Two Major Types of Sampling Methods

1. Two Major Types of Sampling Methods uses some form of random selection requires that each unit have a known (often equal) probability of being selected selection is systematic or haphazard, but not random Probability Sampling Non-Probability Sampling

2. Sampling and representativeness Sampling Population Sample Target Population Target Population  Sampling Population  Sample

3. Statistical Terms in Sampling Variable

4. Statistical Terms in Sampling Variable 1 2 3 4 5 responsibility

5. Statistical Terms in Sampling Variable 1 2 3 4 5 responsibility Statistic

6. Statistical Terms in Sampling Variable 1 2 3 4 5 responsibility Statistic Average = 3.72 sample

7. Statistical Terms in Sampling Variable 1 2 3 4 5 responsibility Statistic Average = 3.72 sample Parameter

8. Statistical Terms in Sampling Variable 1 2 3 4 5 responsibility Statistic Average = 3.72 sample Parameter Average = 3.75 population

9. Sampling Error The population has a mean of 3.75... 1 5 0 1 0 0 frequency 5 0 0 3 . 0 3 . 5 4 . 0 4 . 5 responsibility

10. Sampling Error The population has a mean of 3.75... 1 5 0 ...and a standard deviation of .25 1 0 0 frequency 5 0 0 3 . 0 3 . 5 4 . 0 4 . 5 This means that... responsibility

11. Sampling Error The population has a mean of 3.75... 1 5 0 ...and a standard deviation of .25 1 0 0 frequency 5 0 0 3 . 0 3 . 5 4 . 0 4 . 5 This means that... responsibility about 68% of cases fall between 3.5 - 4.0

12. Sampling Error The population has a mean of 3.75... 1 5 0 ...and a standard deviation of .25 1 0 0 frequency 5 0 0 3 . 0 3 . 5 4 . 0 4 . 5 This means that... responsibility about 64% of cases fall between 3.5 - 4.0 about 95% of cases fall between 3.25 - 4.25

13. Sampling Error The population has a mean of 3.75... 1 5 0 ...and a standard deviation of .25 1 0 0 frequency 5 0 0 3 . 0 3 . 5 4 . 0 4 . 5 This means that... responsibility about 64% of cases fall between 3.5 - 4.0 about 95% of cases fall between 3.25 - 4.25 about 99% of cases fall between 3.0 - 4.5

14. Types of Probability Sampling Designs • Simple Random Sampling • Stratified Sampling • Systematic Sampling • Cluster (Area) Sampling • Multistage Sampling

15. Simple Random Sampling • Need a list of all eligible persons in the population • Every person has equal chance (equal probability) to be selected in the sample • can sample with or without replacement • Rarely used in actual surveys • Difficult • Expensive • Excessive travel time (different location of subjects) • Excessive local introduction and organization time

16. Simple Random Sampling Example: • A random sample of nursing students of KUMS • A random sample of diabetic patients registered at Bahonar clinic

17. Simple Random Sampling List of Residents

18. Simple Random Sampling List of Students Random Subsample

19. Stratified Random Sampling • sometimes called "proportional" or "quota" random sampling • Objective - population of N units divided into non-overlapping strata N1, N2, N3, ... Ni such that N1 + N2 + ... + Ni = N, then do simple random sample of n/N in each strata

20. Stratified random sample: The population is divided into multiple strata based on common characteristics e.g.; • Residence (Urban or rural) • Tribe, ethnicity or race • Family income (poor, moderate, or wealthy)

21. Stratified Random Sampling List of Residents

22. Stratified Random Sampling List of students Nursing medical Pharmacy Strata

23. Stratified Random Sampling List of Residents surgical medical Non-clinical Strata Random Subsamples

24. Systematic Random Sampling Procedure: • number units in population from 1 to N • decide on the n that you want or need • N/n=k the interval size • randomly select a number from 1 to k • then take every kth unit

25. Systematic Sampling: Similar Procedure: • List all persons in the population • Define selection interval: = (Sampled population)/(Sample size) = N/n = An integer for ease of field use

26. Systematic Sampling:(continued) • Select a random starting point (first person in the sample) • Next selection = the random start + the random interval • And so on and so forth…

27. Systematic Random Sampling • Assumes that the population is randomly ordered • Advantages - easy; may be more precise than simple random sample • Example - students study

28. Systematic Random Sampling 1 26 51 76 2 27 52 77 3 28 53 78 4 29 54 79 5 30 55 80 6 31 56 81 7 32 57 82 8 33 58 83 9 34 59 84 10 35 60 85 11 36 61 86 12 37 62 87 13 38 63 88 14 39 64 89 15 40 65 90 16 41 66 91 17 42 67 92 18 43 68 93 19 44 69 94 20 45 70 95 21 46 71 96 22 47 72 97 23 48 73 98 24 49 74 99 25 50 75 100 N = 100

29. Systematic Random Sampling 1 26 51 76 2 27 52 77 3 28 53 78 4 29 54 79 5 30 55 80 6 31 56 81 7 32 57 82 8 33 58 83 9 34 59 84 10 35 60 85 11 36 61 86 12 37 62 87 13 38 63 88 14 39 64 89 15 40 65 90 16 41 66 91 17 42 67 92 18 43 68 93 19 44 69 94 20 45 70 95 21 46 71 96 22 47 72 97 23 48 73 98 24 49 74 99 25 50 75 100 N = 100 want n = 20

30. Systematic Random Sampling 1 26 51 76 2 27 52 77 3 28 53 78 4 29 54 79 5 30 55 80 6 31 56 81 7 32 57 82 8 33 58 83 9 34 59 84 10 35 60 85 11 36 61 86 12 37 62 87 13 38 63 88 14 39 64 89 15 40 65 90 16 41 66 91 17 42 67 92 18 43 68 93 19 44 69 94 20 45 70 95 21 46 71 96 22 47 72 97 23 48 73 98 24 49 74 99 25 50 75 100 N = 100 want n = 20 N/n = 5

31. Systematic Random Sampling 1 26 51 76 2 27 52 77 3 28 53 78 4 29 54 79 5 30 55 80 6 31 56 81 7 32 57 82 8 33 58 83 9 34 59 84 10 35 60 85 11 36 61 86 12 37 62 87 13 38 63 88 14 39 64 89 15 40 65 90 16 41 66 91 17 42 67 92 18 43 68 93 19 44 69 94 20 45 70 95 21 46 71 96 22 47 72 97 23 48 73 98 24 49 74 99 25 50 75 100 N = 100 want n = 20 N/n = 5 select a random number from 1-5: chose 4

32. Systematic Random Sampling 1 26 51 76 2 27 52 77 3 28 53 78 429 54 79 5 30 55 80 6 31 56 81 7 32 57 82 8 33 58 83 934 59 84 10 35 60 85 11 36 61 86 12 37 62 87 13 38 63 88 1439 64 89 15 40 65 90 16 41 66 91 17 42 67 92 18 43 68 93 1944 69 94 20 45 70 95 21 46 71 96 22 47 72 97 23 48 73 98 244974 99 25 50 75 100 N = 100 want n = 20 N/n = 5 select a random number from 1-5: chose 4 start with #4 and take every 5th unit

33. Cluster Sampling • The population is first divided into clusters • A cluster is a small-scale version of the population (i.e. heterogeneous group reflecting the variance in the population. • Take a simple random sample of the clusters. • All elements within each sampled (chosen) cluster form the sample. • Generally requires a larger total sample size than simple or stratified random sampling.

34. Cluster (area) Random Sampling • Advantages - administratively useful, especially when you have a wide geographic area to cover • Examples - randomly sample from city blocks and measure all homes in selected blocks

35. Example: Cluster sampling Section 1 Section 2 Section 3 Section 5 Section 4

36. Simple Random Sample: n = 20, N = 2000

37. Systematic sample: n = 20, N = 2000, k = 45

38. Stratified sample of 20 from 4 strata

39. Cluster Sample of 20 (cluster size = 4)

40. STATISTICAL TABLES: Table A Random Digits

41. SIMPLE RANDOM SAMPLING

42. STRATIFIED RANDOM SAMPLINGGrouped by characteristic

43. SYSTEMATIC SAMPLING

44. CLUSTER SAMPLING

45. TWO STAGE CLUSTER SAMPLING (WITH RANDOM SAMPLING AT SECOND STAGE)

46. Multi-Stage Sampling • Cluster (area) random sampling can be multi-stage • Any combinations of single-stage methods

47. Types of Probability Sampling Designs • Simple Random Sampling • Stratified Sampling • Systematic Sampling • Cluster (Area) Sampling • Multistage Sampling

48. Nonprobability Sampling Designs

49. Major Issues • Likely to misrepresent the population • May be difficult or impossible to detect this misrepresentation

50. Types of Nonprobability Samples • Accidental, haphazard, convenience • Modal Instance • Purposive • Expert • Quota • Snowball • Heterogeneity sampling