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Systematic 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.

Start number:

The researcher selects an integer that must be less than the total number of individuals in the population. This whole number will correspond to the first subject.


The researcher chooses another whole number that will serve as the constant difference between two consecutive numbers in the 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.


• 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.


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

Types of Sampling Methods (4.1)
Types of Sampling Methods (4.1)

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