Author: Trent Buskirk, PhD.
As it is in history, literature, criminology and many other areas, context is important in statistics. Knowing from where your data comes gives clues about what you can do with that data and what inferences you can make from it.
In survey samples context is critical because it informs you about how the sample was selected and from what population it was selected. (more…)
Author: Trent Buskirk, PhD.
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. (more…)
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.
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 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.
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).
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…)
by Karen Grace-Martin and Trent Buskirk
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. Most of the statistics we’re trained on use only one of those types—the simple random sample.
If you don’t have a simple random sample, you need to incorporate that into the way you calculate your statistics in order for the statistics to accurately reflect the population.
Remember the objective of a sample is to represent the population of interest.
Simple random samples do that in a very straightforward way.
Because they’re simple, after all.
Complex samples do it as well, but in a more…roundabout way. Their roundabout nature has many other advantages, though. But you do need to make adjustments to any statistics you calculate from them.
Simple Random Samples (SRS) have a few important features.
That’s pretty self-explanatory, but it has important consequences and requirements.
First, it requires that the list of all individuals in the population is available to the researcher.
Practically, this is never entirely true. But we can often get close. Or we can at least have reason to believe that the individuals who are available are in no systematic way different than the ones who aren’t.
That belief may or may not be reasonable, and it’s a good thing to question in your own research.
One consequence is that all observations are independent and identically distributed (i.i.d.). You’re probably familiar with this term because it’s extremely important in statistics and modeling in particular.
Now, we know that most populations aren’t really infinite. But once they get to a certain size, that part doesn’t matter mathematically.
The overall samples tend to represent a very small fraction of this very, very large population. But don’t let the small sample size fool you.
That’s where the beauty of simple random sampling comes in. Your sample doesn’t have to be that large to adequately represent the population from which it is drawn if the selection was done through simple random sampling.
In fact, most polls of Americans are conducted using simple random samples of telephone numbers and a sample of roughly 1,200 adults in the U.S.
This relatively small sample is enough to represent the full population of approximately 300 million people and to estimate a binary outcome like “will you vote or not in 2014 general elections?” within 3 percentage points.
In the next post in this series, we’ll talk about the other kind of probability sample: Complex Samples.