Is there any way to account for nonresponse bias in a study, besides assigning more weight to those participants that were hard to obtain?
Missing data are one of the hard issues in real studies. Many people simply disregard them and analyze the complete data only. This is bad practice. Resist doing this, even when your advisors or bosses tell you it is OK. A great book on this subject is by Little and Rubin (1987) Statistical Analysis with Missing Data. The approach I use is to impute the missing values with scientifically-driven guesses. For example, suppose income is missing for some units. I would use a regression model to predict their incomes from other characteristics of these units. Then, I fill in the missing incomes with predictions from my regression model, being sure to add some chance deviation from the line in my imputations. This fills in all the holes in the data set so that I can analyze it as usual. A key idea is to impute several values of the missing value to make sure that you can capture the uncertainty due to guessing. There are specific rules for combining the multiply-imputed data sets that are de
