What is an Ordinal Scale?

An ordinal scale is a measurement scale that allows you to rank data in order. The categories on an ordinal scale have a natural order, but the intervals between the categories are not equal. This means that you can say that one data point is higher or lower than another, but you cannot say how much.

Why use an Ordinal Scale?

Here are some reasons for using the ordinal scale:

To quantify ideas with a natural order. An ordinal scale, for instance, could be used to assess ranking, harshness, or level of satisfaction.

To gather data that is simple to use and understand. Respondents can easily comprehend and use ordinal scales. They are a fantastic option for surveys and quizzes because of this.

To gather information that is more resistant to measurement inaccuracy. Because the gaps between the categories on an ordinal scale are not equal, modest measurement errors do not significantly affect the outcomes.

To gather data that is more adaptable than scales that are nominal. Compared to nominal scales, ordinal scales can be used to measure a wider range of concepts.

How do you use an Ordinal Scale?

Decide what it is you wish to measure. What do you want to measure as a variable? For instance, you might wish to gauge how happy customers are with a product, how severe symptoms are, or how well athletes are doing in a tournament.

Choose how many categories you wish to utilize. The notion you are measuring will determine how many categories there are. A scale of 1 to 5, for instance, might be used to gauge satisfaction, with 1 denoting “very dissatisfied” and 5 denoting “very satisfied.” You might use a scale of 1 to 10, with 1 being the best and 10 being the worst, to rank sportsmen.

Give the categories names. Words, numbers, or symbols may be used as labels. For

Use the terms “very dissatisfied,” “dissatisfied,” “neutral,” “satisfied,” and “very satisfied” as examples to gauge satisfaction. To rank athletes, you may use the terms “1st,” “2nd,” “3rd,” and so on.

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.

Characteristics of Ordinal Scale

Order of the categories: An ordinal scale has a natural order to its categories. It is possible to say that one data point is higher or lower than another in light of this. For instance, if you were to rank the competitors in a competition, you could claim that the first-place finisher is better than the second-place finisher.

There are no equal gaps between the categories. On an ordinal scale, the gaps between the categories are not all the same. As a result, it is impossible to quantify how much one data point differs from another. On a scale of 1 to 10, with 1 denoting “mild” and 10 denoting “severe,” you can, for instance, rate the severity of symptoms.

The categories are given numbers, which are merely labels. On an ordinal scale, the numbers given to the categories are merely labels. Regarding the gaps between the categories, they are meaningless. For instance, if you are rating athletes in a competition, the number 1 does not necessarily indicate that the athlete won by a single unit.

Examples of Ordinal Scale

Some of the examples are here below:

Ask respondents to score their level of satisfaction with a product or service on a scale of 1 to 5, with 1 denoting “very dissatisfied” and 5 denoting “very satisfied.”

Athletic performance rankings: You might order competitors’ competitors from best to worst.

Symptom severity: You may have patients rate the intensity of their symptoms on a scale of 1 to 10, with 1 denoting “mild” symptoms and 10 denoting “severe” symptoms.

Education level: You may ask respondents to indicate their education level by choosing from a list of options like “elementary school,” “high school,” “college,” and “graduate school.”

A political topic could be the subject of a question asking respondents to express their opinions.

Advantages of Ordinal Scale

Some of the advantages are:

Simple to use and comprehend: Respondents had little trouble figuring out what the categories meant and how to score them.

Less sensitive to measurement error: Since the intervals between the categories are not the same, modest measurement errors do not significantly affect the outcomes.

Adaptable than nominal scales Ordinal scales can be used to evaluate a larger range of topics.

How do you create an Ordinal Scale?

The processes for making an ordinal scale are as follows:

Decide what it is you wish to measure. What do you want to measure as a variable? For instance, you might wish to gauge how happy customers are with a product, how severe symptoms are, or how well athletes are doing in a tournament.

Choose how many categories you wish to utilize. The notion you are measuring will determine how many categories there are. A scale of 1 to 5, for instance, might be used to gauge satisfaction, with 1 denoting “very dissatisfied” and 5 denoting “very satisfied.” You might use a scale of 1 to 10, with 1 being the best and 10 being the worst, to rank sportsmen.

Give the categories names. Words, numbers, or symbols may be used as labels. Use the terms “very dissatisfied,” “dissatisfied,” “neutral,” “satisfied,” and “very satisfied” as examples to gauge satisfaction. To rank athletes, you may use the terms “1st,” “2nd,” “3rd,” and so on.

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 Ordinal Scale results?

Here are a few examples of how to evaluate findings from an ordinal scale:

Determine the most prevalent category: This might help you get a sense of the overall direction of the data. For instance, if you are assessing customer happiness with a product, you can discover that “satisfied” is the most popular category. This would imply that the majority of consumers are content with the product.

Choose the categories that differ the most: This might assist you in locating the locations where the data is most variable. For instance, you might discover that the categories “mild” and “severe” are the most dissimilar if you are measuring the severity of symptoms. This would imply that there is a significant distinction between mild and severe symptoms.

Sort the categories into order:  You may observe how the categories are arranged in relation to one another by doing this. For instance, if you are ranking competitors, you can discover that the top three competitors are all listed as “first.” This would imply that the performances of these sportsmen are all extremely comparable.

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