What method replaces missing data based on the value of another variable?

The method that replaces missing data based on the value of another variable is known as Predictive Replacement. This technique uses statistical models to predict the missing value based on the relationships observed among variables in the dataset. For instance, if a dataset has a missing value for a variable that is related to another variable, Predictive Replacement can utilize the known values from that related variable to estimate the missing value effectively. This approach helps to maintain the overall dataset size and can lead to improved accuracy in analyses that follow, as it provides a more informed approximation than simply leaving the data missing.

In contrast, Listwise Deletion and Pairwise Deletion focus on handling missing data by removing certain cases or pairs of cases from analysis based on the presence of missing values, which can potentially lead to loss of data and reduced statistical power. Data Validation, on the other hand, refers to the process of ensuring the accuracy and quality of data, rather than directly addressing issues of missing values through imputation or replacement methods. Therefore, Predictive Replacement stands out as the technique that specifically targets the issue of missing data by utilizing existing information within the dataset.