Imputerade

Imputerade, or imputed data, is a term used in statistics and data analysis to describe values that have been estimated and substituted for missing or incomplete observations. The practice arises when datasets contain missing values due to nonresponse, data corruption, or dropouts. Imputation aims to produce a complete dataset suitable for standard analyses while acknowledging uncertainty about the true values. In Swedish-language statistics, imputation is often described in terms of the imputerade or imputations of missing data.

Single imputation fills in missing entries with a single value, such as the mean, median, or a predicted value from a model. Multiple imputation creates several plausible values for each missing entry, producing multiple complete datasets that are analyzed separately and whose results are combined to reflect uncertainty; this approach is associated with the work of Rubin. Common methods include mean imputation, regression imputation, hot-deck imputation, k-nearest neighbors, and model-based approaches like expectation-maximization and multiple-imputation by chained equations (MICE).

Appropriate use depends on the missingness mechanism: missing completely at random (MCAR), missing at random (MAR), or not at random (MNAR). Imputation can reduce bias and preserve sample size but can also distort relationships or understate variability if poorly chosen; thus diagnostics and sensitivity analyses are advised.

Practical guidance includes assessing the extent and pattern of missingness, choosing a method aligned with the missingness mechanism and data context, checking imputations against the observed distributions, and clearly reporting the method and the uncertainty introduced. Software implementations exist in R (mice, missForest) and Python (scikit-learn, fancyimpute). Imputerade techniques are widely used in clinical trials, survey research, and economic data to enable analyses with incomplete datasets.