In statistics, the numbers, called variables, are defined and put into groups based on different scales. Because each measurement scale is different, it is used in statistical studies. In this post, we will learn about **nominal, ordinal, interval, and ratio scales**. We will also discuss **ordinal vs. nominal examples **and **nominal, ordinal, interval, and ratio examples**.

Table of Contents

**In statistics, how do you define scales of measurement? **

After gathering data for research, the following step is to analyze it; how we do so relies on the methods we employ. How we do this depends on the ways we use it. For example, we can give respondents a list of labels (**nominal scale**) to choose from if we want qualitative information. When working with quantitative data, the researcher can use either interval or ratio scales, which make it possible to show the data as numbers. Let’s look at a real-world example of gathering information to determine what cars people want to drive. This information can be put together with the help of a scale with categories like “electric car,” “diesel car,” “hybrid car,” etc. Because of this, we will use a nominal scale to measure. Researchers can use a ratio scale to determine how much the average person in a city weighs. In the following sections, we’ll look at what each of the four measuring systems we discussed above offers.

**Here are the four scales used in statistics: **

- Nominal Scale

- Ordinal Scale

- Interval Scale

- Ratio Scale

The fact that these measurement scales are always in the same order shows that the **ordinal scale** is similar to the **nominal scale**, and the interval scale is identical to the nominal and ordinal scales. The ratio scale is similar to all three.

**Nominal Scale of Measurement**

In a** nominal scale**, the numbers are just used as labels to identify and group the things being measured. A nominal scale is often used for variables that are not numbers or quantities that don’t have a number value.

**Characteristics**

- A nominal scale variable is made up of two or more groups. For this metric system, the correct answer should be in one of the two groups.
- The kind of information being shared is qualitative. Here, the numbers are used to tell what each thing is.
- The numbers don’t say anything about what the object is. Numbers can only be used to count on the nominal scale.

**Nominal Scale Example:**

How would you describe your gender?

M- Male

F- Female

In this case, the variables are like labels, and “M” or “F” is the correct answer.

**Ordinal Scale of Measurement**

Ordinal scales report second-level data because they show rankings and scores without adding any variation. The ordinal number system is a way to show the idea of “order.” Ordinal data is also called qualitative or categorical information in statistics. It can be put into a category, given a name, and even sorted.

**Characteristics **

- An
**ordinal scale**is used to show how the variables are ranked. - Figuring out and describing the size of a variable.
- In addition to the information given by the nominal data, the ordinal scale shows the order of these variables.
- We don’t know anything about the intervals.
- Surveyors can find out if people agree on how factors should be ranked in a short amount of time.

**Ordinal Scale Example: **

- Students’ academic rankings (1st, 2nd, 3rd, etc.).
- Reviews and ratings of restaurants
- Analyzing the Level of Agreement

- Totally agree
- Agree
- Neutral
- Disagree
- Totally disagree

**Interval Scale of Measurement**

The interval scale is the third level of a scale. It’s a scale of numbers where the difference between two things matters in the real world. So, the variables are measured precisely, not in a relative way, where zero can happen at any time.

**Characteristics**

- An interval scale is a quantitative tool that can measure differences in values.
- So, we can get the average and middle values of the variables.
- When you subtract the values of the two variables, you can see how different they are.
- The interval scale is used in statistics because it can measure subjective things like emotions, calendar types, etc.

**Interval Scale Example: **

- The Likert Scale
- Net Promoter Score (NPS)
- Bipolar Matrix Table

**Ratio Scale of Measurement**

The fourth level of measurement is the quantitative ratio scale. With this scale, you can measure a range of possible results. It gives researchers a way to figure out how significant the gaps are between two data sets. One thing that makes the ratio scale unique is that it can be used to compare values. Its very nature is to be starting points or “zeroes.”

**Characteristics**

- One thing about the ratio scale is that it has a negative one.
- It can never have negative numbers because of the zero-point property.
- It gives quantitative research new ways to look at things. The variables can be subtracted, added, multiplied, and divided systematically. Use the ratio scale to find the mean, the middle, and the most common value.
- The ratio scale is unique and valuable because of how it works. Among these features is the ability to switch between different units of measurement, such as kilograms and calories or grams and calories.

**Ratio Scale Example: **

How many kg do you weigh?

- Under 55 kg
- 55 – 75 kg
- 76 – 85 kgs
- 86 – 95 kgs
- Over 95 pounds

**Ordinal vs. Nominal Scale**

Any organization needs a solid understanding of measurement theory to make decisions based on facts and knowledge. The Nominal scale, for example, gives the least amount of information, while the Ratio scale gives the most. Let us look at the differences between ordinal and nominal scales through the following table.

Elements | Nominal Scale | Ordinal Scale |

Definition | The only thing that sets the variables on this scale apart is their names. In nominal scale, there isn’t any implied order in which variables appear. | There is a natural order to these variables, but it is not known what the difference between them is. On this scale, you can’t figure out the difference between two variables. For example, the sizes go from small to medium to large to extra large. |

Quantitative Degree | On this scale, there is no numerical value for any of the variables. Rather, it is a scale for measuring how good something is. | Ordinal variables are connected to quantitative values, but they can’t be evaluated using simple arithmetic. |

Key Differences | You can’t put these variables in any order. The variables on this scale are different. Nominal data cannot be quantified. | The different parts of this scale are given numbers. There is no way to do arithmetic with these variables. You can’t figure out the difference between two variables. |

Examples | GenderReligionNationalityMarital Status | RankRatingQualificationEconomic Status |

**Conclusion**

There are four ways to measure data in statistics: **nominal, ordinal, interval, and ratio scale**. These are just ways to divide different kinds of data into smaller groups. A nominal scale gives a set of values a name or label. Ordinal scales, like those used in customer satisfaction surveys, tell us a lot about the order of the choices. Interval scales tell us the sequence of values and how much difference there is between them. Lastly, ratio scales offer us the ultimate order, interval values, and the capability to calculate ratios because a “true zero” can be defined.