Skip to main content

How do they do it?

The experiment on call scheduling in a telephone survey required specialized programming to make it work. We use Blaise SMS in our telephone facility. My colleagues here, Joe Matuzak and Dave Dybicki, are planning to present what they did to make this experiment work at the International Blaise Users Conference (IBUC) in October.

They asked me to show some of the results. The first problem we faced was how to make sure that the experimental and control groups were called at the same pace. I produce files every day that show how I want the sample sorted. The control group is sorted using a different algorithm. But we had to make sure that the cases were mixed up -- we didn't want to call one group and then the other.

Dave wrote a program that reads the sorted list for each group (experimental and control). It pulls a record from each list and then checks if it is still active. Maybe it was finalized after the sort occured. When it finds 5 active cases from the top of the sort in one group, it pulls 5 active cases from the other group. It continues this process until the whole sample is sorted, with 5 active experimental cases followed by 5 control cases. Or vice versa.

The following table shows the proportion of calls to each type of case by hour. I'm ignoring day of week (mainly weekend vs weekday) just to simplify the table.



It looks like the algorithm works pretty well at keeping the distribution of calls even across hours of the day. There were some other jazzy features that Dave and Joe set up for the experiment. You'll have to catch their presentation at IBUC if you want to learn more about those.

Comments

Popular posts from this blog

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 forms of d

An Experimental Adaptive Contact Strategy

I'm running an experiment on contact methods in a telephone survey. I'm going to present the results of the experiment at the FCSM conference in November. Here's the basic idea. Multi-level models are fit daily with the household being a grouping factor. The models provide household-specific estimates of the probability of contact for each of four call windows. The predictor variables in this model are the geographic context variables available for an RDD sample. Let $\mathbf{X_{ij}}$ denote a $k_j \times 1$ vector of demographic variables for the $i^{th}$ person and $j^{th}$ call. The data records are calls. There may be zero, one, or multiple calls to household in each window. The outcome variable is an indicator for whether contact was achieved on the call. This contact indicator is denoted $R_{ijl}$ for the $i^{th}$ person on the $j^{th}$ call to the $l^{th}$ window. Then for each of the four call windows denoted $l$, a separate model is fit where each household is assu