Big Data and Survey Data

I missed Dr. Groves blog post on this topic. It is an interesting perspective on the strengths and weaknesses of each data source. His solution is to "blend" data from both sources to compensate for the weaknesses of each. Dr. Couper spoke along similar lines at the ESRA conference last year.

An important takeaway from both of these is that surveys have an important place in the future. Surveys gather, relative to big data, rich data on individuals that allow the development and testing of models that may be used with big data. Or provide benchmarks for estimates from big data for which the characteristics of the population are only vaguely known.

In any event, I'm not worried that surveys or even probability sampling have outlived their usefulness. But it is good to chart a course for the future that will keep survey folks relevant to these pressing problems.

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Assessment of Maching Learning Classifiers

I heard another interesting episode of the Data Skeptic podcast . They were discussing how a classifier could be assessed (episode 121). Many machine learning models are so complex that a human being can't really interpret the meaning of the model. This can lead to problems. They gave an example of a problem where they had a bunch of posts from two discussion boards. One was atheist and the other board was composed of Christians. They tried to classify each post as being from one or the other board. There was one poster who posted heavily on the Christian board. His name was Keith. Sadly, the model learned that if the person who was posting was named Keith, then they were Christian. The problem is that this isn't very useful for prediction. It's an artifact of the input data. Even cross-validation would eliminate this problem. A human being can see the issue, but a model can't. In any event, the proposed solution was to build interpretable models in local areas of t...

Tailoring vs. Targeting

One of the chapters in a recent book on surveying hard-to-reach populations looks at "targeting and tailoring" survey designs. The chapter references this paper on the use of the terms among those who design health communication. I thought the article was an interesting one. They start by saying that "one way to classify message strategies like tailoring is by the level of specificity with which characteristics of the target audience are reflected in the the communication." That made sense. There is likely a continuum of specificity ranging from complete non-differentiation across units to nearly individualized. But then the authors break that continuum and try to define a "fundamental" difference between tailoring and targeting. They say targeting is for some subgroup while tailoring is to the characteristics of the individual. That sounds good, but at least for surveys, I'm not sure the distinction holds. In survey design, what would constitute ...

What is Data Quality, and How to Enhance it in Research

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Survey Methods Musings

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