1. Sampling ADV 3500 Fall 2007 Chunsik Lee

2. A sample is some part of a larger body specifically selected to represent the whole. Sampling is the process by which this part is chosen. Sample vs. Population

3. Sample vs. Census Why do we take a sample rather than a complete census?  For efficiency and generalization

4. Sampling methods & procedures The sampling process: Define the population (clear & tangible characteristics) Determine sampling method Specify the sampling frame Determine sample size Select the sample

5. Sampling methods & procedures Two types of sampling procedures Probability sampling We can specify the probability or likelihood that a given element of the population will be included in the sample. Non-probability sampling We cannot specify the likelihood that a given element from the population will be included in the sample.

6. Characteristics of probability samples Always involves chance selection of the elements for inclusion in the sample. Each element will have a non-zero chance of selection. Only with a probability sample can we estimate the likelihood that a sample will represent the population. We can estimate the error associated with the sample.

7. Characteristics of non- probability samples We have no assurance that every element of the population has a chance to be included. We do not have the ability to estimate the error associated with the sample drawn.

8. Types of probability sampling Simple random sampling Systematic random sampling Stratified sampling

9. Probability sampling Simple random sampling Every element in the population will have an equal chance of being selected. Tables of random number or computer generated random numbers are used.

10. Probability sampling Systematic random sampling Initial starting point is selected randomly, then every n th number on the list is selected. Example: You wish to take a sample of 1,000 from a list consisting of 200,000 names. Using systematic selection, every 200th name from the list will be drawn. -- sampling interval = 200 -- 200,000/1,000 = 200

11. Probability sampling Stratified sampling Break population into groups or strata and then take random sample within each group. Treat each stratum as a separate subpopulation for sampling purposes. Strata are homogeneous within and heterogeneous between (or maximally different from each other).

12. Probability sampling Stratified sampling Proportionate stratified random sampling is done in proportion to the group’s representation in the population Disproportionate stratified random sampling is a means of weighting a group’s representation in a sample to accommodate broader research objectives

13. Types of non-probability sampling Convenience sampling Judgment (Purposive) sampling Quota sampling Snowball sampling

14. Non-probability sampling Convenience sampling Take what is available. Used in exploratory situations or non- generalization research (e.g., experimental research)

15. Non-probability sampling Judgment (Purposive) sampling Choose people to achieve a specific analytical objective, typically to make certain that there are sufficient numbers of elements. But, doesn’t consider characteristics of the target population.

16. Non-probability sampling Quota sampling Selected purposively in such a way that the characteristics of interest are “represented” in the sample in the same proportion as they are in the population.

17. Non-probability sampling Snowball sampling Subsequent respondents are obtained through initial respondent referrals. Used to locate rare populations by referrals.