In this series, we’ve already talked about what a complex sample isn’t; why you’d ever bother with a complex sample; and stratified sampling.
All this is in support of our upcoming workshop: Introduction to the Analysis of Complex Survey Data Using SPSS. If you want to learn a lot more on this topic, check that out.
In this article, we’re going to discuss another common design features of complex samples: cluster sampling.
What is Cluster Sampling?
In cluster sampling, you split the population into groups (clusters), randomly choose a sample of clusters, then measure each individual from each selected cluster.
The most common and obvious example of cluster sampling is when school children are sampled. An example I (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…)