Multiple imputation for nonresponse in public-use files replaces each missing value by two
or more plausible values. The values can be chosen to represent both uncertainty about …

Multiple imputation is widely accepted as the method of choice to address item-nonresponse
in surveys. However, research on imputation strategies for the hierarchical structures that …

The use of item responses from questionnaire data is ubiquitous in social science research.
One side effect of using such data is that researchers must often account for item level …

Multiple imputation is an increasingly popular strategy for analyzing data with missing
values (Allison, 2002; Rubin, 1987). In multiple imputation, the analyst creates several …

This paper outlines a strategy to validate multiple imputation methods. Rubin's criteria for
proper multiple imputation are the point of departure. We describe a simulation method that …

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 (MI) is a technique especially designed for handling missing data in
public-use datasets. It allows analysts to perform incomplete-data inference straightforwardly …

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 …

Multistage estimation is frequently and extensively used in behavioral science research.
However, the uncertainty carried over from a previous estimation stage to the next is often …

Florida A & M University University of South Florida University of South Florida This study
investigated the effectiveness of ten missing data treatments within the context of a two …