Surveys and Population-Based Studies

1. Surveys and Population-Based Studies • Definition of a "Survey" A method of collecting information about a human population in which direct (or indirect) contact is made with the units of the study (e.g., individuals, organizations, communities, etc.) by using systematic methods of measurements like questionnaires and interview schedules. (Warwick and Lininger, 1975) • Examples of well-known surveys: • U.S. Decennial Census • Current Population Survey (n=60,000 HHs/mo.) • Health Interview Survey (n=50,000 HHs/yr.) • Other Examples in Groves, et al. (2004)

2. Nonprobability Sampling • Selection by nonrandom methods • Membership in the sample is ultimately left to human judgment • No basis for assuming stochastic behavior of sample estimates • One method: quota sampling

3. Quota Sampling • Quota control/allocation for each interviewer: • Filling category is left to interviewer's discretion (i.e., judgment)

4. Probability Sampling • Ultimate selection left to some randomized (i.e., chance) mechanism • Two types: • Random sampling • Survey sampling

5. Random Sampling • "Population" is infinite and abstract; distribution of measurements follows some assumed form (e.g., a normal distribution) • Sample is the result of independently selecting a measurement at random from the assumed distribution, with sample size as the number of selections

6. Random Sampling • “Random sample” as defined by Hogg & Craig: • "Let X1, X2, . . ., Xn denote n mutually statistically independent • random variables, each of which has the same but possibly • unknown probability density function, f(x). The random variables • X1, X2, . . ., Xn are then said to constitute a random sample from • a distribution that has pdf, f(x). • Example: f(x) for the normal distribution:

7. Population-Based Sampling • Population is finite (i.e., made up of a countable set of members) • Distribution of measurements usually does not follow a neat mathematical form • Ex: Number of health care visits in the past 12 months • Randomization used but selections may not be made independently

8. Probability Sampling • Each population element has a known and nonzero probability of being selected into the sample • EPSEM sample design: • Sample in which selection probability for each element is equal; • Stands for Equal Probability Selection Method. • Also use the term "self-weighting"

9. Advantages of Probability Sampling • Statistical theory (including sampling theory) assumes this method • Not subject to biases of human judgment • Can directly measure the precision (i.e., statistical quality) of estimates produced from sample

10. Utility of Sampling Theory • Basis for settling on ways to estimate population parameters and the precision of those estimates • Basis for much of the decision making in designing the sample

11. Population: Values to be Estimated Analysis: (Population Values Estimated) Sample Design (Probability Sampling) Selected Sample: (Data Collected) Inference in Population-Based Studies • Circle of inference:

12. Population Member Population Hierarchy

13. Population Hierarchy: Some Examples • First grade students in NC schools • Residents of the United States

14. Components of a Population-Based Study • Planning • Study specifications • Target population vs. • Survey population • Budget considerations • Staff communication • Sample size

15. Components of a Population-Based Study • Sampling • Preliminary activities • Search for sampling frame(s) • List(s) of units to be sampled • Develop the sample design • Plan to choose the sample • Consists of a sequence of statistical issues and decisions • Select the sample

16. Components of a Population-Based Study • Data collection instrument • Design questionnaire and forms • Small-scale testing • Manuals for training • Data collection • Preparation (e.g., hiring and training) • Field operations (e.g., monitoring and supervision) • Manual editing/coding • Preparation • Operations

17. Components of a Population-Based Study • Data entry • Preparation • Operations • Machine editing/coding and file processing • Preparation • Run edits • Prepare analysis work files • Analysis and dissemination