1. Agenda  Sampling  probability sampling  nonprobability sampling  External validity

2. Sampling  Drawing a subgroup from a population (vs. Census)

3. Population Sample  Students registered in FAMR 380 fall ’00  All registered HI voters in Sept. 2000  All Adidas shoes made in 1999  Everyone in class today  Registered voters reached by random- digit dialing on 9/14/00 who answered the survey Every 2,000 th pair produced at each plant

4. What is a good sample?  A sample that resembles the population in characteristics  (a representative sample)

5. Representativeness PopulationSample

6. Representativeness PopulationSample 40% Males 60% Females 40% Males 60% Females

7. Representativeness PopulationSample 70% Satisfied 30% Dissatisfied 70% Satisfied 30% Dissatisfied Statistics Parameter

8. Why Representative Sample?  If characteristics of the sample is similar to the population, the statistics of sample are likely to be similar to the parameters

9. Let’s Think …  Research question: How do UH students utilize campus facilities?  Population: UH Students  Sample size: 200  How will you sample?  How can you maximize representativeness of your sample?

10. Random Sampling  Gives everybody in the population an equal chance to be selected as a participant in the sample  Requires the list of everybody in the population

11. SIMPLE RANDOM SAMPLE Population of 40: 25% 50% Sample of 4 : Each person 1/10 chance Sample A Sample BSample C Sample D

12. Systematic Random Sampling  Pick up every ‘n’th subjects  Sensitive to the way the list is ordered

13. SYSTEMATIC SAMPLE Population of 40: 25% 50% For a sample of 4, Take every 10 th one Sample B Sample A

14. Stratified Random Sampling  Divide the population into groups (strata)  Select subject randomly from the stratum  Then proportion of groups in the sample is equal to proportion of groups in population

15. STRATIFIED RANDOM SAMPLE Population of 40: 25% 50% Stratify (layer, category) by color Stratified random sample of 4: Randomly pick from each strata to maintain 25%, 25%, 50% balance

16. Cluster Sampling  Sample a ready-made group within the population (cluster) assuming it has a similar composition to the population  Example: Third grade classrooms

17. Know exact chance of being included BEFORE participant is picked E.g., 1 in 100,.003%, etc. Need # in the population, # in sample DON’T know each participant’s chance of being picked

18. Probability vs. Non-probability  Simple random  Systematic random  Stratified random  Cluster  Each member of the population has a specifiable probability of being chosen  Population info available  Convenience  Snowball  Purposive  Quota  We don’t know the probability of a specific member of the population being chosen  Population info not available Probability Sampling Non-probability Sampling

19. Representativenss & Generalizability  Representativeness = Resembles population characteristics  Generalizability = Able to generalize the results of your study to the whole population  High representativeness = High generalizability  Probability sampling allows higher representativeness than non-probability

20. Non-probability Sampling

21. Convenience Sampling  Get available people in the population  Low representativeness / generalizability

22. Snowball Sampling  Obtain participants through a chain of personal networking-referrals  Useful to locate the ‘hidden’ or ‘difficult to recruit’ population  Low representativeness / generalizability

23. Quota Sampling  Predetermine the proportion of groups in the sample  e.g., male 50%, female 50%  e.g., clinical trials-drug research, etc.

24. Purpose of Quota Sampling  1: To ensure that the sample reflects the proportion of the group in the population  2: To secure enough numbers of group members for analysis  If you set quota for purpose 2, your sample may not reflect the population as a whole

25. Purposive Sampling  Obtain most informed / most ‘typical’ participants  “Judgmental sampling”  High quality of information from each participanta  Low representativeness / generalizability  Quality of sample depends on researcher’s ability to identify group to be studied

26. Why is sampling important?  Usually want to talk about a POPULATION  Easier to get a sub-set of the population (SAMPLE)  In a good sample Results from a good sample should match the parent population (REPRESENTATIVE) Participants should be chosen without bias (RANDOM)  This allows you to GENERALIZE the results-- what holds for the sample should also hold for the larger group

27. External Validity  Degree that results can be extended beyond the limited research setting  Extent findings can be generalized to others  Based on sample ( rats, college students, whites, males, lab setting)

28. External validity ?  Will the findings from this study likely be found  When other individuals are studied?  Volunteers / non-volunteers,  Gender  Under other conditions?  In other settings?

29.  Psychology  The study of college sophomores  People in general?  College students - intelligent, high cognitive skills, young, developing sense of self-identity, social and political attitudes in state of flux, need for peer approval, unstable peer relationships (Cozby, 2001).

30. External validity  Related to sample and sampling technique

31. I hope you have a great day!