* If the oversampling was part of the master sampling design, the Latino voters could have been assigned a lower individual weight to correct for the oversampling (the technical term for this sort of procedure is stratification). This is generally hard to do in telephone surveys. (You can generally fudge it using quotas—which raise theoretical problems as well.)

However, if the oversampling was done post hoc, the only valid correction (at least in terms of classic probability sampling) is to only use the Latino voters who were in the original sample for inference to the population at large. An alternative approach called propensity weighting—which would weight the Latinos less due to their overrepresentation relative to the other ethnic groups—is somewhat controversial (however, Harris Interactive, the online survey research company, has demonstrated to at least some researchers’ satisfaction that Internet surveys with propensity weighting can actually do a better job of estimating population parameters than standard telephone surveys or Knowledge Networks’ competing WebTV-based survey product based on probability sampling; see this article from Political Analysis for details). There’s a subtle difference between the two weighting schemes (propensity weighting versus stratification), but in terms of sampling theory it’s a very important difference.

Interestingly enough, if you don’t care about the actual population parameters, sampling bias is far less problematic. For example, if you wanted to estimate a statistical model explaining support for candidates in the recall, sampling bias really isn’t an issue provided your explanatory variables account for the cause of the sampling bias. So if Latinos are oversampled, and you include a variable representing Latinos in the model, that particular oversampling problem is no longer an issue for making inferences about individual behavior. (However, if you don’t include such a variable, you’ll end up with non-spherical errors which will have unknown effects on your significance tests. It’s not quite omitted variable bias, but it’s still an ugly problem. Hence to be safe it is best to use the sample weights if you have them available, particularly if you don’t know how your sample was stratified.)