Qualitative sampling design is a key step in qualitative research, especially for rural development, researchers this document provides the necessary details on the procedures to follow

1. DANIELTEMESGEN GELAN
PROFESSOR INSTRUCTOR
VISION INTERNATIONAL UNIVERSITY
SAMPLING DESIGN AND
PROCEDURES
IN QUALITATIVE RESEARCH

2. DETERMINING SAMPLE DESIGN
• All the items under consideration in any field of inquiry constitute a
‘universe’ or ‘population’.
• A complete enumeration of all the items in the ‘population’ is known as a census
inquiry.
• when all the items have been covered no element of chance is left and the highest
accuracy is obtained. But in practice, this may not be true.
• Besides, this type of inquiry involves a great deal of time, money, and energy.
• Hence, quite often we select a few items from the universe for our study purposes.

3. Sampling design
• A sample design is a definite plan determined before any data
are actually collected for obtaining a sample from a given
population.
• Samples can be either probability samples or non-
probability samples.
• With probability samples each element has a known probability
of being included in the sample

4. PROBABILITY SAMPLING
• With probability samples, each element has a known probability
of being included in the sample
• Probability sampling is a method that involves randomly selecting a sample or part of the
population you want to research. It is also sometimes called random sampling.
• being random is when each of research unit (e.g., person, business, or organization in your
population) must have an equal chance of being selected.
• Probability samples are those based on simple random sampling,
systematic sampling, stratified sampling, cluster/area sampling

5. NON-PROBABILITY SAMPLING
• Non-probability sampling is a method that uses non-random criteria like the availability,
geographical proximity, or expert knowledge of the individuals you want to research to answer a
research question.
• this type of sampling is at higher risk for research biases than probability sampling, particularly
sampling bias.
• Non-probability samples are those based on convenience
sampling, judgment sampling, and quota sampling techniques

6. SIMPLE RANDOM SAMPLE
• is a randomly selected subset of a population. In this sampling method, each member of
the population has an exactly equal chance of being selected.
• Because it uses randomization, any research performed on this sample should have high
internal and external validity, and be at a lower risk for research biases like sampling bias
and selection

7. PREREQUISITESTO USE SIMPLE RANDOM SAMPLING
Prerequisites to use Simple random samplingYou have a complete list of every member of
the population.
• In the case of a finite universe
• You can contact or access each member of the population if they are selected.
• You have the time and resources to collect data from the necessary sample size.

8. Systematic sampling :
• Systematic sampling: In some instances, the most practical way of sampling is to
select every 15th name on a list, every 10th house on one side of a street, and so
on.
• Systematic random sampling – samples according to a rule
• Problems: same as simple random.The rule must not lead to bias.
• An element of randomness is usually introduced into this kind of sampling by
using random numbers to pick up the unit with which to start.

9. STRATIFIED SAMPLING
• Stratified sampling: appropriate when you want to ensure that specific
characteristics are proportionally represented in the sample.
• You split your population into strata (for example, divided by gender or race), and
then randomly select from each of these subgroups.
• Stratified sampling technique is applied to constitute a homogeneous group, and
obtain a representative sample.
• If the items selected from each stratum are based on simple random sampling the
entire procedure is known as stratified random sampling.

10. Cluster sampling and area sampling:
• Cluster sampling involves grouping the population and then selecting the groups or the clusters
• Under area sampling we first divide the total area into a number of smaller non-overlapping areas,
generally called geographical clusters,
• then a number of these smaller areas are randomly selected, and all units in these small areas are
included in the sample.

11. WHAT ISTHE DIFFERENCE BETWEEN
CLUSTER AND STRATIFIED SAMPLING?
• In Cluster Sampling, cluster/group is considered a sampling unit.
• In Stratified Sampling, elements within each stratum are sampled.
• In Cluster Sampling, only selected clusters are sampled.
• In Stratified Sampling, from each stratum, a random sample is selected.
• In Cluster Sampling, the aim is to reduce cost and increase the efficiency of sampling.
• In Stratified Sampling, the motive is to increase precision to reduce error

12. TYPES OF CLUSTER SAMPLING
In all three cluster sampling, you need to start by dividing the population into clusters and then selecting clusters
for your sample in random order.
• One-stage cluster sampling: Every member within the selected cluster can participate in the research.
• Two-stage cluster sampling: when we select the research sample twice.
• 1st – select random sub-groups
• 2nd – narrow down the sample by selecting a few participants within selected clusters.
• Multi-stage cluster sampling: This allows you to filter your target population & select a specific sample for
research.
• After performing two-stage cluster sampling, you can further select a sample for multi-stage cluster sampling.

13. MULTI-STAGE SAMPLING
• Multi-stage sampling:This is a further development of the idea of cluster sampling.
• This technique is meant for big inquiries extending to a considerably large geographical
area like an entire country.
• Under multi-stage sampling the first stage may be to select large primary sampling units
such as states, then districts, then towns and finally certain families within towns.
• If the technique of random-sampling is applied at all stages, the sampling procedure is
described as multi-stage random sampling

14. PURPOSIVE/ DELIBERATE SAMPLING
• Deliberate sampling is also known as purposive or non-probability sampling.
• This sampling method involves the purposive or deliberate selection of particular units of
the universe for constituting a sample that represents the universe.
• refers to a group of non-probability sampling techniques in which units are selected
because they have characteristics that you need in your sample. In other words, units are
selected “on purpose” in purposive sampling.

15. • Purposive sampling Also called judgmental sampling, this sampling method relies on the researcher’s
judgment when identifying and selecting the individuals, cases, or events that can provide the best
information to achieve the study’s objectives.
• Purposive sampling is common in qualitative research and mixed methods research.
• It is particularly useful if you need to find information-rich cases or make the most out of limited
resources, but is at high risk for research biases like observer bias.

16. WHENTO USE PURPOSIVE SAMPLING
• Purposive sampling is best used when you want to focus in-depth on relatively small samples.
• The main goal of purposive sampling is to identify the cases, individuals, or communities best suited to
help you answer your research question.
• Purposive sampling works best when you have a lot of background information about your research
topic.
• The more information you have, the higher the quality of your sample.

17. PURPOSIVE SAMPLING METHODS
• Purposive sampling methods and examples
• Depending on your research objectives, there are several purposive sampling methods you can use:
• Maximum variation (or heterogeneous) sampling
• Homogeneous sampling
• Typical case sampling
• Extreme (or deviant) case sampling
• Critical case sampling
• Expert sampling

18. CONVENIENCE SAMPLING
• Convenience sampling is primarily determined by convenience to the researcher such as
• Ease of access
• Geographical proximity
• Existing contact within the population of interest
• Convenience samples are sometimes called “accidental samples,” because participants can be selected
for the sample simply because they happen to be nearby when the researcher is conducting the data
collection.

19. QUOTA SAMPLING
• In quota sampling, you select a predetermined number or proportion of units, called a quota.Your
quota should comprise subgroups with specific characteristics (e.g Gender identity,Age,Working status,
Residential location, Housing situation ) and should be selected in a non-random manner.
• Your estimation can be based on previous studies or on other existing data, if there are any.
• In the data collection phase, you continue to recruit units until you reach your quota.
• Your respondents should be recruited non-randomly
• In proportional quota sampling, the major characteristics of the population are represented by
sampling them in regard to their proportion in the population of the study.

20. SNOWBALL SAMPLING
• Snowballing – also known as chain referral sampling – is considered a type of purposive 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 who could potentially participate in or contribute to the
study.
• Snowball sampling is often used to find and recruit “hidden populations,” that is, groups not easily
accessible to researchers through other sampling strat

21. APPROPRIATE SAMPLE DESIGN
• The sample design to be used must be decided by the researcher taking into consideration the nature of the inquiry and
other related factors.
What is the Appropriate Sample Design?
• Representativeness is Always Important
• Degree of accuracy
• Resources
• Time
• Advanced knowledge of the population
• National versus local
• Need for statistical analysis

22. THE END
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