Various methods can be employed to handle incomplete data, including:
Imputation: This technique involves filling in missing values with estimated ones based on the observed data. Common methods include mean imputation, regression imputation, and multiple imputation. Sensitivity Analysis: Testing how sensitive the results are to different assumptions about the missing data can provide insights into the robustness of the findings. Weighting: Applying weights to account for the probability of missing data can help mitigate bias. Data Augmentation: Collecting additional data or using external data sources to compensate for missing information. Advanced Statistical Methods: Techniques such as Maximum Likelihood Estimation and Bayesian Methods can provide more sophisticated ways to handle incomplete data.
