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SAMPLING
Sampling
Sampling is a process
used in statistical analysis in which predetermined number of observation is
taken from larger population. The methodology used to sample from a larger
population depends on the types of analysis being performed but may include
simple random sampling or systematic sampling.
The way in which we
select a sample of individuals to be research participants (random sampling)
will determine the population to which we may generalize our research findings
The procedure that we use for assigning participants to different treatment
groups. (Are the groups equal on all known and unknown factors?) We address
random
Probability
Sampling Methods
Random Sampling Simple. In
this case each individual is chosen entirely by chance and each member of the
population has an equal chance, or probability, of being selected one way of
obtaining random sample is to give each individual in a population a number and
then we use of table of random numbers to decide which individual is to
include.
As
with all probability sampling method, Simple random sampling allows the
sampling error to be calculated and reduce selection bias .A specific
advantages is that it is the most straight forward method of probability sampling.
A disadvantage of sample random sampling us that you may not select enough
individuals your characteristic of interest, especially if that characteristic
is uncommon. It may also difficult to define a complete sampling frame and
inconvenient to contact them, especially in different forms of contact are
required. (E mail, phone, post) and urn sample units are scattered over a.
Whole geographical area.
Systematic
sampling.
Individuals are selected at regular intervals from the sampling
frame. The intervals are chosen to ensure an adequate sampling size. If you
need sample size 'n' from a population of size 'x' you should select every x/n the individual
for the sampling. Systematic sampling is
often more convenient than simple random sampling, and it is easy to administer.
However it may also lead to bias.
Stratified Sampling. In these
methods, the population is first divided into subgroups (or strata) who all
share a similar characteristic. It is
used when we might reasonably expect the measurement of interest to vary
between the different subgroups. The study sample is then obtained by taking
equal sample sizes from each stratum. In stratified sampling, i it may also be
appropriate to choose non equal sample sizes from each stratum. Stratified sampling improves accuracy and representativeness
of the result by reducing sampling bias. However, it requires knowledge of the
appropriate characteristics of the sampling frame. It can be difficult to
decide which characteristics to stratify by.
Cluster sampling. In
a cluster sample, subgroups of the population are used as the sampling, rather
than individuals. The population is
divided into subgroups, known as clusters, which are randomly selected to be
included in the study.
Clusters are identified and included in
a sample on the basis of defining demographic parameters such as age, location,
sex, etc. which makes it extremely easy for a Serve creator to derive effective
inference from the feedback.
Non
Probability Sampling Method
The non - probability
sampling method uses the researcher's discretion of selecting a sample. This
type of sample is derived mostly on the basis of the researcher or
statistician’s ability to get to this sample this type of Sampling is used for
primary research work the primary objective is to derive a hypothesis about the
topic in research. Here each member of the population does not have an equal
chance of being a part of the sample and those parameters are known only post
selection to the sample.
Non probability
sampling can be further classified into four distinct types of samples. They are:
Convenience
sampling. Convenience sampling is perhaps the easiest
method of sampling, because participants are selected based on availability and
willingness to take part. Useful results can be obtained but the results are brown
to significant buyers because those who volunteered to take part may be
different from those who choose not to (volunteer bias) and the sample may not
be representative of other characteristics, such as age or sex.
Quota
sampling.
This method of sampling is offer used by market researchers interviewers
are given a quota of subjects of a specified type two attempt to recruit. For example and interviewer might be told to go out and select 20 adult men ,
20 adult women , 10 teenage girls and 10 teenage boys so that they could
interview them about their television viewing.
Judgmental/
purposive sampling. The judge mental
/ purposive sampling method is a method of developing a sample purely on the
basis and discretion of the researcher purely on the basis of nature of study
along with his / her understanding of the target audience .In the sampling method,
people who only fit the research criteria and objective are selected and the remaining
are kept out. Also known as selective or subjective sampling this technique
relies on the judgement of the researcher when choosing who to ask to
participate researchers may implicitly thus choose a "representative”
sample to suit their needs, or specifically approach individuals with certain characteristics. This approach is often used by the media when
canvassing the public for opinions and in qualitative research.
Snowball sampling. In this method is
commonly used in Social Sciences then investigating hard - to - reach groups.
Existing subjects are asked to nominate further subjects known to them, so the
sample increases in size like a rolling snowball.
Figure no.1 Sampling
Table 1 Advantages
and disadvantages of sampling
Advantages Disadvantages
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References
v https://www.healthknowledge.org.uk/public-health-textbook/research-methods/1a-epidemiology/methods-of-sampling-population
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