The 7 types of sampling and their use in the Sciences
We call "sampling" the statistical procedures that are used to select samples that are representative of the population to which they belong, and that constitutes the object of study of a determined investigation.
In this article we will analyze the different types of sampling that exist, both random and non-systematic .
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Sampling in inferential statistics
In statistics, the concept "sample" is used to refer to any possible subset of a given population. Thus, when we talk about a sample, we are referring to a specific set of subjects that start from a larger group (the population).
Inferential statistics is the branch of this discipline that deals with study samples to make inferences in relation to populations of which they start. It is opposed to descriptive statistics, whose task is, as its name suggests, to describe in detail the characteristics of the sample, and therefore ideally of the population.
However, the process of statistical inference requires that the sample in question be representative of the reference population as long as it is possible to generalize the conclusions obtained on a small scale. With the aim of favoring this task, various sampling techniques, that is, obtaining or selecting samples .
There are two main types of sampling: the random or probabilistic and the non-random, also known as "non-probabilistic". In turn, each of these two broad categories includes different kinds of sampling that are differentiated according to factors such as the characteristics of the reference population or the selection techniques employed.
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Types of random or probabilistic sampling
We talk about random sampling in cases where all subjects that are part of a population have the same probability of being chosen as part of the sample. Samples of this class are more popular and useful than non-random samples, mainly because they have a high representativeness and allow to calculate the error of the sample.
1. Simple random sampling
In this type of sampling, the relevant variables of the sample have the same probability function and are independent of each other. The population has to be infinite or finite with replenishment of elements. Simple random sampling is the most used in inferential statistics , but it is less effective in very large samples.
2. Stratified
Stratified random sampling consists of dividing the population into strata; An example of this would be to study the relationship between the degree of life satisfaction and the socioeconomic level. Then a certain number of subjects from each of the strata is extracted in order to maintain the proportion of the reference population.
3. Conglomerates
In inferential statistics the conglomerates are sets of population elements , such as schools or public hospitals in a municipality. When carrying out this type of sampling, the population is divided (in the examples, a specific locality) into several conglomerates and some of them are randomly chosen to study them.
4. Systematic
In this case, we begin by dividing the total number of subjects or observations that make up the population among those that we want to use for the sample. Subsequently, a random number is chosen from among the first ones and this same value is added constantly; the selected elements will become part of the sample.
Non-random or non-probabilistic sampling
Non-probabilistic samplings use criteria with a low level of systematization that try to ensure that the sample has a certain degree of representativeness. This type of sampling is mainly used when it is not possible to carry out other random type , which is very common because of the high cost of control procedures.
1. Intentional, opinion or convenience
In intentional sampling the researcher voluntarily chooses the elements that will make up the sample, assuming that this will be representative of the reference population. An example that will be familiar to students of psychology is the use of students as an example of opinion on the part of university professors.
2. Snowball or chain sampling
In this type of sampling the researchers establish contact with certain subjects; then they get new participants for the sample until they complete it. Snowball sampling is generally used when working with hard-to-reach populations , as in the case of addicts to substances or members of minority cultures.
3. Sampling by quotas or accidental
We speak of sampling by quotas when the researchers choose a specific number of subjects that meet certain characteristics (eg, Spanish women over 65 with severe cognitive impairment) based on their knowledge of the population strata. Accidental sampling it is frequently used in surveys .
