Missing data are a prevalent and widespread data analytic issue and previous studies have
performed simulations to compare the performance of missing data methods in various …

Missing data have long plagued those conducting applied research in the social, behavioral,
and health sciences. Good missing data analysis solutions are available, but practical …

Multiple imputation (MI), a two-stage process whereby missing data are imputed multiple
times and the resulting estimates of the parameter (s) of interest are combined across the …

Missing data are frequently encountered in biomedical, epidemiologic and social research. It
is well known that a naive analysis without adequate handling of missing data may lead to …

This tech report presents the basic concepts and methods used to deal with missing data.
After explaining the missing data mechanisms and the patterns of missingness, the main …

The impact of missing data on quantitative research can be serious, leading to biased
estimates of parameters, loss of information, decreased statistical power, increased standard …

Missing data are ubiquitous in clinical epidemiological research. Individuals with missing
data may differ from those with no missing data in terms of the outcome of interest and …

Missing data can frequently occur in a longitudinal data analysis. In the literature, many
methods have been proposed to handle such an issue. Complete case (CC), mean …

Statistical analysis with missing data has always been a challenge. However, there have
been tremendous advances in statistical theory related to analysis with missing data. Of …

Incomplete data are ubiquitous in social sciences; as a consequence, available data are
inefficient (ineffective) and often biased. In the literature, multiple imputation is known to be …