Sampling is a process used in statistical analysis in which a predetermined number of observations are taken from a larger population. The methodology used to sample from a larger population depends on the type of analysis being performed.
OR
The act, process, or technique of selecting a representative part of a population for the purpose of determining parameters of the whole population.
Group of the individual having similar characteristics is called population
A small group from a population is supposed to carry all the characteristics of the population or which represent the whole population.
A sample should have three major qualities:
It is the list of all the sample units which are considered as a sample of a population.
The population to be studied/ to which the investigator wants to generalize his results.
The individual member of the sample frame is called the sample unit.
It is the number of individuals who are included in the sample frame.
Sometimes, the entire population will be sufficiently small, and the researcher can include the entire population in the study. This type of research is called a census study because data is gathered on every member of the population.
A population frame is the source material or device from which a sample is drawn. It is a list of all those within a population who can be sampled and may include individuals, households or institutions.
Simple Random Sampling,
Stratified Random Sampling,
Systematic Sampling,
Cluster Sampling
Multistage Sampling.
In this method, each sampling unit of the population has an equal chance of being selected in the sample.
The simple random sampling procedure is as follows:
Every nth class is chosen for the study from a list of cases.
Determined by dividing the size of the population by the desired sample size.
The ratio between sample size and population
(K)= N/n
N =size of population
n = desired sample size
To create a systemic random sample, there are seven steps:
(a) Defining the population
(b) Choosing your sample size
(c) Listing the population
(d) Assigning numbers to cases
(e) Calculating the sampling fraction
(f) Selecting the first unit and
(g) selecting our sample
To the sampling interval, divide the size of the list by the desired sample size. For example, if we want to select 20 health centers from a list of 46 in our sampling frame, our sampling interval would be 46/20 = 2.3.
The first facility chosen in this case can be either 1, 2 or 3, which are all the possible sampling units within the first sampling interval.
The procedure is as follows:
Later facilities are selected by adding the sampling interval to the previous result. If the first result was 0.421.then the next facilities selected would be:
Facility 1
0.421 + 2.3 = 2.721 so Facility 3 (Remember: always round upward)
2.721 + 2.3 = 5.021 so Facility 6
5.021 + 2.3 = 7.321 so Facility 8
And so forth.
If the first result had been 1.749, then the first facility would be Facility 2, and the next facilities selected would be:
Facility 2
1.749 + 2.3 = 4.049 so Facility 5
4.049 + 2.3 = 6.349 so Facility 7
6.349 + 2.3 = 8.649 so Facility 9
And so forth
The method just described gives every unit an equal chance of being selected. This method can also be used with minor modifications to select units allowing for how large they are.
Sometimes it is desirable for clinics serving larger populations to have a greater chance of being included in a sample. This method is called sampling with probability proportional to size.
Systematic sampling is also useful when sampling prescriptions from a patient register. If a register contains 100 pages, each with 25 lines of prescriptions, and you need to select 30 prescriptions, the sampling interval would be:
100 x 25 = 83.3
Thus every 83^{rd} prescription would be sampled. Multistage sampling, described as the fifth method below, could also be used to select a sample from a patient register.
When the population is not homogenous, we consider different sections of the population which are homogenous within themselves. The population is divided into a number of sections called strata. A sample is drawn independently from each stratum using a simple random method.
This method is used when the whole population is made up of many natural groups. In this method, a group is taken as a sampling unit.
Any sampling method where some elements of the population have no chance of selection (these are sometimes referred red to as ‘out of coverage ‘ / ‘ undercovered ‘), or where the probability of selection can’t be accurately determined. The elements do not have a pre-determined chance of being selected. In this method, samples are not picked randomly.
It is also known as accidental, haphazard or accessibility sampling. The sample is selected from elements of a population that are easily accessible. A readily available group of individuals is used.
It relies on referrals from initial subjects to generate additional subjects
When participants are hard to find, for example, a study investigation on cheating in exams
It is a non-probability sampling technique where the assembled sample has the same proportions of individuals as the entire population with respect to known characteristics, traits or focused phenomenon.
Two types of quota
An equal number of sample is selected.
Example
100 samples are selected all from Punjab, Sindh, K.P.K., and Baluchistan.
On the basis of ratio, sample units are selected from all areas of the population.
Example
If we have to select 200 samples from Pakistan then we will select 45% from Punjab, 30% from Sindh, 15% from Baluchistan and 10% from K.P.K.
The sample size is the number of patients or other experimental units included in a study, and one of the first practical steps in designing a trial is the choice of the sample size needed to answer the research question.
In practice, the sample size used in a study is determined based on the expense of data collection, and the need to have sufficient statistical power.
If the sample is too small;
If the sample size is too large;
1) On the basis of population
2) On the basis of prevalence
Used for measurable data like height, weight, blood pressure, etc.
n=
Where: N=total population e = margin of error
n=
Where;
Zα is the z-table value against our assumed alpha (0.05)
δ is a variation or standard deviation
e is a margin of error
Used for immeasurable data like intelligence, beauty or for binomial data like true/false, pass/fail, present/absent, etc.
n
Where;
p is probability or chances of occurrence of an event q is chances of an event to NOT occur? It is also written as 1-p.
If p is 20% (0.2) then q is 80% (0.8).
n=
Where;
n is sample size p is a prevalence
t is t distribution of CI =0.95
m is the margin of error that is 5%
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