By imputation, missing values can be replaced by existing or estimated values to complete a data set. This makes it possible to work with a complete data set, for example for model analyses or for adjustment weighting. In addition, the imputation of missing values helps to reduce the nonresponse bias and to reproduce the covariance structure of the variables and the respective marginal distribution.
The determination of imputation values should always take into account the underlying failure mechanism and the use of auxiliary variables that are as highly correlated as possible with the missing characteristic. Different approaches and donors are available for this purpose, far removed from the imputation model used. Roughly speaking, a distinction is made between simple and multiple imputation as well as hot and cold deck methods for determining suitable donors. Depending on the method, variance-covariance structure and model used, imputation also has an influence on the variance of the estimator to be determined from the data. This must also be taken into account in the subsequent use of the data.
The imputation of missing values for your data set can be commissioned from GESIS both for your complete data set and for selected variables. In consultation with you, we impute missing values using simple or multiple imputation. The dataset for which this is to be done must be made available to GESIS.
There is an expense allowance of 80 euros per hour for our expertise. We will be happy to make you an offer.
Articles in the GESIS Survey Guidelines:
Sampling in Theory