Complex Surveys use a sampling technique other than a simple random sample. Terms you may have heard in this area include cluster sampling, stratified sampling, oversampling, two-stage sampling, and primary sampling unit. learn more

Sampling is such a fundamental concept in statistics that it’s easy to overlook. You know, like fish ignore water. It’s just there. But how you sample is actually very important. There are many different ways of taking probability samples, but they come down to two basic types. learn more

Recently, 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. learn more

Previously, 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. Now, we’re going to talk a bit about one complex sampling technique: stratified sampling. learn more

Earlier 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. In this article, we’re going to discuss another common design features of complex samples: cluster sampling. learn more

What do you do when you hear the word error? Do you think you made a mistake? Well, in survey statistics, error could imply that things are as they should be. That might be the best news yet – error could mean that things are as they should be. Let’s break this down a bit more before you think this might be a typo or even worse, an error. learn more

Sampling is a critical issue in any research study design. Most of us have grappled with balancing costs, time and of course, statistical power when deciding our sampling strategies. How do we know when to go for a simple random sample or to go for stratification or for clustering? Let’s talk about stratified sampling here and one research scenario when it is useful. learn more