Multistage, Purposive, Snowball Sampling.
Multistage Sampling
Multistage sampling is a
sampling method that divides the population into groups (or clusters) for
conducting research. It is a complex form of cluster sampling, sometimes, also
known as multistage cluster sampling. During this sampling method, significant
clusters of the selected people are split into sub-groups at various stages to
make it simpler for primary data collection.
Figure 1 showing Multistage
sampling.
What are the Steps to Conduct Multistage Sampling?
There are four multistage
steps to conduct multistage sampling:
Step one: Choose a sampling frame, considering the population of
interest. The researcher allocates a number to every group and selects a small
sample of relevant separate groups.
Step two: Select a sampling frame of relevant separate sub-groups.
Do this from related, different discrete groups selected in the previous stage.
Step three: Repeat the second step if necessary.
Step four: Using some variation of probability sampling, choose
the members of the sample group from the sub-groups.
What are the Types of
Multistage Sampling?
There are two types of
multistage sampling – multistage cluster sampling and multistage random
sampling. In market research, multistage sampling is the choosing of samples at
stages and choosing smaller sampling units at every step.
Multistage Cluster Sampling.
Multistage
cluster sampling is a complex type of cluster sampling. The researcher divides
the population into groups at various stages for better data collection,
management, and interpretation. These groups are called clusters.
For example, a researcher
wants to know the different eating habits in western Europe. It is practically
impossible to collect data from every household. The researcher will first
choose the countries of interest. From these countries, he/she chooses the
regions or states to survey. And from these regions, he/she further narrows
down his research by choosing specific cities and towns that represent the
region. The researcher does not interview all the residents of the city or
town. He/she further chooses particular respondents from the selected cities to
participate in research. Here we see that clusters are selected at various
stages until the researcher narrows down to the sample required.
Multistage Random Sampling.
The concept of multistage
random sampling technique is similar to multistage cluster sampling. But in
this case, the researcher chooses the samples randomly at each stage. Here, the
researcher does not create clusters, but he/she narrows down the convenience
sample by applying random sampling.
For example, a researcher
wants to understand pet feeding habits among people living in the USA. For
this, he/she requires a sample size of 200 respondents. The researcher selects
10 states out of 50 at random. Further, he/she randomly picks out 5 districts
per state. From these 50 randomly selected states, he/she then chooses 4
pet-owning households to conduct his research.
Advantages.
You don’t need to start with a sampling frame of your target
population.
Compared to a simple random sample, it’s relatively inexpensive
and effective when you have a large or geographically dispersed population.
It’s flexible—you can vary sampling methods between stages based
on what’s appropriate or feasible.
Disadvantages.
Compared
to simple random samples, you’ll need a larger sample size for a multistage sample to achieve the same
statistical inference properties.
The best choice of sampling method at each stage is very
subjective, so you’ll need clear reasoning for your decision to avoid biased
decision-making.
It can lead to
unrepresentative samples because large sections of populations may not be
selected for sampling, leading to under coverage bias and selection bias.
Purposive Sampling
Purposive sampling is a technique
in which the person conducting the research relies on their judgment to choose
the members who will be part of the study. It is a type of nonprobability
sample, and it’s also referred to as a judgmental or expert sample.
A purposive sample is a
non-randomly selected and typically smaller subset of the population intended
to represent it logically. This can be done by understanding the population’s
background by selecting a sample that portrays those variations.
Researchers use sampling methods when they want to access a particular subset of people, where all the survey participants are selected to fit a specific profile.
Types of Purposive
Sampling
Maximum Variation Sampling.
This is also known as
heterogeneous sampling. It is a purposive
sampling technique that captures various customer perspectives of your study of
interest.
Homogeneous Sampling.
Homogeneous
sampling is a purposive sampling method that is the opposite of the maximum
variation method. With homogeneous sampling, a group of people of the same age,
gender, background, or occupation will be chosen. It is often used when
researching a specific trait, feature, or area of interest.
Typical Case Sampling.
Typical
case sampling is used when the researcher or evaluator wants to study a
phenomenon related to the parent sample’s ordinary members. For example,
suppose a survey taker wants to understand how inflation affects people with
average or low income. In that case, only average or low-income earners will be
selected from the overall sample.
Extreme Case Sampling.
Extreme
case purposive sampling is used to study the outliers from a set norm for a
particular phenomenon or trend.
Critical Case Sampling.
Critical
case purposive sampling selects one information-rich case to represent the
population. A researcher expects the information-rich case to provide details
that apply to other similar cases by studying it.
Total Population
Sampling.
Total population purposive sampling is a way of carrying out
sampling where the entire population carrying one or more shared
characteristics is examined or surveyed.
These attributes
can be specific experience, knowledge, or skills.
Expert Sampling.
Expert
purposive sampling is used when the researcher needs to obtain knowledge from
individuals with particular expertise. This skill may be necessary during the
starting phase of qualitative research design because it can help understand
new areas of interest.
Advantages. One of the major advantages of purposive sampling is the different
types of sampling techniques, from homogeneous sampling to critical case
sampling, that can be used to achieve qualitative research design.
With the help of
purposive sampling, it’s easier to generalize your sample than a random sample
where not all participants have the characteristic you are studying.
The margin of error with the purposive
sampling is low.
It is cost-effective and can produce
substantial results in real-time.
Helps
to avoid sampling errors.
Disadvantages. A vast array of inferential statistical procedures are present in
this structure, thus making these statistics invalid.
As the participants are aware that they are a part of the research
project, bias is possible.
Snowball Sampling
Snowball sampling is a
non-probability sampling method where new units are recruited by other units to
form part of the sample. Snowball sampling can be a useful way to conduct
research about people with specific traits who might otherwise be difficult to
identify (e.g., people with a rare disease).
Also known as chain sampling
or network sampling, snowball sampling begins with one or more study
participants. It then continues on the basis of referrals from those
participants. This process continues until you reach the desired sample, or a
saturation point.
Example showing Snowball sampling
isn’t figure 3
When to Use
Snowball Sampling
Snowball sampling is a widely
employed method in qualitative research, specifically when studying
hard-to-reach populations.
These may include:
Populations that are small relative to the general population
Geographically dispersed populations
Populations possessing a
social stigma or particular shared characteristic of interest
In all these cases, accessing members of the population can be
difficult for non-members, as there is no sampling frame available.
Research in the fields of
public health (e.g., drug users), public policy (e.g., undocumented
immigrants), or niche genres (e.g., buskers) often uses snowball sampling.
This sampling method is also
used to study sensitive topics, or topics that people may prefer not to discuss
publicly. This is usually due to a perceived risk associated with
self-disclosure. Snowball sampling allows you to access these populations while
considering ethical issues, such as protecting their privacy and ensuring
confidentiality.
Types of Snowball
Sampling
Linear Snowball Sampling
formation of a sample group
starts with one individual subject providing information about just one other
subject and then the chain continues with only one referral from one subject.
This pattern is continued until enough number of subjects are available for the
sample.
Exponential Non-Discriminative
Snowball Sampling
In this type, the first
subject is recruited and then he/she provides multiple referrals. Each new
referral then provides with more data for referral and so on, until there is
enough number of subjects for the sample.
Exponential Discriminative Snowball Sampling:
In this technique, each subject gives multiple referrals,
however, only one subject is recruited from each referral. The choice of a new
subject depends on the nature of the research study.
Advantages. Depending on your research goals, there are advantages to using
snowball
sampling.
Snowball sampling helps you
research populations that you would not be able to access otherwise. Members of
stigmatized groups (e.g., people experiencing homelessness) may hesitate to
participate in a research study due to fear of exposure. Snowball sampling
helps in this situation, as participants refer others whom they know and trust
to the researcher.
Since snowball sampling involves individuals recruiting other
individuals, it is low-cost and easy to recruit a sample in this way.
Unlike probability sampling, where you draw your sample following
specific rules and some form of random selection, snowball sampling is
flexible. All you need is to identify someone who is willing to participate and
introduce you to others.
Disadvantages. Snowball sampling has disadvantages, too, and is not a good fit
for every research design.
As the sample is not chosen through random selection, it is not
representative of the population being studied. This means that you cannot make
statistical inferences about the entire population and there is a high chance
of research bias.
The researcher has little or
no control over the sampling process and relies mainly on referrals from
already-identified participants. Since people refer others whom they know (and
share traits with), this sampling method has a high potential for sampling
bias.
Relying on referrals may lead to difficulty reaching your sample.
People may not want to cooperate with you, hesitate to reveal their identities,
or mistrust researchers in general.
Table 1 : Difference between Multistage sampling and
Snowball sampling.
|
DIFFERENCES |
MULTISTAGE SAMPLING |
SNOWBALL SAMPLING |
|
Sampling technique |
Probability sampling |
Non-probability |
|
Meaning / Main steps |
Divide sampling process into stages |
Collect data from references you get from
other sample |
|
Mainly used / Purpose |
Multiple sampling is required with multiple
stages |
Collecting data from population who are
difficult to reach. |
|
Major limitation |
Time-consuming |
Reluctance in referring |
