The most basic kind of measurement scale is a nominal scale. It is used to group data into groups that are mutually exclusive. The numbers given to the categories are merely names because there is no inherent order to them. For instance, you could use a nominal scale to group individuals into masculine or female categories. Although male and female could be attributed to the numbers 1 and 2, respectively, there is no inherent order to these numbers.
What is a Nominal Scale?
A measurement scale known as a nominal scale employs labels to divide data into categories that are mutually exclusive. The numbers given to the categories are merely names because there is no inherent order to them. For instance, you could use a nominal scale to group individuals into masculine or female categories. Although male and female could be attributed to the numbers 1 and 2, respectively, there is no inherent order to these numbers.
Why use an Ordinal Scale?
An ordinal scale is a measurement scale that ranks data in order from least to greatest. The categories have a natural order, but the distances between the categories are not known. For example, you could use an ordinal scale to rank people’s satisfaction with a product on a scale of 1 to 5, with 1 being “very dissatisfied” and 5 being “very satisfied.”
Ordinal scales are often used in surveys and questionnaires. For example, you might ask respondents to rate their satisfaction with a product on a scale of 1 to 5. The numbers assigned to the categories have meaning, but the distances between the categories are not known.
How do you use a Nominal Scale?
A nominal scale can be used in the following ways:
What are the categories? Choosing the categories you wish to use is the first step. For instance, if you were interested in categorizing people’s preferences for the colors red, blue, green, yellow, and purple, you could do so.
Give the category’s name. After you have determined the categories, you must give each one a label. The labels may contain words, letters, or numbers. You may, for instance, designate the digits 1, 2, 3, 4, and 5 to the appropriate groups of red, blue, green, yellow, and purple.
Assemble data. You can gather data after creating the categories and giving them labels. Respondents can be questioned to find out.
Characteristics of Nominal Scale
The following are some traits of nominal scales:
- The groups are exclusive to one another. A data point can only fall within one category, according to this. A data point cannot be both red and blue, for instance, if you are utilizing the categories red, blue, green, yellow, and purple.
- The list of categories is an extensive piece of data and therefore can fall into one of the categories. There should be no data points that do not fall into any of the categories you are utilizing, such as red, blue, green, yellow, or purple.
- The categories’ assigned numbers are merely labels. Regarding order or magnitude, they are meaningless. As an illustration, the number 1 does not mean that red is “better” than blue.
Examples of Nominal Scales
Below are some instances of nominal scales:
- Either a man or a woman
- Race: Asian, Hispanic, Asian-American, White, etc.
- Single, married, divorced, widowed, etc., are all valid marital statuses.
- Red, blue, green, yellow, purple, and other hues are favorites.
- Democrat, Republican, Independent, etc. political parties.
- Zip codes include 54321, 12345, etc.
- A, B, AB, and O blood types
- Brown, blue, green, or hazel eyes, among others.
- Origin: Mexico, Canada, the United States, etc.
Advantages of Nominal Scales
They are simple to use and comprehend. The meaning of the categories and how to select one are well understood by the respondents.
Their sensitivity to measurement mistakes is lower. Because the categories are not arranged in any particular sequence, tiny measurement errors do not significantly affect the outcomes.
Compared to other scales, they are more adaptable. Nominal scales can be used to quantify a greater variety of ideas.
How do you create a Nominal Scale?
Decide what it is you wish to measure. What do you want to measure as a variable? For instance, you might want to assess the respondents’ gender, favorite color, or political party preference.
Decide which categories to utilize. What many categories are you going to use to evaluate the idea? To measure gender, for instance, you might use the terms “male” and “female.” Use the categories “red,” “blue,” “green,” “yellow,” and “purple” to determine your preferred color.
Give the categories names. After you have determined the categories, you must give each one a label. The labels may contain words, letters, or numbers. You may, for instance, designate the digits 1 and 2 to the distinctions between “male” and “female,” respectively. Or, you might represent the categories with the letters M and F.
Analyze the scale. After building the scale, you must test it to ensure that it functions properly. You may check if they grasp the categories and how to choose one by having a few people use the scale.
How do you interpret Nominal Scale results?
Here are a few examples of how to evaluate findings from a nominal scale:
- Frequency counts: The frequency count for each category can be determined. This will reveal the number of data points that belong to each category.
- Calculate the proportion of data points in each category using percentages. This will enable you to see the data distribution in more depth.
- Bar charts: You can make bar charts to see how the data is distributed. This is an effective method for determining how the data is allocated among the various categories.
- Pie charts: Pie charts can be made to show how the data is distributed. This is yet another helpful method for observing the distribution of the data among the various categories.