Skip to main content

Call Record Problems

A couple of years ago I did an experiment where I recommended times to called sampled units in a face-to-face survey based on an area probability cluster sample. The recommendations were based on estimates from multi-level logistic regression models. The interviewers ignored the recommendations.

In meetings with the interviewers, several said that they didn't follow the recommendations since they call every case on every trip to an area segment. The call records certainly didn't reflect that claim. But it got me thinking that maybe the call records don't reflect everything that happens.

Biemer, Chen and Wang (2011) reported a survey of interviewers where the interviewers did report that they do not always create a call record for a call. They reported that sometimes they would not report a call in order to keep a case alive (since the number of calls on any case was limited) or because they just drove by the sampled unit and saw that no one was home. Biemer, Chen, and Wang also show that this selective reporting of calls can damage nonresponse adjustments that use the number of calls. Making bias worse.

It seems like there are two options. 1) Understand the process that generates the call records and how errors occur (this might allow us to adjust for the errors); 2) Improve the process to remove those errors. Either way, it seems like option 1 is the first step.

Comments

  1. call recording and business voip are the best ways to record calls and use click to call features for small to medium sized business'

    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...

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 ...

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...