Other Sampling Methods

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 selectedindividuals. • 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.