1. Other Sampling Methods
Lecture 7
Sections 2.6 – 2.8
Tue, Jan 25, 2005

2. Stratified Random Sampling
Stratified random sample – A sample selected by
First, dividing the population into mutually exclusive groups, or strata,
Then, taking a simple random sample from each stratum.
Normally the members of each stratum share a common characteristic.
For example, male vs. female.

3. Why Stratified Samples?
We may be genuinely interested in the differences between the strata.
By taking samples from each stratum, we can measure those differences.
For example, pollsters studying elections routinely stratify their samples by gender and ethnic group.

4. Why Stratified Samples?
It is often the case that the variability within a stratum is much less than the variability between strata.
If that is so, then we can get a much better estimate of the population parameter than if we took one random sample from the entire population.
Note: The “margin of error” is based on the variability.

5. Why Stratified Samples?
A researcher may use a stratified sample just to ensure that each group is properly represented in the sample, just in case that matters.

6. Let’s Do It! Let’s do it! 2.7, p. 92 – Stratified Random Sampling.
Substitute “freshman” for “female” and “upperclassman” for “male.”
Substitute “average number calls to parents per month” for “average number of haircuts per year.”

7. Cluster Sampling
Cluster Sampling – The units of the population are grouped into clusters. One or more clusters are selected. All members of the selected clusters are in the sample.
Note that it is the clusters that are selected at random, not the individuals.

8. Let’s Do It! Let’s do it! 2.12, p. 104 – Cluster Sampling of Students.
After selecting the cluster, compute the sample average number of phone calls home.

9. Stratified Sampling vs. Cluster Sampling
In stratified sampling
The members of a stratum have some characteristic in common (homogeneous).
From all of the strata we take randomly selected individuals.
In cluster sampling
The members of each cluster are intended to resemble the entire population (heterogeneous).
From randomly selected clusters we take all of the individuals.

10. Let’s Do It! Let’s do it! 2.12, p. 104 – Cluster Sampling of Students.
Let’s do it! 2.13, p. 105 – Which Sampling Method Could Have Been Used?

11. Systematic Sampling 1-in-k systematic sampling
Label the members of the population 1 through N.
Select one member at random from the first k.
Select every k-th member from there on.

12. Example Example 2.18, p. 98 – A 1-in-4 Systematic Sample.
Think about it, p. 99.