Friday, December 18, 2015

Bayesian Adaptive Survey Design

Just a short blog post. I recently attended the 4th Workshop on Adaptive and Responsive Survey Design. There were many good papers delivered at this workshop. There was a particular focus on Bayesian approaches to the estimation of survey design parameters or paradata modeling. The link has some of the slides and papers.

Friday, December 11, 2015

Mode Sequence

A few years ago, I did an experiment with two sequences of modes for a screening survey. The modes were mail and face-to-face. We found that the sequence didn't matter much for the response rate to the screener, but that the arm that started with face-to-face and then used mail had a better response rate to the main interview given to those who were found to be eligible in the screening interview.

There are other experiments that use different sequences of modes. Some of these find that the sequence doesn't matter. For example, Dillman and colleagues looked at mail-telephone and telephone-mail and these had about the same response rate. On the other hand, Millar and Dillman found that for mail-web mixed-mode surveys the sequence does seem to matter, although certainly the number and kind of contact attempts are also important.

It does seem that there are times when the early attempts might interfere with the effectiveness of later attempts. That is, we "harden the refusal" early. If we could identify the cases for which this is true, then we could skip right to the effective treatment, instead of offering it to them when they've already decided not to participate.

Friday, December 4, 2015

Myopic Calling Strategies

I'm interested in sequential decision-making problems.In these problems, there is a tension between exploration and exploitation. Exploitation is when you take actions with more certainty about the rewards. The goal of exploitation is to get maximum reward to the next action given what is currently known. Exploration is when you take actions with less certainty. The goal is to discover what the rewards are for actions about which little is known.

A strategy that always exploits is called myopic since it always tries to maximize the reward of the current action without any view to long-term gains.

Calling algorithms certainly face this tension. For example, evenings might be the best time on average to contact households. If I know nothing else, then that would be my guess about when to place the next call. But it would be foolish to stay with that option if it continues to fail. If I have failures in that call window, I might explore another call window to try and see if the reward is greater in that window for this particular household.

The following is a simple example, taken from Kulka et al. (1988). The goal is to establish contact. The contact strategy \(a_j\) can take on any of the following five values: WDM=weekday morning, WDA=weekday afternoon, WDE=weekday evening, SAT=Saturday, SUN=Sunday. We want to know which 3-call (\(j=1,2,3\)) sequence produces the highest contact rate. Using our notation, if \(Y_i=1\) denotes contact for the \(i^{th}\) case on any of the 3 calls, then the goal is to find the 3-call sequence that leads to the highest \(Pr(Y_i=1)\). A myopic strategy would choose \(a_1\) by comparing the probability of contact for each of the five possible treatments. The choice of \(a_2\) and \(a_3\) would be made in the same way. A non-myopic strategy would look at all 125 (\(5*5*5\)) possible sequences and determine which one had the highest overall probability of contact. That's basically what Kulka and colleagues did (looking at all possible combinations).

We could extend this approach across several stages of the survey process by looking at how the contact strategy impacts the ability to gain cooperation at later stages. For instance, a three-call sequence that placed three calls in the middle of the night might have a high contact rate, but would likely have a low rate of completing interviews.