## 18.4 General strategies

Strategies for handling missing data include:

• Complete-case/available-case analysis: drop cases that make analysis inconvenient.
• If variables are known to contribute to the missing values, then appropriate modeling can often account for the missingness.
• Imputation procedures: fill in missing values, then analyze completed data sets using complete-date methods
• Weighting procedures: modify “design weights” (i.e., inverse probabilities of selection from sampling plan) to account for probability of response
• Model-based approaches: develop model for partially missing data, base inferences on likelihood under that model

### 18.4.1 Complete cases analysis

If not all variables observed, delete case from analysis

• Simplicity
• Common sample for all estimates
• Loss of valid information
• Bias due to violation of MCAR

### 18.4.2 Available-case analysis

• Use all cases where the variable of interest is present
• Potentially different sets of cases for means of X and Y
• and complete pairs for $$r_{XY}$$
• Tempting to think that available-case analysis will be superior to complete-case analysis
• But it can distort relationships between variables by not using a common base of observations for all quantities being estimated.

### 18.4.3 Imputation

Fill in missing values, analyze completed data set