- It’s called categorical, or nominal data. Is this statement TRUE or FALSE?
The aim of this problem is to familiarize us with the concepts of variables that can be measured and can assume distinct values and can have different qualitative and quantitative characteristics.
Variables are categorized into two categories: categorical and numeric. Each category is ranked into two subcategories: nominal or ordinal holds categorical variables, and discrete or continuous holds numeric variables.
Expert Answer
To solve this problem, we will look into a few perspectives of classification and characterization. The first perspective is that we can define the nominal or ordinal data categories as the domains of the $4$ data measurement rankings in statistics and research, whereas the other two belong to interval and ratio data. These $4$ data measure rankings are subcategories of categorical and numeric data as we have discussed above.
Furthermore, these nominal and ordinal data classes are organized under the categorical class, while interval and ratio data are classified under the numeric data class. This categorization is found to be on the quantitativeness of a data model.
Categorical data is not quantitative data, which means it does not possess a numerical value. Thus, both nominal and ordinal data can be declared as non-quantitative, which may represent a string of text, date, or any other alphabetical sentence.
Now coming onto the $2^{nd}$ perspective that is the nominal data can be described as data that is employed for labeling variables, without any numerical value. Sometimes we call this type of data- “named” data – which is coined from the phrase nominal.
To further understand nominal data, we can come up with a simple example of Race, which is a nominal variable having a diverse set of categories, but there can not be a hard and fast rule to categorize it from highest to lowest or from lowest to highest.
Likewise, ordinal data is a kind of categorical data with an order. In ordinal data, variables are listed in an ordered manner. The ordinal variables are mostly numbered so as to denote the order of the list. Nevertheless, the numbers are not mathematically calculated or determined but are simply assigned as labels for opinions.
Thus, we can conclude that the group of non-parametric variables belongs to the nominal data while the group of non-parametric ordered variables belongs to the ordinal data.
Numerical Result
Qualitative data which cannot be classified is called categorical, nominal data. Thus, the provided statement is TRUE.
Example
Primary, Master’s, Ph.D., high school, and bachelor’s are all nominal data when viewed separately. But when ranked on a scale and organized in a given hierarchy (Primary, high school, bachelor’s, Master’s, and Ph.D.), they are viewed as ordinal data.
The primary qualitative disparity between ordinal and nominal data is that there lies an order set in ordinal data. Since these data types are based on a categorical nature, their mean and standard deviations cannot be calculated on a particular scale.
Thus, qualitative or categorical data can’t be estimated or measured in the form of numbers.