Approaches Toward Missing Data

Missing data vex researchers, and there's no single "correct" way to deal with this not-infrequent real-world problem. A recent question posted on the StataList (here) visually illustrates what this can look like (in terms of N variation) across alternative specifications of a multilevel regression model drawing from a data set that contains missing data in some variables.

As this output illustrates, as one proceeds across models 1-4 what is clear is that the addition (and subtraction) of variables introduce missing data. How to approach this and what to do about it motivated the initial query. And in the discussion that follows identifies (some of the) various "standard" approaches.