Sequence in a Mixed Mode Survey

We recently tried to implement a mixed mode approach to a large screening survey that is usually done face-to-face (FtF). We wanted to be sure that modes with lower response rates didn't "contaminate" the sample. If not, there may be cost savings in using a mailed version of the screening survey.

We varied the sequence of the mixed mode approach to see if that had any impact. We did FtF-Mail, Mail-FtF, and FtF.

We also monitored response rates to the main interview, which is conducted only with eligible persons. The response rates to the screening survey were very similar across the three arms of the trial. But it turns out that one mode combination did better on the main interview response rate with cases in locked buildings. That mode combination was FtF-Mail. This might be fluke, but definitely worth exploring.

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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

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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