Sampling

Systematic sampling is
a random sampling technique that researchers often choose because of their
simplicity and regular quality

In systematic random
sampling, the investigator first randomly chooses the first piece or subject of
the population. Next, the researcher will select each nth subject in the list.

The procedure of
systematic random sampling is very easy and can be done manually. The results
are representative of the population unless certain characteristics of the
population are repeated for each nth individual, which is very unlikely.

The process of
obtaining the systematic sample is very similar to an arithmetic progression.

The whole number is
typically selected so that the researcher obtains the correct sample size.

For example, the
researcher has a total population of 100 individuals and needs 12 subjects.
First choose your starting number, 5.

Then, the researcher
chooses his interval, 8. The members of his sample will be individuals 5, 13,
21, 29, 37, 45, 53, 61, 69, 77, 85, 93.

Other researchers use
a modified systematic random sampling technique where they first identify the
necessary sample size. Next, divide the total number of the population by the
size of the sample to obtain the sampling fraction. The sampling fraction is
then used as the constant difference between the subjects.

## Advantages of systematic sampling

• The main advantage
of using systematic sampling over simple random sampling is its simplicity. It
allows the researcher to add a degree of system or process in the random
selection of subjects.

• Another advantage of
systematic random sampling over simple random sampling is the guarantee that
sampling will be done equally over the population. There is a possibility of a
simple random sampling that allows a selection by conglomerates of subjects.
This is systematically eliminated in systematic sampling.

## Disadvantage of systematic sampling

• The selection
process may interact with a hidden periodic feature within the population. If
the sampling technique coincides with the periodicity of the trait, the
sampling technique will no longer be random and the representativeness of the
sample will be compromised.

## Notes

• Given that
systematic random sampling is a type of probabilistic sampling, the researcher
must ensure that all members of the population have the same chances of being
selected as the starting point or initial subject.

• The researcher must
be sure that the constant interval chosen between the subjects does not reflect
a certain pattern of features present in the population. If there is a pattern
in the population and coincides with the interval established by the
researcher, the randomness of the sampling technique is compromised. Non-probabilistic
sampling

## Non-probabilistic sampling

is a sampling technique where samples are collected in a process that
does not give all individuals in the population equal opportunities to be
selected.

In any type of
research it is difficult to achieve authentic random sampling.

Most researchers have
temporary, monetary and labor limitations and, thanks to them, it is almost
impossible to take a random sample of the entire population. Generally, it is
necessary to use another sampling technique, the non-probabilistic sampling
technique.

Unlike probabilistic
sampling, the non-probabilistic sample is not a product of a random selection
process. The subjects in a non-probabilistic sample are usually selected based
on their accessibility or the personal and intentional criteria of the
researcher.

The disadvantage of the non-probabilistic sampling method is that no evidence is taken of an
unknown portion of the population. This implies that the sample can represent
the entire population with precision or not. Therefore, the results of the research can not be used in generalizations regarding the entire population.

## Types of non-probabilistic sampling

### Sampling for convenience

Convenience sampling is probably the most common sampling technique. In convenience sampling,
samples are selected because they are accessible to the researcher. The
subjects are chosen simply because they are easy to recruit. This technique is
considered the easiest, the cheapest and the least time consuming. Consecutive
sampling

### Consecutive sampling

is very similar to convenience sampling, except that it attempts to include ALL
subjects accessible as part of the sample. This non-probabilistic sampling
technique can be considered the best non-probabilistic sample, since it
includes all the subjects that are available, which makes the sample better
represent the entire population.

### Sampling by installments

Sampling by quotas is
a non-probabilistic sampling technique in which the researcher ensures a fair
and proportionate representation of the subjects, according to what trait is
considered the basis of the quota.

For example, if the
base of the quota is of the year level in the university and the researcher
needs an equal representation, with a sample size of 100, you must select 25
students of the 1st year, 25 of the 2nd year, 25 of the 3rd year and 25 of 4th
year. The bases of the quota are generally age, gender, education, ethnicity,
religion and socioeconomic level.

### Discretionary sampling

Discretionary sampling
is more commonly known as intentional sampling. In this type of sampling, the
subjects are chosen to be part of the sample with a specific objective. With
discretionary sampling, the researcher believes that some subjects are more
suitable for research than others. For this reason, those are deliberately
chosen as the subject

### Snowball sampling

Snowball sampling is
usually carried out when there is a very small population. In this type of
sampling, the researcher asks the first subject to identify another potential
subject who also meets the research criteria. The disadvantage of using a
snowball sample is that it is hardly representative of the population.

## When to use non-probabilistic sampling

• This type of
sampling can be used when you want to show that there is a certain trait in the
population.

• It can also be used
when the researcher aims to do a qualitative, pilot or exploratory study.

• It can be used when
randomization is impossible, as when the population is almost unlimited.

• It can be used when
the research is not aimed at generating results that are used to make
generalizations about the entire population.

• It is also useful
when the researcher has a limited budget, time and labor.

• This technique can
also be used in an initial study that will be carried out again using random
probabilistic sampling.

## Probabilistic sampling and randomization

Probabilistic sampling is a sampling technique whereby samples are collected in a process that gives
all individuals in the population the same opportunities to be selected.

In this sampling
technique, the researcher must ensure that each individual has the same
opportunities to be selected and this can be achieved if the researcher uses
randomization.

The advantage of using a random sample is the absence of sampling and systematic biases. If the random
selection is done correctly, the sample will be representative of the entire
population.

The effect of this is
an absent or minimal systematic bias that is the difference between the results
of the sample and the results of the population. The sampling bias is also
eliminated since the subjects are chosen at random.

## Types of probabilisticsampling

### Simple random sampling

Simple random sampling
is the easiest way of probabilistic sampling. The only thing the researcher has
to do is make sure that all members of the population are included in the list
and then randomly select the desired number of subjects.

There are many methods
to do this. It can be as mechanical as taking strips of paper from a hat with
names written while the researcher is blindfolded or it can be as easy as using
computer software to make the random selection.

Formula

### Stratified random sampling

Stratified random
sampling is also known as proportional random sampling. This is a probabilistic
sampling technique in which subjects are initially grouped into different
categories, 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 keep in mind that the strata do not overlap.

Generally, researchers
use stratified random sampling if they want to study a certain subgroup within
the population. Simple random sampling is also preferable because it guarantees
more accurate statistical results.

### Systematic random sampling

Systematic random
sampling can be compared to an arithmetic progression where the difference
between two consecutive numbers is the same. For example, suppose you are in a
clinic and you have 100 patients.

1. The first thing you
have to do is choose an integer that is less than the total number of the
population. This will be your first subject, for example (3).

2. Select another
whole number that will be the number of individuals among the subjects, for
example, (5).

3. Your subjects will
be patients 3, 8, 13, 18, 23 and so on.

There is no clear
advantage in the use of this technique.

## Random sampling by conglomerates

Random cluster
sampling is done when simple random sampling is impossible due to population
size. Imagine doing simple random sampling when the population in question is
the entire population of Asia.

In conglomerate
sampling, research first identifies borders, in the case of our example. They
can be the countries of Asia.

The researcher
randomly selects a number of identified areas. It is important that all areas
(countries) within the population have the same chances of being selected.

The researcher can
include all the individuals within the selected areas or randomly select the subjects from the identified areas.

## Random mixed /multi-stage sampling

This probabilistic sampling technique involves a combination of two or more sampling techniques
listed above. In most complex investigations carried out in the field or in the laboratory, it is not appropriate to use a single type of probabilistic sampling.

Most of the investigations are carried out in different stages and in each stage a different random sampling technique is applied.

## Explanatory video

https://www.youtube.com/watch?v=pTuj57uXWlk&t=3s