What are the different types
of probability sampling methods? Explain with the help of suitable examples
Probability
sampling is a sampling technique wherein the samples are gathered in a process
that gives all the individuals in the population equal chances of being
selected.
In this sampling technique, the researcher must guarantee that every individual has an equal opportunity for selection and this can be achieved if the researcher utilizes randomization.
Types of Probability Sampling
Systematic Random Sampling
The advantage
of using a random sample is the absence of both systematic and sampling bias. If
random selection was done properly, the sample is therefore representative of
the entire population.
The effect of this is a minimal or absent systematic bias which is
the difference between the results from the sample and the results from the
population. Sampling bias is also eliminated since the subjects are randomly
chosen.
Simple Random Sampling
Simple random sampling is the easiest form
of probability sampling. All the researcher needs to do is assure that all
the members of the population are included in the list and then randomly select
the desired number of subjects.
There are a lot of methods to do this. It can be as mechanical as
picking strips of paper with names written on it from a hat while the
researcher is blindfolded or it can be as easy as using a computer software to
do the random selection for you.
Stratified Random Sampling
Stratified random sampling is also known as proportional
random sampling. This is a probability sampling technique wherein the subjects
are initially grouped into different classifications such as age, socioeconomic
status or gender.
Then, the researcher randomly selects the final list of subjects
from the different strata. It is important to note that all the strata must
have no overlaps.
Researchers usually use stratified random sampling if they want
to study a particular subgroup within the population. It is also preferred over
the simple random sampling because it warrants more precise statistical
outcomes.
Systematic random sampling can be likened to an arithmetic
progression wherein the difference between any two consecutive numbers is the
same. Say for example you are in a clinic and you have 100 patients.
1.
The first thing you do is pick an integer that is less than the
total number of the population; this will be your first subject e.g. (3).
2.
Select another integer which will be the number of individuals
between subjects e.g. (5).
3.
You subjects will be patients 3, 8, 13, 18, 23, and so on.
There is no clear advantage when using this technique.
Cluster Random Sampling
Cluster random sampling is done when simple random sampling
is almost impossible because of the size of the population. Just imagine doing
a simple random sampling when the population in question is the entire
population of Asia.
1.
In cluster sampling, the research first identifies boundaries,
in case of our example; it can be countries within Asia.
2.
The researcher randomly selects a number of identified areas. It
is important that all areas (countries) within the population be given equal
chances of being selected.
3.
The researcher can either include all the individuals within the
selected areas or he can randomly select subjects from the identified areas.
Mixed/Multi-Stage Random Sampling
This probability sampling technique involves a combination of
two or more sampling techniques enumerated above. In most of the complex
researches done in the field or in the lab, it is not suited to use just a
single type of probability sampling.
Most of the researches are done in different stages with each stage
applying a different random sampling technique.
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