In our last two posts, we explained (1) that every member of a simple random sample had an equal probability of selection and (2) that there are some really good reasons why complex samples can work better, despite being more complex.
Today, we’re going to talk a bit about one complex sampling technique: stratified sampling.
What is Stratified Sampling?
In stratified sampling, the target population is first classified into subgroups or strata. (Grammar note: “strata” is plural for “stratum” just as “data” is plural for “datum.”).
A simple random sample is then selected within every stratum.
That’s it.
For example, let’s say you’re doing a linguistics study within the US. You want to make sure that you have enough (more…)
In our last article, we talked about simple random samples. Simple random samples are, well…simple, but they’re not always optimal or even possible.
Probability samples that don’t meet the assumptions of Simple Random Samples are called Complex Samples.
You’ll also hear the term Complex Survey, which is really just a survey that incorporates some sort of complex sampling design. Because of their size and research goals, surveys are usually* the only type of research study that uses complex samples.
(*but not always. I have seen intervention studies, for example, that used complex sampling).
What is a Complex Sample?
The most defining feature of a complex sample is that sample members do not have equal probability of being selected.
That sounds simple enough. But… (more…)
