# 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.

## Popular Answers

Aditya Kumar Katragadda· NuStats, Austin, TexasA 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)

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.

## All Answers (11)

DeletedAditya Kumar Katragadda· NuStats, Austin, TexasA 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)

Muhammad Ibrahim· Govt. M A O CollegeChris Macintosh· University of UtahJason Leung· The Chinese University of Hong Kongdr vinod Sen· Central University of GujaratReyhaneh FarhadiA 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.

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.

subhash Davar· Kurukshetra UniversityYuting Chen· University of Torontosee last paragraph of the pdf file below. I feel it is more clear and makes sense. the scale data type includes interval and ratio. e.g., when you want to calculate the average of several variables.

http://www.sfu.ca/~palys/SPSS%20Lesson%201.pdf

Rayalu K K V· IASE, AP, Indiaif one understand what is concept ofnominal perfectly there may not be confusion with other. one can not add or subtract among fruits, cars, bangles,doors....etc. even the numbers representing number of things can be added. but the addition or subtraction of those numbers is meaningless when total can be asked.. but frequency.. how many of each are there is worth to know and give meaning. so evaluate or justify your self. when defining nominal. add those sum and and frequency that is count and ask yourself what meaning will come

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