Multistage Sampling, Purposive Sampling, Snowball Sampling
Sampling is the process
of selecting observations to provide an adequate description and robust
inferences of the population. Sample is representative of the population. There
are two types of sampling. They are Random sampling and Non random sampling. Non-random sampling methods
select locations for sampling by either: according to regular (i.e.,
systematic) patterns, targeting specific features or events, using personal or
anecdotal information, or without any specific plan. Care must be exercised
when using non-random sample selection methods because the samples may not be
representative of the entire population. If this is the case, then inference
cannot extend beyond the set of sampling units. Random sampling methods rely on
randomization at some point in the sample design process in an attempt to
achieve statistically unbiased samples. Random sampling methods are a form
of design-based inference where
1): the population being measured is assumed to have fixed parameters at the
time they are sampled, and 2) that a randomly-selected set of samples for the
population represents one realization of all possible sample sets (i.e., the
sample set is a random variable).
Figure 1- Types of sampling methods
Multistage sampling
included in random sampling method. Purposive and snowball sampling are
included in Non- random sampling method.
Multistage sampling
Multistage
sampling also known as multistage cluster sampling is a more complex form of
cluster sampling which contains two or more stages in sample collection. In
simple terms, in multistage sampling large clusters of population are divided
into smaller clusters in several stages in order to make primary data
collection more manageable. It has to be acknowledged that multistage sampling
is not as effective as true random sampling ; however, it addresses certain
disadvantages associated with true random sampling being overly expensive and
time consuming(‘multistage
sampling—Google Search’, n.d.).
Figure 2- Multistage sampling
Application of multistage sampling: an example. Contrary to its name,
multistage sampling can be easy to apply in business studies. Application of
this sampling method can be divided into 4 stages.
1. Choosing sampling
frame, numbering each group with a unique number and selecting a small sample
of relevant discrete groups.
2. Choosing a sampling
frame of relevant discrete subgroups. This should be done from relevant
discrete groups selected in the previous stage.
3. Repeat the second
stage above, if necessary.
4. Choosing the members
of the sample group from the subgroups using some variations of probability
sampling.
example:- your research objective is
to evaluate online spreading patterns of
households in the US through online questionnaires .You can form your sample
group comprising 120 households in the following manner:
1. Choose 6 states in
the USA using simple random sampling.
2. Choose 4 districts
with in each state using systematic sampling method.
3. Choose 5 households
from each district using simple random methods. This will result 120 households
to be included in your sample group.
Figure 3- Example for Multistage sampling
Advantages and
disadvantages of multistage sampling
Advantages of Multistage sampling
|
Disadvantages of Multistage sampling
|
Effective in primary data collection from
geographically dispersed population when face to face contact in required.
|
High level of
subjectivity.
|
Cost effectiveness and time effectiveness.
|
Research findings can
never be 100% representatives of population.
|
High level of
flexibility.
|
The presence of group
level information is required.
|
Purposive sampling
Purposive sampling also
known as judgment sampling, selective or subject sampling. It is a sampling
technique in which researcher relies on his or her own judgment when choosing
members of population to participate in the study. Purposive sampling is a non-
probability sampling method and it occurs when elements selected for the
samples are chosen by the judgment of
the researcher. Researchers often
believe that they can obtain a representative sample by using a sound judgment,
which will result in saving time and money.
TV reporters stopping
certain individuals on the street in order to ask their opinions about certain
political changes constitutes the most popular example of this sampling method.
In purposive sampling
personal judgment needs to be used to choose cases that help answer research
questions or achieve research questions(Crossman, n.d.).
Figure 4- Purposive sampling
Applications of Purposive sampling:- an example. Your research objective is to determine the patterns of use
of social media by global IT consulting companies based in the US .Rather than
applying random sampling and choosing subject who may not be available , you
can use purposive sampling to choose IT companies whose availability and
attitude are compatible with the study.
Advantages of purposive sampling
|
Disadvantages of purposive sampling
|
Purposive sampling is one of the most cost
effective and time effective sampling methods available.
|
Vulnerably to errors
in judgment by researchers.
|
Purposive sampling may
be the only appropriate method available if there are only limited numbers of
primary data sources who can contribute to the study.
|
Low level of
reliability and high levels of bias.
|
The sampling technique
can be effective in exploring anthropological situations where the discovery
of meaning can benefit from an intuitive approach.
|
Inability to
generalize research findings.
|
Snowball sampling
Snowball sampling also
known as chain referral sampling. It is a non- probability sampling method used
when characteristics to be possessed by samples are rare and difficult to find.
This sampling method involves primary data sources nominating another potential
primary data sources to be used in research. In other words, this method is
based on referrals from initial subjects to generate additional subjects.
Therefore when applying this sampling method members of the sample group are
recruited via chain referral(‘Snowball
sampling’, 2019).
Types of snowball sampling
Linear snowball sampling:-
Formation of a sample group starts with only one subject and the subject
provides only one referral. The referral is recruited into the sample group and
he/she also provides only one new referral.
This pattern is continued until the sample group is fully formed.
Figure 5- Linear method
Exponential non discriminative snowball sampling:- The first subject recruited to the sample
group provides multiple referrals. Each new referral is explored until primary
data from sufficient amount of samples are collected.
Figure 6- Exponential non discriminative method
Exponential discriminative snowball sampling:- Subjects give multiple referrals, however
only one new subject is recruited among them. The choice of a new subject is
guided by the aim and objectives of the study.
Figure 7- Exponential discriminative method
Advantages of snowball sampling
|
Disadvantages of snowball sampling
|
The ability to recruit
hidden populations.
|
Oversampling a
particular network of peers can lead to bias.
|
The possibility to
collect primary data in cost effective manner.
|
Respondents may be
hesitant to provide names of peers and asking them to do so may raise ethical
concerns.
|
Studies with snowball
sampling can be completed in a short duration of time.
|
There is no guarantee
about the representatives of samples. It is not possible to determine the
actual pattern of distribution of population.
|
A very little planning
is required to start primary data collection.
|
It is not possible to
determine the sampling error and make statistical inferences from the sample
to the population due to the absence of random selection of samples.
|
Conclusion
Sampling
methods are classified as either probability or non- probability. In probability
samples, each member of the population has a known non-zero probability of
being selected. Probability methods include random sampling, systematic
sampling, and stratified sampling. In non- probability sampling, members are
selected from the population in some nonrandom manner. These include
convenience sampling, judgment sampling, quota sampling, and snowball sampling.
The advantage of probability sampling is that sampling error can be calculated.
Sampling error is the degree to which a sample might differ from the
population. When inferring to the population, results are reported plus or
minus the sampling error. In non -probability sampling, the degree to which the
sample differs from the population remains unknown. Sampling is a tool that
is used to indicate how much data to collect and how often it should be
collected. This tool defines the samples to take in order to quantify a system,
process, issue, or problem
Reference
https://research-methodology.net/sampling-in-primary-data-collection/multi-stage-sampling/
https://research-methodology.net/sampling-in-primary-data-collection/purposive-sampling/
https://research-methodology.net/sampling-in-primary-data-collection/snowball-sampling/
Crossman, A. (n.d.).
What You Need to Understand About Purposive Sampling. Retrieved 20 August 2019,
from ThoughtCo website: https://www.thoughtco.com/purposive-sampling-3026727
multistage sampling—Google Search. (n.d.). Retrieved 20 August 2019,
from
https://www.google.com/search?q=multistage+sampling&rlz=1C1CHBF_enIN727IN727&oq=mulyis&aqs=chrome.2.69i57j0l5.9469j0j8&sourceid=chrome&ie=UTF-8
