types of samping discussed

1. Sampling
Yordanos S.

2. 2
Learning objectives
At the end of the session the participant/student
will be able to:
 Differentiate source population, study
population and sample population
 Calculate sample size for the proposed study
 Apply appropriate sampling techniques for the
selection of study units

3. Population
• In research, measurements are taken from few
people and estimates are derived from these
measurements.
• All kinds of errors prior, during and after the
study can be responsible for bias in the final
results.
• This bias can be caused by measurement
errors, as well as through poorly chosen source
and study populations.
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4. Population cont..
• Bias can also be introduced during the sampling
procedure.
• The generalizability of the results could be
limited by these types of bias.
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5. Target population
• Refers to the entire group of individuals or objects
to which researchers are interested to generalize
the conclusions.
• But, because of practicalities, entire target
population often cannot be studied.
• Also known as the theoretical population.
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6. Study population
• This population is a subset of the
target/source population and is also
known as the accessible population.
• It is from this accessible population that
researchers draw their samples.
• E.g Female patients who are older than 50
years admitted with a diagnosis of
diabetes mellitus.
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7. Sample population
• Is a population selected and included in the
study.
• Samples are subsets of study populations
used in research because often not every
member of study population can be
measured.
• However, the results drawn from the
investigation of the sample are interpreted
and applied directly to the study population.
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8. 8
Sampling methods
• Sampling is a process of choosing a section of the
population for study
• The conclusions drawn from the study are often
based on generalizing the results observed in the
sample to the entire population from which the
sample was drawn
• The accuracy of the conclusions will depend on
how representative the sample is for the target
population

9. 9
Why sampling?
• There are several reasons why samples are
chosen for a study, rather than studying the
entire population
• A researcher wants to minimize the costs of
– Data collection
– processing and
– reporting on the results

10. 10
How to do sampling
• Sample should be representative of the population
• This requires knowledge of the variables and their
distribution in the population
• A representative sample has all the important
characteristics of the population from which it is
drawn

11. Sampling methods
• There are two types of sampling methods:
A. Probability Sampling methods
B. Non-probability methods
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12. Probability sampling methods
• Points to be considered
– Heterogeneity of the population
– Area coverage
– Frame availability
– Analysis to be performed
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13. Probability sampling methods
• Simple random sampling
• Systematic random sampling
• Stratified Random sampling
• Cluster random sampling
• Stratified-cluster sampling
• Multistage random sampling
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14. Simple random sampling
• Each individual in the population should
have an equal chance to be selected
• Sampling frame is necessary
• Select the required number of study units
using lottery method (for small
population) or a table of random numbers
(for large population)
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15. Systematic Random sampling
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16. Stratified random sampling
• The total population is divided into smaller
groups (strata) to complete the sampling
process.
• The strata is formed based on some common
characteristics in the population.
• After dividing the population into strata,
randomly select the sample.
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17. Stratified random sampling cont…
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18. Stratified random sampling cont..
 Types of allocation in stratified sampling
1) Proportional allocation – if the same sampling
fraction is used for each stratum
2) Non-proportional allocation
– if a different sampling fraction is used for
each stratum or
- if the strata are unequal in size and a fixed
number of units is selected from each stratum
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19. Stratified Random sampling cont…
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20. Cluster sampling
• Cluster (group of population elements)
constitutes the sampling unit, instead of a
single element of the population.
• The main reason for cluster sampling is
cost efficiency
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21. Cluster sampling cont..
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22. Cluster sampling cont..
Simple one-stage cluster sampling
• List all the clusters in the population
• From the list, select the clusters – usually
by simple random sampling
• All units (elements) in the sampled clusters
are selected.
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23. Cluster sampling cont..
Simple two-stage cluster sample
• List all the clusters in the population.
• First, select the clusters.
• The units (elements) in the selected
clusters of the first-stage are then sampled
in the second-stage.
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24. Cluster sampling cont..
Multi-stage sampling
• When sampling is done in more than one stage.
• In practice, clusters are also stratified.
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25. Stratified-cluster sampling cont..
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26. Non-probability sampling methods
• Convenience sampling
• Purposive sampling
• Snowball sampling
• Quota sampling
• Extreme or deviant case sampling
• homogenous sampling
• maximum variation sampling (heterogeneous sampling)
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27. Non-probability sampling methods
cont..
Convenience sampling
For convenience sake the study units that
happen to be available at the time of data
collection are selected
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28. Non-probability sampling methods cont..
Quota sampling
• Ensures that a certain number of sample units
from different categories with specific
characteristics are represented
• The investigator interviews as many people in each
category of study unit as he can find until he has
filled his quota
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29. Non-probability sampling methods
cont..
Purposive sampling
• Involves selection of the most productive sample
to answer a research question
• Ongoing interpretation of data will indicate who
should be approached, including identification of
“missing” voices.
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30. Non-probability sampling methods
cont..
Snowball or chain sampling:
• Mainly applied when researcher is not familiar with the
research area
• Is used when the desired sample characteristic is rare.
• It may be extremely difficult or cost prohibitive to
locate respondents in these situations.
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31. Non-probability sampling methods
cont..
• Snowball sampling relies on referrals from initial
subjects to generate additional subjects.
• It introduces bias because the technique itself
reduces the likelihood that the sample will
represent a good cross section from the
population
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32. Non-probability sampling methods
cont..
Extreme or deviant case sampling
Useful to test emerging theories by
learning from highly unusual
manifestations and/or atypical situations
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33. Non-probability sampling methods
cont...
Homogenous sampling
• Useful to identify most common phenomenon
• Focus on similar type of respondents
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35. Sample size determination and Types
of data
Yordanos S.

36. Sample size
• In general it is much better to increase the
accuracy of data collection than to increase
sample size after a certain point
• Also try to get a representative sample rather than
to get a very large sample
• Nowadays computers have made the calculation
of sample size easier
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37. Rules of thumb
1. For smaller samples (N ‹ 100), there is little point
in sampling. Survey the entire population.
2. If the population size is around 500, 50% should
be sampled.
3. If the population size is around 1500, 20% should
be sampled.
4. At least 20 respondents for each independent
variable should be considered
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38. 38
Single population proportion
formula
Where:
n - is the sample size
Zα/2 -is the value of Z from standard normal curve at α/2
For α= 0.05 the Z0.025=1:96
For α =0:1 the Z0.05 = 1:65 and so on.
p= Best estimate of population proportion (When using
the formula, if you let p* = 0.5, this produces the
maximum possible value for n for any given E and α)
E=Margin of error or maximum acceptable difference
2
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39. Single population proportion cont..
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Margin of error (E)
• The margin of error (E) measures the precision of
the estimate
• Small value of E indicates high precision
• It lies in the interval (0%, 5%]
• For p close to 50%, E is assumed to be close to
5%
• For smaller value of p, E is assumed to be lower
than 5%
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40. Some Considerations
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41. Single population proportion cont..
Example:
• We wish to estimate the proportion of males in
‘Country X’ who smoke.
• What sample size do we require to achieve a
95% confidence interval of width ± 5% ( that is
to be within 5% of the true value) ? In a study
some years ago that found approximately 30%
were smokers.
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42. Single population proportion
p=0.3
E=.05
(1-α)=0.95
 Z(1-α)= 1.96 for 95% confidence level
Then n = (1.96)2(0.3)(0.7)/(0.05)2 = 322.7
≈ 323
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43. Single population proportion cont..
Design Effect
• It is a correction of bias in the variance introduced in
the sampling design, by selecting subjects due to
the use of clusters.
• The design effect is 1 (i.e., no design effect) when
taking a simple random sample.
• The design effect varies using cluster sampling
• It is usually estimated that the design effect is 2 in
multistage sampling having cluster sampling.
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44. Group Assignment with presentation
1.What are data?
2.What are the types of data?