Incomplete data is a common problem of survey research. Recent work on multiple
imputation techniques has increased analysts' awareness of the biasing effects of missing …

Sample estimates derived from data with missing values may be unreliable and may
negatively impact the inferences that researchers make about the underlying population due …

Missing data are common in psychology research and can lead to bias and reduced power if
not properly handled. Multiple imputation is a state-of-the-art missing data method …

Higher education researchers using survey data often face decisions about handling
missing data. Multiple imputation (MI) is considered by many statisticians to be the most …

Multiple imputation replaces an incomplete dataset with m> 1 simulated complete versions
that are analyzed separately by standard methods. We present a natural extension of …

Multiple imputation is a popular technique used to handle item-level missing data. Recent
studies, however, have generated serious concerns about the best practices for statistical …

When conducting research synthesis, the collection of studies that will be combined often do
not measure the same set of variables, which creates missing data. When the studies to …

Health-science researchers often measure psychological constructs using multi-item scales
and encounter missing items on some participants. Multiple imputation (MI) has emerged as …

Missing values are a practical issue in the analysis of longitudinal data. Multiple imputation
(MI) is a well‐known likelihood‐based method that has optimal properties in terms of …

The article focuses on the use of multiple imputation (MI), generally termed missingness
mechanism, to analyze incomplete datasets which can lead to incorrect inferences. Other …