More thoughts on the cost of paradata....

Matt Jans had some interesting thoughts on costs on his blog. I like the idea of small pilot tests. In fact, we do a lot of turning on and off of interviewer observations and other elements. In theory, this creates nearly experimental data that I have failed to analyze. My guess is that the amount of effort created by these few elements is too small to be detected given the sample sizes we have (n=20,000ish). That's good, right? The marginal cost of any observation is next to zero.

At a certain point, adding another observation will create a problem. It will be too much. Just like adding a little more metal to a ball bearing will transform it into a... lump of metal. Have we found that point yet?

Last week, we did find an observation that was timed using keystroke data. We will be taking a look at those data.

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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

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

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