Package:

Extends:

Version:

1

Is Abstract:

no

Is Pattern:

no

Definition:

Set of distinct values, characterized by properties of those values, and by operations on those values.

(From ISO/IEC 11404 - General purpose datatypes)

Example:

1) Temperature can be measured in Fahrenheit, Celsius, and Kelvin. For the first 2 units of measure, one may take differences but not ratios. It is wrong to say 25 deg F is a third as warm as 75 deg F. Same with Celsius. However, 75 deg F is 50 deg F warmer than 25 deg F. Same with Celsius. Same with Kelvin. However, in Kelvin, 25 deg K IS a third as warm as 75 deg K. Therefore, we say Celsius and Fahrenheit measures have an Interval datatype, whereas Kelvin measures are Ratio.
2) Take the typical Sex Codes classification
<0, male>
<1, female>
Does the fact that 0 and 1 have an ordering mean anything here? You don't want it to, as codes are arbitrary, so you declare the Nominal datatype. This corresponds to the fact that male and female as categories have no ordering. But, the declaration makes it clear.
3) An example that is a little more confusing is educational attainment. To keep it simple, let the categories and codes be as follows:
<1, elementary>
<2, high school>
<3, college>
<4, post-graduate>
One could say these values (or categories) have an ordering, thus you would assign Ordinal to this. But, Ordinal with numeric codes suggests to people they can take averages, which leads to loads of confusion. This is not Interval data, and that means differences are not necessarily comparable. Taking averages requires comparable intervals, and doing so here leads to nonsense. Finally, it is an interesting question whether assigning Nominal to this might work just as well. The ordering is not so well-defined.
4) A preference scale on the other hand needs to be Ordinal, as preferences have an intrinsic order. As we said above, the codes are not important, so you could have the following (simple) scale:
<3, dislike>
<1, neutral>
<2, like>
The codes are arbitrary. Typically, though, we choose them as mnemonics to coincide with the available computation. But, look what will happen with averages here!
5) Currencies are often listed in units.hundredths. For example, 5 dollars and 22 cents is 5.22. This leads to the usage of Real datatypes, yet a Scaled datatype (see ISO/IEC 11404 for a detailed explanation) is far more appropriate.
There is a difference between the datatype in some application (often currencies can only be represented as Real numbers) and the intended one (Scaled in the case of currencies). So, the Represented Variable has the intended datatype specified, whereas the Instance Variable has the application one.

Property:

Name | Cardinality | Datatype | Description |
---|---|---|---|

schemeEntry | 1..1 | A specific datatype drawn from a vocabulary such as ISO 11404, Excel, SAS, R, etc. |

Relationship:

Is extendable:

Is property:

0

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