Nominal, ordinal and scale is a way to label data for analysis. In SPSS the researcher 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. Each of these has been explained below in detail.
In the primary research, a questionnaire contains questions pertaining to different variables. Some of those variables cannot be ranked, some can be ranked but cannot be quantified by any unit of measurement. While some can be ranked as well as can be quantified. Upon importing the data for any variable into the SPSS input file, it takes it as a scale variable by default since the data essentially contains numeric values. It is important to change it to either nominal or ordinal or keep it as scale depending on the variable the data represents.
Difference between nominal, ordinal and scale in SPSS
In SPSS input file, it is required to define the variables on the basis of nominal, ordinal or scale. At the same time, it needs to code the variables according to the categories those variables are divided into.
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 gender of individual or religious affiliation. The nominal scale can also be coded by the researcher in order to ease out the analysis process, for example; M=Female, F= Female.
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 a degree of satisfaction among the consumers, preference degree from very high to very low, and degree of concern towards the certain issue. Generally, it is preferable to assign numeric codes to represent the degree of something among respondents. For example 1=Highly satisfied, 2=satisfied, 3= neutral, 4= dissatisfied, 5= highly dissatisfied.
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 Rupees, or score of a student in GRE exam. For example in a classroom of 60 students, each one would have given GRE entrance test, and therefore Scale is used to determine the average score for the class, or the highest and lowest score in the class so on and so forth..
Generally, for an analysis, represent all options in a close-ended questionnaire in the form of numbers by coding them. “Gender” can be “Male” or “Female” but do not give “M” or “F”. Define the options as 1= Male; 2= Female. Therefore we keep the option under “Measure” as “Nominal” only.
- Risk tolerance by stocks categorization using ratio analysis - September 10, 2020
- Methodology to analyze the dynamic behavior of investors in the Indian stock market - September 4, 2020
- An introduction to stock market trend analysis - July 31, 2020