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 …

Data imputation involves representing missing values in a dataset. Missing data create a
number of potential challenges for statistical analysis. Missing values can increase the …

We propose a remedy to the substantial discrepancy between the way political scientists
analyze data with missing values and the recommendations of the statistics community. With …

Within epidemiological and clinical research, missing data are a common issue which are
often inappropriately handled in practice. Multiple imputation (MI) is a popular tool used tofill …

Much of the recent research into imputation methodology has focused on developing
optimal procedures for a single variable or set of variables, where the patterns of …

The objective of this research is to examine, validate, and recommend techniques for
handling the problem of missingness in observational data. We use a rich observational …

In epidemiological research, patient reported outcomes are often measured by a multi-item
questionnaire. In a multi-item questionnaire a construct is measured by combining the …

Scores on self-report questionnaires are often used in statistical models without accounting
for measurement error, leading to bias in estimates related to those variables. While …

Multiple Imputation and its Application The most up-to-date edition of a bestselling guide to
analyzing partially observed data In this comprehensively revised Second Edition of Multiple …

Following its introduction nearly 30 years ago (Rubin, 1978), multiple imputation (MI) has
become an important and influential approach for dealing with the statistical analysis of …