Monitoring Daily Response Propensities

I've been working on this paper for a while. It compares models estimated in the middle of data collection with those estimated at the end of data collection. It points out that these daily models may be vulnerable to biased estimates akin to the "early vs. late" dichotomy that is sometimes used to evaluate the risk of nonresponse bias.The solution is finding the right prior specification in a Bayesian setup or using the right kind and amount of data from a prior survey so that estimates will have sufficient "late" responders.

But, I did manage to manufacture this figure which shows the estimates from the model fit each day with the data available that day ("Daily") and the model fit at the end of data collection ("Final"). The daily model is overly optimistic early. For this survey, there were 1,477 interviews. The daily model predicted there would be 1,683. The final model predicted 1,477.

That's the average "optimism." That "optimism" might be bad if I think that things are going better than they actually are. On the other hand, if all I cared about was the ranking of cases into "high," "medium," and "low" probability, then this optimistic bias might not matter... but then again, it might.

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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

We often talk about “data quality” or “data integrity” when we are discussing the collection or analysis of one type of data or another. Yet, the definition of these terms might be unclear, or they may vary across different contexts. In any event, the terms are somewhat abstract -- which can make it difficult, in practice, to improve. That is, we need to know what we are describing with those terms, before we can improve them. Over the last two years, we have been developing a course on Total Data Quality , soon to be available on Coursera. We start from an error classification scheme adopted by survey methodology many years ago. Known as the “Total Survey Error” perspective, it focuses on the classification of errors into measurement and representation dimensions. One goal of our course is to expand this classification scheme from survey data to other types of data. The figure shows the classification scheme as we have modified it to include both survey data and organic f...

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