Nominal, ordinal and scale

May 3rd, 2006 mark

Today Erik from the Netherlands sent us the following question:

What is the diffrence between nominal, ordinal and scale?

In SPSS you can specify the level of measurement as scale (numeric data on an interval or ratio scale), ordinal, or nominal. Nominal and ordinal data can be either string alphanumeric) or numeric.But what is the difference?

Nominal.
A variable can be treated as nominal when its values represent categories with no intrinsic ranking; for example, the department of the company in which an employee works. Examples of nominal variables include region, zip code, or religious affiliation.A variable can be treated as nominal when its values represent categories with no intrinsic ranking; for example, the department of the company in which an employee works. Examples of nominal variables include region, zip code, or religious affiliation.

Ordinal.
A variable can be treated as ordinal when its values represent categories with some intrinsic ranking; for example, levels of service satisfaction from highly dissatisfied to highly satisfied. Examples of ordinal variables include attitude scores representing degree of satisfaction or confidence and preference rating scores.

A variable can be treated as ordinal when its values represent categories with some intrinsic ranking; for example, levels of service satisfaction from highly dissatisfied to highly satisfied. Examples of ordinal variables include attitude scores representing degree of satisfaction or confidence and preference rating scores.For ordinal string variables, the alphabetic order of string values is assumed to reflect the true order of the categories. For example, for a string variable with the values of low, medium, high, the order of the categories is interpreted as high, low,mediumwhich is not the correct order. In general, it is more reliable to use numeric codes to represent ordinal data.

Scale.
A variable can be treated as scale when its values represent ordered categories with a meaningful metric, so that distance comparisons between values are appropriate. Examples of scale variables include age in years and income in thousands of dollars.A variable can be treated as scale when its values represent ordered categories with a meaningful metric, so that distance comparisons between values are appropriate. Examples of scale variables include age in years and income in thousands of dollars.

(Source: SPSS User Guide)

Entry Filed under: Questions and answers, Statistics

6 Comments Add your own

  • 1. SPSSlog.com » How t&hellip  |  May 20th, 2006 at 5:11 am

    [...] - Adjusting Measurment This is the last place where you can choose the right measurment for the variable. You can choose between scale, ordinal and nominal. Which measurment you’ve choose for what question, you can read here. [...]

  • 2. Eduardo Herrera  |  August 22nd, 2006 at 11:41 am

    Congratulations,
    It,s effort is welcome, I thank you for maintaining this center of aid in SPSS,
    Eduardo
    Head Professor of Anatomy

  • 3. sanjay kumer dash  |  April 11th, 2007 at 12:05 am

    repeatation made me confused

  • 4. WS  |  February 7th, 2008 at 1:09 pm

    The answer here is misleading and so is the label “Scale” used in SPSS. The first two categories are correct, but the last category should be replaced in SPSS with its proper label: “Ratio.” (I am unaware where this label came from.)

    The only difference between Ordinal and Scale [sic] (Ratio) is that the latter has a absolute end point, which indicates the absence of the measure.

    If you took the temperature of your room, that data would be entered as ordinal, or ratio depending on what measure you used (Celsius vs. Kelvin) because the former can reach negatives (e.g., my room in the day is 17 degrees Celsius, but at night, it drops to -5 degrees Celsius). Therefore, 0 degrees Celsius does not qualify as ratio data. But measuring the temperature in Kelvin does, because Kelvin as an absolute end point. 0 Kelvin is the lower temperature score that can be recorded. There is no -1 Kelvin.

    The above SPSS examples (age and income) are good examples or ratio data because they both have absolute end point values. Height is also a good example, and so is KM/h (because you can’t be -3 cm tall or be going -60 KM/h; no, not eve in reverse).

    Non scholae sed vitae discimus

  • 5. Anna  |  June 5th, 2008 at 3:22 am

    WS> thank you so much for the clarification. It helps a lot!

  • 6. leonard  |  August 13th, 2008 at 7:12 pm

    thanx a lot……

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