Skip to main content

Tiny Data...

I came across this interesting post about building a Bayesian model with careful specification of priors. The problem is that they have "tiny" data. So the priors play an important role in the analysis.

I liked this idea of "tiny" data. The rush to solve problems for "big data" has obscured the fact that are interesting problems for situations where you don't have much data.

Frost Hubbard and I looked at a related problem in a recently published article. We look at the problem of estimating response propensities during data collection. In the early part of the data collection, we don't have much data to estimate these models. As a result, we would like to use "prior" data from another study. However, this prior information needs to be well-matched to the current study -- i.e. have the same design features, at least approximately.

This doesn't always work. For example, I might have a new study with a different incentive than I've used before. How do I estimate the impact of that incentive early on? This is like the "tiny" data problem. It makes sense to try and formalize our prior information (in the case of a new design feature like the incentive example). This is usually a combination of expert opinion and literature review.

I would argue that turning this information into a formal Bayesian prior is useful for a couple of reasons. First, it gives us a way to learn whether our priors and methods for forming them are adequate. Second, it gives us a way to generalize our knowledge across surveys. Otherwise, the expert judgments aren't quantified in a way that others can easily use.

Comments

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…

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…

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…