The 7 types of sampling and their use in the Sciences

There are many ways to study small groups in order to extrapolate conclusions thanks to statistics.

We call "sampling" the statistical procedures used to select samples that are representative of the population to which they belong, and which constitutes the object of study of a given investigation.

In this article we will analyze the different types of sampling that exist, both random and non-systematic..

Sampling in inferential statistics

In statistics, the concept "sample" is used to refer to any possible subset of a given population. Thus, when we speak of a sample, we are referring to a given set of subjects from a larger group (the population).

Inferential statistics is the branch of this discipline that deals with the study of samples in order to make study samples to make inferences about the populations from which they are drawn. from which they are based. It is opposed to descriptive statistics, whose task consists, as its name indicates, in describing 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 so that it is possible to generalize the conclusions obtained on a small scale. In order to facilitate this task, several sampling techniques have been developed, i.e. sampling techniques, i.e., obtaining or selecting samples, have been developed to facilitate this task..

There are two main types of sampling: random or probability sampling and non-random sampling, also known as "non-probabilistic". In turn, each of these two broad categories includes different types of sampling that are distinguished according to factors such as the characteristics of the reference population or the selection techniques used.

Types of random or probability sampling

We speak of random sampling in those cases in which all subjects that are part of a population have the same probability of being chosen as part of the sample. as part of the sample. Sampling of this type is more popular and useful than non-random sampling, mainly because it is highly representative and allows the sample error to be calculated.

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 must be infinite or finite with replacement of elements. Simple random sampling is the most commonly used in inferential statistics, but it is less efficient in very large samples.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 socioeconomic level. A certain number of subjects are then drawn from each of the strata in order to maintain the proportion of the reference population.

3. Clustering

In inferential statistics clusters are sets of population elements, such as schools or schoolssuch as schools or public hospitals in a municipality. When carrying out this type of sampling, the population (in the examples, a specific locality) is divided into several clusters and some of them are randomly selected for study.

4. Systematic

In this case, we begin by dividing the total number of subjects or observations that make up the population by the number 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 samples

Non-probability sampling uses criteria with a low level of systematization 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 sampling methods. when it is not possible to carry out other types of random sampling, which is very commonThis is very common due to the high cost of control procedures.

1. Intentional, opinion or convenience sampling

In purposive sampling, the researcher voluntarily chooses the elements that will make up the sample, assuming that it will be representative of the reference population. An example that will be familiar to psychology students is the use of students as an opinion sample by university professors.

2. Snowball or chain sampling

In this type of sampling, researchers establish contact with particular subjects; they then recruit new participants for the sample until the sample is complete. Snowball sampling is generally used when working with hard-to-reach populations. when working with hard-to-reach populations, as in the case of addictssuch as in the case of substance abusers or members of minority cultures.

3. Quota or accidental sampling

We speak of quota sampling when the researchers choose a specific number of subjects who meet certain characteristics (e.g., those who meet certain criteria). Spanish women over 65 years of age with severe cognitive impairment) based on their knowledge of the population strata. Accidental sampling is frequently used in surveys.