1. Chapter 1: The Nature of Statistics 1.4 Other Sampling Designs

2. Drawbacks to simple random sampling May fail to provide sufficient coverage when information about subpopulations is required May be impractical when the members of the population are widely scattered geographically

3. Systematic Random Sampling Step 1 – Divide the population size by the sample size and round the result down to the nearest whole number, m Step 2 – Use a random-number table (or a similar device) to obtain a number, k, between 1 and m Step 3 – Select for the sample those members of the population that are numbered k, k+m, k+2m, …, k+(sample size – 1)m

4. Systematic Random Sampling Easier to execute than simple random sampling Usually provides results comparable to simple random sampling Only exception…prescence of some kind of cyclical pattern in the listing of the members of the population (male, female, male, female,…) – Relatively rare

5. Cluster Sampling Step 1 – Divide the population into groups (clusters) Step 2 – Obtain a simple random sample of the clusters Step 3 – Use all the members of the clusters obtained in Step 2 as the sample

6. Disadvantage to Cluster Sampling Each cluster needs to mirror the entire population – Not usually the case, as members of a cluster are frequently more homogeneous than the members of the population as a whole

7. Stratified Random Sampling with Proportional Allocation Proportional allocation – Strata are sampled in proportion to their size Step 1 – Divide the population in subpopulations (strata) Step 2 – From each stratum, obtain a simple random sample of size proportional to the size of the stratum (total sample size times stratum size divided by population size) Step 3 – Use all the members obtained in Step 2 as the sample

8. Multistage Sampling Combining of types of sampling Used by pollsters and government agencies i.e. Cut up into clusters, then do different kinds of sampling to each cluster

9. Practice Problems P. 21-22 1.33 – 1.37