1. Five types of sampling zRandom (or simple random) zStratified random zCluster sampling zSystematic zArea probability

2. Random zEvery subject is known zEvery subject has equal or know probability of selection

3. Random zAdvantages: yDon’t have to know the characteristics of a population yTends to be completely representative zDisadvantages: yComplete list is difficult to obtain yAlways a chance of drawing a misleading sample yNeeds a larger sample size

4. Stratified random zPopulation classified into two or more strata zSample drawn from each one zCases drawn in proportion to representation in population zCases can be oversampled, if needed

5. Stratified random zAdvantages: yCan be sure no relevant group is omitted yGreater precision possible with lower sample size zDisadvantages: yNeed to know about the population yProportions must be known yDifficulty in locating cases

6. Systematic random zSelection of every nth name zUsually quicker

7. Cluster zDone for efficiency zPopulation is broken down into smaller groups zUseful when no sampling frame is available

8. Area zCombines cluster and systematic zBased on geography