Sampling involves selecting a subset of a population to study rather than examining the entire population. There are two main types of sampling: probability sampling, where units are selected randomly, and non-probability sampling, where units are selected purposefully. Some common probability sampling methods include simple random sampling, systematic sampling, stratified sampling, and cluster sampling. Non-probability sampling techniques include quota sampling, purposive sampling, availability sampling, and snowball sampling.

1. M A R I B E T H B . V I E R N E S , R C H / L P T
SAMPLING PROCEDURE

2. Sampling
Sampling means choosing from a large
population the respondents or subjects to
answer your research questions. The entire
population is involved but for your research
study, you choose only a part of the whole.

3. Important statistical terms
Population:
a technical term in research
which means a big group of
people from where you choose
the sample
Sample:
A subset of the population

4. Why sampling?
Get information about large populations
 Less costs
 Less field time
 It’s impossible to study the whole population

5. Groups of Sampling Methods
 Probability Sampling
This is a sampling method that makes you base
your selection of respondents on pure chance.
 Non-probability Sampling
Sampling techniques included in this category
are not chosen randomly, but purposefully.

6. Probability Sampling
 Simple random sampling
 Systematic sampling
 Cluster (area) sampling
 Stratified sampling
 Multistage sampling

7. Probability Sampling
 Simple-random sampling- choosing of respondents based on
pure chance
 Systematic sampling- Select a random starting point and
then select every kth subject in the population
 Stratified sampling- choosing a sample that will later on be
subdivided into strata, sub-groups, or sub-samples during the
stage of the data analysis.
 Cluster sampling- selecting respondents in clusters, rather than
in separate individuals such as choosing 5 classes of 40 students
each from a whole population of 5,000 students.

8. Multistage Sampling
• This technique, is essentially the process of taking random
samples of preceding random samples.
• An effective strategy because it banks on multiple
randomizations. As such, extremely useful.
• Multistage sampling used frequently when a complete list
of all members of the population not exists and is
inappropriate.
• Moreover, by avoiding the use of all sample units in all
selected clusters, multistage sampling avoids the large,
and perhaps unnecessary, costs associated with traditional
cluster sampling.

9. Multistage - researcher first samples groups
(clusters) obtains names of individuals within each
group (cluster) and then samples within the
cluster.

10. Difference Between Strata and Clusters
• Although strata and clusters are both non-
overlapping subsets of the population, they differ
in several ways.
• All strata are represented in the sample; but only a
subset of clusters are in the sample.
• With stratified sampling, the best survey results
occur when elements within strata are internally
homogeneous. However, with cluster sampling,
the best results occur when elements within
clusters are internally heterogeneous

11. List of clients
Simple Random Sampling

12. List of clients
Random subsample
Simple Random Sampling

13. Simple Random Sampling

14. Systematic Sampling
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N = 100
Want n = 20
N/n = 5
Select a random number from 1-5: chose 4

15. 1 26 51 76
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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
Systematic Sampling

16. Systematic sampling

17. Systematic sampling

18. Stratified sampling
Draw a sample from each stratum
 Divide the population into at least two different groups with common
characteristic(s), then draw SOME subjects from each group (group is
called strata or stratum)
 Results in a more representative sample

19. Stratified Sampling
Objective: Population of N units divided into
nonoverlapping strata N1, N2, N3, ... Ni such
that N1 + N2 + ... + Ni = N; then do simple
random sample of n/N in each strata.

20. Stratified Sampling
List of clients

21. List of clients
Strata
African-American OthersHispanic-American
Stratified Sampling

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

23. Cluster sampling
Section 4
Section 5
Section 3
Section 2Section 1

24.  Cluster Sampling
 Divide the population into groups
(called clusters), randomly select
some of the groups, and then collect
data from ALL members of the
selected groups

25. Non-probability Sampling
 Quota sampling- choosing specific samples that you know
correspond to the population in terms of one, two, or more
characteristics.
For example, an interviewer may be told to sample 200
females and 300 males between the age of 45 and 60.
Interviewers might be tempted to interview those who look
most helpful. The problem is that these samples may be biased
because not everyone gets a chance of selection.
 Purposive sampling (judgemental)- The researcher chooses the
sample based on who they think would be appropriate for the
study. This is used primarily when there is a limited number of
people that have expertise in the area being researched
You chose who you think should be in the study

26. Non-probability Sampling
 Availability sampling-
 Snowball sampling- selecting samples from several
alternative samples like drug dependents, human
traffickers, street children.