1. Sampling

2. Sampling Issues Sampling Terminology Probability in Sampling
Probability Sampling Designs
Non-Probability Sampling Designs
Sampling Distribution

3. Sampling Terminology

4. Two Major Types of Sampling Methods
Probability Sampling
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
Non-Probability Sampling

5. Who do you want to generalize to?
Groups in Sampling
Who do you want to generalize to?

6. The Theoretical Population
Groups in Sampling
The Theoretical Population

7. The Theoretical Population What population can you get access to?
Groups in Sampling
The Theoretical Population
What population can you get access to?

8. The Theoretical Population
Groups in Sampling
The Theoretical Population
The Study Population

9. The Theoretical Population How can you get access to them?
Groups in Sampling
The Theoretical Population
The Study Population
How can you get access to them?

10. The Theoretical Population
Groups in Sampling
The Theoretical Population
The Study Population
The Sampling Frame

11. The Theoretical Population
Groups in Sampling
The Theoretical Population
The Study Population
The Sampling Frame
Who is in your study?

12. The Theoretical Population
Groups in Sampling
The Theoretical Population
The Study Population
The Sampling Frame
The Sample

13. The Theoretical Population
Where Can We Go Wrong?
The Theoretical Population
The Study Population
The Sampling Frame
The Sample

14. The Theoretical Population
Where Can We Go Wrong?
The Theoretical Population
The Study Population
The Sampling Frame
The Sample

15. The Theoretical Population
Where Can We Go Wrong?
The Theoretical Population
The Study Population
The Sampling Frame
The Sample

16. The Theoretical Population
Where Can We Go Wrong?
The Theoretical Population
The Study Population
The Sampling Frame
The Sample

17. Statistical Terms in Sampling
Variable

18. Statistical Terms in Sampling
Variable
responsibility

19. Statistical Terms in Sampling
Variable
responsibility
Statistic

20. Statistical Terms in Sampling
Variable
responsibility
Statistic
Average = 3.72
sample

21. Statistical Terms in Sampling
Variable
responsibility
Statistic
Average = 3.72
sample
Parameter

22. Statistical Terms in Sampling
Variable
response
Statistic
Average = 3.72
sample
Parameter
Average = 3.75
population

23. Statistical Inference
Statistical inference: make generalizations about a population from a sample.
A population is the set of all the elements of interest in a study.
A sample is a subset of elements in the population chosen to represent it.
Quality of the sample = quality of the inference
Would this class be a good representation of all Persian Doctors? Why or why not?
This class would not be a good sample of all Persian Dentists, we are more interested in research methodology, so we are different!!
This class would not be a good sample of all UNO students. The reason, everyone is a business student and it is therefore biased. It contains no representation from Arts and Sciences, Fine Arts, Engineering, Education, CPACS, or ITT.
It might be a good sample to make inferences about the College of Business but not all students at UNO.

24. The Sampling Distribution
sample
sample
sample

25. The Sampling Distribution
sample 4 . 2 3 8 6 5
sample 4 . 2 3 8 6 5
sample 4 . 2 3 8 6 5

26. The Sampling Distribution
sample 4 . 2 3 8 6 5
sample 4 . 2 3 8 6 5
sample 4 . 2 3 8 6 5
Average
Average
Average

27. The Sampling Distribution
sample 4 . 2 3 8 6 5
sample 4 . 2 3 8 6 5
sample 4 . 2 3 8 6 5
Average
Average
Average 4 . 2 3 8 6 1 5
...is the distribution of a statistic across an infinite number of samples
The Sampling Distribution...

28. Random Sampling

29. Types of Probability Sampling Designs
Simple Random Sampling
Stratified Sampling
Systematic Sampling
Cluster Sampling
Multistage Sampling

30. Some Definitions N = the number of cases in the sampling frame
n = the number of cases in the sample
NCn = the number of combinations (subsets) of n from N
f = n/N = the sampling fraction

31. Simple Random Sampling
Objective - select n units out of N such that every NCn has an equal chance
Procedure - use table of random numbers, computer random number generator or mechanical device
can sample with or without replacement
f=n/N is the sampling fraction

32. Simple Random Sampling
Example:
People who subscribe Novin Pezeshki last year
People who visit our site
draw a simple random sample of n/N

33. Simple Random Sampling
List of Residents

34. Simple Random Sampling
List of Residents
Random Subsample

35. 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 + N Ni = N, then do simple random sample of n/N in each strata

36. Stratified Sampling
The population is first divided into groups called strata. If stratification is evident
Example: medical students; preclinical, clerckship, internship
Best results when low intra strata variance and high inter strata variance
A simple random sample is taken from each stratum.
Advantage: If strata are homogeneous, this method is “more precise” than simple random sampling of same sample size
As precise but with a smaller total sample size.
If there is a dominant strata and it is relatively small, you can enumerate it, and sample the rest.

37. Stratified Sampling - Purposes:
to insure representation of each strata - oversample smaller population groups
sampling problems may differ in each strata
increase precision (lower variance) if strata are homogeneous within (like blocking)

38. Stratified Random Sampling
List of Residents

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

40. Stratified Random Sampling
List of Residents
surgical
medical
Non-clinical
Strata
Random Subsamples of n/N

41. 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

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

43. Systematic Random Sampling
N = 100

44. Systematic Random Sampling
N = 100
want n = 20

45. Systematic Random Sampling
N = 100
want n = 20
N/n = 5

46. Systematic Random Sampling
N = 100
want n = 20
N/n = 5
select a random number from 1-5: chose 4

47. Systematic Random Sampling
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

48. 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.

49. Cluster Random Sampling
Advantages - administratively useful, especially when you have a wide geographic area to cover
Example: Randomly sample from city blocks and measure all homes in selected blocks

50. Cluster Sampling vs. Stratified Sampling
Stratified sampling seeks to divide the sample into heterogeneous groups so the variance within the strata is low and between the strata is high.
Cluster sampling seeks to have each cluster reflect the variance in the population…each cluster is a “mini” population. Each cluster is a mirror of the total population and of each other.

51. Multi-Stage Sampling Cluster random sampling can be multi-stage
Any combinations of single-stage methods

52. Multi-Stage Sampling choosing students from medical schools:
Select all schools, then sample within schools
Sample schools, then measure all students
Sample schools, then sample students

53. Nonrandom Sampling Designs

54. Types of nonrandom samples
Accidental, haphazard, convenience
Modal Instance
Purposive
Expert
Quota
Snowball
Heterogeneity sampling

55. Accidental or Haphazard Sampling
“Man on the street”
Medical student in the library
available or accessible clients
volunteer samples
Problem: we have no evidence
for representativeness

56. Convenience Sampling
The sample is identified primarily by convenience.
It is a nonprobability sampling technique. Items are included in the sample without known probabilities of being selected.
Example: A professor conducting research might use student volunteers to constitute a sample.

57. Convenience Sampling
Advantage: Relatively easy, fast, often, but not always, cheap
Disadvantage: It is impossible to determine how representative of the population the sample is.
Try to offset this by
collecting large sample
size.

58. Modal Instance Sampling
Sample for the typical case
Typical medical students age?
Typical socioeconomic class?
Problem: may not represent the modal group proportionately

59. Purposive Sampling
Might sample several pre-defined groups (e.g., patients who does not attend at follow up visits)
Deliberately sampling an extreme group

60. Expert Sampling
Have a panel of experts make a judgment about the representativeness of your sample
Advantage: at least you can say that expert judgment supports the sampling
Problem: the “experts” may be wrong

61. Quota Sampling
select people nonrandomly according to some quotas

62. Snowball Sampling
one person recommends another, who recommends another, who recommends another, etc.
good way to identify hard-to-reach populations
for example, adolescents who abuse recreational drugs

63. Heterogeneity Sampling
make sure you include all sectors - at least several of everything - don't worry about proportions (like in quota sampling)
for instance, when brainstorming issues across stakeholder groups

64. Sampling Random Non Random Haphazard Simple Convenience Systematic
Modal Instance
Cluster
Purposive
Multi Stage
Expert
Stratified
Snowball
Proportionate
Disproportionate
Heterogeneity
Quota

65. Any question?