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...

Commonly in survey research, multiple, different analyses are conducted by one or more than one researcher on the same data set. The conclusions from these analyses should be consistent despite the presence of missing data. Multiple imputation is frequently used to ensure consistency of analyses. Two methods for multiple imputation of missing data are a combination of hot deck and regression im...

BACKGROUND AND OBJECTIVES Baseline creatinine (BCr) is frequently missing in AKI studies. Common surrogate estimates can misclassify AKI and adversely affect the study of related outcomes. This study examined whether multiple imputation improved accuracy of estimating missing BCr beyond current recommendations to apply assumed estimated GFR (eGFR) of 75 ml/min per 1.73 m(2) (eGFR 75). DESIGN,...

Multiple imputation has become viewed as a general solution to missing data problems in statistics. However, in order to lead to consistent asymptotically normal estimators, correct variance estimators and valid tests, the imputations must be proper. So far it seems that only Bayesian multiple imputation, i.e. using a Bayesian predictive distribution to generate the imputations, or approximatel...

BACKGROUND Multiple imputation has become very popular as a general-purpose method for handling missing data. The validity of multiple-imputation-based analyses relies on the use of an appropriate model to impute the missing values. Despite the widespread use of multiple imputation, there are few guidelines available for checking imputation models. ANALYSIS In this paper, we provide an overvi...

In the Pre-Elementary Education Longitudinal Study (PEELS), imputation of item missing data was done using AutoImpute (AI) software, which uses semi-parametric modeling to form imputation classes. In this paper, we summarize PEELS experience with AI, investigate the bias aspect of the imputed data for the PEELS teacher questionnaire data, and study the variance estimation of imputed data using ...

When some of the records used to estimate the imputation models in multiple imputation are not used or available for analysis, the usual multiple imputation variance estimator has positive bias. We present an alternative multiple imputation approach that enables unbiased estimation of variances and, hence, calibrated inferences in such contexts. First, using all records, the imputer samples m v...