The 7 types of sampling and their use in Science

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

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 speaking of a sample, reference is being made to a determined 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 about populations from which you depart. 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 at a small scale. In order to facilitate this task, various sampling techniques, i.e. obtaining or selecting samples.

There are two main types of sampling: random or probabilistic and non-random, also known as “non-probability”. In turn, each of these two large categories includes various kinds of sampling that are distinguished in terms of depending on factors such as the characteristics of the reference population or selection techniques employees.

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Types of random or probabilistic sampling

We speak of random sampling in cases where All subjects in a population have the same probability of being selected. as part of the sample. Samples of this kind are more popular and useful than non-random ones, mainly because they are highly representative and allow the sample error to be calculated.

1. simple random sampling

In this type of sampling, the relevant sample variables have the same probability function and are independent of each other. The population has to be infinite or finite with replacement of elements. Simple random sampling is the most widely 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 studying the relationship between the degree of life satisfaction and the socioeconomic level. Next, a determined number of subjects is extracted from each of the strata in order to maintain the proportion of the reference population.

3. of conglomerates

In inferential statistics Clusters 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 clusters and some of them are randomly chosen to study them.

4. Systematic

In this case, you begin by dividing the total number of subjects or observations that make up the population by the number you want to use for the sample. Subsequently, a random number is chosen from among the first ones and this same value is constantly added; the selected elements will become part of the sample.

Non-random or non-probabilistic sampling

Non-probabilistic sampling uses criteria with a low level of systematization that seek 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 others of a random type, which is very common due to the high cost of control procedures.

1. Intentional, opinionated or convenience

In intentional 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 determined subjects; then they get new participants for the sample until it is completed. 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. Quota or accidental sampling

We speak of quota sampling when researchers choose a specific number of subjects that meet certain characteristics (p. and. Spanish women over 65 years of age with severe cognitive impairment) based on their knowledge of the population strata. accidental sampling often used in surveys.