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Centralization vs Local Control in Face-to-Face Surveys

A key question that face-to-face surveys must answer is how to balance local control against the need for centralized direction. This is an interesting issue to me. I've worked on face-to-face surveys for a long time now, and I have had discussion about this issue with many people.

"Local control" means that interviewers make the key decisions about which cases to call and when to call them. They have local knowledge that helps them to optimize these decisions. For example. if they see people at home, they know that is a good time to make an attempts. They learn people's work schedules, etc. This has been the traditional practice. This may be because before computers, there was no other option.

The "centralized" approach says that the central office can summarize the data across many call attempts, cases, and interviewers and come up with  an optimal policy. This centralized control might serve some quality purpose, as in our efforts here to promote more ba…
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Every Hard-to-Interview Respondent is Difficult in their Own Way...

The title of this post is a paraphrase of a saying coined by Tolstoi. "Happy families are all alike; every unhappy family is unhappy in its own way." I'm stealing the concept to think about survey respondents. 

To simplify discussion, I'll focus on two extremes. Some people are easy respondents. No matter what we do, no matter how poorly conceived, they will respond. Other people are difficult respondents. I would argue that these latter respondents are heterogenous with respect to the impact of different survey designs on them. That is, they might be more likely to respond under one design relative to another. Further, the most effective design will vary from person to person within this difficult group. 

It sounds simple enough, but we don't often carry this idea into practice. For example, we often estimate a single response propensity, label a subset with low estimated propensities as difficult, and then give them all some extra thing (often more money). 

I susp…

Survey Data and Big Data... or is it Big Data and Survey Data

It seems like survey folks have thought about the use of big data  mostly as a problem of linking big data to survey data. This is certainly a very useful thing to do. The model starts from the survey data, and adds big data. This reduces the burden on respondents and may improve the accuracy of data.

But I am also having conversations that start from big data, and then fill the gaps with survey data. For instance, in looking for suitable readings on using big data and survey data, I found several interesting articles that come from folks working with big data who use survey data to validate the logical inferences they make from the data as with this study of travel based upon GPS data, or to understand missing data in electronic health records as with this study.

Now I'm also hearing discussion of how surveys might be triggered by events in the big data. The survey can answer the "why" question. Why the change? This makes for an interesting idea. The big data are the st…

What is the right periodicity?

It seems that intensive measurement is on the rise. There are a number of different kinds of things that are difficult to recall sufficiently over longer periods of time where it might be preferred to ask the question more frequently with a shorter reference period. For example, the number of alcoholic drinks consumed by day. More accurate measurements might be achieved if the questions was asked daily about the previous 24 hour period.

But what is the right period of time? And how do you determine that? This might be an interesting question. The studies I've seen tend to guess at what the correct periodicity is. I think it's probably the case that it would require some experimentation to determine that, including experimentation in the lab.

There are a couple of interesting wrinkles to this problem.

1. How do you set the periodicity when you measure several things that might have different periodicity? Ask the questions at the most frequent periodicity?

2. How does nonrespons…

Slowly Declining Response Rates are the Worst!

I have seen this issue on several different projects. So I'm not calling out anyone in particular. I keep running into this issue. Repeated cross-sectional surveys are the most glaring example, but I think it happens other places as well.

The issue is that with a slow decline, it's difficult to diagnose the source of the problem. If everything is just a little bit more difficult (i.e. if contacting persons, convincing people to list a household, finding the selected person, convincing them to do the survey, and so on), then it's difficult to identify solutions.

One issue that this sometimes creates is that we keep adding a little more effort each time to try to counteract the decline. A few additional more calls. A slightly longer field period. We don't then search for qualitatively different solutions.

That's not to say that we shouldn't make the small changes. Rather, that they might need to be combined with longer term planning for larger changes. That's…

Responsive Survey Design Short Course

I don't do a whole lot of advertising on the blog, but I did want to post about a set of short courses that we will be offering here in Ann Arbor next summer. These courses are the first three days of what will eventually be a full two-week course. We have some great instructors lined up. We are going to teach techniques of responsive survey design that can be used across a variety of studies. If you are interested, follow this link for more information.

The Cost of a Call Attempt

We recently did an experiment with incentives on a face-to-face survey. As one aspect of the evaluation of the experiment, we looked at the costs associated with each treatment (i.e. different incentive amounts).

The costs are a bit complicated to parse out. The incentive amount is easy, but the interviewer time is hard. Interviewers record their time for at the day level, not at the housing unit level. So it's difficult to determine how much a call attempt costs.

Even if we had accurate data on the time spent making the call attempt, there would still be all the travel time from the interviewer's home to the area segment. If I could accurately calculate that, how would I spread it across the cost of call attempts? This might not matter if all I'm interested in is calculating the marginal cost of adding an attempt to a visit to an area segment. But if I want to evaluate a treatment -- like the incentive experiment -- I need to account for all the interviewer costs, as best…