This document discusses key concepts in sampling including population, sample, sampling techniques, and ethics. It defines population as the group being studied and sample as a subset of the population. Probability sampling methods like simple random and stratified sampling aim for representativeness, while non-probability methods like convenience and snowball sampling do not. Ethical considerations for qualitative research include informed consent, confidentiality, and avoiding harm to participants.

2. ⦁ Basic Terminologies
⦁ Population
⦁ Sample and Sampling
⦁ Advantages & Disadvantages of
Sampling
⦁ Probability Sampling
⦁ Non-Probability Sampling

4. ⦁ Population or Universe: It refers to the
group of people, items or units under
investigation and includes every individual.
⦁ Sample: a collection consisting of a part or
subset of the objects or individuals of population
which is selected for the purpose, representing the
population
⦁ Sampling: It is the process of selecting a sample
from the population. For this population is divided
into a number of parts called Sampling Units.

8. Population…
…the larger group from which
individuals are selected to
participate in a study

9. Target population
A set of elements larger than or different
from the population sampled and to which
the researcher would
like to generalize
study findings.

10. The process of selecting a number of
individuals for a study in such a way
that the individuals represent the
larger group from which they were
selected

11. A sample is “a smaller (but
hopefully representative)
collection of units from a
population used to determine
truths about that population”
(Field, 2005)
The sampling frame
A list of all elements or other
units containing the elements in
a population.

13. ⦁ To gather data about the population in order
to make an inference that can be generalized
to the population

14. ⦁ Large population can be conveniently
covered.
⦁ Time, money and energy is saved.
⦁ Helpful when units of area are
homogenous.
⦁ Used when percent accuracy is not
acquired.
⦁ Used when the data is unlimited.

15. ⦁ Economical: Reduce the cost compare to entire
population.
⦁ Increased speed: Collection of data, analysis and
Interpretation of data etc take less time than the
population.
⦁ Accuracy: Due to limited area of coverage,
completeness and accuracy is possible.
⦁ Rapport: Better rapport is established with the
respondents, which helps in validity and reliability of
the results

16. ⦁ Biasedness: Chances of biased selection leading
to incorrect conclusion
⦁ Selection of true representative sample:
Sometimes it is difficult to select the right
representative sample
⦁ Need for specialized knowledge: The researcher
needs knowledge, training and experience in
sampling technique, statistical analysis and
calculation of probable error
⦁ Impossibility of sampling: Sometimes population is
too small or too heterogeneous to select a
representative sample.

17. ⦁ A true representative of the population.
⦁ Free from error due to bias.
⦁ Adequate in size for being reliable.
⦁ Units of sample should be independent and
relevant
⦁ Units of sample should be complete precise
and up to date
⦁ Free from random sampling error
⦁ Avoiding substituting the original sample for
convenience.

18. Define the target population
Select a sampling frame
Conduct fieldwork
Determine if a probability or nonprobability
sampling method will be chosen
Plan procedure for selecting
sampling units
Determine sample size
Select actual sampling units
Stages in the
Selection
of a Sample

20. 1. Probability Sampling: A probability sample
is one in which each member of the
population has an equal chance of being
selected.
2. Non-Probability Sampling: Nonprobability
Sample a particular member of the
population being chosen is unknown.
⦁ In probability sampling, randomness is the
element of control. In Non-probability
sampling, it relies on personal judgment.

22. 1. Simple Random Sampling: Here all
members have the same chance
(probability) of being selected. Random
method provides an unbiased cross
selection of the population.
For Example,
We wish to draw a sample of 50 students from
a population of 400 students. Place all 400
names in a container and draw out 50 names
one by one.

23. Advantages
1. Easy to conduct
2. High probability of achieving a
representative sample
3. Meets assumptions of many statistical
procedures
Disadvantages
1. Identification of all members of the
population can be difficult
2. Contacting all members of the sample can
be difficult
Simple Random Sampling

24. Selection process
 Identify and define the population
 Determine the desired sample size
 List all members of the population
 Assign all members on the list a consecutive
number
 Select an arbitrary starting point from a table of
random numbers and read the appropriate number
of digits
Simple Random Sampling

26. 2. Systematic Sampling: Each member of the sample
comes after an equal interval from its previous
member.
For Example, for a sample of 50 students, the sampling
fraction is 50/400 = 1/8 i.e. select one student out of
every eight students in the population. The starting
points for the selection is chosen at random.

29. 3. Stratified Sampling: The population is divided
into smaller homogenous group or strata by
some characteristic and from each of these
strata members are selected randomly.
Finally from each stratum using simple random
or systematic sample method is used to select
final sample.

30. Advantages
1. More accurate sample
2. Can be used for both proportional and
non-proportional samples
3. Representation of subgroups in the sample
Disadvantages
1. Identification of all members of the
population can be difficult
2. Identifying members of all subgroups can
be difficult
Stratified Sampling

31. Selection process
 Identify and define the population
 Determine the desired sample size
 Identify the variable and subgroups (i.e., strata) for
which you want to guarantee appropriate
representation
 Classify all members of the population as
members of one of the identified subgroups
Stratified Sampling

32. 4. Cluster Sampling (Area Sampling): A researcher/
enumerator selects sampling units at random and
then does complete observation of all units in the
group.
For example, the study involves Primary schools.
Select randomly 15 schools. Then study all the children
of 15 schools. In cluster sampling the unit of sampling
consists of multiple cases. It is also known as area
sampling, as the selection of individual member is made
on the basis of place residence or employment.

33. Advantages
1. Very useful when populations are large and
spread over a large geographic region
2. Convenient and expedient
3. Do not need the names of everyone in the
population
Disadvantages
1. Representation is likely to become an issue
Cluster Sampling

34. Selection process
 Identify and define the population
 Determine the desired sample size
 Identify and define a logical cluster
 List all clusters that make up the population of clusters
 Estimate the average number of population members per
cluster
 Determine the number of clusters needed by dividing the
sample size by the estimated size of a cluster
 Randomly select the needed numbers of clusters
 Include in the study all individuals in each selected cluster
Cluster Sampling

35. Cluster Sampling

36. Cluster Sampling

38. 1. Purposive Sampling: In this sampling method,
the researcher selects a "typical group" of
individuals who might represent the larger
population and then collects data from this
group. Also known as Judgmental Sampling.

39. 2. Convenience Sampling : It refers to the
procedures of obtaining units or members
who are most conveniently available. It
consists of units which are obtained because
cases are readily available.
In selecting the incidental sample, the researcher
determines the required sample size and then
simply collects data on that number of
individuals who are available easily.

40. 3. Quota Sampling: The selection of the sample is
made by the researcher, who decides the quotas
for selecting sample from specified sub groups of
the population.
⦁ For example, an interviewer might be need data from
40 adults and 20 adolescents in order to study
students’ television viewing habits.
Selection will be
⦁ 20 Adult men and 20 adult women
⦁ 10 adolescent girls and 10 adolescent boys

41. 4. Snowball Sampling:
⦁ In snowball sampling, the researcher
Identifying and selecting available
respondents who meet the criteria for
inclusion.
⦁ After the data have been collected from the
subject, the researcher asks for a referral of
other individuals, who would also meet the
criteria and represent the population of
concern.
⦁ chain sampling, chain-referral, sampling
referral sampling

45.  It is the researcher’s ethical responsibility to
safeguard the story teller by maintaining the
understood purpose of the research…
 The relationship should be based on trust between
the researcher and participants.
 Inform participants of the purpose of the study.

46.  Being respectful of the research site, reciprocity,
using ethical interview practices, maintaining
privacy, and cooperating with participants.
 Patton (2002) offered a checklist of general ethical
issues to consider, such as:
 reciprocity
 assessment of risk
 confidentiality,
 informed consent
 and data access and ownership.

47.  Qualitative researchers must be aware of the
potential for their own emotional turmoil in
processing this information
 During the interview process, participants may
disclose sensitive and potentially distressing
information in the course of the interview..