Stratified sampling

1. Slide 12- 1 Stratified Sampling Simple random sampling is not the only fair way to sample. More complicated designs may save time or money or help avoid sampling problems. All statistical sampling designs have in common the idea that chance, rather than human choice, is used to select the sample.

2. Slide 12- 2 Stratified Sampling (cont.) Designs used to sample from large populations are often more complicated than simple random samples. Sometimes the population is first sliced into homogeneous groups, called strata , before the sample is selected. Then simple random sampling is used within each stratum before the results are combined. This common sampling design is called stratified random sampling .

3. STEPS IN STRATIFIED SAMPLING: 1. Identify and define the population. 2. Determine the desired sample size. 3. Identify the variable and subgroups (strata) for which you want to guarantee appropriate, equal representation.

4. STEPS IN STRATIFIED RANDOM SAMPLING 4. Classify all members of the population as members of the one identified subgroup. 5. Randomly select, using a table of random numbers; an “appropriate” number of individuals from each of the subgroups, appropriate meaning an equal number of individuals.

5. ADVANTAGES OF STRATIFIED RANDOM SAMPLING: More precise sample. Can be used both proportions and stratification sampling. Sample represents the desired strta.

6. Slide 12- 6 Stratified Sampling (cont.) Stratified random sampling can reduce bias. Stratifying can also reduce the variability of our results. When we restrict by strata, additional samples are more like one another, so statistics calculated for the sampled values will vary less from one sample to another.

7. DISADVANTAGES OF STRATIFIED RANDOM SAMPLING: Need names of all population members. There is difficulty in reaching all selected in the sample. Researcher must have names of all populations.