OptinMon 26 - Complex Samples

What is a Complex Sample? Part 1: Simple Random Samples

January 2nd, 2014 by

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.

Why all this is important

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.

What is Simple random sampling?

Simple Random Samples (SRS) have a few important features.

1. Each element in the population has an equal probability of being selected to the sample. 

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.

2. The sample is a tiny proportion of an infinite population, but…

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.

 


Member Training: Complex Survey Sampling – An Overview

November 1st, 2013 by

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.

Complex Samples require statistical methods that take the exact sampling design into account to ensure accurate results.

In this webinar, guest instructor Dr. Trent Buskirk will give you an overview of the common sampling techniques and their effects on data analysis.


Note: This training is an exclusive benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series. Each Stat’s Amore Training is approximately 90 minutes long.

Not a Member? Join!

About the Instructor

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Trent D. Buskirk, Ph.D. is the Vice President of Statistics and Methodology, Marketing Systems Group.

Dr. Buskirk has more than 15 years of professional and academic experience in the fields of survey research, statistics, as well as SPSS, SAS, and R.

Dr. Buskirk has taught for more than a decade at the University of Nebraska and Saint Louis University where he was an Associate Professor of Biostatistics in the School of Public Health.

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It’s never too early to set yourself up for successful analysis with support and training from expert statisticians. Just head over and sign up for Statistically Speaking.

You'll get access to this training webinar, 130+ other stats trainings, a pathway to work through the trainings that you need — plus the expert guidance you need to build statistical skill with live Q&A sessions and an ask-a-mentor forum.


Stratified Sampling for Oversampling Small Sub-Populations

June 11th, 2012 by

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%. (more…)