1. Unless otherwise noted, the content of this course material is licensed under a Creative Commons 3.0 License. http://creativecommons.org/licenses/by/3.0/ Copyright 2008, Huey-Ming Tzeng, Sonia A. Duffy, Lisa Kane Low. The following information is intended to inform and educate and is not a tool for self-diagnosis or a replacement for medical evaluation, advice, diagnosis or treatment by a healthcare professional. You should speak to your physician or make an appointment to be seen if you have questions or concerns about this information or your medical condition. You assume all responsibility for use and potential liability associated with any use of the material. Material contains copyrighted content, used in accordance with U.S. law. Copyright holders of content included in this material should contact open.michigan@umich.edu with any questions, corrections, or clarifications regarding the use of content. The Regents of the University of Michigan do not license the use of third party content posted to this site unless such a license is specifically granted in connection with particular content objects. Users of content are responsible for their compliance with applicable law. Mention of specific products in this recording solely represents the opinion of the speaker and does not represent an endorsement by the University of Michigan.

2. Sampling Contributors Sonia A. Duffy, PhD, RN Lisa Kane Low, PhD, CNM, FACNM Huey-Ming Tzeng, PhD, RN

3. Sampling Theory Concepts Population Sampling criteria Sampling frame 3 Sampling

4. Population Target population  Example: The customer satisfaction survey for all patients that went through the University of Michigan Health System in 2006 Accessible population  Example: All patients that lived Elements of a population  Subjects could be people or units 4 Sampling

5. Sampling (Eligibility) Criteria Inclusion criterion  Who is in?  Need to specify demographic and clinical characteristics Exclusion criterion  Who do you want to keep out to avoid bias because they would provide poor data, be likely lost, or have ethical concerns? 5 Sampling

6. An Example of Inclusion and Exclusion Criterion Inclusion criteria  Example: All patients admitted to and discharged from University of Michigan Health System (inpatient care units) during 2006 Exclusion criterion  Examples:  Under 18 years  Non-English speaking  Cognitively impaired  Deceased 6 Sampling

7. Sampling Frame List of potential candidates to be in your study  Example: Get a list of all patients discharged from the inpatient car settings of the University of Michigan Health System to do the inpatient satisfaction survey 7 Sampling

8. Probability (Random) Sampling Methods Simple random sampling Systematic sampling Stratified random sampling Cluster sampling 8 Sampling

9. Simple Random Sampling Example: Randomly select 1,200 of 12,000 patients (10%) that were discharged from University of Michigan Health System (inpatient care units) during 2006  Use a random number table or use a random number generator (like pulling from hat) to sample subjects Purposes  Every person has similar chance of entering the study  Reduces bias 9 Sampling

10. Systematic Sampling Use an ordered list Randomly draw a number between 1 and 10 (e.g., 3)  Example: Start with patient 3 and take every nth (e.g., 10th) patient 10 Sampling

11. Stratified Sampling Stratify by unit Example:  1,200 participants from a total of 6 units; 200 patients per unit  Then, we may do random sampling or systematic random sampling  On unit X, we may sample every 6 th patient. On the other units, we may sample every 18 th patient 11 Sampling

12. Nested or Hierarchal Sampling Each stratum is treated as an extraneous variable because people in that stratum are similar  Example: Patients, treated by doctors, on units, in hospitals 12 Sampling

13. Cluster Sampling Randomly pick clusters to sample  Example: Randomly pick states, then zip codes, then neighborhoods, then blocks, then survey everybody on that block Makes better use of surveyors’ time than doing a random sample of US population and finding everyone all over the place Provides a bigger sample at a lower cost 13 Sampling

14. Sampling Theory Concepts The subject acceptance rate or response rate  The percentage of individuals consenting to be subjects Representativeness Generalizability 14 Sampling

15. Response Rate Want to go for a high response rate  A higher response rate increases the representativeness of sample and generalizability of the study results The characteristics of the responders can be different than the ones of the non- responders 15 Sampling

16. Factors Affecting Response Rates Length of survey How intense is the intervention? Is there any incentive for the participants? Is it an RCT?  People do not usually like to be randomized 16 Sampling

17. Representativeness Does the sample represent the general population of the persons with the specified problem?  Example: Does my sample of 1,200 inpatients discharged from University of Michigan Health System compare to the total population of 12,000 patients on age, gender, cancer site and stage, etc.? 17 Sampling

18. Generalizability Who is the sample generalizble to? The results are generalizable to the sampling frame  Example: The research results from the random sample of 1,200 inpatients would be generalizable to the population of 12,000 inpatients, who were discharged from University of Michigan Health System 18 Sampling

19. Nonprobability (Nonrandom) Sampling Convenience (accidental) sampling Quota sampling Purposive sampling Network sampling 19 Sampling

20. Convenience Sampling Subjects who are available and very approachable  Example: All patients who are hospitalized in my working unit 20 Sampling

21. Quota Sampling Want to have a certain number of participants per group  Example: For an end-of-life focus group study, we would want equal numbers of 5 ethnic groups in each focus group 21 Sampling

22. Purposive Sampling Seek out selected people to interview  Example: Homeless people 22 Sampling

23. Network Sampling (Snowballing) Used for difficult to reach populations  Example: The researchers talk to one person from the target population. This person may lead the researchers to the next potential participant 23 Sampling

24. Sample Size Factors influencing sample size:  Effect size  Type of study conducted  Number of variables studied  Measurement sensitivity  Data analysis techniques 24 Sampling

25. Power Analysis Standard power of 0.8 Level of significance  The alpha value can be set at.05,.01,.001 Effect size  Small =.2  Medium =.5  Large =.8 Sample size 25 Sampling

26. Effect Size Small, medium, or large effect of dependent (outcome) variable  Example: Change on the blood pressure. Do we want to get a change of 10mg., 20mg., or 30mg. mercury? Look at other studies to see what kinds of effect sizes they get and what kind of sample sizes they had to get those 26 Sampling

27. Example Sample A convenience sample of  55 adults scheduled for first time elective CABG surgery without cardiac catheterization  Who had not had other major surgery within the previous year  Who were not health professionals  Were randomly assigned to one of two instruction conditions Based on a formulation of 80% power  A medium critical effect size of 0.40 for each of the dependent variables, and a significance level of.05 for one-tailed t-tests means  A sample size of 40 was deemed sufficient to test the study hypotheses 27 Sampling

28. Critique The Sample What was the sampling frame? Were the inclusion and exclusion criteria identified? What sampling methods were used? Was there rationale for the sampling method? What was the response rate? Was there a power analysis? Was the sample large enough? Were the characteristics of the sample described? Was the sample representative of the population they were studying? Who is the sample generalizable to? 28 Sampling

29. How Do I Deal With Sample Size In the Real World? Know that to detect a small effect, you need a larger sample Know that for every extraneous variable, you need a bigger sample Know that if you have a small sample, you may be underpowered Look to see if your results are in the expected direction 29 Sampling

30. Sample Section for Your Research Proposal What is your sampling frame? What are the inclusion and exclusion criteria? What sampling methods will be used? What is the rationale for the sampling method? About how big will the sample be? Explain how subjects will be assigned to groups Who is the sample generalizable to? Discuss strengths and weaknesses of sampling method 30 Sampling