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French Equivalent: Classification

A set of discrete, exhaustive and mutually exclusive observations, which can be assigned to one or more variables to be measured in the collation and/or presentation of data.

In SDMX, "Classification Systems" refer to a description of the classification systems being used and how they conform with internationally accepted standards guidelines, or good practices. It also refers to the description of deviations of classification systems compared to accepted statistical standards, guidelines, or good practices, when relevant.

The terms "classification" and "nomenclature" are often used interchangeably, despite the definition of a "nomenclature" being narrower than that of a "classification".

The structure of classification can be either hierarchical or flat. Hierarchical classifications range from the broadest level (e.g. division) to the detailed level (e.g. class). Flat classifications (e.g. sex classification) are not hierarchical.

The characteristics of a good classification are as follows:

- the categories are exhaustive and mutually exclusive (i.e. each member of a population can only be allocated to one category without duplication or omission);

- the classification is comparable to other related (national or international) standard classifications;

- the categories are stable, i.e. they are not changed too frequently, or without proper review, justification and documentation;

- the categories are well described with a title in a standard format and backed up by explanatory notes, coding indexes, coders and correspondence tables to related classifications (including earlier versions of the same classification);

- the categories are well balanced within the limits set by the principles for the classification (i.e. not too many or too few categories). This is usually established by applying significance criteria (e.g. size limits on variables such as employment, turnover, etc.);

- the categories reflect realities of the field (e.g. the society or economy) to which they relate (e.g. in an industry classification, the categories should reflect the total picture of industrial activities of the country); and

- the classification is backed up by the availability of instructions, manuals, coding indexes, handbooks and training.

Examples of classification are NACE Rev. 1 (Statistical Classification Of Economic Activities), NUTS (Nomenclature of Territorial Units for statistics), and ISCO-88 (International Standard Classification of Occupations). ISIC is the United Nations International Standard Industrial Classification of All Economic Activities.

Source Publication:
"United Nations Glossary of Classification Terms" prepared by the Expert Group on International Economic and Social Classifications; unpublished on paper.

Cross References:
Analytical Unit - Eurostat
Classification scheme
Classifications, standard
Observation unit - ISIC Rev. 3
Statistical Data and Metadata Exchange (SDMX)
Statistical Data and Metadata Exchange (SDMX)
Statistical Data and Metadata Exchange (SDMX)
Statistical units – ISIC
Taxonomy - ISO


Statistical Theme: Classifications

Glossary Output Segments:

Created on Tuesday, September 25, 2001

Last updated on Tuesday, April 9, 2013