Sampling is the process of selecting units (e.g., people, organizations) from a population of interest so that by studying the sample we may fairly generalize our results back to the population from which they were chosen. Let's begin by covering some of the key terms in sampling like "population" and "sampling frame." Then, because some types of sampling rely upon quantitative models, we'll talk about some of the statistical terms used in sampling. Finally, we'll discuss the major distinction between probability and Nonprobability sampling methods and work through the major types in each

3. Probability Method

4. Types of Probability Sampling Designs
• Simple random sampling
• Stratified sampling
• Systematic sampling
• Cluster (area) sampling
• Multistage sampling

5. 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

6. 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.

7. Simple Random Sampling
Examples:
• Small service agency.
• Client assessment of quality of service.
• Get list of clients over past year.
• Draw a simple random sample of n/N.

8. Simple Random Sampling
List of clients
Random subsample

9. Stratified Random Sampling
•Sometimes called "proportional" or "quota" random sampling.
•Objective: Population of N units divided into no overlapping strata
N1, N2, N3, ... Ni such that N1 + N2 + ... + Ni = N; then do simple
random sample of n/N in each strata.

10. Stratified Sampling - Purposes:
•To insure representation of each strata, oversample smaller
population groups.
•Administrative convenience -- field offices.
•Sampling problems may differ in each strata.
•Increase precision (lower variance) if strata are
homogeneous within (like blocking).

11. Stratified Random Sampling
List of clients
Random subsamples of n/N
Strata
African-American OthersHispanic-American

12. Types of Stratified Random Sampling
• Proportionate:
• If sampling fraction is equal for each stratum
• Disproportionate:
• Unequal sampling fraction in each stratum

13. 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.
• Take every kth unit.

14. Systematic Random Sampling
• Assumes that the population is randomly ordered.
• Advantages: Easy; may be more precise than simple random sample.
• Example: The library (ACM) study.

15. 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
Start with #4 and take every 5th unit

16. Cluster Sampling
 The primary sampling unit is not the
individual element, but a large cluster of
elements. Either the cluster is randomly
selected or the elements within are
randomly selected
 Why?
1. Frequently used when no list of population available or because
of cost
2. Ask: is the cluster as heterogeneous as the population? Can we
assume it is representative?

17. Types of cluster samples
Area sample:
Primary sampling unit is a geographical area.
Multistage area sample:
Involves a combination of two or more types of
probability sampling techniques. Typically,
progressively smaller geographical areas are
randomly selected in a series of steps.

18. Cluster (Area) Random Sampling
Procedure:
• Divide population into clusters.
• Randomly sample clusters.
• Measure all units within sampled clusters.

19. 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.

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

21. Multi-Stage Sampling
• Select all schools; then sample within schools.
• Sample schools; then measure all students.
• Sample schools; then sample students.
Example: Choosing students from schools

22. Non Probability Method

23. Convenience Sample
 The sampling procedure used to
obtain those units or people most
conveniently available
 Why: speed and cost
 External validity?
 Internal validity
 Is it ever justified?

24.  Advantages
 Very low cost
 Extensively used/understood
 No need for list of population elements
 Disadvantages
 Variability and bias cannot be measured or
controlled
 Projecting data beyond sample not justified.

25. Judgment or Purposive Sample
 The sampling procedure in which an
experienced research selects the
sample based on some appropriate
characteristic of sample members… to
serve a purpose

26.  Advantages
 Moderate cost
 Commonly used/understood
 Sample will meet a specific objective
 Disadvantages
 Bias!
 Projecting data beyond sample not
justified.

27. Quota Sample
 The sampling procedure that ensure
that a certain characteristic of a
population sample will be represented
to the exact extent that the
investigator desires

28.  Advantages
 moderate cost
 Very extensively used/understood
 No need for list of population elements
 Introduces some elements of stratification
 Disadvantages
 Variability and bias cannot be measured or
controlled (classification of subjects0
 Projecting data beyond sample not justified.

29. Snowball sampling
 The sampling procedure in which the
initial respondents are chosen by
probability or non-probability
methods, and then additional
respondents are obtained by
information provided by the initial
respondents

30.  Advantages
 low cost
 Useful in specific circumstances
 Useful for locating rare populations
 Disadvantages
 Bias because sampling units not
independent
 Projecting data beyond sample not
justified.