Nominal, ordinal and scale

May 3rd, 2006

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

15 Comments

  • 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……

  • 7. need help  |  November 11th, 2008 at 11:33 pm

    How do you change a interval/ratio such as 2 persons ages into a nominal variable?

  • 8. wwwhitney  |  December 30th, 2008 at 11:00 am

    WS, you’ll probably never read this, but thanks for the clarification! Makes total sense now.

  • 9. Purabi  |  February 14th, 2009 at 2:43 pm

    Thank you. it was very helpful

  • 10. Muhammad waqas khalique  |  February 24th, 2009 at 10:04 am

    It is fine but the examples are not eough.i am searching on these scales but i am unable to find examples.Kindly send me examples as more as pssible

  • 11. theo  |  March 14th, 2009 at 9:42 am

    Congradulations for your assistance!

    I would like to post a question i have, regarding the level of measurement i should use in a guestion.
    The question is: “Indicate your monthly family income”
    The possible answers are: 501-1000, 1001-1500, 1501-2000, 2001-2500, 2501-3000, 3000+
    My question is: should i use ordinal or scale and why?

    Thank you in advance

  • 12. martha  |  June 29th, 2011 at 3:22 pm

    okay so I am still confuse. how about if your responses for a statement are Strongly Disagree, somewhat disagree, neutral, somewhat agree and Strongly Agree. is that scale or ordinal?

  • 13. martha  |  June 29th, 2011 at 3:23 pm

    okay so what if you have a statement with response categories of:
    Strongly Disagree, somewhat disagree, neutral, somewhat agree or Strongly Agree. is it scale or ordinal?

  • 14. KAMLA MUSHTAQ  |  June 30th, 2011 at 2:21 am

    it’ll beter to give more detail and also give more examples about these topics

  • 15. lovelyn  |  July 21st, 2011 at 9:10 pm

    whatever!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!


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