From average response rate to personalized protocol

Survey methods research first efforts to understand nonresponse started by looking at response rates. The focus was on finding methods that raided response rates. This approach might be useful when everyone has response propensities close to the average. The deterministic formulation of nonresponse bias may even reflect this sort of assumption.

Researchers have since looked at subgroup response rates. Also interesting, but assuming that these rates are a fixed characteristic leaves us helpless.

Now, it seems that we have begun working with an assumprton that there is heterogenous response to treatments and that we should, therefore, tailor the protocol and manipulate response propensities.

I thought this development has a parallel in clinical trials where there is a new emphasis on personalized medicine.

We still have important questions to resolve. For example, what are we trying to maximize?

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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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