Step 1

Nominal and ordinal data are part of the four data measurement scales in research and statistics, with the other two being an interval and ratio data. These four data measurement scales are subcategories of categorical and numerical data.

The Nominal and Ordinal data types are classified under categorical, while interval and ratio data are classified under numerical. This classification is based on the quantitativeness of a data sample.

Categorical data is a data type that not quantitative i.e. does not have a number. Therefore, both nominal and ordinal data are non-quantitative, which may mean a string of text or date.

Step 2

Nominal data is defined as data that is used for naming or labelling variables, without any quantitative value. It is sometimes called “named” data - a meaning coined from the word nominal.

There is usually no intrinsic ordering to nominal data. For example, Race is a nominal variable having a number of categories, but there is no specific way to order from highest to lowest and vice versa.

Ordinal data is a type of categorical data with an order. The variables in ordinal data are listed in an ordered manner. The ordinal variables are usually numbered, so as to indicate the order of the list. However, the numbers are not mathematically measured or determined but are merely assigned as labels for opinions.

Nominal data is a group of non-parametric variables, while Ordinal data is a group of non-parametric ordered variables. Although, they are both non-parametric variables, what differentiates them is the fact that ordinal data is placed into some kind of order by their position.

For example, very hot, hot, cold, very cold, warm are all nominal data when considered individually. But when placed on a scale and arranged in a given order (very hot, hot, warm, cold, very cold), they are regarded as ordinal data.

The major character difference between ordinal and nominal data is that ordinal data has a set order to it. This set order is the bedrock of all other character differences between these two data types.

For instance, both ordinal and nominal data are evaluated using nonparametric statistics due to their categorical nature. Therefore, the mean and standard deviation cannot be evaluated for these data types.

However, the use of parametric statistics for ordinal data may be permissible in some cases. This is done with methods that are a close substitute to mean and standard deviation.

Qualitative data that cannot be ranked is called categorical, nominal data. Hence, the given statement is TRUE.

Nominal and ordinal data are part of the four data measurement scales in research and statistics, with the other two being an interval and ratio data. These four data measurement scales are subcategories of categorical and numerical data.

The Nominal and Ordinal data types are classified under categorical, while interval and ratio data are classified under numerical. This classification is based on the quantitativeness of a data sample.

Categorical data is a data type that not quantitative i.e. does not have a number. Therefore, both nominal and ordinal data are non-quantitative, which may mean a string of text or date.

Step 2

Nominal data is defined as data that is used for naming or labelling variables, without any quantitative value. It is sometimes called “named” data - a meaning coined from the word nominal.

There is usually no intrinsic ordering to nominal data. For example, Race is a nominal variable having a number of categories, but there is no specific way to order from highest to lowest and vice versa.

Ordinal data is a type of categorical data with an order. The variables in ordinal data are listed in an ordered manner. The ordinal variables are usually numbered, so as to indicate the order of the list. However, the numbers are not mathematically measured or determined but are merely assigned as labels for opinions.

Nominal data is a group of non-parametric variables, while Ordinal data is a group of non-parametric ordered variables. Although, they are both non-parametric variables, what differentiates them is the fact that ordinal data is placed into some kind of order by their position.

For example, very hot, hot, cold, very cold, warm are all nominal data when considered individually. But when placed on a scale and arranged in a given order (very hot, hot, warm, cold, very cold), they are regarded as ordinal data.

The major character difference between ordinal and nominal data is that ordinal data has a set order to it. This set order is the bedrock of all other character differences between these two data types.

For instance, both ordinal and nominal data are evaluated using nonparametric statistics due to their categorical nature. Therefore, the mean and standard deviation cannot be evaluated for these data types.

However, the use of parametric statistics for ordinal data may be permissible in some cases. This is done with methods that are a close substitute to mean and standard deviation.

Qualitative data that cannot be ranked is called categorical, nominal data. Hence, the given statement is TRUE.