### Call Windows as a Pattern

The paradata book, edited by Frauke Kreuter, is out! I have a chapter in the book on call scheduling.

One of the problems that I mention is how to define call windows. The goal should be to create homogenous units. For example, I made the following heatmap that shows contact rates by hour for a face-to-face survey. The figure includes contact rates for all cases and for the subset of cases that were determined to be eligibile

I used this heatmap to define contiguous call windows that were homogenous with respect to contact rates. I used ocular inspection to define the call windows.

I think this could be improved. First, clustering techniques might produce more efficient results. I assumed that the call windows had to be contiguous, this might not be true.

Second, along what dimension do we want these windows to be homogenous? Contact rates is really a proxy. We want them to be homogenous with respect to the results of next call on any case, or really our final goal of interviewing the case.

It might be that the heatmap of contact rates gets us much of the way there, but it would be nice to know that for sure.

1. Fascinating! It's amazing how much adding color can help with interpretation. My only comment is on that dimension. It looks like red is high contact rate cells and green is low rate cells. With the proper key, that's easier to understand (or even just by looking at a few cells), and it probably doesn't matter which colors you use, compared to the different between using color v. not. But to make it even more intuitive, you might think of using blue for the low rate cells (heat goes from hot=red to cool=blue, not green). If you're trying to use a stop light analogy, you might want to flip the colors so that green is for the high cells (the good cells) and red as the low cells (the bad cells). I don't know if there's any research to support this, but to me red says "Stop and look...problem here!" and green says "Ok...these are fine, move on".

Keep up the great work!

1. I was thinking that the "hot" colors meant times we wanted to call. But you may be right, it could be interpreted as flagging a problem.

My daughter just graduated from "safety town." A key lesson for her was... red means stop.

### "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…