Adequately addressing missing data is a pervasive issue in the social sciences. Failure to
correctly address missing data can lead to biased or inefficient estimation of parameters …

Missing data are frequently encountered in practice. A broader class of missing data is
called incomplete data, which includes data with measurement error, multilevel data with …

Multiple Imputation of Missing Data in Practice: Basic Theory and Analysis Strategies
provides a comprehensive introduction to the multiple imputation approach to missing data …

Multiple imputation (MI) is a method for repairing and analyzing data with missing values. MI
replaces missing values with a sample of random values drawn from an imputation model …

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 …

Social scientists typically devote considerable effort to mitigating measurement error during
data collection but then ignore the issue during data analysis. Although many statistical …

Missing data are a common problem in statistics. Imputation, or filling in the missing values,
is an intuitive and flexible way to address the resulting incomplete data sets. We focus on …

Multiple Imputation in Practice: With Examples Using IVEware provides practical guidance
on multiple imputation analysis, from simple to complex problems using real and simulated …

Missing data is a problem that occurs frequently in many scientific areas. The most
sophisticated method for dealing with this problem is multiple imputation. Contrary to other …

Missing values present challenges in the analysis of data across many areas of research.
Handling incomplete data incorrectly can lead to bias, over-confident intervals, and …