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Sampling III: Nonprobability Samples and Choosing Informants

If your objective is to estimate a parameter or a proportion from a sample to a larger population, and your research calls for the collection of data about attributes of individuals (whether those individuals are people or organizations or episodes of a sitcom), then the rule is simple: Collect data from a sufficiently large, randomly selected, unbiased sample. If you know that you ought to use an unbiased sample and you have the means to get an unbiased sample and you still choose to use a nonprobability sample, then expect to take a lot of flak.

There are, however, three quite different circumstances under which nonprobability samples are exactly what are called for:

1. Nonprobability samples are always appropriate for labor-intensive, in-depth studies of a few cases. Most studies of narratives are based on fewer than 50 cases, so every case has to count. This means choosing cases on purpose, not randomly. In-depth research on sensitive topics requires nonprobability sampling. It can take months of participant observation fieldwork before you can collect narratives about topics like sexual and reproductive history or bad experiences with mental illness or use of illegal drugs.

Come to think of it, just about everything is a sensitive topic when you dig deeply enough. Sexual history is an obviously sensitive topic, but so is the management of household finances when you get into how people really allocate their resources. People love to talk about their lives, but when you get into the details of a life history, you quickly touch a lot of nerves. Really in-depth research requires informed informants, not just responsive respondents—that is, people whom you choose on purpose, not randomly. [1] [2]

The major nonprobability sampling methods are: quota sampling, purposive sampling (also called judgment sampling), convenience sampling, and chain-referral (snowball) sampling. A special kind of mixed method, that combines elements of probability and nonprobability sampling, is the case control design (see box 4.6).

  • [1] Nonprobability samples are also appropriate for large surveys when, despite our bestefforts, we just can’t get a probability sample. In these cases, use a nonprobabilitysample and document the bias. That’s all there is to it. No need to agonize about it.
  • [2] And, as I said at the beginning of chapter 5, when you are collecting cultural data, ascontrasted with data about individuals, then expert informants, not randomly selectedrespondents, are what you really need. Think of the difference between asking someone‘‘How old was your child when you first gave him an egg to eat?’’ versus ‘‘At what agedo children here first eat eggs?’’ I deal with the problem of selecting cultural experts(people who are likely to really know when most mothers introduce eggs around here)later in this chapter.
 
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