A few posts ago, I talked about the value of balancing response across subgroups defined by data on the frame (or paradata from all cases). The idea was that this provides some empirical confirmation of whether the subgroups are related to the variables of interest.
Paradoxically, if we balance the response rates across these subgroups, then we reduce the utility of these variable for adjustment later. That's the downside of this practice.
As I said earlier, I think this does provide confirmation of our hypothesis. It also reduces our reliance on the adjustment model, although we have to assume the model is correct and there aren't unobserved covariates that are actually driving the response process.
Is there an additional advantage to this approach? It seems that we are least trying to provide an ordered means of prioritizing the sample. We can describe it. Even if there are departures, we can say something about how the decisions were made to prioritize certain cases. Without this approach, we can only surmise. We often assume that the process is random, but this is probably not accurate.
Paradoxically, if we balance the response rates across these subgroups, then we reduce the utility of these variable for adjustment later. That's the downside of this practice.
As I said earlier, I think this does provide confirmation of our hypothesis. It also reduces our reliance on the adjustment model, although we have to assume the model is correct and there aren't unobserved covariates that are actually driving the response process.
Is there an additional advantage to this approach? It seems that we are least trying to provide an ordered means of prioritizing the sample. We can describe it. Even if there are departures, we can say something about how the decisions were made to prioritize certain cases. Without this approach, we can only surmise. We often assume that the process is random, but this is probably not accurate.
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