… structure on certain covariance matrices (Savalei & Bentler, 2009). For these and other
reasons, we believe that marrying SEM and multiple imputation is often a preferable strategy for …

… two missing data procedures, full information maximum likelihood (FIML) and multiple
imputation … the expected patterns of discrepancy between the two estimators. Via Monte Carlo …

… Next, we review the mechanisms that lead to missingness and the multiple imputation
model. We conclude with a worked example of missing data analysis and multiple imputation …

… Third, we should have a reliable variance formula and a means of constructing … of multiple
imputation, it is worth noting that these remain our aims; the aim of multiple imputation is not to …

… many techniques to generate single or multiple imputations are available, this book concentrates
on the generation of multiple imputations … Other techniques to generate imputations that …

… tobacco smoking on depressive symptoms using a multiple imputation technique. … multiple
imputation technique to deal with missing values, and a fixed effects logistic regression model …

… In this study, we investigated the performance of various MI methods for imputing three-level
incomplete data when the target analysis model is a three-level random effects model with a …

… This book presents a basic introduction to structural equation modeling (SEM). Readers will
find that we have continued our tradition of keeping examples basic and easy to follow. The …

… analysis, several studies have shown that it is particularly important for the imputation
model to take the multilevel structure into account if analyses based on the imputed data are to …

… will tend to be a better strategy. He proposed a strategy that he referred to as “multiple imputation,
then deletion” (… Under MID, all subjects are used in the imputation process. Values are …