Multiple imputation for non‐response replaces each missing value by two or more plausible
values. The values can be chosen to represent both uncertainty about the reasons for non …

To judge whether the difference between two point estimates is statistically significant, data
analysts often examine the overlap between the two associated confidence intervals. We …

We investigate the procedure of checking for overlap between confidence intervals or
standard error intervals to draw conclusions regarding hypotheses about differences …

Several multiple imputation techniques are described for simple random samples with
ignorable nonresponse on a scalar outcome variable. The methods are compared using …

Background: Body mass index (BMI), waist circumference (WC), and the waist-stature ratio
(WSR) are considered to be possible proxies for adiposity. Objective: The objective was to …

Some of the outstanding cognitive capabilities of humans are commonly attributed to a
disproportionate enlargement of the human frontal lobe during evolution. This claim is based …

Studies in the social and behavioral sciences frequently suffer from missing data. For
instance, sample surveys often have some individuals who either refuse to participate or do …

" OBJECTIVES: The objectives of this report are to document the methods developed at the
National Center for Health Statistics (NCHS) to bridge the Census 2000 multiple-race …

Multiple imputation is a technique for handling data sets with missing values. The method
fills in the missing values several times, creating several completed data sets for analysis …

The National Health Interview Survey (NHIS) provides a rich source of data for studying
relationships between income and health and for monitoring health and health care for …