Skip to main content

Probability Sampling

In light of the recent kerfuffle over probability versus non-probability sampling, I've been thinking about some of the issues involved with this distinction. Here are some thoughts that I use to order the discussion in my own head:

1. The research method has to be matched to the research question. This includes cost versus quality considerations. Focus groups are useful methods that are not typically recruited using probability methods. Non-probability sampling can provide useful data. Sometimes non-probability samples are called for.

2. A role for methodologists in the process is to test and improve faulty methods. Methodologists have been looking at errors due to nonresponse for a while. We have a lot of research for using models to reduce nonresponse bias. As research moves into new arenas, methodologists have a role to play there. While we may (er... sort of) understand how to adjust for nonresponse, do we know how to adjust for an unknown probability of getting into an online panel? That's not my area, but certainly something worth looking at. Probably election polling is most developed in this area.

Related to this, how to we know when something is bad? Tough question, but methodologists ought to lead the way in developing methods to evaluate it.

3. At least some probability surveys are still needed. For example, in election polling, likely voter models are an important ingredient of estimates. Such models can be tested and developed on panel surveys like the National Election Study, and then applied to other samples. Mick Couper reviews uses of surveys in the age of "big data." Yes, we do still need them.

Comments

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