Skip to main content

Refusal Conversions and Timing of the Call

In the previous post, I talked about an interesting problem that a call scheduling experiment produced in relationship to refusal conversions. In the experiment, calls prior to a first refusal were more efficient under the experimental algorithm. But calls after the refusal were less efficient (in terms of contact) such that the experimental condition was only as efficient as the control when looking at call pre- and post-first refusal. Ouch.

My question is: what can be done to change the call scheduling of calls after the refusal to improve their efficiency? My colleagues here in Survey Research Operations suggested that calling back at the same time as the first refusal might be bad. You might get the same person.

For that to be the case, it seems as if the person who refused would have to screening their calls. While the control algorithm calls back at different times and finds someone else at home.

As a test of this hypothesis, I changed the algorithm to put the window in which the first refusal was taken at the bottom of the list. In other words, avoid calling at the window in which the first refusal was taken. We'll see how or if this works...

Comments

  1. Very interesting, James. Do update next month if you can...
    Do you have difficulty implementing these changes to the call algorithm? I have refrained from asking for changes beyond the first call attempt for now.

    ReplyDelete
  2. We have the sample management system set up to take as an input a set of files that I create on a daily basis. The SMS then updates the prioritization for each window/time zone combination.

    I have an automated process that creates my inputs files (the prioritization). I reprogrammed it to make this change.

    I'm thinking that things other than the timing of the call are more important -- the interviewer, incentive, etc. But it's still weird that I'm getting this result (less efficiency in the refusal conversion part).

    ReplyDelete

Post a Comment

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