis. Simple ad-hoc fixes, like deletion or
mean imputation, only work under highly restrictive conditions, which are often not met in …

The last 20 years has seen an uptick in research on missing data problems, and most
software applications now implement one or more sophisticated missing data handling …

Whenever multiple regression is applied to a multiply imputed data set, several methods for
combining significance tests for R 2 and the change in R 2 across imputed data sets may be …

Multiple imputation has enjoyed widespread use in social science applications, yet the
application of imputation-based inference to structural equation modeling has received …

To ensure meaningful comparison of test scores across groups or time, measurement
invariance (ie, invariance of the general factor structure and the values of the measurement …

Structural equation modeling (SEM) applications routinely employ a trilogy of significance
tests that includes the likelihood ratio test, Wald test, and score test or modification index …

Likelihood ratio tests (LRTs) are a popular tool for comparing statistical models. However,
missing data are also common in empirical research, and multiple imputation (MI) is often …

Models for analysing multivariate datasets with missing values require strong, often
unassessable, assumptions. The most common of these is that the mechanism that created …

Abstract Introduction Personalized feedback interventions (PFIs) are associated with small
but reliable decreases in alcohol consumption among college students. While they often …