1. Error and Sample Sizes PHC 6716 June 1, 2011 Chris McCarty

2. Types of error Non-sampling error – Error associated with collecting and analyzing the data Sampling error – Error associated with failing to interview the entire population

3. Non-Sampling Error Coverage error ▫Wrong population definition ▫Flawed sampling frame ▫Interviewer or management error in following sampling frame Response error ▫Badly worded question results in invalid or incorrect response ▫Interviewer bias changes response Non-response error ▫Respondent refuses to take survey or is away ▫Respondent refuses to answer certain questions Processing errors ▫Error in data entry or recording of responses Analysis errors ▫Inappropriate analytical techniques, weighting or imputation are applied

4. Sampling Error Sampling error is known after the data are collected by calculating the Margin of Error and confidence intervals Surveys don’t have a Margin of Error, questions do Power analyses use estimates of the parameters involved in calculating the margin of error It is common to see sample sizes of 400 and 1000 for surveys (these are associated with 5% and 3% margins of error) In most cases the size of the population being sampled from is irrelevant The margin of error should be calculated using the size of the subgroups sampled

5. Margin of Error Formula H = Half interval expressed in units of standard deviation z = z score associated with level of confidence (typically 95%) s = standard deviation n = sample size

6. The z score The z value is the z score associated with a level of confidence Typically (almost exclusively) surveys use 95% This means that if the survey were replicated 100 times, 95 times out of 100 the estimate would be within the margin of error The z score associated with 95% is 1.96

7. The standard deviation (s) For a continuous variable the standard deviation is typically not known Previous research may suggest some reasonable range for the margin of error After you have collected the data the standard deviation is known

8. Example: Age of Floridians Sample of 406 Floridians Age range 18 to 92 Mean age of sample = 52.3 Standard deviation = 17.6 95 times out of 100 sample estimate would be between 50.58 and 54.01 (Frequentist interpretation)

9. Margin of Error for a Proportion p = proportion

10. Example: Floridians employed Sample of 415 Floridians 55.29 percent employed 44.47 percent not employed 95 times out of 100 the estimate of the percent employed would be between 50.59 and 59.99

11. Margin of Error with Finite Population Adjustment

12. Example: Floridians employed with finite population adjustment With the finite population adjustment the margin of error is.01 percent lower

13. No real value to adjustment until you reach 10 percent of population H adjusted falls to zero as you approach a census H unadjusted never does

14. Formula to determine sample size given a desired margin of error

15. Calculator sites http://www.americanresearchgroup.com/moe.html http://www.surveysystem.com/sscalc.htm

16. Power Analysis nH (%) 1009.8 2006.9 3005.7 4004.9 5004.4 6004.0 7003.7 8003.5 9003.3 10003.1

17. Dillman formula