Sampling technique and Sample size determination
Introduction :
Sample is representative of a whole
populations . The method used to selection of sample is known as sampling . It
is very important in research methodology . which is taken by using probability
and non- probability sampling method according to data .
Basic terminologies in sampling :
- Universe/Population : Including all the people or items all
the characteristics one wishes to understand.
- Sampling unit : The smallest unit of the population to be
sampled
- Sample : An element or sampling unit from which information
is collected is called sample. A sample must be optimum, representative,
reliable and flexible
- Sample size: Sample size refers to the number of items to be
selected from the population to constitute sample
- Respondent : A sampling unit from which information is
collected
Steps in sampling :
- Deciding the population to be covered
- Defining the sampling units
- Acquiring the frame list of the population elements
- Deciding the sample size
- Selection
Advantage
of sampling :
·
Cost effective
·
Time saving
·
Reduce the work loads
·
Suitable for the large population
·
Use when population is infinity
Types of Probability
sampling
- Probability sampling : It's involves random
selection, allowing you to make statistical inferences about the whole
group.
- Non- Probability sampling : It's involves
non-random selection based on convenience or other criteria, allowing you
to easily collect initial data .
 | |
| Types of sampling techniques in research methodology |
Probability
sampling methods :
Probability
sampling means that every member of the population has a chance of being
selected. It is mainly used in quantitative research. If you want to
produce results that are representative of the whole population, you need to
use a probability sampling technique.
There are four main types of
probability sample :
1.Simple Random Sampling :
In a simple random sample, every
member of the population has an equal chance of being selected. Your
sampling frame should include the whole population. To conduct this type of
sampling, you can use tools like random number generators or other techniques
that are based entirely on chance.
Example:
You want to select a simple random
sample of 100 employees of Company X. You assign a number to every employee in
the company database from 1 to 1000, and use a random number generator to
select 100 numbers.
2.Systematic Sampling :
Systematic sampling is similar to
simple random sampling, but it is usually slightly easier to conduct. Every
member of the population is listed with a number, but instead of randomly
generating numbers, individuals are chosen at regular intervals.
K=N/n
Example:
- All employees of the company are listed in alphabetical
order. From the first 10 numbers, you randomly select a starting point:
number 6. From number 6 onwards, every 10th person on the list is selected
(6, 16, 26, 36, and so on), and you end up with a sample of 100 people.
- If you use this technique, it is important to make sure that
there is no hidden pattern in the list that might skew the sample. For
example, if the HR database groups employees by team, and team members are
listed in order of seniority, there is a risk that your interval might
skip over people in junior roles, resulting in a sample that is skewed
towards senior employees.
3.Stratified Sampling :
This sampling method is appropriate
when the population has mixed characteristics, and you want to ensure that
every characteristic is proportionally represented in the sample. You divide
the population into subgroups (called strata) based on the relevant
characteristic (e.g. gender, age range, income bracket, job role). From the
overall proportions of the population, you calculate how many people should be
sampled from each subgroup. Then you use random or systematic sampling to
select a sample from each subgroup.
3.1 Proportionate stratified sampling
The sample is taken from each group
(stratum) in the same proportion as they appear in the total populations . Example:
Suppose a district has 1,000 farmers growing different crops:
|
Crop |
No
of farmers |
%of
population |
|
Rice |
500 |
50% |
|
Wheat |
300 |
30% |
|
Vegetables |
200 |
20% |
|
Total |
1000 |
100% |
4. Cluster sampling :
Cluster sampling also involves dividing the
population into subgroups, but each subgroup should have similar
characteristics to the whole sample. Instead of sampling individuals from
each subgroup, you randomly select entire subgroups. If it is practically
possible, you might include every individual from each sampled cluster. If the
clusters themselves are large, you can also sample individuals from within each
cluster using one of the techniques above.
This method is good for dealing with large and
dispersed populations, but there is more risk of error in the sample, as there
could be substantial differences between clusters. It’s difficult to guarantee
that the sampled clusters are really representative of the whole population.
Example: Suppose a researcher wants to study the adoption of
organic farming practices among farmers in Karnali province of Nepal, but
it is difficult and costly to visit every farmer individually across all
districts.
·
Instead of selecting individual farmers
directly, the researcher divides the population into clusters, such as villages
or wards.
· Steps in
cluster sampling:
·
All villages in Karnali Province are listed.
·
From that list, 10 villages are randomly
selected as clusters.
·
Within each selected village, all farmers
are surveyed (or a large number of them).
·
The data collected from these farmers
represent the whole province. Here, the villages act as clusters, not
individual farmers.
5. Multistage sampling
In this technique the items are
selected at different stages at random and is used when the universe is very
large. Sampling happens at multiple levels: province → district → village
→ farmer
Example:
- Suppose a researcher wants to study the
“use of improved seed varieties among maize farmers in Nepal”, but
covering the whole country at once is not practical.
- In multistage sampling, the researcher
selects samples in more than one stage, moving from large units to
smaller units.
Stages of selection:
- First stage (Provinces):
Out of 7 provinces of Nepal, 3
provinces are randomly selected.
- Second stage (Districts):
From each selected province, 2
districts are randomly chosen.
- Third stage (Municipalities/Villages):
From each selected district, 3
municipalities or villages are selected.
- Fourth stage (Farmers):
From each selected municipality, 20
maize farmers are randomly selected.
Non-probability sampling methods :
In a non-probability sample,
individuals are selected based on non-random criteria, and not every individual
has a chance of being included. This type of sample is easier and cheaper to
access, but it has a higher risk of sampling bias, and you can’t use it to make
valid statistical inferences about the whole population.
Non-probability sampling techniques
are often appropriate for exploratory and qualitative research. In
these types of research, the aim is not to test a hypothesis about a broad
population, but to develop an initial understanding of a small or
under-researched population.
It's Types are :
- Convenience Sampling
- Voluntary Response Sampling
- Purposive Sampling
- Snowball Sampling
1.Convenience sampling/Accidental :
A convenience sample simply includes
the individuals who happen to be most accessible to the researcher. This is an
easy and inexpensive way to gather initial data, but there is no way to tell if
the sample is representative of the population, so it can’t produce
generalizable results.
Example: A researcher wants to study
the use of organic fertilizers. Instead of selecting farmers from the whole
district, they interview only the farmers living near the research station
because they are easy to reach.
2.Voluntary response sampling
Similar to a convenience sample, a
voluntary response sample is mainly based on ease of access. Instead of the
researcher choosing participants and directly contacting them, people volunteer
themselves (e.g. by responding to a public online survey). Voluntary response
samples are always at least somewhat biased, as some people will inherently be
more likely to volunteer than others.
Example: A local agriculture office
posts an online survey link on its website and social media asking, “Are you
satisfied with the government’s fertilizer subsidy program? Share your opinion
by filling out this form.” Only the farmers who feel strongly about the
issue(either very satisfied or very dissatisfied) choose to respond and submit
the survey themselves.
3. Purposive sampling/Judgement :
This type of sampling involves the
researcher using their judgement to select a sample that is most useful to the
purposes of the research. It is often used in qualitative research, where the
researcher wants to gain detailed knowledge about a specific phenomenon rather
than make statistical inferences. An effective purposive sample must have
clear criteria and rationale for inclusion.
Example: To understand successful
organic farming techniques, a researcher selects only those farmers who have
been practicing organic farming for more than 5 years. These specific farmers
are chosen purposively because they are expected to have deeper knowledge and
experience.
4.Snowball sampling :
If the population is hard to access,
snowball sampling can be used to recruit participants via other
participants. The number of people you have access to “snowballs” as you
get in contact with more people.
Example: A researcher wants to study
farmers who grow saffron in Nepal, which is very rare.
First, the researcher finds one or two
saffron farmers through an agricultural office. These farmers are then asked to
suggest other who also grow saffron. The new farmers again provide more
contacts.
Conclusion :
Sampling technique helps
in taking sample from large population by using probability and non-
probability sampling technique which is time and cost saving .
Important Exam Questions on
Sampling Techniques
Q1: Define a sample and sampling
techniques. Also, mention the various types of sampling techniques.
Q2: What are the advantages of
sampling?
Q3: Differentiate between probability
and non-probability sampling.