Stratified Sampling

What is Complex Sampling? Part 4: Cluster Sampling

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 [Read more…] about What is Complex Sampling? Part 4: Cluster Sampling

What is a Complex Sample Part 3: Stratified Sampling

by Karen Grace-Martin 2 Comments

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 [Read more…] about What is a Complex Sample Part 3: Stratified Sampling

Stratified Sampling for Oversampling Small Sub-Populations

by guest contributer 5 Comments

by Ritu Narayan

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.

One Scenario for Stratified Sampling

Suppose you are studying minority groups and their behavior, say Yiddish speakers in the U.S. and their voting. Yiddish speakers are a small subset of the US population, just .6%. [Read more...] about Stratified Sampling for Oversampling Small Sub-Populations