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ScaleĪ variable can be treated as scale when its values represent ordered categories with a meaningful metric, so that distance comparisons between values are appropriate. For example 1=Highly satisfied, 2=satisfied, 3= neutral, 4= dissatisfied, 5= highly dissatisfied. Generally, it is preferable to assign numeric codes to represent the degree of something among respondents.
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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 a certain issue. For example, levels of service satisfaction from highly dissatisfied to highly satisfied. OrdinalĪ variable can be treated as ordinal when its values represent categories with some intrinsic ranking. The nominal scale can also be coded by the researcher in order to ease out the analysis process, for example M=Female, F= Female.
SPSS CODE CATEGORICAL VARIABLES ZIP
Examples of nominal variables include region, zip code, or gender of individual or religious affiliation. For example the department of the company in which an employee works. NominalĪ variable can be treated as nominal when its values represent categories with no intrinsic ranking. At the same time, it needs to code the variables according to the categories those variables are divided into. In the SPSS input file, it is required to define the variables on the basis of nominal, ordinal or scale. Difference between nominal, ordinal and scale in SPSS It is important to change it to either nominal or ordinal or keep it as scale depending on the variable the data represents. 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. While some can be ranked as well as can be quantified. Some of those variables cannot be ranked, some can be ranked but cannot be quantified by any unit of measurement. In the primary research, a questionnaire contains questions pertaining to different variables. Each of these has been explained below in detail. Nominal and ordinal data can be either string alphanumeric or numeric.