1. SAMPLE DESIGN: WHO WILL BE IN THE SAMPLE ? (CONTINUED) Lu Ann Aday, Ph.D. The University of Texas School of Public Health

2. COMBINED DESIGNS A.Area probability sample design (example: PPS) B. Random digit dialing (RDD) C. List sample

3. PROBABILITY PROPORTIONATE TO SIZE (PPS) – EXAMPLE (Aday & Cornelius, 2006, Table 6.2) STEPSEXAMPLE 1. Estimate the desired sample size (n).77 2. Fix the desired cluster size (n c ).7 3. Calculate the number of clusters (c ) needed to achieve the desired sample size: n/n c.77/7 = 11 4. Estimate the total number of units in the universe from which the sample will be drawn (N).2200 Col. B, Table 5. Calculate the cumulative total of the number of units across all clusters in the universe.Col. C, Table 6. Calculate the sampling interval (k) for selecting clusters for the universe: N/c.2200/11=200 7. Pick a random starting point (r ) to select clusters within the designated sampling interval (Step 6), using a random numbers table. 50 8. Calculate the selection numbers (HU #) for the blocks to be sampled by entering the random starting point, adding the sampling interval, and then repeat the process to identify sampled blocks. Col. D, Table 9. Assign cluster numbers to each designated block.Col. E, Table 10. Confirm % in strata for sample agree with % in universe.Col. B, E (%), Table

4. RANDOM DIGIT DIALING: WAKSBERG-MITOFSKY DESIGN Target population: residents of the State of California STAGE 1 1. Implement random or systematic selection of area code-central office code combinations for area: (xxx) xxx. 2. Add two random digits to each area code-central office code combination. 3. Prepare list of possible 8 digit numbers, which become PSUs with clusters of 100 numbers each: (xxx) xxx-xx00 thru xx99. 4. Assign last two digits of the number randomly, such as (xxx) xxx-xx24. 5. Dial the resulting number. 6a. Eligible household number—complete the interview. Retain PSU of 100 numbers. 6b. Ineligible household number—terminate the interview. Eliminate the PSU of 100 numbers from further calls.

5. RANDOM DIGIT DIALING: WAKSBERG-MITOFSKY DESIGN (cont.) Target population: residents of the State of California STAGE 2 7. Randomly assign two new digits to end of cluster of numbers for same or new PSU (as appropriate). 8. Repeat process until desired sample size is reached.

6. LIST SAMPLE Target population: U.S. dentists in active practice STAGE 1: Identify eligible dentists. STAGE 2: 1. Determine sampling fraction. 2. Draw systematic random sample of eligible dentists.

7. SAMPLING RARE POPULATIONS Screening- Ask respondents whether they/household have the attribute of interest and drop those from sample that do not. Disproportionate sampling- Assign a higher sampling fraction to stratum that has attribute of interest. Network sampling- Ask the respondents if they know others in family network (defined in certain way) who have attribute of interest. Dual frame sampling- Use a second sampling frame containing elements with attribute of interest to supplement original frame.

8. PROCEDURES FOR SELECTING THE RESPONDENT: SELECTION TABLES Kish tables- Ask about all potentially eligible individuals in the household, list them and then use Kish tables. Troldahl-Carter-Bryant (TCB)- Ask how many persons live in the household, how many of them are women, and then use TCB selection charts.

9. PROCEDURES FOR SELECTING THE RESPONDENT: RESPONDENT CHARACTERISTICS Hagan and Collier Method- Ask to speak with one of four types of age-sex individuals and if no one of that gender, ask for counterpart of opposite gender (youngest adult male/youngest adult female/oldest adult male/oldest adult female). Last/Next Birthday Method- Ask to speak with the person who had a birthday last or who will have one next.

10. SURVEY ERRORS: Deciding Who Will Be in the Sample Systematic ErrorsVariable Errors Noncoverage bias (frame bias) Noncoverage bias (respondent selection bias) Design effects Solutions to errors Match the sample frame to the target population. Use multiple sample frames, if needed, to more fully capture the target population of interest. Employ methods for randomly selecting the study respondents. Try to balance the complexity (especially the cluster nature) of the sample design needed to address the study objectives in relationship to survey costs.

11. REFERENCES Bennett, S., Woods, T., Liyanage, W.M., & Smith, D.L. (1991). A simplified general method for cluster-sample surveys of health in developing countries. World Health Statistics Quarterly 44: 98- 106.