This document discusses different sampling methods for surveys. It defines key terms like population, sample, and sampling error. It describes probability sampling methods like random sampling, systematic sampling, stratified sampling, and multi-stage cluster sampling which aim to select representative samples. It also describes non-probability sampling methods like convenience sampling, purposive sampling, snowball sampling, and quota sampling which do not aim for representativeness. Sample size considerations and sources of error are also addressed.

1. SAMPLING
LECTURE 4

2. Key terms you should be familiar with
by the end of this lecture
 Population
 Sample
 Sampling frame
 Representative sample
 Probability sample
 Non-probability sample
 Sampling error
 Non-sampling error
 Non-response
 Census

3. Sampling
 It is impossible to survey everyone in a population so it is
necessary to work with a sample.
 A sample is subset or a segment of the population that is
selected for investigation.
 There are different ways in which to build a sample. How you
build a sample will depend on to what extent you want to
generalise the findings.
 Two types of sampling probability and non-probability
sampling.

4. PROBABILITY SAMPLE
 A sample that is selected using
a random selection.
 In sampling, random has a very
different meaning to how it is
conventionally understood.
 A random sample is one where
each unit in the population has
a known chance of being
selected.
 Generally assumed to be more
likely to result in a
representative sample.
NON-PROBABILITY SAMPLE
 A sample that does not use
randomization techniques to
select members.
 Therefore, some units in the
population are more likely to be
selected than others.
 Typically done when
randomization is not feasible or
possible.
 Bias is more of a concern with
this type of sampling.

5. Types of probability sampling
https://www.youtube.com/watch?v=pTuj57uXWlk

7. RANDOM SAMPLE
Define the
population
• Postgraduate students
at UJ
• N= the population
• N= 6,700 students
Devise sampling
frame
• Exclude those who do
not meet the sampling
criteria.
Decide sample size
• 𝑛 = sample size
• Sample size will be
dependent on time,
resources and size of
the population.
• Sample size of 670
Assign numbers and
generate sample
Sampling fraction =
𝑛
𝑁
𝑆𝑎𝑚𝑝𝑙𝑖𝑛𝑔 𝑓𝑟𝑎𝑐𝑡𝑖𝑜𝑛 =
670
6700
i.e 10 in 100
• Using a computer
programme
(SPSS) or table of
random numbers.

8. Random sampling
Advantages
 Unbiased
 Representative
 (Although no sample
can be free of sampling
error).
Disadvantages
 Costly (time and money)
 If your research questions
seeks to target particular
groups, a random sample
may not be able to provide
enough cases to answer
your research question.

10. SYSTEMATIC SAMPLE
Define the
population
• Postgraduate
students at UJ
• N= the population
• N= 6,700 students
Devise
sampling frame
• Exclude those who
do not meet the
sampling criteria.
Decide sample
size
• 𝑛 = sample size
• Sample size will be
dependent on
time, resources
and size of the
population.
• Sample size of 67
Choose x in
every y
Sampling fraction =
𝑛
𝑁
𝑆𝑎𝑚𝑝𝑙𝑖𝑛𝑔 𝑓𝑟𝑎𝑐𝑡𝑖𝑜𝑛 =
67
6700
i.e 10 in 100
• Using sampling
fraction
• Make a random
start from the
first 100
student.
• Start with
student no 46
• Then we would
choose 146,
246, 346 etc.

11. Systematic sampling
Advantages
 Unbiased
 Representative
 (Although no sample
can be free of sampling
error).
Disadvantages
 Costly (time and money)
 If your research questions
seeks to target particular
groups, a random sample
may not be able to provide
enough cases to answer
your research question.

13. Stratified random sample
Faculty Population Hypothetical random
or systematic sample
Stratified sample
Art, Design and
Architecture
500 75 50
Education 1,000 83 100
Engineering and the Built
Environment
1,400 161 140
Health Sciences 900 95 90
Humanities 1,100 106 110
Management 1,000 97 100
Science 800 53 80
Total 6,700 670 670
• Using our sampling fraction of 10 in 100 we would expect to have 50 Art and Design students. However
due to sampling error this is unlikely if we used a random or systematic sample.
• A stratified sample ensures that each Faculty is represented in proportion.

14. Stratified sampling
Advantages
 Unbiased.
 Representative.
 If your research questions
seeks to target particular
groups, stratified sampling
may be more accurately
able to target these groups.
 (Note: No sample can be
free of sampling error).
Disadvantages
 Costly (time and money)

15. Multi-stage cluster sampling
 National survey of postgraduate students. (3,000 students out of a total population
of 30,000).
 A random survey may be costly (travel) but may also under-represent certain
institutions.
 There are 26 universities in South Africa but you may not be able to afford to travel
to all of them.
 Thus we might decide to produce a random sample of 12 universities and sample
250 students from each university.
𝑆𝑎𝑚𝑝𝑙𝑖𝑛𝑔 𝑓𝑟𝑎𝑐𝑡𝑖𝑜𝑛 =
3,000
30,000
i.e 10 in 100

16. Multi-stage cluster sampling
 But you might not have the time and money to go all over the country.
 You could group universities into regions and choose to sample 4 universities from
3 regions, sampling 250 students each.
Gauteng (region 1) KwaZulu Natal (region
2)
Eastern Cape (region 3)
Tshwane University of
Technology
University of KwaZulu-
Natal
Rhodes University
University of
Johannesburg
Durban University of
Technology
University of Fort Hare
University of Pretoria University of Zululand Nelson Mandela
Metropolitan University
University of the
Witwatersrand
Mangosuthu University
of Technology
Walter Sisulu University

17. Multi-stage cluster sampling
Advantages
 Unbiased.
 Representative.
 If your research questions
seeks to target particular
groups, stratified sampling
may be more accurately
able to target these groups.
 (Note: No sample can be
free of sampling error).
Disadvantages
 Costly (time and money)

18. SAMPLE SIZE IN PROBABILITY SAMPLING
 The importance of probability sampling is the ability to generalise to a population.
 Your ability to do this will depend, in part, on your sample size. A sample of a few
dozen people is unlikely to be representative of a population. For statistically
relevant results there needs to be at least 100 responses. However, a robust sample
size should be larger, perhaps at least 1,000 responses.
 Sample size is likely to be impacted by time, cost and non-response.
 For instance, if you are sampling 250 students at a university it is unlikely that all of
the 250 that are asked will participate. It may therefore be necessary to draw a
larger sample.

19. NON-SAMPLING ERROR
 Difference between the population and the sample
that arise from weaknesses of the sampling
approach or non-response.
 Poor question wording or interviewing.
 Poor data capture.

20. SAMPLING ERROR
 https://www.youtube.com/watch?v=uGuWrPFStdg

21. PROBABILITY
 A sample that is selected using
a random selection.
 In sampling, random has a very
different meaning to how it is
conventionally understood.
 A random sample is one where
each unit in the population has
a known chance of being
selected.
 Generally assumed to be more
likely to result in a
representative sample.
NON-PROBABILITY SAMPLE
 A sample that does not use
randomization techniques to
select members.
 Therefore, some units in the
population are more likely to be
selected than others.
 Typically done when
randomization is not feasible or
possible.
 Bias is more of a concern with
this type of sampling.

22. CONVENIENCE SAMPLING

23. Convenience sampling
Advantages
 Quick, easy and cheap.
 Can be useful in pilot
studies and for
generating hypotheses.
Disadvantages
 Highly vulnerable to
selection bias and influences
beyond the control of the
researcher.
 High level of sampling error.
 Studies that use
convenience sampling have
reduced credibility.

24. PURPOSIVE SAMPLING
 Where participants are selected according to some kind of criteria.
 Might be necessary when you have a specific research target or a research target
that might be difficult to access.
 For example if you were conducting research on women who are HIV-positive you
would sample participants that have meet that criteria.
Purposive sampling, one of the most common sampling strategies, groups
participants according to preselected criteria relevant to a particular research
question (for example, HIV-positive women in Ivory Park).

25. Purposive sampling
Advantages
 Cost and time-effective.
 Appropriate if there are only
limited number of primary data
sources who can contribute to
the study.
 Useful in qualitative and/or
anthropological situations
where the discovery of
meaning can benefit from an
intuitive approach.
Disadvantages
 Susceptible to errors in
judgment by researcher.
 Not a representative
sample.
 Inability to generalize
research findings.

26. SNOWBALL SAMPLING
 In this method, participants or
informants with whom contact has
already been made use their social
networks to refer the researcher to
other people.
 Often used in difficult to access
groups.
 For instance, undocumented
migrants.
 Could result in bias.

27. Snowball sampling
Advantages
 The ability to recruit hidden
populations that other sampling
procedures could not access.
 Cost and time effective.
Disadvantages
 Not a representative sample.
 Limits to generalisability.
 Oversampling a particular network
of peers can lead to bias.
 Respondents may be hesitant to
provide names of peers and
asking them to do so may raise
ethical concerns.

28. QUOTA SAMPLING
 Similar to purposive sampling but quota sampling is more specific with respect to
sizes and proportions of subsamples, with subgroups chosen to reflect
corresponding proportions in the population.
 Studies employ purposive rather than quota sampling when the number of
participants is more of a target than a steadfast requirement – that is, an
approximate rather than a strict quota.
Chocolate buyers Quota size
(N=200)
Men 40% 80
Women 60% 120

29. Quota sampling
Advantages
 Quicker and easier than a stratified
sample.
 If you are interested in particular
social groups this sampling method is
more targeted than other sampling
methods but not as targeted as others
(i.e stratified sampling).
Disadvantages
 Not representative and therefore
limits the ability to generalise from
the findings.
 While this sampling technique might
be very representative of the quota-
defining characteristics, other
important characteristics may be
disproportionately represented in the
final sample group.

30. SAMPLE SIZE IN QUALITATIVE RESEARCH
When conducting qualitative research there is no minimum or maximum sample size unlike
in quantitative research. Sample sizes, which may or may not be fixed prior to data
collection, depend on the resources and time available, as well as the study’s objectives.
Sample sizes can often be determined on the basis of theoretical saturation (the point in
data collection when new data no longer bring additional insights to the research
questions).

31. Summary
Probability
 Random sample
 Systematic sample
 Stratified
 Multi-stage cluster sampling
Non-probability
 Convenience
 Purposive
 Snowball
 Quota