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

This article compares two missing data procedures, full information maximum likelihood
(FIML) and multiple imputation (MI), to investigate their relative performances in relation to …

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 …

Current research on multiple imputation suggests that including auxiliary variables in the
imputation model may increase the accuracy and efficiency of coefficient estimation, yet few …

Missing data and nonnormality are two common factors that can affect analysis results from
structural equation modeling (SEM). The current study aims to address a challenging …

Although structural equation modeling software packages use maximum likelihood
estimation by default, there are situations where one might prefer to use multiple imputation …

As with other statistical methods, missing data often create major problems for the estimation
of structural equation models (SEMs). Conventional methods such as listwise or pairwise …

Craig K. Enders although most of the theoretical and computational underpinnings of
contemporary missing data handling procedures were developed in the 1970s and 1980s …

Obtaining predictions from regression models fit to multiply imputed data can be challenging
because treatments of multiple imputation seldom give clear guidance on how predictions …

Although missing data methods have advanced in recent years, methodologists have
devoted less attention to multilevel data structures where observations at level-1 are nested …