SAMPLING
Sampling is a statistical procedure that is concerned
with the selection of the individual observation; it helps us to make
statistical inferences about the population.
In
sampling, we assume that samples are drawn from the population and sample means
and population means are equal. A population can be defined as a whole
that includes all items and characteristics of the research taken into study.
However, gathering all this information is time-consuming and costly. We
therefore make inferences about the population with the help of samples.
There
are two branches in statistics, descriptive and inferential statistics. Of
these two main branches, statistical sampling concerns itself primarily
with inferential statistics. The basic idea behind this type of statistics
is to start with a statistical sample. After we have this sample, we then
try to say something about the population. We very quickly realize the
importance of our sampling method.
There
are a variety of different types of samples in statistics. Each of these
samples is named based upon how its members are obtained from the population.
It is important to be able to distinguish between these different types of
samples. Below is a list with a brief description of some of the most
common statistical samples.
Different Types Of Sample
Random sample
Simple random sample
Voluntary response sample
Convenience sample
Systematic sample
Cluster sample
Stratified sample
Random sampling. In data
collection, every individual observation has equal probability to be selected
into a sample. In random sampling, there should be no pattern when drawing a
sample. Significance: Significance is the per cent of chance that a relationship may be found in sample data due to
luck. Researchers often use the 0.05% significance level.
Figure1Random
sampling
Type of Random. Simple random sampling: By using the
random number generator technique, the researcher draws a sample from the
population called simple random sampling. Simple random samplings are of two
types. One is when samples are drawn with replacements, and the second is when
samples are drawn without replacements.
Multistage stratified
random sampling: In multistage stratified random sampling, the proportion of strata is selected from a homogeneous group using simple random
sampling. For example, from the nth class and nth stream, a sample is drawn
called the multistage stratified random sampling.
Cluster sampling: Cluster
sampling occurs when a random sample is drawn from certain aggregation geographical
groups.
Multistage cluster
sampling: Multistage cluster sampling occurs when a researcher draws a random sample from the smaller unit of an aggregation group.
Stratified sample. A stratified
sample results when a population is split into at least two non-overlapping
sub-populations.
Stratified
random sampling is a type of probability sampling using which a research an organization can branch off the entire population into multiple
non-overlapping, homogeneous groups (strata) and randomly choose final members
from the various strata for research which reduces cost and improves
efficiency. Members in each of these groups should be distinct so that every
member of all groups get equal opportunity to be selected using simple
probability. This sampling method is also called “random quota sampling”.
Age,
socioeconomic divisions, nationality, religion, educational achievements and
other such classifications fall under stratified random sampling.
Types of Stratified Random Sampling
Proportionate stratified random sampling
With
proportionate stratification, the sample size of each stratum is proportionate
to the population size of the stratum. This means that each stratum has the
same sampling fraction.
Disproportionate stratified random sampling
With
disproportionate stratification, the sampling fraction may vary from one
stratum to the next.
Table
1 Advantages
of Random Sampling & Stratified Sampling
Random Sampling
|
Stratified Sampling
|
·
It
offers a chance to perform data analysis that has less risk of carrying error
|
·
A
stratified sample can provide greater precision than a simple random sample
of the same size
|
·
There
is an equal chance of selection
|
·
It
provides greater precision, a stratified sample often requires a smaller
sample, which saves money
|
·
It
requires less knowledge to complete the
·
research
|
·
We
can ensure that we obtain sufficient sample points to support a separate
analysis of any subgroup
|
·
It
is a simple form of data collection
|
·
Each the element of the population can be assigned to one, and only one, stratum
|
CONCLUSION
Random sampling is the purest form of probability sampling.
Each member of the population has an equal and known chance of being selected.
When there are very large populations, it is often difficult or impossible to
identify every member of the population, so the pool of available subjects
becomes biased. And Stratified sampling is a commonly used probability
method that is superior to random sampling because it reduces sampling error. A
stratum is a subset of the population that shares at least one common
characteristic.
REFERENCE
(“stratified
sampling method—Google Search,” n.d.)
Comments
Post a Comment