1. Data Collection Sampling

2. Target Population The group of people to whom the researcher wishes to generalize the results of the study

3. Accessible Population n -The smaller portion of the target population to whom the researcher actually has access

5. Sample n -The group of people who supply data for the study (Study group)

6. Sampling n the process of selecting a portion of the target population (sample) in such a way that the individuals chosen represent, as nearly as possible, the characteristics of the target population.

7. Sampling Unit n -A single member of the target population.

8. Sampling Bias -An overrepresentation or underrepresentation of some characteristic in the sample relative to the target population Unconscious Conscious

9. n The extent to which bias is a concern is a function of the homogeneity or heterogeneity of the target population. n When a variation (relevant to the research question) occurs in a population, then it must occur in the sample

10. Strata n -Subpopulations of the target population

11. Sampling error n -the fluctuation of a statistic from one sample to another drawn from the same population. (Can be estimated with probability sampling) Note: the larger the sample, the less sampling error.

12. Probability Sampling n -Sampling procedures use some form of randomization to select samples from the population.

13. Non Probability Sampling n Sampling procedures using other than random procedures.

14. NON PROBABILITY SAMPLING n CONVENIENCE SAMPLING n PURPOSIVE SAMPLING n QUOTA SAMPLING

15. Convenience Sampling (Accidental Sampling) n Involves the use of the most convenient and readily available subjects for the sample. – CMan on the street interviews – CTeacher uses students – CVolunteers

16. Convenience/accidental sampling n Problem: Sample bias because of “self selection”--available subjects may be highly atypical of the population with regard to critical variables.

17. SNOWBALL SAMPLING” n Variation of above, used when subjects are hard to find. One subject recommends another. Even more prone to bias.

18. n Convenience sampling is the most widely used yet weakest form of sampling. There is no way to evaluate all of the biases that may be operating.

19. QUOTA SAMPLING n Researcher uses some knowledge of the population to build some representativeness into the sampling plan n divides population into different strata and samples from each of them n USUALLY BETTER THAN JUST CONVENIENCE

20. n THE BASIS OF THE CHARACTERISTICS CHOSEN SHOULD REFLECT IMPORTANT DIFFERENCES IN THE DEPENDENT VARIABLE – Cage – Cgender – Cethnicity – Csocioeconomic status – Ceducation – Cmedical diagnosis – Coccupation

21. Quota Sampling n Problem: you cant always determine which characteristics in the sample are going to be reflected in the dependent variable

22. PURPOSIVE SAMPLING “Judgmental Sampling” n PROCEEDS ON THE BELIEF THAT THE RESEARCHER KNOWS ENOUGH ABOUT THE POPULATION AND ITS ELEMENT TO HANDPICK THE SAMPLE – Cselects “typical” persons – Cselects widest variety

23. Purposive or Judgemental Sampling n Assumption: n judgemental errors will tend to balance out. n Risk of conscious bias greatly multiplied n Should be avoided if the population is heterogeneous.

24. PROBABILITY SAMPLING n SIMPLE RANDOM n STRATIFIED RANDOM n CLUSTER The probability of any member of the target population being included in the sample can be calculated. n SYSTEMATIC SAMPLING (Can be either probability or non probability)

25. SIMPLE RANDOM SAMPLING Cidentify population Cestablish sampling frame Cnumber elements in sampling frame consecutively Crandomly select from list

26. n Random sampling does not guarantee representativeness, it does guarantee that difference between the sample and the population are purely a function of chance.

27. STRATIFIED RANDOM SAMPLE n The population is divided into two or more strata by relevant characteristics and subjects are randomly chosen from these strata n Slightly better than simple random, especially if the sample is not very large.

28. CLUSTER SAMPLING n Multistage sampling process n Used when target population is very large n Results in more sampling error n Statistical analysis more complicated

29. SYSTEMATIC SAMPLING n Selection of every Kth case from a list of possible subjects. n ( K represents any number)

30. SAMPLE SIZE n N Determined by: n COHEN’S POWER ANALYSIS Determine “effect size of treatment” Use in power analysis formula Achieves the least measurement error

31. N DETERMINED BY CONVENTION The bigger the better Ccost and convenience C10% minimum for descriptive studies C15 subjects/group for experiments C5 for each cell in factorial