Nominal Data Characteristics

Nominal Data

Definition of Nominal data:

Nominal data is a simple naming system and no numeric value such as profession. An order related to other numbered items without applying it to nominal data just a name a thing and the way of thinking most popular about nominal data and variables is they are just a named. Categorical data and measure categories are also called Nominal data and Nominal scales have only two categories for example, male or female is called “dichotomous”. A categorical variable (sometimes called a nominal variable) is one that has two or more categories but there is no intrinsic ordering to the categories.  For example, gender is a categorical variable having two categories (male and female) and there is no intrinsic ordering to the categories.  Hair colour is also a categorical variable having a number of categories (blonde, brown and red) and again there is no agreed way to order these from highest to lowest.  A purely categorical variable is one that simply allows you to assign categories but you cannot clearly order the variables.  If the variable has a clear ordering, then that variable would be an ordinal variable as described below.

Characteristics of Nominal Data:

• Cannot be quantified in Nominal data.
• Any type of order cannot be assigned.
• Distinct categories only allocated to the values.
• No meaningful order on those categories.
• Responses or observations does not matter in Order.
• Don’t hold distance in Nominal Scales.
• There is no true or real zero in True Zero. Zero is uninterpretable in Nominal Scale.

Example of Nominal Data:

• Gender of women or men
• Religion either Muslim, Buddhist, Christian
• Hair colour are green, yellow, brown

Appropriate statistics for Nominal scales:

• Mode, count and frequencies.

Displays for Nominal Data:

• Histograms or bar charts.

Ordinal Data

Definition of Ordinal Data:

Position on the scale placed by some kind of order. Data purposefully assigned to number that have a sense of rank or order but the difference of magnitude between those numbers are not known or cannot be measure. For example, they may indicate superiority. However, cannot do arithmetic with ordinal numbers because they only show sequence. “In between” categorical and quantitative variables considered as Ordinal data and variables. The difference between the two is that there is a clear ordering of the variables.  For example, suppose you have a variable, economic status, with three categories (low, medium and high).  In addition to being able to classify people into these three categories, you can order the categories as low, medium and high. Now consider a variable like educational experience (with values such as elementary school graduate, high school graduate, some college and college graduate). These also can be ordered as elementary school, high school, some college, and college graduate.  Even though we can order these from lowest to highest, the spacing between the values may not be the same across the levels of the variables. Say we assign scores 1, 2, 3 and 4 to these four levels of educational experience and we compare the difference in education between categories one and two with the difference in educational experience between categories two and three, or the difference between categories three and four. The difference between categories one and two (elementary and high school) is probably much bigger than the difference between categories two and three (high school and some college).  In this example, we can order the people in level of educational experience but the size of the difference between categories is inconsistent (because the spacing between categories one and two is bigger than categories two and three).  If these categories were equally spaced, then the variable would be an interval variable.

Characteristics of Ordinal Data:

• Specific order can be in Ordinal data.
• The assigned numbers are not arbitrary, unlike with Nominal data.

• The responses or observations matters of the order.
• Distance do not hold Ordinal scales.
• There is no true or real zero. Zero cannot finish an item, observation or category.
• This type of data scales does not allow for the calculation of an average or mean since the difference of magnitude between each assigned number is not the same.
• Example: an average of the degree of heart failure a group of patients have cannot be described with a mean. A patient cannot have class 2.5 heart failure because they don’t really know what that means.

Example of Ordinal Data:

• The first, second and third person in a competition.
• When perform a survey and ask respondents to express their level of satisfaction with the choice of those words: very satisfied, satisfied neutral, dissatisfied, very dissatisfied.
• When a respondent should put a value from 1 to 3 to a statement. Often the words “agree, neutral, disagree” are used.
• Economic status: low, medium and high.

Appropriate statistics for Ordinal Scales:

• Count, frequencies and mode.

Displays of Ordinal Data:

• Histograms or bar charts

Interval Data

Definition of Interval Data:

The difference between two values where a measurement is Meaningful. The most common example of interval data is temperature, the difference between a temperature of 100 degrees and 90 degrees is the same difference as between 90 degrees and 80 degrees. An interval variable is similar to an ordinal variable, except that the intervals between the values of the interval variable are equally spaced.  For example, suppose you have a variable such as annual income that is measured in dollars, and we have three people who make $10,000, $15,000 and $20,000. The second person makes $5,000 more than the first person and $5,000 less than the third person, and the size of these intervals is the same.  If there were two other people who make $90,000 and $95,000, the size of that interval between these two people is also the same ($5,000).

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