2. Definitions
Sampling: This is a technique of selecting individual members or a subset of the
population to make statistical inferences from them and estimate the characteristics
of the whole population.
The population includes all members from a specified group, all possible
outcomes or measurements that are of interest.
The sample consists of some observations drawn from the population, so a part of
a subset of the population. The sample is the group of elements who participated in
the study.
The sampling frame is the information that locates and defines the dimensions of
the universe. 2
3. Characteristics of a Good Sample
A good sample should satisfy the below conditions-
Representativeness: The sample should be the best representative of the
population under study.
Accuracy: Accuracy is defined as the degree to which bias is absent from the
sample. An accurate (unbiased) sample is one that exactly represents the
population.
• Size: A good sample must be adequate in size and reliability
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4. Different types of Sampling techniques
Sampling techniques can be subdivided into two groups:
Probability and Non-probability Sampling
1. Probability sampling involves random selection, allowing you to make
statistical inferences about the whole group.
There are four types of probability sampling techniques
Simple random sampling
Cluster sampling
Systematic sampling
Stratified random sampling
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5. 2. Non-probability sampling involves non-random selection based on
convenience or other criteria, allowing you to easily collect initial data. There
are four types of non-probability sampling techniques.
Convenience sampling
Judgmental or purposive sampling
Snowball sampling
Quota sampling
The choice between using a probability or a non-probability approach to
sampling depends on a variety of factors:
1. Objectives and scope of the study
2. Method of data collection
3. Precision of the results
4. Availability of a sampling frame and resources required to maintain the frame
5. Availability of extra information about the members of the population
Monday,
January 22,
2024
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6. Probability Sampling: This is preferred when conducting major studies,
especially when a population frame is available, ensuring that we can select and
contact each unit in the population.
• Simple Random Sampling: In simple random sampling technique, every item in
the population has an equal and likely chance of being selected in the sample.
Advantages
Minimum sampling bias as the samples are collected randomly
Selection of samples is simple as random generators are used
The results can be generalized due to representativeness
Disadvantages
The potential availability of all respondents can be costly and time consuming
Larger sample sizes
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7. Systematic sampling: Here, the researcher first randomly picks the first item from
the population. Then, the researcher will select each nth item from the list.
Steps in selecting a systematic random sample:
Calculate the sampling interval (the number of observations in the population
divided by the number of observations needed for the sample)
Select a random start between 1 and sampling interval
Repeatedly add sampling interval to select subsequent households
Advantages
1. Cost and time efficient
2. Spreads the sample more evenly over the population
Disadvantages
1. Complete population should be known
2. Sample bias If there are periodic patterns within the dataset
Monday,
January 22,
2024
This training is supported under the COMCEC Project Funding 7
8. Stratified random sampling: In Stratified random sampling, the entire
population is divided into multiple non-overlapping, homogeneous groups
(strata) and randomly choose final members from the various strata for
research.
Advantages
1. Greater level of representation from all the groups
2. If there is homogeneity within strata and heterogeneity between strata, the
estimates can be as accurate
Disadvantages
1. Requires the knowledge of strata membership
2. Might take longer and more expensive
Complex methodology
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9. Cluster sampling: Cluster sampling divides the population into multiple
clusters for research. Researchers then select random groups with a
simple random or systematic random sampling technique for data
collection and data analysis.
Steps involved in cluster sampling:
1. Create the clusters from the population data
2. Select each cluster as a sampling frame
3. Number each cluster
4. Select the random clusters .
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10. Advantages
1. Saves time and money
2. It is very easy to use from the practical standpoint
3. Larger sample sizes can be used
Disadvantages
1. High sampling error
2. May fail to reflect the diversity in the sampling frame
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11. Non-probability sampling: Non-Probability samples are preferred when
accuracy in the results is not important
Convenience sampling: This is the easiest method of sampling and the
participants are selected based on availability and willingness to participate in
the survey.
Advantages
1. It is easy to get the sample
2. Low cost and participants are readily available
Disadvantages
1. Can’t generalize the results
2. Possibility of under or over representation of the population
3. Significant bias
Monday,
January 22,
2024
This training is supported under the COMCEC Project Funding 11
12. Quota sampling: Here, the researchers divide the survey population into
mutually exclusive subgroups. These subgroups are selected with respect to
certain known features, traits, or interests. Samples from each subgroup are
selected by the researcher.
Advantages
1. Cost effective
2. Doesn’t depend on sampling frames
3. Allows the researchers to sample a subgroup that is of great interest to the
study
Disadvantages
1. Sample may be overrepresented
2. Unable to calculate the sampling error
3. Great potential for researcher bias and the quality of work may suffer due to
researcher incompetency and/or lack of experience
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13. Judgement (or Purposive) Sampling: Here, a researcher relies on his or her
judgment when choosing members of the population to participate in the study.
Advantages
1. Cost and time effective sampling method
2. Allows researchers to approach their target market directly
3. Almost real-time results
Disadvantages
1. Vulnerability to errors in judgment by researcher
2. Low level of reliability and high levels of bias
Inability to generalize research findings
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14. Snowball Sampling: In this method, the samples have traits that are difficult to find.
So, each identified member of a population is asked to find the other sampling units.
Those sampling units also belong to the same targeted population.
Advantages
1. Researchers can reach rare subjects in a particular population
2. Low-cost and easy to implement
3. It doesn’t require a recruitment team to recruit the additional subjects
Disadvantages
1. The sample may not be a representative
2. Sampling bias may occur
3. Because the sample is likely to be biased, it can be hard to draw conclusions about the
larger population with any confidence
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