Skip to main content

Balancing Response through Reduced Response Rates

A case can be made that balanced response -- that is, achieving similar response rates across all the subgroups that can be defined using sampling frame and paradata -- will improve the quality of survey data. A paper that I was co-author on used simulation with real survey data to show that actions that improved the balance of response usually led to reduced bias in adjusted estimates. I believe the case is an empirical one. We need more studies to speak more generally about how and when this might be true.

On the other hand, I worry that studies that seek balance by reducing response rates (for high-responding groups) might create some issues. I see two types of problems. First, low response rates are generally easier to achieve. It takes skills and effort to achieve high response rates. The ability to obtain high response rates, like any muscle, might be lost if it is not used. Second, if these studies justify the lower response rate by saying that estimates are not significantly changed by the lower response rate, then they run the risk of moving down a slippery slope.

Think of a hypothetical 10-call data collection protocol. The first step toward balance might be to reduce some groups to a 9-call protocol. They find that the 10- and 9-call protocols are not significantly different. In the next step, they compare the 8- and 9-call protocols and decide that they are not significantly different. And then 7 to 8... and so on... None of these steps are large. But the difference between 1- and 10-call might be significant.

Finally, as in this last example, obtaining high response rates on at least some surveys or some subsample within a survey provide a means for evaluating the risk of nonresponse bias on other surveys or the rest of the sample.
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