Statistical Grouping

Statistical Grouping


a method of processing and analyzing statistical data. In this method, the set of phenomena under study is divided into classes and subclasses that are of a uniform makeup with respect to certain characteristics; each such class or subclass is described by a system of statistical indexes. The grouped data may be presented in tables.

Grouping is the principal method used in the statistical study of social phenomena and is a prerequisite for the application of various statistical procedures and analytical techniques. For example, grouping is required in order to make use of such generalizing indexes as averages.

In prerevolutionary Russian statistics, particularly in the statistics of the zemstvos (local self-government bodies), considerable experience was acquired in grouping various kinds of entities, and substantial work was done on developing not only tables with grouping according to a single characteristic but also more complex tables, where data are grouped according to more than one characteristic. The theoretical questions associated with the use of the grouping method, however, did not receive a scientific grounding until the work of V. I. Lenin. He had a high opinion of the cognitive value and practical importance of grouping. With respect to tables based on grouping according to more than one characteristic, Lenin wrote, “It may be said without exaggeration that they would revolutionize the science of agricultural economics” (Poln. sobr. soch., 5th ed., vol. 24, p. 281). Of fundamental importance are Lenin’s recommendations on the need for preliminary political-economic analysis of the nature of patterns and for delineation of types of phenomena before beginning experiments involving the grouping of raw data.

Grouping is used not only in analyzing the structure of a population but also in delineating types of phenomena and in studying the interrelationships between different characteristics or factors. Examples of groupings that express the structure of a population are the grouping of a human population by age (with class intervals of one year or, more often, of five years) and the grouping of enterprises by size (Table 1).

By combining classes or establishing nonuniform intervals, qualitative differences between individual classes can be ascertained, and the technical-economic or socioeconomic types of the entities in question (for example, enterprises or farms) can then be determined. Thus, the grouping of a country’s population according to age may be done on the basis of, besides simple chronological classes, such special classes as women from 16 to 54 in age and men from 16 to 59 in age. The use of these special classes permits the calculation of the national economic index known as the country’s labor resources. The boundaries of the intervals are somewhat arbitrary and may differ in different countries. This arbitrariness, however, is not of fundamental importance. From a

Table 1. Grouping of industrial enterprises1 of the USSR according to number of workers (1973, percent of total)
Average annual number of workers in enterpriseNumber of enterprisesGross outputAverage annual number of industrial production personnelAverage annual value of fixed industrial production assets
1 Enterprises with independent budgets (excluding power stations, power networks, and district heating systems)
Less than 101 . . . . . . . . . . . . . . . .
101–200 . . . . . . . . . . . . .
201–500 . . . . . . . . . . . . .22.914.013.911.2
501–1,000 . . . . . . . . . . . . .11.314.414.913.2
1,000–3,000 . . . . . . . . . . . . .8.425.926.625.8
3,001–10,000 . . . . . . . . . . . . .2.524.024.126.5
10,001 or more . . . . . . . . . . . . .0.311.611.616.4

detailed quantitative grouping of enterprises and farms one can move on to the identification of a few basic qualitative groups, such as small, medium-sized, and large enterprises and farms. A number of general economic problems can then be clarified—for example, the process of concentration of production, the growth in efficiency of production, and the increase in productivity of labor. Lenin’s New Data on the Laws Governing the Development of Capitalism in Agriculture (ibid., vol. 27, pp. 129–227) provides a brilliant example of a profound analysis that makes use of grouping in showing the complex nature of the regularities and relationships between the size of a farm and its productivity.

The most complicated task associated with grouping consists in identifying and describing in detail types of socioeconomic phenomena. Such types represent an expression of the forms of a certain social process or the essential characteristics common to many individual phenomena. In his analysis of the stratification of the peasantry, Lenin made use of grouping in a thorough and comprehensive manner; he revealed the process of formation of the principal social classes in prerevolutionary Russia, in the Western European countryside, and in the agriculture of the USA.

Soviet statistics has had considerable experience with typological grouping. For example, the balance of the national economy of the USSR assumes a complex and ramified system of statistical groupings. Other examples of typological grouping in Soviet statistics include the grouping of the population into social classes (Table 2), the grouping of fixed production assets according to socioeconomic types of production units, and the grouping of the aggregate social product.

Table 2. Class composition of the population of the USSR (percent)
Total population (including unemployed members of families) . . . . . . . . . . . . . .100.0100.0100.0
Total industrial and nonindustrial workers . . . . . . . . . . . . . .17017.6829
Industrial workers . . . . . . . . . . . . . .146124609
Kolkhoz peasants and craftsmen in cooperatives . . . . . . . . . . . . . .2917 1
Private peasants and craftsmen not in cooperatives . . . . . . . . . . . . . .66774900
Bourgeois, landowners, merchants, and kulaks . . . . . . . . . . . . . .16.34.6

Bourgeois statistics does not make sufficient use of grouping. When grouping is used, it is applied, for the most part, in an incorrect manner and does not contribute to characterization of the true state of affairs in the capitalist countries. For example, the grouping of agricultural enterprises according to land area exaggerates the position of small-scale production in agriculture, and the grouping of the population by occupation does not reveal the true class structure of bourgeois society.

The socioeconomic characteristics of socialist society provide new applications for statistical grouping. Grouping is employed in analyzing the fulfillment of national economic plans, in determining the reasons why certain enterprises and sectors fall behind, and in identifying unused resources; for example, enterprises may be grouped according to the degree of plan fulfillment or the level of profitability. Of great importance for characterizing the introduction of scientific and technological progress into industry is the grouping of enterprises according to such technical-economic characteristics as the degree of automation and mechanization and the amount of electric power available to labor.


References in periodicals archive ?
Since statistical grouping is a method of data systematizing by which the amount of recorded data compresses depending on one or more features, we analysed the collected data both according to magnitude and earthquakes depth and we also combined these essential earthquakes features.
However, 'Hot Habanero Orange' and 'Hot Fatalli' had significantly fewer flowers and total counted organs than all other cultivars, and for pedicels, they were in the lowest statistical grouping and had the fewest (Table 1).
dorsalis on 2 leaves per pepper plant (Site A), 'Agriset 4108', and 'Numex Big Jim' were in the highest statistical grouping for proportions of infested plants suggesting they were among the quickest to become infested.
annuum 'Hot Habanero Orange' seemed to be the second-least susceptible cultivar and was usually in the lowest statistical grouping for numbers of S.
All data were statistically analyzed (SAS institute, 1988), by least significant difference (LSD) to separate the means among ecotypes within a salinity level, with emphasis on identifying the top (best) statistical grouping for the measured parameters.
A Resistance Performance Index = the number of times a cultivar ranked in the top statistical grouping, was calculated for each cultivar as a measure of overall resistance (Engelke et al.
For the other flower color or bicolor groupings, there is a range of susceptibility among cultivars as well, typically with at least 1 cultivar ranking in the top statistical grouping and expressing resistance.
([dagger]) The number of times a grass was in the best statistical grouping for characteristics exhibiting a significant F test at P [less than or equal to] 0.10.
Diamond was similar in winter color response to Emerald, with both cultivars ranking in the top statistical grouping six out of six times during the 1994-1995 evaluation trials for 25 entries in the NTEP Zoysiagrass trial planted at TAMU-Dallas (Engelke et al., 1996).
It's silly to suggest that people should never be dealt with in statistical groupings and that portraying them as such isn't a revealing exercise.

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