Tracking Research: A Lack of Experimental Studies

I've been reading a number of papers on tracking (aka tracing or locating) of panel members in longitudinal research. Many of the papers are case studies, reporting on what particular studies did. Very few actually conduct experiments.

Survey methodologists have produced a few recent experimental papers. Research on HRS showed that higher incentives had persistent effects on response at later waves. McGonagle and colleagues looked at the effects of between-wave contact methods and incentives. Fumagelli and colleagues  also explore between wave contact methods.

These experiments all involve contacting panel members. I found one interesting paper that actually experimented with the order of the steps in the tracking process. Usually, the order starts with the cheapest things to do and goes to the more expensive. If steps have a similar cost, then just choose an order. This paper by Koo et al actually randomized the order of the steps (two different websites). A haven't seen any other papers like this one. It's something that I think would be fun and useful with which to experiment.

Popular posts from this blog

My dissertation was entitled "Adaptive Survey Design to Reduce Nonresponse Bias." I had been working for several years on "responsive designs" before that. As I was preparing my dissertation, I really saw "adaptive" design as a subset of responsive design.

Since then, I've seen both terms used in different places. As both terms are relatively new, there is likely to be confusion about the meanings. I thought I might offer my understanding of the terms, for what it's worth.

The term "responsive design" was developed by Groves and Heeringa (2006). They coined the term, so I think their definition is the one that should be used. They defined "responsive design" in the following way:

1. Preidentify a set of design features that affect cost and error tradeoffs.
2. Identify indicators for these costs and errors. Monitor these during data collection.
3. Alter the design features based on pre-identified decision rules based on the indi…

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 assum…