1. SAMPLING

2. The Best Approach: Avoid sampling (studying everybody)

3. When sampling is needed: Probability sampling –A random process to guarantee that each unit of the population has a specified chance of selection. –A scientific approach. –Rigorous basis for statistical treatment. Nonprobability sampling –Convenient & practical but difficult to generalize the results.

4. Determining Factors: An awareness of the main classes The judgement to choose one that will be practical and scientific The compulsiveness to carry it out well

5. Probability Nonprobability Simple random s Systematic s Stratified random s Cluster s Consecutive s Convenience s Judgmental s

6. Simple random sampling Principle The process of enumerating every unit of the accessible population, and then selecting the sample at random. Example The invr could make a list of all student at U of M Nursing, then use a table of random numbers to select a subset of these individuals for study.

7. Systematic sampling Principle Selection by a periodic process Cons Susceptible to errors Chance of manipulation No logistic advantages over simple random s. Example Framingham study: taking every second person from a list of town residents.

8. Stratified random sampling Principle Dividing the population into subgroups according to characteristics such as sex or race, and taking a random sample from each of these “strata” Example Studying toxemia in pregnancy, you decide to stratify the population according to racial group, and to sample equal #s from each race. This would yield incidence estimates for blacks and whites that have comparative precision.

9. Cluster sampling Principle The process of taking a random sample of natural groupings (clusters) of individuals in the population. Appropriate for a large # of clusters with heterogeneous groups Example Hospitals for clusters at a large epidemiological study

10. Probability Nonprobability Simple random s Systematic s Stratified random s Cluster s Consecutive s Convenience s Judgmental s

11. Consecutive sampling The best of the nonprobability techniques and often practical. = taking the complete accessible population over the duration of the study. Maybe to short to adequately represent seasonal factors or “secular trends.”

12. Convenience sampling Frequent flyers in clinical studies The process of taking those members of the accessible population who are easily available. Advantages in cost and logistics. Acceptable for physiological phenomena but not suitable for epidemiological studies.

13. Judgmental sampling Hand-picking from the accessible population those individuals judged most appropriate for the study. Resembling convenience sampling and has similar flaws

14. Choosing the sampling design for selecting study subjects from the accessible population Consecutive sample Often the best option Simple random sample (To reduce the number in the Sample if a consecutive sample Would be too large) Stratified random sample (To increase the size of specified subgroups in the sample) Cluster random sample (To draw an inexpensive but representative sample from a large population that is widely Spread or difficult to enumerate) Convenience sample Judgmental sample (To draw an easy, inexpensive sample when almost any sample will be representative)

15. RECRUITMENT

16. The Goals of Recruitment To recruit enough subjects to meet the sample size requirements of the study –The commonest problem –Solution Pretest Large enough population Contingency plan To recruit a sample that is unbiased –Response rate influences the external validity –Technical errors (internal validity)

17. Enhancing the response rate A non-response rate can seriously distort the observed prevalence of a disease when the disease itself is a cause of non-response. –Solution Non-respondents: Systematic series of repeated contact (mail, telephone, home visit) Refusal to respond: efficient and attractive initial encounter, brochures, individual discussion, translator or $$

18. RECAPPING THE ERRORS IN CHOOSING THE STUDY SUBJECTS Design errors (e.g., smoking survey) –The target population is not well suited to the research question (high school vs. middle school) –Accessible population does not sufficiently represent the target population (cluster sampling) –The intended sample does not sufficiently represent the accessible population because of biased sampling design (volunteers) Implementation errors –Due to random sampling error(chance) –Due to systematic sampling error (bias) Failure to make contact Subject refuses to participate