Skip to main content

Mixed Modes -- Don't forget the mixing parameter

I've been thinking about mixed-mode surveys a great deal over the last few months. And I notice that research publications tend to use a lot of shorthand to describe the approach -- e.g. "Mail-Telephone." Of course, they describe it in more detail, but the shorthand definition focuses on the modes. Since the shorthand describes the sequence, we end up comparing different sequences. But there are other important design features at play that make these comparisons tenuous.

Of course, these other design parameters include the dosage of each mode in the sequence. Different dosages may result in different proportions of the interviews be conducted in each mode. For example, in the mail-telephone design, more mailings can increase the proportion of interviews in the mail mode. A recent article by Klausch, Schouten, and Hox includes a parameter for the mixture of modes \(\pi\).

I'm concerned that we may do lots of experimentation to design a mixed mode survey that is consistent with the current single-mode approach in terms of the estimates, but then let the mixing parameter vary after the switch is made. Of course, this is another reason to try to disentangle measurement error and nonresponse error in these surveys. Then the changes in the errors due to changes in the mixing parameter can still be controlled or, at least, understood.


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…