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Meaning of the word representativeness

representativeness in the crossword dictionary

representativeness

Dictionary of medical terms

representativeness (French representation, representation) in statistics

correspondence of the studied characteristics of the sample population to the characteristics of the general population; taken into account when organizing a sample study.

Encyclopedic Dictionary, 1998

representativeness

REPRESENTATIVITY (from the French representatif - indicative) in statistics - the correspondence of the characteristics obtained as a result of sample observation to indicators characterizing the entire population. The discrepancy between these indicators represents a representativeness error, which can be random or systematic.

Representativeness

(from the French représentatif ≈ representing something, indicative) in statistics, the main property of a sample population, consisting in the proximity of its characteristics (composition, average values, etc.) to the corresponding characteristics of the general population from which it was selected (subject to certain rules ) selective (see Selective observation). A judgment about the degree of R. is made based on consideration of the sample population in two directions. First, it is compared with the general population with respect to all characteristics recorded in both. Thus, to judge R. of the totality of families selected for observation family budgets, compare their distribution by level wages workers with a similar distribution according to general statistical data or (in the absence of general data on distribution) compare average wage levels, etc. Secondly, a judgment about the degree of R. can be made on the basis of the variability of the characteristics under study in the sample population. Thus, if, according to a survey of family budgets, for example, per capita consumption of bread from family to family varies much less than meat consumption, then this gives reason to consider the R. of this sample in relation to bread consumption to be greater than in relation to meat.

R. is measured by the “error of representativeness,” that is, the difference between the characteristics of the sample and general populations. However, the actual (real) value of this difference remains unknown, as a result of which the measure of R. is its probable value, determined according to the rules of mathematical statistics, or the mean square of its possible values (see also Sampling method).

A. Ya. Boyarsky.

Wikipedia

Representativeness

Representativeness- correspondence of sample characteristics to the characteristics of the population or population as a whole. Representativeness determines the extent to which it is possible to generalize the results of a study using a particular sample to the entire population from which it was collected.

Representativeness can also be defined as the property of a sample population to represent the parameters of the general population that are significant from the point of view of the research objectives.

Examples of the use of the word representativeness in literature.

At the level of conditions of the possibility of thinking, Ricardo, separating the formation of value from its representativeness, was able to identify the interconnectedness of economy and history.

Generally. Representativeness determines the extent to which it is possible to generalize the results of a study using a particular sample to the entire population from which it was collected.

Example

Let's assume that the population is all the students of the school (600 people from 20 classes, 30 people in each class). The subject of study is attitudes towards smoking. A sample of 60 high school students represents the population much less well than a sample of the same 60 people that includes 3 students from each grade. The main reason This is due to the unequal age distribution in classes. Consequently, in the first case, the representativeness of the sample is low, and in the second case, the representativeness is high (all other things being equal).

Literature

Ilyasov F.N. Representativeness of survey results in marketing research // Sociological Research. 2011. No. 3. pp. 112-116.

Wikimedia Foundation. 2010.

Synonyms:

See what “Representativeness” is in other dictionaries:

- (French representatif indicative, characteristic), representativeness, a measure of the ability to restore, reproduce an idea of the whole from its part or a measure of the ability to extend the idea of a part to include this part... ... Philosophical Encyclopedia

The property of a sample to reflect the characteristics of the population being studied. In English: Representativeness Synonyms: Representativeness See also: Sample populations Financial Dictionary Finam ... Financial Dictionary

Representativeness, distinctiveness, demonstrativeness Dictionary of Russian synonyms. representativeness noun, number of synonyms: 3 representativeness (8) ... Synonym dictionary

- (from the French representatif indicative) the representativeness of a sample of economic indicators (most often statistical) used to analyze economic processes and phenomena. Representativeness depends both on the reliability of the available... ... Economic dictionary

representativeness- and, f. representatif adj. Representativeness, demonstrativeness. NS 2. Light, elegant stylization of the city landscape under an ancient engraving conveys the unique flavor of the era. The panel is characterized by solemnity and representativeness. bringing him closer to... ... Historical Dictionary Gallicisms of the Russian language

The validity of transferring the results obtained from the analysis of the sample population to the general population. Dictionary of business terms. Akademik.ru. 2001 ... Dictionary of business terms

- (from the French representatif representing), in statistics, methods for determining the parameters of a sample population (parts of an object, a set), the study of which makes it possible to reasonably represent the state of the general... ... Modern encyclopedia

- (from the French representatif indicative) in statistics, the correspondence of characteristics obtained as a result of sample observation to indicators characterizing the entire population. The discrepancy between the indicated indicators represents... ... Big Encyclopedic Dictionary

- (from the French representatif demonstrative) English. representativeness; German Reprasen tativitat. 1. Representativeness. 2. Indicator k.l. observations in statistics and other sciences. 3. The property of the sample to reflect the characteristics of the population being studied... ... Encyclopedia of Sociology

- (from the French representatif representing something) the most important property of a particular sample of information, consisting in its reflection (representation) of the features of the entire general population (for example, the entire cenopopulation). About the representativeness of the sample... ... Ecological dictionary

representativeness- The property of a sample population to reproduce the parameters and significant elements of the structure of the general population. The term “representative sample” was first introduced in relation to socio-economic research by the Norwegian statistician A. Kiaer in ... Technical Translator's Guide

Books

Medieval Intellectual Culture, A. M. Shishkov. Given tutorial is a reference publication on the history of intellectual culture of the Middle Ages, as it was reflected in the works of philosophers, theologians, naturalists and...
The role of soil in the formation and conservation of biological diversity. The collective monograph presents materials and results of many years of research on the role and importance of soils in the formation and conservation of biological diversity, carried out by employees...

The property of sampling, due to which the results of a sample study allow one to draw conclusions about the general population and the empirical object as a whole, is called representativeness.

Representativeness (representativeness) of the sample is the ability of a sample to reproduce certain characteristics of the population within acceptable errors. A sample is called representative if the measurement result a certain parameter for a given sample coincides, taking into account the permissible error, with the known result of measuring the general population. If a sample measurement deviates from known parameter population is greater than the selected error level, then such a sample is considered unrepresentative.

The proposed definition first of all establishes relationship between sample and population research. It is the general population that is represented by the sample, and only the general population can be extended to the trends identified in the sample study. It should now be clear why such attention was previously paid to the problems of correctly defining the population and describing it in research documentation and publications. The sample cannot represent a population other than the one from which the units for measurement were actually selected. If the researcher is mistaken about the actual boundaries of the population, then his conclusions will be incorrect. If he mistakenly or intentionally expands or distorts the boundaries of the population in reporting materials, publications, or presentations based on the results of the study, then this misleads users and can be considered as falsification of results.

The test of representativeness is carried out by comparing individual parameters of the sample and the general population. A common misconception is that representative samples exist “at all.”

The representativeness or non-representativeness of a sample can be determined solely in relation to individual variables. Moreover, the same sample can be representative in some respects and unrepresentative in others.

As a rule, in the professional discourse of sociologists, representativeness is presented as a dichotomous property - a sample is either representative or not. But this is not a completely correct approach. In reality, a sample may reproduce some parameters of the population more accurately and others less accurately. Therefore, it is more correct (although with practical point vision and less convenient) to talk about degree of representativeness specific sample according to specific parameters.

As with the sample as a whole, key point in determining the representativeness of a sample is the justification of the error within which the sample is considered representative for the purposes of the study. The opposite is also possible - fixing the size factual errors and a statement of the fact that the sample represents the general population with certain errors. Again, the nature of the use of research findings plays a key role in this. Consequently, the same sample may be considered sufficiently representative for some purposes (for example, to predict voter turnout in upcoming elections), but not sufficiently representative for others (for example, to determine candidate ratings and predict voting results).

What parameters should be used to check the representativeness of the sample? First, there are few such parameters in most research situations. After all, it is possible to compare the results of a sample measurement with data on the general population only if the latter are available. And the research is being carried out because there is just not enough such data. Therefore, even at the stage of object modeling and subsequent development of tools, it is advisable to provide for the measurement of one or more control parameters for which data characterizing the general population is available. This will provide the necessary empirical basis for testing representativeness.

Secondly, one should strive to check the representativeness of the sample on parameters that are significant for subject area research. In modern practice, control of representativeness by basic demographic parameters - gender, age, education, etc. has become widespread. These data, as a rule, are available for any territorial object, since they are recorded during population censuses and subsequently recalculated by statistical institutions using well-founded mathematical models . For this reason, the mandatory inclusion of several demographic variables in the data sheet has become a generally accepted professional norm. However, such a practice can be classified as naive and subject to justified criticism. The fact is that basic demographic parameters that are publicly available for comparison do not always play the role of structuring factors in relation to the subjects of sociological research. Their nature in itself is not social, and their influence on the objects of research is often quite indirect. Therefore, demographically representative samples may actually hide significant problems in the form of system errors and uncontrolled biases. On the contrary, the demographic representativeness of samples that are effective from the point of view of the goals and objectives of the study may turn out to be low.

Here interesting example from practice. In 2009, one of the research companies working in the Urals carried out a survey in the city of Kizel Perm region. During fieldwork, the researchers encountered serious obstacles to recruiting the sample envisaged by the research plan - the lack of a sufficient number of available respondents, deterioration weather conditions. Apparently, the research company was not fully prepared to carry out work on such a large-scale project. Its production facilities worked at maximum capacity to ensure that 6,000 respondents were surveyed over a fairly large area within a week. As a result, the actual sample in many survey sites was, by the researchers' own admission, filled with everyone who could be recruited to participate in the study. The demographic quotas established by the terms of reference were violated in most areas of the survey. In some areas, the distortion in the proportions of the sample in relation to the quota target reached 2.5 times for certain categories of the population, which actually cast doubt on the very fact of using quota sampling. It seemed that the customer of the study had every reason to make reasonable claims against the researchers.

However, an examination carried out on behalf of the arbitration court found that such significant distortions of quotas and, accordingly, the obvious unrepresentativeness of the resulting sample in terms of basic demographic parameters practically did not lead to distortion of the research data! By reweighing the data array, the experts obtained the effect of a representative sample based on controlled parameters. Almost all frequency distributions of data tested by experts showed statistically insignificant differences between the results of processing the actual and reweighted arrays. De facto, this means that, despite gross violations of survey technology and practical disregard for quota assignments, the researchers provided the customer with the same data that he could have counted on if the sampling procedures had been fully followed and demographic representativeness had been ensured.

How could this happen? The answer is simple - the demographic parameters used to control representativeness had practically no significance (and this was confirmed by correlation analysis) influence on the subject variables of the study - estimates by the population socio-economic provisions and parameters of his socio-political activity. In addition, the sample size was very large relative to the general population (in fact, the study covered a quarter of the adult population of the municipal district), which, as a result of the law, large numbers led to the stabilization of the observed distributions long before the required number of respondents were interviewed.

The practical implication from this cautionary tale is that effort and resources should be directed toward ensuring and controlling representativeness with respect to those sampling parameters that the researcher expects to have a significant impact on the subject of the study. This means that parameters to control representativeness must be selected specifically for each research project according to its subject specificity. For example, assessments of socio-economic status are always strongly related to the real well-being of the respondent’s family, his position in the labor market and in the business sphere. Accordingly, it is advisable to use these parameters to control representativeness. Another thing is that it can be difficult to obtain objective data characterizing the general population. Needed here creativity and perhaps a compromise. For example, the level of well-being can be monitored by the presence of a car in the respondent’s family, because statistics of registered cars in the region may be available.

Interestingly, research reports and publications almost always refer to representative samples. Are unrepresentative samples really that rare? Of course not. There are quite a few samples that are problematic in terms of representativeness in certain parameters in research practice. Rather, there are even more of them than samples, the representativeness of which can be assessed not formally (by demographic parameters), but essentially. However, their public mention in professional sociological circles is, unfortunately, taboo. And none of the researchers is ready to admit that the representativeness of his sample in terms of parameters essential for the subject area of measurement is problematic or unverifiable.

In fact, discovering signs of non-representative sampling is not a disaster. Firstly, existing technologies“repairing” (reweighing) the sample in many cases makes it possible to completely eliminate the effect of unrepresentativeness regarding the parameter that worries the sociologist or his client. The essence of the reweighting method is to assign certain categories of observations (in the case of a survey, respondents) weighting coefficients, compensating for insufficient or excessive actual representation of these categories in the sample. Subsequently, these weights are taken into account when carrying out all calculation operations with the data array, which makes it possible to obtain distributions that fully correspond to a balanced (corresponding to the calculation quotas) data array. Modern statistical programs, such as BRvv, allow calculations to be made taking into account weighting coefficients in automatic mode, which makes this procedure quite easy to perform.

Secondly, even if it is not possible to obtain a “good” representative sample, “moderate” representativeness may be sufficient to solve many research problems. Recall that representativeness is a measure of fit rather than a dichotomous marker. And only certain research tasks - mainly related to the accurate prediction of certain events - require samples to be truly high (statistically confirmed) representativeness.

For example, in order to predict the market share of a new product in marketing research, a sample is required that covers and represents potential clients. However, most often marketers do not have sufficient data about who actually makes up their circle of clients, especially potential ones. In this situation, it is generally impossible to check the representativeness of the sample - after all, it is not known what parameters it should reproduce. Nevertheless, many marketing tasks are successfully solved, since to identify customer preferences, respond to advertising materials, analyze reviews of New Product Statistically representative samples are not needed - it is enough to cover a typical clientele, which is easy to find right in stores. Non-representative samples are quite suitable for solving search problems, identifying strong trends, analyzing the specifics of individual categories (represented by small independent subsamples), comparing such categories with each other (bivariate analysis), analyzing relationships between variables and other tasks in which the accuracy of the obtained statistical distributions is limited. of secondary importance.

The concept of representativeness often appears in statistical reporting and in the preparation of speeches and reports. It is perhaps difficult to imagine any type of presentation of information without it.

Representativeness - what is it?

Representativeness reflects the extent to which 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 developed 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.

Also, the representativeness of information is defined as the ability of sample data to present the parameters and properties of the population that are important from the point of view of the research being conducted.

Representative sample

The principle of sampling is to select the most important and accurately reflect the properties of the overall data set. For this purpose they are used various methods, which allow you to obtain accurate results and a general understanding using only selective materials that describe the qualities of all data.

Thus, there is no need to study all the material, but just consider selective representativeness. What is this? 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 non-probabilistic. Probability is a sample that is made by calculating the most important and interesting data, which are further representatives of the general population. This is a deliberate choice or random selection, however, justified by its content.

Non-probability is one of the types of random sampling, compiled according to the principle of a regular lottery. In this case, the opinion of the person who compiles such a sample is not taken into account. Only blind drawing 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 when conducting social surveys. In this case, survey participants are not selected from the crowd based on any specific criteria, and information is obtained from the first 50 people who took part in it.
Purposeful samples are different in that they have a number of requirements and conditions for selection, but still rely on chance, without the goal of achieving good statistics.
Quota sampling is another variation of non-probability sampling that is often used to study large populations of data. There are many conditions and norms used for it. Objects are selected that must correspond to them. That is, using the example of a social survey, we can assume that 100 people will be interviewed, but only the opinions of a certain number of people who meet the established requirements will be taken into account when compiling a statistical report.

Probability samples

For probability samples, a number of parameters are calculated that the objects in the sample will correspond to, and among them different ways Exactly those facts and data can be selected that will be presented as representative of the sample data. These methods of calculating the required data can be:

Simple random sampling. It consists in the fact that among the selected segment a completely random lottery is selected required amount data that 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, which indicates the serial number of the data selected from the total population will be 5, then the subsequent data that will be selected may be, for example, 15, 25, 35 and so on. This example clearly explains that even a random choice can be based on systematic calculations of the necessary input data.

Consumer sample

Meaningful sampling is a method that consists of considering each individual segment, and based on its assessment, a population is compiled that reflects the characteristics and properties common base data. In this way it is gained large quantity data that meets the requirements of a representative sample. It is possible to easily select a number of options that will not be included in the total without losing the quality of the selected data that represents the total population. In this way, the representativeness of the study results is determined.

Sample size

Not the last issue that needs to be addressed is the size of the sample to be representative of the population. The sample size does not always depend on the number of sources in the population. However, the representativeness of the sample population directly depends on how many segments the result should ultimately be divided into. The more such segments, the more data gets into the effective sample. If the results require a general designation and do not require specifics, then, accordingly, the sample becomes smaller, since, without going into detail, the information is presented more superficially, which means its reading will be general.

The concept of representativeness bias

Representativeness error is a specific discrepancy between the characteristics of the population and the sample data. When conducting any sample study, it is impossible to obtain absolutely accurate data, as with a complete study of general populations and a sample represented by only part of the information and parameters, while a more detailed study is possible only when studying the entire population. Therefore, some errors and errors are inevitable.

Types of errors

There are some errors that arise when compiling a representative sample:

Systematic.
Random.
Intentional.
Unintentional.
Standard.
Limit.

The reason for the appearance of random errors may be the discontinuous nature of the study of the general population. Typically, the random error of representativeness is insignificant in size and nature.

Systematic errors, meanwhile, arise when the rules for selecting data from the general population are violated.

The mean 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. Then the larger the volume, the smaller 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 of probable errors under given conditions of their occurrence.

Intentional and unintentional errors of representativeness

Data bias errors can be either intentional or unintentional.

Then the reasons for the occurrence of intentional errors is the approach to data selection using the method of determining trends. Unintentional errors arise at the stage of preparing a sample observation and forming a representative sample. To prevent such errors, you need to create good foundation for the sample making up lists of sampling units. It must be fully consistent with the sampling purposes, be reliable, and cover all aspects of the study.

Validity, reliability, representativeness. Error calculation

Calculation of the representativeness error (Mm) of the arithmetic mean (M).

Standard deviation: sample size (>30).

Representativeness error (MR) and (P): sample size (n>30).

In the case when you have to study a population where the sample size is small and less than 30 units, then the number of observations will be 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 is reflected by a certain value of the maximum error.

Representational systems

Not only is a representative sample used in the process of assessing the presentation of information, but the person receiving the information himself uses representative systems. Thus, the brain processes some by creating a representative sample from the entire flow of information in order to qualitatively and quickly evaluate the data provided 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 everything it can, depending on what information needs to be isolated from the general flow. Thus, they distinguish:

The visual representational system, where the organs of visual perception of the eye are involved. People who frequently use such a system are called visual learners. With the help of this system, a person processes information received in the form of images.
Auditory representational system. Main body, which is used is hearing. Information supplied in the form of sound files or speech is processed by this system. People who perceive information better by hearing are called auditory learners.
The kinesthetic representational system is the processing of information flow by perceiving it through the olfactory and tactile channels.

The digital representational system is used together with others as a means of receiving information from the outside. perception and comprehension of the received data.

So, representativeness - what is it? A simple selection from a set or an integral procedure when processing information? We can definitely say that representativeness largely determines our perception of data flows, helping to isolate the most weighty and significant ones from it.

The concept of representativeness in sociological research

In other words, representativeness is the quality of the sample. The sample may be representative or non-representative. If a sociological study used a large group of people, then the sample will be representative.

Definition 2

A sample is a selected number of elements from a population. A representative sample is characterized by the fact that all elements of the general population are represented in the same proportion.

The representativeness of a sociological research sample is determined by two random components: errors that were made during registration and random errors.

Example 1

For example: if the object of sociological research is complex and has several elements, then a larger number of interviewers will be required. Not all interviewers are always well qualified, which can lead to errors during registration. In contrast, conducting a sample survey by interviewers who are more trained and instructed leads to fewer errors, that is, random errors.

Constructing a sample comes down to three main problems:

determine the sample size (that is, build a certain procedure so that the sample is representative);
determine the sample size (the number that needs to be surveyed);
assessment of sample quality (analysis of the accuracy of the results).

Note 1

It is important to remember that the sample and population ratios should not exceed 5%. If this proportion is violated, then the conclusions of such a sociological study will not correspond to reality.

Sample types

Samples are divided into: random and targeted.

Random sampling is the most accurate and representative. The essence of this sample is that, thanks to random selection, all units in the general population have the same chance of being included in the sample population. This type of sampling is usually used before elections, referendums and other public events. In addition to the fact that this sample gives us accuracy, it is difficult to use. In order to conduct a random sample, the sociologist must have a list of elements of the population, which is not always easy. Random sampling requires a large sample size to obtain accurate results.

Varieties of random sampling are serial, regionalized, mechanical and others.

Serial or cluster sampling has the form of series. Is in selection individual elements(family, group, school, team, etc.), which are subject to continuous research.
Regionalized sampling is used in cases where the entire data array needs to be divided into homogeneous parts. Such parts can be city districts.
The principle of mechanical sampling is that all elements of the general population are included in one list and the required number of respondents is selected from it through equal integrals. Mechanical sampling has a population-to-sample ratio. For example: If population 2000 people, and the sample is 200, then this means that with general list every tenth one is selected.

Purposive sampling is a type of sampling where selection is carried out according to the criteria of accessibility, typicality, equality, etc. Purposeful sampling is divided into spontaneous sampling, snowball sampling and quota sampling.

Spontaneous sampling is sampling of the first person you meet. The disadvantage of this sample is that it is impossible to establish the population in advance.
The snowball method involves increasing information. Each interviewed respondent provides contacts of colleagues, friends, acquaintances who can take part in the study, etc.
Quota sampling. In this sample, all data is a quota. When using quota sampling, respondents are selected purposefully, adhering to quota parameters. The characteristics that are selected according to quotas are gender, education, age, skill level, or others, which are determined by the goals and objectives of the sociological research itself.