Statistical groupings: basic concepts, stages, grouping of materials, tasks

In the method of statistical groupings, the totality of the phenomena studied is divided into classes and subclasses that have a homogeneous structure according to certain characteristics. Each such division is described by a system of statistical indicators. Grouped data can be presented in tables.

This action is the main method used in the actual study of social phenomena. It arises as a prerequisite for the application of various groups of statistical data, procedures and analytical methods. For example, classification is necessary in order to use any generalized indices, for example, averages.

Contribution V.I. Lenin

In pre-revolutionary Russian statistics, in particular in various zemstvos (these are local self-government bodies), considerable experience was gained in the grouping of various types of organizations. And also at this time, considerable work was done to develop not only tables with classification according to one characteristic, but also more complex schemes. In them, all data is grouped by two or more parameters. However, the theoretical issues associated with the use of statistical grouping methods have not been scientifically substantiated. This state of affairs persisted until the works of V.I. Lenin. He had a high opinion of the cognitive value and practical importance of classification. Regarding tables based on the characteristics of a statistical grouping, according to more than one characteristic, Lenin wrote: “It can be said without exaggeration that they will revolutionize science and, of course, agricultural economics.”

Of fundamental importance are the recommendations of Vladimir Ilyich on the need for a preliminary political and economic analysis of the nature of patterns and determination of the types of phenomena before starting experiments with the classification of initial data.

Stages of statistical groupings

Systematization is used not only in the analysis of the structure of the population, but also in determining the types of phenomena and in studying the relationships between various characteristics or factors. Examples of groups that express the structure of the population are classifications of people by age (at intervals of one year or, more often, five years) and enterprises by size.

By combining classes or establishing uneven intervals, it is possible to establish qualitative differences between individual systems, and then determine the techno-economic or socio-economic types of the relevant entities (for example, enterprises or farms). Thus, grouping the country's population by age can be based on, in addition to simple chronological objects, such special divisions as women aged 16 to 54 years and men from 16 to 59 years. Using these special classes allows you to calculate the national economic index, known as the country's labor resources. The boundaries of the intervals are somewhat arbitrary and may vary in different states.

Task

A detailed quantitative classification of enterprises and firms allows us to move on to identifying several major quality groups, such as small, medium, and large organizations. After this, a number of general economic problems can be clarified, for example, the process of concentration of production, the growth of industrial efficiency and the increase in labor productivity. The new data by Vladimir Ilyich Lenin on the laws governing the development of capitalism in agriculture are a brilliant example of in-depth analysis that uses grouping to demonstrate the complex nature of patterns. And also the relationship between the size of the enterprise and its full productivity.

The most important and difficult task of statistical groups is to identify and describe in detail the types of socio-economic phenomena. Such subjects are an expression of the forms of a particular social process or basic characteristics. They appear to be common to many individual phenomena. In his analysis of the stratification of the peasantry, Vladimir Ilyich Lenin used the group thoroughly and comprehensively. First of all, he revealed the process of the formation of the main social classes in pre-revolutionary Russia, in the West European village and in the US agriculture.

And, as it turned out, the Soviet data have considerable experience in typological and statistical groupings. For example, the balance of the national economy of the USSR involves a complex and ramified classification system. Other examples of a typological statistical grouping in the Soviet space include the systematization of the population by social classes. As well as the union of fixed assets by socio-economic types of industrial units. And you can also give an example, such as grouping the statistical aggregate of a social product.

Bourgeois classification makes insufficient use of systematization. When a grouping is applied, it is, for the most part, incorrect and does not contribute to characterizing the true state of affairs in capitalist countries. For example, the classification of agricultural enterprises by land area exaggerates the position of small-scale production in this channel. But the grouping of the population by profession does not reveal the true class structure of bourgeois society.

The socio-economic characteristics of the socialist state provide new applications for the statistical grouping. Classification is used to analyze the implementation of national economic plans, to determine the reasons for the lag of some enterprises and sectors. And also identifying unused resources. For example, enterprises can be grouped according to the degree of implementation of the plan or the level of profitability. Of great importance for the characterization of the introduction of scientific and technological progress in industry is the grouping of enterprises, according to such technical and economic data as the degree of automation and mechanization and the amount of electricity available for labor.

Grouped data is information generated by combining separate groups of statistical observations about the presence of a variable into separate classes, so the frequency distribution of these systems serves as a convenient means of generalizing and analyzing all materials.

Information

Data can be defined as groups with material that represent the qualitative or quantitative attributes of a variable or set of non-constant. This is analogous to the statement that classes can be any set of information that describes an entity. Systems, in a grouping of statistical data, can be classified into grouped and ungrouped objects.

Any information that a person collects in the first place is unclassified. Ungrouped statistical groups are data, but only in unprocessed form. An example of such systems is any list of numbers you can think of.

First type of classifications

Grouped data is information that has been organized into groups known as classes. This type has already been classified, and thus some level of analysis has been carried out. This means that all information is no longer raw.

A data class is a group that is associated with a specific user property. For example, if the head of the enterprise collected people whom he hires in a certain year, he could group them into systems by age: twenty, thirty, forty years, and so on. And each of these groups is called a class.

In turn, this is not the last division. Each of these classes has a specific width, and this is called spacing or size. This concept is very important when it comes to building histograms and frequency diagrams. All classes can have the same or different size, depending on how all the information is grouped. The system interval is always an integer.

Class constraints and its boundaries

The first concept refers to the actual values ​​that can be seen in the final table. Class restrictions are divided into two categories: the lower limit of the system and the upper limit. Of course, to ensure the correctness and information content, all the divisions are used in the preparation of tables.

But, on the other hand, class boundaries are not always respected in the frequency table. This concept gives the true range of systems and, like various restrictions, is also divided into the boundaries of the lower and upper values.

Living and non-living groups

Science seeks to understand and explain natural phenomena. Scientists understand things by classifying them. This applies to both living creatures and inanimate groups of statistical materials.

In turn, such types can be divided into groups depending on the contrasting properties. For example, if students have compiled lists in their scientific journals about various materials and subjects that they studied, they can use this data to expand their knowledge and information about the systems that they studied.

All knowledge can be sorted or classified according to various contrasting properties. Here are some examples:

• Metals versus various non-metals.
• Stone terrain instead of desert or meadow.
• Visible crystals against invisible minerals.
• Natural process instead of artificial.
• Substances are denser than water or less significant than a given liquid.
• Magnetic versus non-magnetic.

And you can also make group differences on the following grounds:

• The state of substances at room temperature (solid, liquid, gas).
• The fusibility of metals.
• Physical properties and so on.

Materials:

• Various articles that serve as examples of the categories above.
• Magnets for checking the properties of materials.
• Water container to check if objects are swimming or drowning.
• Scientific journals.

Work procedure

How exactly does everything happen:

1. Students work in groups. Each is given some materials and asked to find ways to group items into categories. They develop the criteria that they will use, and then sort the elements accordingly. Tables of results are recorded in their scientific journals.
2. After grouping the materials, they are again sorted by other criteria. The next step will also be a list of results. And after that, an additional row of elements is written that were sorted differently due to changes in the criteria.
3. Students record observations and tables in their scientific journals.

results

Students record a series of tables that show how their subjects are sorted based on each of the criteria. For example, a group of students has a paper clip, a small piece of granite, a cork, a plastic toy. And then a pair of sorting tables may look like the one below.

1. Items are sorted by magnetism.
   React to the magnet: paper clip, granite. Do not react: cork, plastic.
2. Items are sorted by density compared to water.
   Float: cork, plastic. Drowning: paper clip, granite.

After that, students make presentations for the class. They discuss why different items are classified differently depending on the criteria used.

Students repeat these observations every time, applying different properties.

Discussion

At this stage:

1. Students can extend these observations to other materials, already without any practical research.
2. Examples are samples of different types of rocks. Students will learn how to make more careful observations and write exactly what they see with the help of loops and other objects that they use.
3. If students have created a property index file recorded on cards, they can also be sorted. This will be useful if the index contains additional materials that are not in the class.

A common way to process continuous quantitative data is to subdivide the entire range of meanings into several sub-ranges. It is necessary to assign to each material a constant value of the class in which it falls. It is worth noting that the data set varies from continuous to discrete.

The concept of statistical grouping

Systematization is performed by determining the set of ranges, and then counting the amount of data falling into each of them. Subbands do not overlap. They should cover the entire range of the data set.

One of the most successful ways to visualize grouped systems is a histogram. It is a set of rectangles, where the base of the figure covers values ​​in the range associated with it. And the height corresponds to the amount of information.