Skip to main content

More on R-Indicators

I mentioned the R-Indicators in a recent post. In addition to the article in Survey Methodology, they also have a very useful website. The website includes a number of papers and presentations on the topic.
Labels:

Comments

  1. Andy PeytchevDecember 1, 2009 at 12:41 PM

    In some respects, the R-index is contradictory to your fraction of missing data. For example, more auxiliary data can lead to lower R-index (bad) and lower fraction of missing data (good). Do you see a middle ground approach, despite how different they are in theory, assumptions, and implementation?

    ReplyDelete
  2. James WagnerDecember 2, 2009 at 9:45 AM

    I can only say something very general on this question at the moment.

    Both of these measures are model-dependent, that is, they are only as good as the model they choose for their implementation. As a result, we'll need multiple views of the problem. In addition, we'll be uncertain of the results produced by either statistic. So basing actions on those statistics may need to be tempered in some fashion. If multiple statistics converge to the same solution, we should probably feel pretty good about that solution.

    I hope to address these questions. I'm working on it.

    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