Skip to main content

Adaptive Design in Panel Surveys

I enjoyed Peter Lugtig's blog post on using adaptive design in panel surveys. I was thinking about this again today. One of the things that I thought would be interesting to look at would be to view the problem of panel surveys as maximizing information gathered.

I feel like we view panel studies as a series of cross-sectional studies where we want to maximize the response rate at each wave. This might create non-optimal designs. For instance, it might be more useful to have the first and the last waves measured, rather than the first and second waves. From an imputation perspective, in the latter situation (first and last waves) it is easier to impute the missing data.

The problem of maximizing information across waves is more complicated than maximizing response at each wave. The former is a sequential decisionmaking problem, like those studies by Susan Murphy as "adaptive treatment regimes." It might be the case, that a lower response rate in early waves might lead to overall higher information -- if it can lead to more data later. It's certainly a complicated problem, but one worth considering.

For example, would postponing refusal conversion across several waves increase the probability of responding to more waves?  A recent article by Burton and colleagues looked at the effect of refusal conversion on panel composition. People tended to stay in after being converted, but eventually dropped out. This is a useful evaluation of refusal conversion. It might also be useful to examine whether delaying refusal conversion increases the number of waves of response.
Labels:

Comments

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
36 comments
Read more

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
Post a Comment
Read more

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
4 comments
Read more