singleimputation

Single imputation is a method used in statistics to handle missing data. It involves replacing each missing value with a single plausible value. This single value is typically derived from the observed data using various techniques. Common single imputation methods include mean imputation, median imputation, mode imputation, and regression imputation. Mean imputation replaces missing values with the average of the observed values for that variable. Median imputation uses the median, which is less sensitive to outliers. Mode imputation is used for categorical variables and replaces missing values with the most frequent category. Regression imputation predicts missing values based on a regression model built from the observed data. While single imputation is straightforward to implement and preserves sample size, it can lead to biased estimates and an underestimation of the variability of the data. This is because it does not account for the uncertainty associated with the imputed values. The imputed values are treated as if they were observed, which can artificially reduce standard errors and distort relationships between variables. Therefore, while single imputation is a simple solution for missing data, it is often recommended to use multiple imputation or other more sophisticated methods when possible to obtain more accurate and reliable results.