Another choice that we've got would be filling in a mean

that's usually within some cells defined on characteristics that you need to

know about all the units in your sample.

And you may or not add a random error to that.

The third choice is what's called cold deck.

So the idea there is this works if you've got a continuing survey

where you've got a previous edition of the survey.

If a unit responded in the previous version,

then you go back, get that value or some possibly

indexed forward value of it and fill it in for the current missing value.

So it's called cold because you're referring back

to a dataset that is already in your hands.

It's not the current one that you're dealing with.

Now, in contrast to that is something called hot deck, and

what that amounts to is you look at your current dataset.

If you've got a missing value, then you find a similar case that

has complete data for the variable, and you just grab that value and fill it in.

So that's usually done within cells, also.

A fifth possibility is regression prediction.

So based on covariates that are available for all units, those that are missing or

non-missing, you generate a regression prediction, and you may or

may not add a random error to that.

Now, somewhat related to that is a method called predictive mean matching.

You find a unit that's got the closest

observed value to one predicted by regression for your missing case.

So you've got a missing case.

You make a regression prediction based on some covariates.

So you've gotta fit that regression from the complete cases.

Then you look at that prediction, find a complete case that actually has reported

data that's close to that prediction, and then you fill in that value.

So it's got the virtue of taking advantage of any sort of regression

relationship between covariates and your analysis variable.