Skip to main content

Responsive Design and Quota Sampling

I conducted a webinar on responsive design this week. I had several interesting questions. One of these was a question about responsive design and quota sampling.  The question was whether these two approaches are, in fact, different?

Of course, there are similarities in that the response process is being controlled -- somewhat -- by the researchers. And this may lead to "allocating" nonresponse to some groups over others. For example, if some group is responding at higher rates, we might allocate resources to the lower responding group. Quota sampling will stop data collection for groups that have reached their quota.

There are differences, however. Responsive design attempts to provide balanced response, but doesn't necessarily force that to happen. Further, responsive design is attempting to control the data collection process using a variety of approaches. Quota sampling only has one approach -- stop when the quota is full. 

I do worry that there may be a convergence, where responsive design become quota sampling. To me, the most interesting problems are actually re-allocating resources to improve the balance of respondents while maintaining or increasing response rates. Decreasing response rates in order to improve balance seems too easy. There are some examples where this can be shown to be helpful. I think we need more evidence to be convinced of this.


Labels:

Comments

  1. junaid rahmanAugust 20, 2015 at 7:23 AM

    There is quite an investment when it comes to spending money for the services rendered by professional designing firms. After having thoroughly checked and selecting a logo design service you could contact them and get more details about their working methods and mode of payment, etc. how to design a questionnaire

    ReplyDelete

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