13.6 Criteria for Choosing an Effective Approach

Choosing an appropriate imputation method depends on the following criteria:

Unbiased Parameter Estimates: The technique should ensure that key estimates, such as means, variances, and regression coefficients, are unbiased, particularly in the presence of MAR or MNAR data.
Adequate Power: The method should preserve statistical power, enabling robust hypothesis testing and model estimation. This ensures that important effects are not missed due to inflated type II error.
Accurate Standard Errors: Accurate estimation of standard errors is critical for reliable p-values and confidence intervals. Methods like single imputation often underestimate standard errors, leading to overconfident conclusions.

Preferred Methods: Multiple Imputation and Full Information Maximum Likelihood

Multiple Imputation (MI):

MI replaces missing values with multiple plausible values drawn from a predictive distribution. It generates multiple complete datasets, analyzes each dataset, and combines the results.
Pros: Handles uncertainty well, provides valid standard errors, and is robust under MAR.
Cons: Computationally intensive, sensitive to model mis-specification.

Full Information Maximum Likelihood (FIML):

FIML uses all available data to estimate parameters directly, avoiding the need to impute missing values explicitly.
Pros: Efficient, unbiased under MAR, and computationally elegant.
Cons: Requires correctly specified models and may be sensitive to MNAR data.

Methods to Avoid

Single Imputation (e.g., Mean, Mode):
- Leads to biased estimates and underestimates variability.
Listwise Deletion:
- Discards rows with missing data, reducing sample size and potentially introducing bias if the data is not MCAR.

Practical Considerations