Multiple Imputation with Survey Weights: A Multilevel Approach

Multiple Imputation in a Complex Sample Survey

Multiple imputation for missing survey data is relatively new concept. As defined by one of its leading proponents, "multiple imputation is the technique that replaces each missing or deficient value with two or more acceptable values representing a distribution of possibilities" (Rubin 1987, p.2). Multiply-imputed data reflects the uncertainty contained in the imputation process in a way not p...

A SAS Macro for Applying Multiple Imputation to Multilevel Data

Single-level multiple imputation procedures (e.g., PROC MI) are not appropriate for multilevel data sets where observations are nested within clusters. Analyzing multilevel data imputed with a single-level procedure yields variance estimates that are biased toward zero and may yield other biased parameters. Given the prevalence of clustered data (e.g., children within schools; employees within ...

Multiple Imputation with Mplus

A two-step semiparametric method to accommodate sampling weights in multiple imputation.

Multiple imputation (MI) is a well-established method to handle item-nonresponse in sample surveys. Survey data obtained from complex sampling designs often involve features that include unequal probability of selection. MI requires imputation to be congenial, that is, for the imputations to come from a Bayesian predictive distribution and for the observed and complete data estimator to equal t...

A multiple imputation approach for clustered interval-censored survival data.

Multivariate interval-censored failure time data arise commonly in many studies of epidemiology and biomedicine. Analysis of these type of data is more challenging than the right-censored data. We propose a simple multiple imputation strategy to recover the order of occurrences based on the interval-censored event times using a conditional predictive distribution function derived from a paramet...