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 kind of presentation of information for review.
Representativeness - what is it?
Representativeness reflects how the selected objects or parts correspond to the content and meaning of the data set from which they were selected.
Other definitions
The concept of representativeness can be revealed in different contexts. But in its meaning, representativeness is the correspondence of the features and properties of selected units from the general population, which accurately reflect the characteristics of the entire general database as a whole.
The representativeness of information is also defined as the ability of sample data to represent the parameters and properties of the population that are important from the point of view of the study.
Representative sample
The principle of sampling is to selectthe most important and accurately reflecting the properties of the total data set. For this, various methods are used that allow obtaining accurate results and a general idea of the population, using only sample materials that describe the qualities of all data.
Thus, it is not necessary to study the entire material, but it is enough to consider the sample 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 improbable. Probability is a sample that is made by calculating the most important and interesting data, which are further representatives of the general population. Is it a deliberate choice or a random selection, nonetheless justified by its content.
Improbable - this is one of the varieties of random sampling, compiled according to the principle of a regular lottery. In this case, the opinion of the one who makes up such a sample is not taken into account. Only a blind lot is used.
Probability sampling
Probability samples can also be divided into several types:
- One of the simplest and most understandable principles is non-representative sampling. For example, this method is often used in social surveys. At the same time, survey participants are not selected from the crowd on any specific grounds, and information is obtained from the first 50 people who took part in it.
- Deliberatesamples differ in that they have a number of requirements and conditions in the selection, but still rely on random coincidence, not pursuing the goal of achieving good statistics.
- Quota-based sampling is another variation of non-probabilistic sampling that is often used to examine large datasets. It uses a lot of terms and conditions. Objects are selected that should correspond to them. That is, using 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 a statistical report.
Probability samples
For probabilistic samples, a number of parameters are calculated to which the objects in the sample will correspond, and among them, in different ways, exactly those facts and data that will be presented as representativeness of the sample data can be selected. These ways of calculating the necessary data can be:
Simple random sampling. It consists in the fact that among the selected segment, the required amount of data is selected by a completely random lottery method, 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 subsequentthe data to be selected could be, for example, 15, 25, 35, and so on. This example clearly explains that even a random selection can be based on systematic calculations of the necessary input data
Sample of consumers
Intentional Sampling is a method that considers each individual segment and, based on its assessment, a population is compiled that reflects the characteristics and properties of the overall database. In this way, more data is collected that meets the requirements of a representative sample. It is easy to 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 results of the study 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 general population. However, the representativeness of the sample population directly depends on how many segments the result should be divided into. The more such segments, the more data gets into the resulting sample. If the results require a general notation and do not require specifics, 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.
Mistake conceptrepresentativeness
Representativeness error is a specific discrepancy between the characteristics of the population and sample data. When conducting any sample study, it is impossible to obtain absolutely accurate data, as in a complete study of general populations and a sample provided with only 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
Distinguish some of the errors that occur when compiling a representative sample:
- Systematic.
- Random.
- Deliberate.
- Unintentional.
- Standard.
- Limit.
The reason for the appearance of random errors may be the discontinuous nature of the study of the general population. Typically, random error of representativeness is of negligible size and nature.
Meanwhile, systematic errors occur when the rules for selecting data from the general population are violated.
Mean error is the difference between the sample mean and the underlying 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 smaller the value of the average error.
Marginal error is the largest possible difference between the average values of the sample taken and the total population. Such an error is characterized as the maximum probable errorunder given conditions of their appearance.
Intentional and unintentional errors of representativeness
Data offset errors can be intentional or 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 occur even at the stage of preparing a sample observation, forming a representative sample. In order to avoid such errors, it is necessary to create a good sampling frame for the lists of sampling units. It must be fully consistent with the objectives of the sampling, be reliable, covering all aspects of the study.
Validity, reliability, representativeness. Error calculation
Calculate the representativeness error (Mm) of the arithmetic mean (M).
Standard deviation: sample size (>30).
Representativeness error (Mr) and relative value (R): sample size (n>30).
In the case when you have to study a 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 size of the error is directly proportional to the size of the sample. The representativeness of information and the calculation of the degree of possibility of making an accurate forecast reflects a certain amount of marginal error.
Representational systems
Not only is a representative sample used in the process of evaluating the presentation of information, but the person receiving the information himself,uses representative systems. Thus, the brain processes a certain amount of information, creating a representative sample from the entire flow of information in order to qualitatively and quickly evaluate the submitted data and understand the essence of the issue. Answer the question: "Representativeness - what is it?" - on the scale of human consciousness is quite simple. To do this, the brain uses all the subservient sense organs, depending on what kind of information needs to be isolated from the general flow. Thus, they distinguish:
- Visual representational system, where the organs of visual perception of the eye are involved. People who often use such a system are called visuals. With the help of this system, a person processes information coming in the form of images.
- Audial representational system. The main organ that is used is hearing. Information supplied in the form of sound files or speech is processed by this particular system. People who perceive information better by ear are called auditory.
- The kinesthetic representational system is the processing of the flow of information by perceiving it through the olfactory and tactile channels.
The digital representational system is used along with others as a means of obtaining information from outside. This is a subjective-logical perception and understanding of the received data
So, representativeness - what is it? A simple selection from a set orintegral procedure in the processing of information? We can definitely say that representativeness largely determines our perception of data flows, helping to isolate the most significant and significant from it.
