Sampling

1. Sampling ADV 3500 Fall 2007 Chunsik Lee

2. Sample vs. Population • 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.

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 nth 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.