Imputation of "e" as an extension of a survival model approach

The genesis of the idea for imputing "e" came from my process for estimating the fraction of missing information for an ongoing survey. I had to impute eligibility for cases each day so that I could impute survey values for the subset of eligible case (including those with imputed eligibility). I thought, "hey, I'm already imputing 'e.' I just need to set it up that way."

Along the way, I had to compare the method to the life table product-limit approach advocated by Brick et al. (POQ, 2002). I found a very nifty article by Efron (JASA, 1988) that compares life table methods to logistic regression. Essentially, for the discrete time case, the life table model produces the same results as if we had a logistic regression model with a dummy variable for each time point. Efron then paramterized the model with fewer parameters ($t$, $t^2$, and $t^3$), I believe, and shows how this compares to the life table product-limit nonparametric estimate.

This article helped me think about how my method related to the life table method and was a nice starting point for the analysis. I could start with a model that includes a dummy variable for every call number. Then I could simplify and use some transform of the number of calls (I chose the log transform) and even add parameters.

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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

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