Imputaatiooletukset

Imputaatiooletukset refer to the assumptions made when imputing missing data. Imputation is the process of replacing missing values with substituted values. These assumptions are crucial because they can significantly influence the results of statistical analyses. A common assumption in imputation is that the missing data are Missing Completely At Random (MCAR). This means that the probability of a value being missing is independent of both the observed and unobserved data. Another frequent assumption is Missing At Random (MAR), where the probability of missingness depends only on the observed data, not on the missing values themselves. A more restrictive assumption is Missing Not At Random (MNAR), where the probability of missingness depends on the unobserved missing values.

The choice of imputation method often depends on the assumed pattern of missingness. For instance, simple imputation methods like mean or median imputation implicitly assume MCAR. More sophisticated methods, such as multiple imputation, can handle MAR and, with appropriate modeling, potentially MNAR. If the imputation assumptions are violated, the imputed data may introduce bias, leading to incorrect conclusions. Therefore, understanding and justifying the imputation assumptions is a critical step in data analysis. Sensitivity analyses are often performed to assess how different imputation assumptions might affect the study results. The goal is to use imputation methods that are as close as possible to the true data-generating process, while acknowledging the inherent uncertainties.