Types of Sampling techniques
Types of Sampling Techniques
Research sampling is
basically of two types:
Probability Sampling
(Random Sampling)
1.
Simple random sampling
2.
Systematic
random sampling
3.
Stratified random
sampling
Non Probability
1.
Quota Sampling
2.
Convenience sampling
3. Purposive
sampling
4. Self-selection
sampling
5.
Snowball sampling
A). Probability Sampling
The purpose of random selection
is the creation of a sample whose units are representative of (i.e., have very
similar characteristics to) the population they represent. With random
selection, each unit has an equal chance (i.e., equal probability) of being
selected. The use of random selection not only improves the chance of creating
a representative sample, but also provides you with methods to estimate how
likely (i.e., probable) this will be.
With probability sampling, units can be randomly selected
with the aid of random number tables or
a random number
generator. However, the procedure to
select units from the sampling frame differs depending on the type of probability sampling technique that
is used. Nonetheless, these procedures are very clearly defined, making it easy
to follow them.
Types of Probability sampling
To get a sense of what these
three types of probability sampling technique are, imagine that a researcher
wants to understand more about the career goals of students at a single
university. Let's say that the university has roughly 10,000 students. These
10,000 students are our population (N). Each of the 10,000 students is known as a unit
(although sometimes other terms are used to describe a unit; see Sampling: The basics). In order to
select a sample (n) of students from this population of 10,000
students, we could choose to use simple random sampling, systematic random sampling and stratified random sampling:
·
Simple random sampling
With simple random sampling,
there is an equal chance (probability) that each of the 10,000 students could
be selected for inclusion in our sample. If our desired sample size was around
200 students, were would select 200 students at random, probably using random
number tables.
·
Systematic random sampling
Systematic random sample is a variation on the simple
random sample. Like simple random sampling, there is an equal chance
(probability) that each of the 10,000 students could be selected for inclusion
in our sample. Whilst you typically use random number tables to select the
first unit for inclusion in your sample, the remaining units are selected in an
ordered way (e.g., every 9th student).
·
Stratified random sampling
Unlike the simple random sample and the systematic random
sample, sometimes we are interested in particular strata (meaning groups)
within the population (e.g., males vs. females; houses vs. apartments, etc.).
With the stratified random sample, there is an equal chance (probability) of
selecting each unit from within a particular stratum (group) of the population
when creating the sample.
B) Non Probability
Sampling
Non-probability sampling represents a group of sampling techniques that help researchers to select units from a population that they are interested in studying. A core characteristic of non-probability sampling techniques is that
samples are selected based on the subjective judgement of the researcher, rather than random selection (i.e., probabilistic methods), which is the cornerstone of probability sampling techniques.
Non-probability sampling represents a valuable group of sampling
techniques that can be used in research that follows qualitative, mixed methods, and even quantitative research designs.
Non-probability sampling techniques can often be viewed in such a way
because units are not selected for inclusion in a sample based on random
selection, unlike probability sampling techniques. As a result, researchers
following a quantitative research design often feel that they are forced to use non-probability sampling techniques
because of some inability to use probability sampling (e.g., the lack of access
to a list of the population being studied).
Types of non-probability sampling
There are five types of non-probability sampling
technique that you may use when doing a dissertation at the undergraduate and
master's level: quota sampling, convenience
sampling, purposive sampling, self-selection sampling and snowball sampling.
·
Quota sampling
With proportional quota sampling, the aim is to end up with
a sample where the strata (groups) being studied (e.g., males vs. females
students) are proportional to
the population being studied. If we were to examine the differences in male and
female students, for example, the number of students from each group that we
would include in the sample would be based on the proportion of male and female
students amongst the 10,000 university students.
For example, imagine we were interested in comparing the
differences in career goals between male and female students at the University. If this was the case, we would want to
ensure that the sample we selected had a proportional number of male and female students relative to the
population. Therefore, the total number of male and female students included in
our quota would only be equal if 5,000 students from the university were male
and the other 5,000 students were female.
·
Convenience sampling
A convenience sample is simply one where the units that
are selected for inclusion in the sample are the easiest to access. In our
example of the 10,000 university students, if we were only interested in
achieving a sample size of say 100 students, we may simply stand at one of the
main entrances to campus, where it would be easy to invite the many students
that pass by to take part in the research.
·
Purposive sampling
Purposive sampling, also known as judgmental, selective or subjective sampling, reflects a group of sampling
techniques that rely on the judgement of
the researcher when it comes to selecting the units (e.g., people,
cases/organisations, events, pieces of data) that are to be studied. These
purposive sampling techniques include maximum variation sampling, homogeneous sampling, typical case sampling, extreme (or deviant) case sampling, total population sampling and expert sampling. Each of these purposive sampling
techniques has a specific goal, focusing on certain types of units, all for
different reasons. The different purposive sampling techniques can either be
used on their own or in combination with other purposive sampling techniques
·
Self-selection sampling
Self-selection sampling is appropriate when we want to
allow units or cases, whether individuals or organisations, to choose to take
part in research on their own accord. The key component is that research
subjects (or organisations) volunteer to
take part in the research rather than being approached by the researcher
directly.
·
Snowball sampling
Snowball sampling is particularly appropriate when the
population you are interested in is hidden and/or hard-to-reach. These include
populations such as drug addicts, homeless people, individuals with AIDS/HIV,
prostitutes, and so forth.
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