The Logic of Sampling

1. The Logic of Sampling

2. Key Sampling Concepts • Sampling (two types) • Element • Population • Sample • Sampling Frame • Representative Sample

3. Application of Sampling Terms

4. Types of Samples • Probability -- Strictly following two rules. • Non Probability -- Failing to follow the two rules

5. Types of Probability Samples • Simple Random Sample (SRS) • Systematic Random Sample • Stratified Random Sample • Cluster Sample

6. Simple Random Sample • Every element has an equal chance of selection • No element can be selected more than once

7. Example of a good random sample outcome

8. Simple Random Sample • Every element has an equal chance of selection • No element can be selected more than once

9. Systematic Random Sample Sampling Frame Nth Element A simple random sample employing a sample frame.

10. Stratified Random Sample

11. Cluster Sample

12. Non probability samples • Convenience (available to researcher) • Snowball (available connections) • Quota (stratified without randomness) • Informant (case study/social history) • Focus Groups

13. What’s the difference?How important is the difference? Probability samples can be generalized to a population; while non-probability samples cannot. Non-probability offer an in depth understanding and are most often: “I don’t know what I am seeking until after I find it.” Following is an illustration:

14. Overview of Sample Problems

15. Sample Size Selection The problem with the following formula: It is calibrated for dichotomous data. The sample size will increase with the number of options given to the subject.

18. Do NOT Forget!!!! Regardless of what formula one uses, always increase the sample size by 20%.

19. Excel • RANDBETWEEN – Returns a random number between the numbers you specify. • RAND – Returns a random number greater than or equal to 0 and less than 1, evenly distributed (changes on recalculation).