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1. Learning Objectives
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 Distinguish between probability and non-probability
sampling
 Identify factors to consider when determining sample size
 Differentiate the techniques of probability sampling
methods
 Able to select appropriate sampling method for different
studies

2. Sampling
2

3. Sampling
 Studying an entire population is not always
feasible, primarily due to constraints like time,
expenses, and resource limitations.
 Researchers answer research questions using a
sample of participants.
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4. What is Sampling?
The process of selecting a portion of the population to
represent the entire population
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Population
Sample
 Using data to say something (make an inference) with confidence, about
a whole (population) based on the study of a only a few (sample).
Sampling
Frame
Sampling Process
What you
want to talk
about
What you
actually
observe in
the data
Inference
 Sample is a collection of individuals selected from a larger population

5. Sampling Terminology
• Reference population (or target population): the
population of interest to whom the researchers would like
to make generalizations
• Study population: the actual group in which the study is
conducted = Sample
• Study unit: the units on which information will be
collected: persons, housing units, etc.
• Sampling frame: The list of units from which the sample is to be
selected

6. Researchers are interested to know about factors
associated with ART use among HIV/AIDS patients
attending certain hospitals in Region X
Target population = All ART
patients in the Region X
Sampling population = All
ART patients in, e.g. 3,
hospitals in the Region
Sample (400 patients)

7. What is their difference ?
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A B

8. Errors in sampling
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A.Sampling error: Errors introduced due to errors in the selection of a
sample.
◦They cannot be avoided or eliminated( not mirror image)

9. Sampling Error…
Difference in characteristics of a sample and population
from which the sample is drawn.
Can minimized by using ;
appropriate sampling techniques,
increasing the sample size,
Employing appropriate statistical methods
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10. Non-sampling Error…
• Errors that occur during the data collection and analysis
process
• It can arise from various sources,
• measurement errors, data entry mistakes, processing
errors…
Can be Minimized by;
• Using standardized data collection procedures
• Training and supervising data collectors
• Conducting quality checks.
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12. Probability sampling
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Any method of sampling that utilizes some form of random
selection.
Every sampling unit has a known and non-zero probability of
selection into the sample
Involves the selection of a sample from a population based on
chance.

13. Probability sampling…
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 The method to chosesampling depends on several factors, such as
◦the available sampling frame,
◦how spread out the population is,
◦how costly it is to survey members of the population
◦Homogeneity of the target population

14. Types of probability sampling techniques
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15. Simple random sample (SRS)
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Objective: T
o select n units out of N

16. Simple random sampling…
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Therequired number of individuals are selected at random from
the sampling frame, a list or a database of all individuals in the
population
Each member of a population has an equal chance of being included
in the sample.
To use a SRS method:
◦Make a numbered list of all the units in the population
◦Each unit should be numbered from 1 to N (where N is the size of
the population).

17. Simple random
sampling
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The randomness of the sample is ensured by:
◦Use of “lottery’ methods
◦Table of random numbers
◦Computer programs

18. Random number table
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 It conducted using constructed table of random numbers
 The occurrence of any number in one part of the table is
independent of the occurrence of any number in any other part
of the table.

19. Example
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Suppose your school has 500 students and you need to conduct a
short survey on the quality of the food served in the cafeteria
You decide that a sample of 10 students should be sufficient for your
purposes
In order to get your sample, you assign a number from 1 to 500
to each student in your school.
Pick a starting point in the table (a row and column number) and
look at the random contain three digits as well.
Random number
table…

20. Limitations of SRS
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◦Requires a sampling frame.
◦Difficult if the reference population is dispersed.
◦Minority subgroups of interest may not be selected

21. 2. Systematic random sampling
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22. Systematic random sampling…
 Selection of individuals from the sampling frame systematically
rather than randomly
 Taking individuals at fixed intervals (every kth) based on the sampling
fraction.
 The starting point is chosen at random
• Important if the reference population is arranged in some order:
• Order of registration of patients
• Numerical number of house numbers
• Student’s registration books

23. Steps in systematic random
sampling
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Number the units in the population from 1 to N
Decide on the n (sample size) that you want or need
k = N/n = the interval size
Randomly select an integer between 1 to k
Then, take every kth unit
Note: Systematic sampling should not be used when a cyclic
repetition is inherent in the sampling frame.

24. Example
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25. What is k?
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3
8
systematic random
sampling…

26. Stratified sample
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27. Stratified Random Sample

28. Stratified random sampling
• It is done when the population is known to be have
heterogeneity with regard to some factors and those factors
are used for stratification.
• Using stratified sampling, the population is divided into
homogeneous, mutually exclusive groups called strata, and
• A population can be stratified by any variable that is available
for all units prior to sampling (e.g., age, sex, province of
residence, income, etc.).

29. A separate sample is taken independently from each
stratum.
Elements within each strata are homogeneousbut are
heterogeneous across strata.
◦A simple random or a systematic sample is taken from each
strata
Stratified random sampling…

30. Advantages of stratified sampling
• Every unit in a stratum has the same chance of being
selected.
• Adequate representation of minority subgroups of
interest can be ensured by stratification and by varying
the sampling fraction between strata as required.

31. Stratified random
sampling
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1. Proportional allocation: allocating sampling proportional to
the total population of each strata using the formula:
2. Equal allocation: allocating equal sample for each strata
There are different sample allocation methods in order to
select sample from each

32. Example: Proportionate
Allocation
• Village A B C D Total
• HHs 100 150 120 130 500
• S. size ? ? ? ? 60

33. Cluster sampling
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34. Cluster sampling
 Cluster sampling is the most widely used to reduce the cost.
 The clusters should be homogeneous, unlike stratified
sampling where the strata are heterogeneous
 Cluster sampling is a probability sampling method in which
you divide a population into clusters, such as districts or
schools, and then randomly select some of these clusters as
your sample.

35. Steps in cluster sampling
• A number of clusters are selected randomly to represent the
total population, and then all units within selected clusters
are included in the sample.
• No units from non-selected clusters are included in the
sample—they are represented by those from selected
clusters.
• This differs from stratified sampling, where some units are
selected from each group.

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

37. Multi-stage sampling
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38. Multi-stage sampling
• Similar to the cluster sampling, except that it involves picking a
sample from within each chosen cluster, rather than including all
units in the cluster.
• This type of sampling requires at least two stages.
• The primary sampling unit (PSU) is the sampling unit in the first
sampling stage.
• The secondary sampling unit (SSU) is the sampling unit in the
second sampling stage, etc.

39. Multi-stage sampling
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40. • In the first stage, large groups or clusters are identified and
selected.
• These clusters contain more population units than are needed
for the final sample.
• In the second stage, population units are picked from within the
selected clusters (using any of the possible probability sampling
methods) for a final sample.
• multi-stage sampling still saves a great amount of time and effort
by not having to create a list of all the units in a population.
• To reduce sampling error we use design effect.

41. Summary
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42. Non-probability sampling
 Nonprobability sampling does not involve random selection
 Units selected from a population using a subjective (i.e.
non-random) method.
 Most sampling methods are purposive in nature because
we usually approach the sampling problem with a specific
plan in mind.
 Non-probability sampling strategies are used when it is
practically impossible to use probability sampling
strategies.
 they are quick, inexpensive and convenient.

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Types of Non-probability sampling

44. The most common types of non-probability
sampling
• Convenience sampling (or haphazard sampling)
• Purposive sampling (or judgment sampling)
• Quota sampling
• Snowball sampling (or respondent-driven
sampling)

45. Types of non-probability
sampling
1. Convenience sampling
Drawn at the convenience of the researcher.
Does not lead to any conclusion.
2. Volunteer sampling
• when people volunteer to be involved in the study.
E.g. for psychological experiments or pharmaceutical trials
(drug testing)

46. Types of non-probability sampling
3. Judgmental sampling/ Purposive sampling
Sampling based on some judgment, feelings or
experience of the researcher. If inference drawing is
not necessary, these samples are quite useful.
• E.g. used in pre-testing of questionnaires and
focus groups.

47. Types of non-probability sampling
4. Quota sampling
• Requires that a certain number be selected in each
category--usually done on a first-come first included
basis.
• Sampling stops when enough are included in each
category
5. Snowball sampling
– Used in studies involving respondents who are rare
to find. To start with, the researcher compiles a
short list of sample units from various sources.
– Each of these respondents are contacted to provide
names of other probable respondents.

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