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2. Stratified Random Sampling • Sampling important groups with a population separately, then combine these samples into one large stratified sample • Choose a separate SRS in each stratum • Combine these SRSs to form a full sample • Example: Population election counties are divided into Urban, Suburban and Rural. • Separate SRSs are then selected from each region • Capture a representative sample of all counties which could not be obtained from traditional SRS • Can produce more exact information that SRS of the same size by taking advantage of the fact that individuals in the same stratum are similar to one another.

3. Stratified Random Sampling • In some populations, each member of that population may not have the same chance of being chosen as other members do. • E.g. choosing counties across the US in such a way that both urban and rural counties are represented in our sample • There are several hundred counties in urban areas but thousands in rural areas. • In such cases Srs may not be the best way to obtain a representative sample of US counties • Strata – population groups comprising of similar units or individuals

4. Multistage Sample Design • Choosing samples in stages • Large-scale samples • Part of stratified sample design • Population surveys, opinion surveys • Eliminates the economic cost of interviewing Example: Stages of sampling: • Chose stratified sample of counties that urban and rural counties • Within each county selected, select neighborhoods to be sampled at random • Within each chosen neighborhood, chose households at random

5. Cautions about Sample Surveys • For sampling methods to work properly – to obtain a representative sample of a population, it is first necessary to know exactly what the population is and its characteristics. • Such lists are rarely available • Under-representation • Poor, homeless, prison inmates • Opinion polls over telephones will miss the 6% of population that do not have phones

6. Cautions about Sample Surveys • Non- response • When selected individuals are not contacted or do not respond • Usually 30% • Results in bias • Interviewing skills – important not to introduce bias • Types of questions asked • Attitude during interviewing • Wording of questions – confusing, misleading, intimidating

7. Inference about population • Larger samples give more accurate results than smaller samples