1. Sampling

2. Sampling Can’t talk to everybody Select some members of population of interest If sample is “representative” can generalize findings

3. Some Terms PopulationSample Population Parameter Estimator Sample Statistic Stratum

4. More Terms Sampling Frame Sampling Unit Sample Bias

5. When Good Surveys go Bad Literary Digest 1936 Poll Draw on Approx 4 million respondents Predict Landon Victory What went wrong? Bad sample Auto Registrations Phones Readers of Literary magazine

6. Sampling Bigger is generally preferable to smaller Quality trumps quantity Margin of Error 4000 ± 2 1500 ± 3 1000 ± 4 600 ± 5 400 ± 6 200 ± 8 100 ± 11

7. Sampling/Margin of Error Poll shows 53-47 prefer Bush Sample of 1000 ± 4 Could be 49-51 Could be 57-43

8. An Example 2000 NES 1798 People Answered Age Question Actual Ages –Average 47.2 –Range 18-97 11Samples of 10% –Estimate- 45.4-49.6 –Std. Deviation of Estimate 1.2 –Mean of Estimates- 47.1

9. Example Continued Actual Average- 47.2 1% Sample- 39.3 5% Sample-49.1 25% Sample- 47.9 50% Sample- 48 75% Sample-46.8 99% Sample- 47.25

10. Sampling Bigger is generally preferable to smaller Quality trumps quantity Margin of Error 4000 ± 2 1500 ± 3 1000 ± 4 600 ± 5 400 ± 6 200 ± 8 100 ± 11

11. Sampling/Margin of Error Poll shows 53-47 prefer Bush Sample of 1000 ± 4 Could be 49-51 Could be 57-43

12. Types of Samples- Simple Random Simple Random RDD RDD Requires numbered list of population members Pick random elements until you meet sample size

13. Systematic Sampling List population Determine Sampling Interval E.g. if you have 1000 and want 200 cases, take every 5 th case (1000/200=5) E.g. if you have 1000 and want 200 cases, take every 5 th case (1000/200=5) Start on random list number (for example 1-5) Include every 5 th case thereagter Problem- POPULATION MUST NOT BE RANKED BY A CHARACTERISTIC

14. Stratified Sample Probability Sample Group Elements by some trait Select Number of Each element to reflect distribution in population Example City is 70% White, 20% Latino, 10% African American, want sample of 1000 Randomly Select 700 Whites, 200 Latinos, 100 African Americans City is 70% White, 20% Latino, 10% African American, want sample of 1000 Randomly Select 700 Whites, 200 Latinos, 100 African Americans Oversampling- Some traits may not be common, select extra members of that population E.g. African Americans make up about 10% of population, might collect extra African Americans for more detailed analysis. E.g. African Americans make up about 10% of population, might collect extra African Americans for more detailed analysis.

15. Cluster Samples Initial Frame is Clusters of Units Take sample of initial units (e.g. telephone exchanges, zip codes, city blocks, etc). Get details of make up of selected units Take random sample within units Still random, just done in steps Works best in fairly homogenous populations

16. Non Probability Samples Cases Where no good way to specify population Too expensive for probability sampling Preference for studying certain cases

17. Types of Non-Probability Samples Purposive Convenience Sample Quota Sample Snowball Sample

18. For Next Time Content Analysis