Multiple Imputation Techniques for Survey Data with Multiple Rating Scales

Multiple Imputation for Missing Data

Multiple imputation provides a useful strategy for dealing with data sets with missing values. Instead of filling in a single value for each missing value, Rubin’s (1987) multiple imputation procedure replaces each missing value with a set of plausible values that represent the uncertainty about the right value to impute. These multiply imputed data sets are then analyzed by using standard proc...

Multiple imputation: dealing with missing data.

In many fields, including the field of nephrology, missing data are unfortunately an unavoidable problem in clinical/epidemiological research. The most common methods for dealing with missing data are complete case analysis-excluding patients with missing data--mean substitution--replacing missing values of a variable with the average of known values for that variable-and last observation carri...

Single versus multiple imputation for genotypic data

Due to the growing need to combine data across multiple studies and to impute untyped markers based on a reference sample, several analytical tools for imputation and analysis of missing genotypes have been developed. Current imputation methods rely on single imputation, which ignores the variation in estimation due to imputation. An alternative to single imputation is multiple imputation. In t...

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...

Multiple Imputation with Mplus