When it comes to knowing the examples and differences between nominal vs ordinal data, it’s essential to understand it in its entirety. As a data scientist, data analyst, or researcher, you’ll always encounter different data types.

However, the way you classify and categorize this information can impact the statistical approaches you use. So, you must understand the distinction between them to avoid erroneous conclusions and decision-making.

In this article, you’ll learn about the differences between the nominal vs ordinal data types, their characteristics, and examples. You’ll also have a clear understanding of the various levels of measurement.

Table of Contents

**Levels of Data Measurement**

The first thing you should do before you analyze a dataset is to identify the data type it contains. There are four categories of data: nominal, ordinal, interval, and ratio. While they are usually referred to as data types, they’re called the different levels of measurement.

But what are the levels of data measurement? It entails the precision with which a variable has been measured and dictates the techniques that can be applied to draw conclusions from the data.

However, the various categories of data aren’t usually easy to differentiate; rather, they’re arranged in a hierarchy, where each level builds upon the one before it.

Levels of Measurement | ||||

Nominal | Ordinal | Interval | Ratio | |

Categorized | ✓ | ✓ | ✓ | ✓ |

Ranked order | ✓ | ✓ | ✓ | |

Equal variable distance | ✓ | ✓ | ||

Has a true zero | ✓ |

**Nominal Data:** It is the standard type of data that groups data by assigning names or labels to values like animal types, hair color, or gender. It has no hierarchy.

**Ordinal Data: **This entails categorizing data according to rank, such as social status in groups like “poor,” “middle income,” or “wealthy.” But these groups don’t have any set intervals between them.

**Interval Data:** This quantitative data type categorizes variables on a scale in a ranked order, with equal distances between each variable. The data in this level of measurement is typically numerical. Some examples include SAT scores, classified income ranges, and temperature in degrees.

**Ratio Data:** Ratio data refers to a numerical, quantitative data type with similarities to interval data, except that a true zero exists. Meanwhile, you cannot measure the value of “0” in a ratio scale because “0” as a true zero means nothing or no variable.

In addition, since there’s no numerical zero on the scale, the ratio data can’t include negative values. Examples of this level of measurement include the number of members on a team or measurements of weight and height.

**What is Ordinal Data?**

Ordinal data is a qualitative form of data that groups variables into descriptive categories. The categories used in this data form are ranked on a hierarchical scale (for instance, from high to low).

Furthermore, ordinal data is the next most complex measurement type after nominal data. While it is more complicated, it’s quite simplistic. Since this data form is applied to classify items with order, these categories can aid you to rank and compare various positions, milestones, etc, even though they have unequal intervals between them.

In addition, you can use ordinal data to evaluate differences or understand ordered preferences.

**Characteristics of Ordinal Data**

The characteristics of ordinal data include the following:

- You can use ordinal data to calculate the median, mode, range, and frequency distribution of variables.

- Ordinal data is classified under the categorical and non-numeric data. However, it can still be labeled with numerical values.

- Even though this data form can be ranked, their values are unevenly distributed.

- As the name (ordinal) implies, this type of information is always ranked hierarchically.

**What is Nominal Data?**

Nominal data, otherwise known as categorical data, is an information type that’s used in various fields like data analysis, statistics, and research. It further consists of labels/categories that aid in data arrangement and classification.

Moreover, one striking feature about the nominal/categorical data is that none of its categories have any inherent ranking/order. Rather, these categories are mutually exclusive, distinct, and separate.

In a nutshell, you can use categorical data to group information into separate categories without any particular ranking or order. And these labels are indicated with terms or names without any specific ranking or order among them.

**Characteristics of Nominal Data**

Below is a close look at the characteristics of nominal data:

- This type of data is alphabetical in most cases.
- It comprises labels or categories that are totally distinct and separate from each other.
- The only measure of central tendency is the mode.
- Information classified as nominal are characterized by descriptive labels as opposed to any quantitative or numeric value.
- You cannot calculate its mean even though you arrange the data in alphabetical order.
- Categorical data can’t be ordered or ranked in a specific hierarchy because no category is more superior than the other.
- The categories in nominal data are mutually exclusive, and do not overlap.

**Nominal Data Vs Ordinal Data**

Having looked at each data type and their characteristics separately, you might be curious about their differences. So, let’s compare nominal vs ordinal data and see their differences.

Nominal Data | Ordinal Data |

Has no quantitative value and you can’t compare them with each other or carry out numerical operations with them. | Has a sequence, and you can assign numbers to the data. However, you can compare one item with another one in terms of ranking. |

Examples include gender, eye color, types of animals. | Examples include economic status, customer feedback, seniority level, education level, school grades. |

It is categorical or qualitative. | It’s between qualitative and quantitative data. |

Nominal data is categorized without any specific rank or order. | Ordinal data is classified in a natural order. |

No meaningful numerical values. | No meaningful numerical values. |

Distinct and Discrete. | Discrete and Distinct. |

Bar charts, frequency counts, percentages. | Ordinal regression, ranking, median, non-parametric tests, ordered bar charts |

Used for classification and grouping based on category. | Used for evaluating ordered preferences, hierarchy, or rankings. |

**Nominal Vs Ordinal Data Examples**

Since you have known the difference between nominal vs ordinal data, it is also critical that you understand examples of how they are used/applied.

So, this section will cover the examples of nominal vs ordinal data alongside their usage.

**Nominal Data Examples**

Below are some examples of how nominal data is used to categorize and group information into non-ranked and distinct categories:

**Movie genres –**Movie genres are nominal because the categories (such as “drama,” “comedy,” or “horror”) have no ranking. Each genre is unique on its own but we cannot exactly tell if one is better than the other based on this data only.

**Types of fruits –**Fruit types are nominal data because every fruit has its specific category without any order or hierarchy. Each category is distinct and discrete.

**Car colors –**They’re also examples of nominal data, as they have clear categories without an inherent ranking or order. Each car is represented under a particular color category, with no numerical connection between colors.

**Employment status –**It could be employed, unemployed, or retired. So, it is nominal since it has clear categories without a specific order or ranking.

**Ordinal Data Examples**

Still under the nominal vs ordinal data examples, let’s look at how ordinal data is used in domains and fields.

**Economic classes –**Classes like “upper class” and “middle class” can be categorized as ordinal depending on their ranking.

**Customer satisfaction ratings –**Customer satisfaction ratings or surveys typically request respondents/customers to rate their experience on a scale, from “poor” to “excellent.” Therefore, it is ordinal since it can be ranked.

**Educational levels –**We can also use ordinal data to represent educational levels like “high school diploma,” “Bachelor’s degree,” “Masters degree,” and “Ph.D.” These levels are ordered.

**Conclusion**

In data analysis, understanding the differences between nominal and ordinal data is synonymous to navigating a compass. It assists you to make correct interpretations and sound decisions.

Nominal data such as classifying favorite jersey numbers or ice cream flavors have no inherent order. Whereas, ordinal data like ranking competitors in a race or rating a movie from one to five stars clearly have a natural order/hierarchy.

So, these nominal vs ordinal data examples highlight the basic differences between the two types of data. It also guides the type of analytical approach you use.