SingleImputationMethoden

Single imputation is a statistical approach for handling missing data by replacing each missing value with a single estimated value. It aims to produce a complete dataset for analysis but does not reflect uncertainty about the true value that is missing. This method contrasts with multiple imputation, which generates several plausible values to capture imputation uncertainty and combines results accordingly.

Common single imputation methods include mean or median imputation, where missing values are replaced with the variable’s central tendency; regression imputation, which predicts missing values from observed relationships with other variables; hot deck imputation, which substitutes a missing value with a similar observed value from a donor case; last observation carried forward for time-series data; and k-nearest neighbors imputation, which uses nearby observations to impute missing entries. The choice of method depends on the data structure, missingness mechanism, and the analysis goals.

Key limitations of single imputation include underestimation of variance and standard errors, which can lead to overly confident conclusions. It can also distort the distribution of the imputed variable and introduce bias if data are not missing at random or if the imputation model is misspecified. Because single imputation does not account for uncertainty about the true value, it is generally discouraged for formal inference or when missingness is substantial.

In practice, single imputation is sometimes used for quick analyses, exploratory data checks, or when missingness is minimal and the risk of bias is deemed acceptable. For rigorous analyses, multiple imputation or model-based approaches that incorporate missing-data uncertainty are preferred.