1. 1 COMM 301: Empirical Research in Communication Kwan M Lee Lect5_1

2. 2 Sampling Things to know by the end of the lecture: –What are the key concepts in sampling? –What are the main types of probability sampling? –Know how to do the main types of probability sampling

3. 3 Sampling Moving from issues about internal validity to those about external validity External validity: –How accurately the findings of a study may be generalized to other groups Depends on how representative are the subjects studied Key to selecting representative subjects is good sampling

4. 4 Sampling Key concepts in sampling –population (universe): group of people (non-human elements) with particular characteristics of interest to the study –parameters: specific characteristics that must be present for an individual (or for an object) to fit the population e.g. ages between 10-19, income more than 10K, couples married within last 2 years, etc –census: study of every element in the population (universe)

5. 5 Sampling Key concepts in sampling (continued): –Sample: representative subset of population (universe) –Representativeness: how closely a sample matches its population in terms of the characteristics we want to study –Sampling error: degree to which a sample’s characteristics differ from the population’s

6. 6 Sampling Sampling error depends on –sample size larger sample size cuts sampling error, but at a decreasing rate Example (see lect6_2 sampling error table) –homogeneity of population (universe) higher homogeneity (the more the population members are alike) cuts sampling error Example: Also see lect6_2 sampling error table

7. 7 Sampling Probability Sampling Sampling methods: probability vs. non-probability Probability sampling –selects elements from population (universe) guided by a set of mathematical rules –allow calculation of sampling error (crucial advantage over non probability error)

8. 8 Probability Sampling Methods of probability sampling –simple random sampling –systematic sampling –stratified sampling –multistage cluster sampling Common to all is the sampling frame, a list of all elements in the population (universe)

9. 9 Probability Sampling: Simple random sampling Simple random sampling –basic form –each element in population (universe) is given equal chance to be selected –use lottery or random numbers Example

10. 10 Probability Sampling: Systematic sampling Systematic sampling Example Caution: make sure that the elements in the sampling frame are not organized in some pattern

11. 11 Probability Sampling: Stratified sampling Stratified sampling –proportional representation on a certain variable Example

12. 12 Probability Sampling: Multistage cluster sampling Multistage cluster sampling –simple in concept, complicated in execution –used to deal with very large populations, when using a sampling frame is not feasible

13. 13 Probability Sampling: Multistage cluster sampling (cont.) Multistage cluster sampling example –Sample 1000 people from US population –Using geographic boundaries from 50 states, select randomly 5 states from each state, select randomly 5 counties (25 counties total) from each county, select randomly 5 cities or equivalent (125 cities total) from each city, select 8 individuals (1000 individuals total)

14. 14 Probability Sampling: Multistage cluster sampling (cont.) Multistage cluster sampling problems –over- or under-representation at each layer –homogeneity of each cluster Statistical adjustments possible

15. 15 Tips in Probability Sampling Tips in practice –coping with the amount of information be careful and thorough keep things neat do a small batch and review Response rate –Another VERY important issue for survey research!