Introduction to Multiple Imputation

An Introduction to Multiple Imputation of Complex Sample Data using SAS ®

This paper presents practical guidance on the proper use of multiple imputation tools in SAS® 9.2 and the subsequent analysis of multiple imputed data sets from a complex sample survey data set. Use of the MI and MIANALYZE procedures and SAS survey procedures for typical descriptive and inferential analyses is demonstrated. The analytic techniques presented can be used on any operating system a...

Multiple Imputation with Mplus

Model - Based Multiple Imputation

managing Multiple imputation can improve data quality.

Local Multiple Imputation

Dealing with missing data via parametric multiple imputation methods usually implies stating several strong assumptions about both the distribution of the data and about underlying regression relationships. If such parametric assumptions do not hold, the multiply imputed data are not appropriate and might produce inconsistent estimators and thus misleading results. In this paper, a fully nonpar...

Review: a gentle introduction to imputation of missing values.

In most situations, simple techniques for handling missing data (such as complete case analysis, overall mean imputation, and the missing-indicator method) produce biased results, whereas imputation techniques yield valid results without complicating the analysis once the imputations are carried out. Imputation techniques are based on the idea that any subject in a study sample can be replaced ...