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…)
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.
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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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.
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%.