### Interesting Experiment

I recently read an article about a very interesting experiment. Luiten and Schouten report on an experiment to improve the Statistics Netherlands' Survey of Consumer Sentiment. Their task was to improve representativity (defined as increasing the R-Indicator) of the survey without increasing costs and without lowering the response rate. This sounds like a difficult task. We can debate the merits of lowering response rates in "exchange" for improved representativity. But who can argue with increasing representativity without major increases in costs or decreases in response rates.

The experiment has a number of features all built with the goal of meeting these constraints. One of the things that makes their paper so interesting is that each of the design features is "tailored" to the specifics of the sampled units. For those of you who like the suspense of a good survey experiment, spoiler alert: they managed to meet their objectives.

### "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…

### 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…

### Is there such a thing as "mode"?

Ok. The title is a provocative question. But it's one that I've been thinking about recently. A few years ago, I was working on a lit review for a mixed-mode experiment that we had done. I found that the results were inconsistent on an important aspect of mixed-mode studies -- the sequence of modes.

As I was puzzled about this, I went back and tried to write down more information about the design of each of the experiments that I was reviewing. I started to notice a pattern. Many mixed-mode surveys offered "more" of the first mode. For example, in a web-mail study, there might be 3 mailings with the mail survey and one mailed request for a web survey. This led me to think of "dosage" as an important attribute of mixed-mode surveys.

I'm starting to think there is much more to it than that. The context matters  a lot -- the dosage of the mode, what it may require to complete that mode, the survey population, etc. All of these things matter.

Still, we ofte…