# Difference between ordinal and scale in SPSS

Is there any difference in SPSS to specify a variable as ordinal or scale? I know they are different in meaning, but it seems SPSS treat them the same in calculation.

Is there any difference in SPSS to specify a variable as ordinal or scale? I know they are different in meaning, but it seems SPSS treat them the same in calculation.

- for tables and graphs you must define correctly if a variable is ordinal or scale, and for the usage of labels in ordinal variables you should define the properties of the variable correctly... it's an "opperative" usage, you could change the variable type when you need it as ordinal or scale, depending on which type of analysis you'll do in SPSS
- 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.

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.

(Source: SPSS User Guide) - You are absolutely right for defining scales in SPSS
- In practice, SPSS does not REQUIRE you to make the definition. I've seen many data sets where no one bothered to define in SPSS if the variable was ordinal or scale. However, as noted, that means that improper calculations can sometimes be made. It is up to the researcher to understand the difference and choose tests appropriately. Defining the level of measurement does have an advantage sometimes in that SPSS will not allow some variables to be used incorrectly - particularly in the case of nominal variables.
- Thanks all of you for the comments!
- in the SPSS if you want measured Central tendency (mean, Median and Mode ), than you must have some short of knowledge about nominal, Ordinal and scale. the Choice of mean, median and mode is restricted by the level of measurement of a variable you defined. if the level of the measurement for a variable is nominal, you can calculate only mode, if the level of measurement of a variable is ordinal then you can calculate mode or median. if the level of measurement of a variable is interval/ratio, you can calculate mode and median,
- ordinal scale:

A rank-ordered scale of measurement in which equal differences between numbers do not represent equal differences between the things measured.

nominal scale:

scale of measurement in whch numbers are used simply as names and not as quantites. - there are total four types of scales, namely Nominal, Ordinal, Interval and Ratio. Depending on the type of the scales, respective treatment can be given to those variables. for example nominal variable can only be counted hence no mean or standard deviation can be calculated on the same. ordinal variable can be counted and median can be calculated. Interval and ratio ( combined as scale in SPSS) are quantitative variables. most quantitative analysis can be performed on these. The basic difference between qualitative and quantitative variables is the FIXED Distance. qualitative variables do not have fixed distance. quantitative variables have fixed distance.

Nominal - Qualitative variable with out order (only categorisation possible) example : gender, departments, etc.

Ordinal - Qualitative variable with order (categorisation and order) example Rank, credit rating as High risk, medium risk, Low risk etc.

Interval - Quantitative variable without fixed origin.( has fixed distance but no fixed origin) example - temperature

Ratio - Quantitative variable with fixed origin. all quantitative variables are in this category , salary, expenditure, etc. - scale is simply a measurement of a phenomenon or characteristic, Working out a figure and saying that it is male or female amounts to scaling ie.classification popularly termed as nominal scale. It could be multinomial. and it is not necessary that we have only 0 and 1 scale. The nominal scale could be 1,2, 3 or more categories in which you place objects or persons. It depends on the classification one is interested in. Even continuous data can be translated into categories. For example, income data may be grouped into four or five categories. For a statistiscal analysis,, you may require to know number of people or respondents into particulal category.If one is interested in rank order, it can be ranked from lowest category to highest category. For example, a trait can be rated as highest, down to a minimum and not necessarily to a zero level. let me say that the SPSS will do what you want. Do try to understand the statistics

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## Popular Answers

Shailaja Leslie Rego· Narsee Monjee Institute of Management StudiesNominal - Qualitative variable with out order (only categorisation possible) example : gender, departments, etc.

Ordinal - Qualitative variable with order (categorisation and order) example Rank, credit rating as High risk, medium risk, Low risk etc.

Interval - Quantitative variable without fixed origin.( has fixed distance but no fixed origin) example - temperature

Ratio - Quantitative variable with fixed origin. all quantitative variables are in this category , salary, expenditure, etc.