Explain what average imputation is.

Average imputation is a statistical method used in handling missing data. In this method, the missing value on a certain variable is replaced by the mean of the available cases. Here are the steps to perform average imputation:

1. Identify the variable with missing data: The first step is to identify which variable or variables have missing data that you want to impute.

2. Calculate the mean of the variable: Once you've identified the variable, calculate the mean of this variable excluding the missing values.

3. Replace the missing values: After calculating the mean, replace the missing values in the variable with this mean value.

4. Analyze the data: Now that you've replaced the missing values, you can proceed with the analysis of your data.

It's important to note that while average imputation is easy to use, it can lead to biased estimates of the variances and covariances. It also doesn't take into account the uncertainty around the imputation, leading to underestimated standard errors.