The concept of representativeness is often found in statistical reporting and in the preparation of speeches and reports. Perhaps without it it is difficult to imagine any of the types of submission of information for review.
Representativeness - what is it?
Representativeness reflects how the selected objects or parts correspond to the content and meaning of the totality of the data from which they were selected.
Other definitions
The concept of representativeness can be disclosed in different contexts. But in its sense, representativeness is the correspondence of the features and properties of the selected units from the total population, which accurately reflect the characteristics of the entire general database as a whole.
Also, the representativeness of information is defined as the ability of sample data to present the parameters and properties of the population, important from the point of view of the study.
Representative sample
The principle of sampling is the selection of the most important and accurately reflecting the properties of the total population of data. To do this, various methods are used that allow you to get accurate results and a general idea of the general population, using only selective materials that describe the quality of all data.
Thus, it is not necessary to study all the material, but rather to consider selective representativeness. What is it? This is a selection of individual data in order to have an idea of the total mass of information.
Depending on the method, they are distinguished as probabilistic and improbability. Probabilistic is a sample that is produced by calculating the most important and interesting data, which are further representatives of the general population. This is a deliberate choice or a random sample, however, justified by its content.
Unbelievable - this is one of the varieties of random sampling, compiled according to the principle of an ordinary lottery. In this case, the opinion of the person making such a sample is not taken into account. Only blind lot is used.
Probabilistic sampling
Probabilistic samples can also be divided into several types:
- One of the simplest and most understandable principles is a non-representative sample. For example, this method is often used when conducting social surveys. At the same time, the survey participants are not selected from the crowd by any specific criteria, and information is obtained from the first 50 people who took part in it.
- Intentional samples differ in that they have a number of requirements and conditions for selection, but nevertheless rely on random coincidence, without the aim of achieving good statistics.
- Quota-based sampling is another variation of the improbability sampling that is often used to study large data sets. A lot of conditions and norms are used for it. Objects are selected that must match them. That is, on the example of a social survey, it can be assumed that 100 people will be interviewed, but only the opinion of a certain number of people who will meet the established requirements will be taken into account when compiling the statistical report.
Probabilistic Samples
For probabilistic samples, a number of parameters are calculated that the objects in the sample will correspond to, and among them, precisely those facts and data can be selected that will be presented as representativeness of the sample data. Such methods of calculating the necessary data can be:
- Simple random sampling. It consists in the fact that among the selected segment by a completely random lottery method, the necessary amount of data is selected, which will be a representative sample.
- Systematic and random sampling makes it possible to create a system for calculating the necessary data based on a randomly selected segment. Thus, if the first random number that indicates the serial number of the data selected from the total population is 5, then the subsequent data to be selected can be, for example, 15, 25, 35, and so on. This example clearly explains that even random selection can be based on systematic calculations of the necessary input data.
Consumer sample
A meaningful sample is a method that consists in examining each individual segment, and based on its assessment, a population is compiled reflecting the characteristics and properties of the general database. In this way, more data is collected that meets the requirements of a representative sample. You can easily select a number of options that will not be included in the total number without losing the quality of the selected data representing the total population. In this way, the representativeness of the research results is determined.
Sample size
Not the last issue to be addressed is the sample size for a representative representation of the population. The sample size does not always depend on the number of sources in the population. However, the representativeness of the sample directly depends on how many segments the result should be divided as a result. The more such segments, the more data falls into the result set. If the results require a general notation and do not require specificity, then, accordingly, the sample becomes smaller, because without going into details, the information is presented more superficially, which means that its reading will be general.
The concept of representative error
Representativeness error is a specific discrepancy between the characteristics of the population and sample data. When conducting any sample research, it is impossible to obtain absolutely accurate data, as with a complete study of populations and a sample represented by only a part of the information and parameters, while a more detailed study is possible only when studying the entire population. Thus, some inaccuracies and errors are inevitable.
Types of errors
There are some errors that occur when compiling a representative sample:
- Systematic.
- Random.
- Intentional.
- Unintentional.
- Standard.
- The limit.
The reason for the occurrence of random errors may be the incomplete nature of the study of the total population. Typically, a random error of representativeness is of small size and character.
Systematic errors, meanwhile, arise in violation of the rules for selecting data from the total population.
The average error is the difference between the average values of the sample and the main population. It does not depend on the number of units in the sample. It is inversely proportional to the sample size. Then the larger the volume, the lower the value of the average error.
Marginal error is the largest possible difference between the averaged values of the sample taken and the total population. Such an error is characterized as the maximum of probable errors under given conditions for their appearance.
Intentional and unintentional representational errors
Data bias errors are intentional and unintentional.
Then the reasons for the appearance of deliberate errors is the approach to the selection of data by the method of determining trends. Unintentional errors arise even at the stage of preparing a sample observation, the formation of a representative sample. To prevent such errors, it is necessary to create a good basis for the sample that compiles the lists of sampling units. It should be fully consistent with the objectives of the sampling, be reliable, covering all aspects of the study.
Validity, reliability, representativeness. Error calculation
Calculation of the error of representativeness (mm) of the arithmetic mean value (M).
Standard deviation: sample size (> 30).
Representativeness error (Mr) and relative magnitude (P): sample size (n> 30).
In the case when you have to study the population, where the number of samples is small and is less than 30 units, then the number of observations will become less by one unit.
The magnitude of the error is directly proportional to the sample size . The representativeness of the information and the calculation of the degree of possibility of making an accurate forecast reflects a certain amount of marginal error.
Representative systems
Not only in the process of evaluating the flow of information is a representative sample used, but also the person receiving the information uses representative systems. Thus, the brain processes a certain amount of information, creating a representative sample of the entire flow of information in order to qualitatively and quickly evaluate the data supplied and understand the essence of the issue. To answer the question: "Representativeness - what is it?" - on the scale of human consciousness is quite simple. For this, the brain uses all the sensory organs of the senses, depending on what kind of information needs to be extracted from the general stream. Thus, they distinguish:
- A visual representative system where the organs of visual perception of the eye are involved. People who often use such a system are called visuals. Using this system, a person processes information coming in the form of images.
- Audio representative system. The main organ that is used is hearing. Information provided in the form of sound files or speech is processed by this system. People who better perceive information by ear are called audials.
- The kinesthetic representative system is the processing of the flow of information by perceiving it using olfactory and tactile channels.
- A digital representative system is used together with others as a means of obtaining information from outside. This is a subjective logical perception and understanding of the data.
So, representativeness - what is it? A simple selection from the set or an integral procedure in the processing of information? We can definitely say that representativeness largely determines our perception of data streams, helping to isolate the most compelling and significant ones from it.