Skip to main content

How much has the response rate shaped our methods?

In recent posts, I've been speculating about what it might mean to optimize survey data collections to something other than the response rate. We might also look at the "inverse" problem -- how has the response rate shaped what we currently do? Of course, the response rate does not dominate every decisions that gets made on every survey. But it has had a far-reaching impact on practice. Why else would we need to expend so much energy reminding ourselves that it isn't the whole story?

The outlines of that impact are probably difficult to determine. For example, interviewers are often judged by their response rates (or possibly conditional response rates). If they were to be judged by some other criterion, how would their behavior change? For example, if interviewers were judged by how balanced their set of respondents were, how would that impact their moment-to-moment decision-making? What would their supervisors do differently? What information would sample management systems deliver to interviewers? What would project managers look at on a day-to-day basis?

It seems to me that it is difficult to see where the influence of the response rate begins and ends. At the moment, we are taking baby steps away from judging everything in terms of the response rate. We check sample balance, while seeking to maximize response rates. If the sample balance begins to be out of whack, we intervene to control the process a bit. But this is still a long way from what it might look like to be maximizing some function other than the response rate.


Popular posts from this blog

"Responsive Design" and "Adaptive Design"

My dissertation was entitled "Adaptive Survey Design to Reduce Nonresponse Bias." I had been working for several years on "responsive designs" before that. As I was preparing my dissertation, I really saw "adaptive" design as a subset of responsive design.

Since then, I've seen both terms used in different places. As both terms are relatively new, there is likely to be confusion about the meanings. I thought I might offer my understanding of the terms, for what it's worth.

The term "responsive design" was developed by Groves and Heeringa (2006). They coined the term, so I think their definition is the one that should be used. They defined "responsive design" in the following way:

1. Preidentify a set of design features that affect cost and error tradeoffs.
2. Identify indicators for these costs and errors. Monitor these during data collection.
3. Alter the design features based on pre-identified decision rules based on the indi…

Future of Responsive and Adaptive Design

A special issue of the Journal of Official Statistics on responsive and adaptive design recently appeared. I was an associate editor for the issue and helped draft an editorial that raised issues for future research in this area. The last chapter of our book on Adaptive Survey Design also defines a set of questions that may be of issue.

I think one of the more important areas of research is to identify targeted design strategies. This differs from current procedures that often sequence the same protocol across all cases. For example, everyone gets web, then those who haven't responded to  web get mail. The targeted approach, on the other hand, would find a subgroup amenable to web and another amenable to mail.

This is a difficult task as most design features have been explored with respect to the entire population, but we know less about subgroups. Further, we often have very little information with which to define these groups. We may not even have basic household or person chara…

An Experimental Adaptive Contact Strategy

I'm running an experiment on contact methods in a telephone survey. I'm going to present the results of the experiment at the FCSM conference in November. Here's the basic idea.

Multi-level models are fit daily with the household being a grouping factor. The models provide household-specific estimates of the probability of contact for each of four call windows. The predictor variables in this model are the geographic context variables available for an RDD sample.

Let $\mathbf{X_{ij}}$ denote a $k_j \times 1$ vector of demographic variables for the $i^{th}$ person and $j^{th}$ call. The data records are calls. There may be zero, one, or multiple calls to household in each window. The outcome variable is an indicator for whether contact was achieved on the call. This contact indicator is denoted $R_{ijl}$ for the $i^{th}$ person on the $j^{th}$ call to the $l^{th}$ window. Then for each of the four call windows denoted $l$, a separate model is fit where each household is assum…