Skip to main content

Measurement Error in Paradata

Paradata are often quite messy. I guess it shouldn't be that surprising since they are often the by-product of a process (survey interviewing) that can be messy. And, at least initially, they were a means to an end and not the end itself.

But there are some issues that run a little deeper than just messy. Brady West had a very interesting paper at AAPOR that looked at measurement error in interviewer observations. On a large face-to-face survey, we ask interviewers to make guesses about key characteristics of selected persons. These guesses are (relatively) highly correlated with survey outcome variables. This is a useful property for many reasons -- monitoring for the risk of bias, adjustment, etc. But, as Brady points out, the measurement or misclassification error reduces their effectiveness.

I've been thinking about another kind of error. In talking with interviewers on the same face-to-face survey, they say the visit ever sampled housing unit every time they visit a segment (i.e. sampled neigbhorhood in a geographically clustered sample). This doesn't show up in the call records. I'm thinking that they may be driving through the segment and then selecting cases to call based on this initial pass through. Possibly based on clues they say -- car in driveway, people outside, etc. If so, the call records would have a kind of selection bias built in. And, it would be likely that interviewers were highly variable in their ability to detect these clues.
Labels:

Comments

Post a Comment

Popular posts from this blog

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...
3 comments
Read more

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 ...
36 comments
Read more

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...
1 comment
Read more