Survey Methods Musings

It seems like survey folks have thought about the use of big data  mostly as a problem of linking big data to survey data. This is certainly a very useful thing to do. The model starts from the survey data, and adds big data. This reduces the burden on respondents and may improve the accuracy of data. But I am also having conversations that start from big data, and then fill the gaps with survey data. For instance, in looking for suitable readings on using big data and survey data, I found several interesting articles that come from folks working with big data who use survey data to validate the logical inferences they make from the data as with this study of travel based upon GPS data, or to understand missing data in electronic health records as with this study. Now I'm also hearing discussion of how surveys might be triggered by events in the big data. The survey can answer the "why" question. Why the change? This makes for an interesting idea. The big data are the...

The term "big data" was meant to cover a wide variety of types of data. Surveys were left out of the definition. Bob Groves attempted to remedy this by coining the terms "organic" and "designed" data. These terms were meant to capture the strengths and weaknesses of big data, on the one hand, and survey data, on the other hand. Organic data are not generated for research purposes, but usually inexpensive to obtain (but not necessarily cheap to analyze). Survey data are designed for research but are often expensive to obtain. I'm finding that these terms might get in the way of thinking about some actual problems. For instance, travel surveys are looking at combining survey data with GPS data. GPS data can be large and complex, i.e. "big data." On the other hand, features of these data are designed by the researchers in travel studies. That is, the researchers ask persons to carry a GPS device or download an app to their smartphones. These d...

I had an opportunity to revisit an article by Burns and colleagues that looks at using data from smartphones (they have a nice appendix of all the data they can get from each phone) to predict things that might trigger episodes of depression. Of course, the data don't contain any specific measures of depression. In order to get those, the researchers had to.... surveys. Once they had those, then they could find the associations with the censor data from the phone. Then they could deliver interventions through the phone. There are 38 sensors on the phone. The phone delivers data quite frequently. So even a small number of phones (n=8 in this trial) there was quite a large amount of data generated. A bigger trial would have even more data. So this seems like a big data application. And, in this case the "organic" data from the phone need some "designed" (i.e. survey) data in order to be useful. This is also interesting in that the smartphone is delivering a...

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 o...