Imputationsdiagnostik

Imputationsdiagnostik refers to the statistical methods and diagnostic procedures used to assess the quality and appropriateness of data imputation techniques. Data imputation is the process of replacing missing values in a dataset with substituted values. While imputation is a common practice to handle incomplete data, it can introduce bias or distort the underlying data structure if not performed correctly. Imputationsdiagnostik aims to identify and quantify these potential issues.

Key aspects of imputationsdiagnostik involve evaluating how well the imputed values represent the true, unobserved values and how the imputation process affects subsequent analyses. This can include examining the distribution of imputed values, comparing them to observed values, and assessing the impact of imputation on statistical estimates such as means, variances, and regression coefficients. Diagnostic plots, such as residual plots or plots comparing distributions before and after imputation, are frequently employed.

Furthermore, imputationsdiagnostik considers the assumptions underlying different imputation methods. For instance, simple imputation methods like mean imputation might preserve the mean but distort variance. More sophisticated methods like multiple imputation can provide more robust estimates, but their diagnostics also need to be carefully considered. The goal is to ensure that the imputation process does not lead to misleading conclusions from the data analysis.