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Showing posts from November, 2014

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 …

Interviewer Travel and New Forms of Data

The Director of the Census Bureau, John Thompson, recently blogged about a field test for the 2020 Decennial Census Nonresponse Follow-up. They are testing a number of new features, including the use of smartphones in data collection.

I've been working with GPS data from smartphones used by field interviewers. The data are complex, but may offer new insights into interviewer travel. Think of travel as a broad concept -- it's not just an expense or efficiency issue. The order in which calls are made may also relate to field outcomes like contact and response rates.

Perhaps these GPS data can help us understand how interviewers currently make decisions about how to work their sample. For example, do they move past sampled housing units when they first arrive to the area segment? Is this action associated with higher contact rates?

Of course, travel is also an expense or efficiency issue. I wouldn't want pushing for more efficient travel to interfere with other aspects of the…