Costs of Face-to-Face Call Attempts

I've been working on an experiment where evaluating cost savings is an important outcome. It's difficult to measure costs in this environment. Timesheets and call records are recorded separately. It's difficult to parse out the travel time from other time.

One study actually shadowed a subset of interviewers in order to generate more accurate cost estimates. This is an expensive means to evaluate costs that may not be practical in many situations.

It might be that increasing computerization does away with this problem. In a telephone facility, everything is timestamped so we can calculate how long most call attempts take. It might be that we will be able to do this in face-to-face studies soon/already.

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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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