Data is the backbone of statistics.

Ensuring the data you gather is accurate and dependable is a crucial component of any research project.

You can perform this with the assistance of data measurement scales.

In statistics, data measurement scales are used to categorize data into different levels of measurement based on their properties and characteristics.

As a researcher, you should clearly understand these measurement scales, as they can make or break your research. And this blog will help you with it.

You’ll also find real-life examples to help you understand the concepts and avoid messing up your research.

## What Is A Data Measurement Scale?

Before defining the measurement scale, you must understand a variable’s significance in statistical data analysis. (However, if you know this topic, you can skip this section).

A variable is a measurable quantity whose value can change throughout its existence.

For example, consider an instance of employed individuals.

Here, the variables that can define the subjects can be gender, age, location, marital status, industry type, skills, and position. The values of the variables can vary throughout the individual’s work history.

For example, you want to calculate an employee’s average paid time off. It’s practically impossible to do so, by the way.

Selecting random individuals to represent the rest of the employees would be the best thing you could do next. These individuals will be your sample audience.

Now, with the help of other statistical tests, you can calculate the average paid time off taken by an employee in your organization.

Here, the variable’s level of measurement will determine the type of statistical test you can use.

In short, how you measure a variable is considered a measurement level. There are four fundamental types of measurement scales, namely the nominal scale, the ordinal scale, the interval scale, and the ratio scale.

**You Might Also Like:** A Guide to Measurement & Data Scales.

## What Are the Four Measurement Scale Types?

Now that you understand the characteristics of a measurement scale, let’s take a closer look at each one and some examples of how they are used in statistics.

### Nominal Scale: The 1st Measurement Level

The nominal measurement scale is the first and weakest level of measurement among the four scales.

This scale, as the name suggests, is just a naming scale. Here, the variables are merely ‘named’ or tagged in no specific order.

For a nominal scale example, consider gender, race, religion, and political affiliation.

You cannot rank or measure them by any means.

### Ordinal Scale: The 2nd Measurement Level

On this scale, the variables are ‘tagged’ and can be arranged in a specific order. However, you cannot use this scale to determine the exact difference between each variable on a scale.

For example, consider the education levels one has to undertake (elementary school, middle school, high school, college, graduate school, etc.)

There’s an order to this ranking, but you cannot calculate the distinct difference between each level of education.

**Related: **Ordinal Vs. Nominal Scale.

### Interval Scale: The 3rd Measurement Level

The interval measurement scale is the second strongest measurement level among the four scales.

On this scale, each variable is tagged, arranged in an order, *and* has a specific interval with other variables. However, the only issue with this scale is that it fails to provide any sense of ratio between the variables.

The Celsius or Fahrenheit temperature scale would be an excellent example of it.

### Ratio Scale: The 4th Measurement Level

The ratio scale is the most informative of all four scales.

You’ll find equal intervals between each variable and a true zero point here. Meaning you can not only arrange the variables in a specific order, but you can also calculate the difference between them along with the actual zero value.

A ratio scale combines the nominal, ordinal, and interval scales.

An example of a ratio scale is the height and weight of an individual. Here, you’ll find equal intervals between each variable, with the added advantage of an absolute zero value.

## Characteristics Of A Data Measurement Scale

There are mainly four characteristics of a data measurement scale. These are:

### Identity

Identity refers to the unique essence or value used to measure each variable. These values can only be used in mathematical operations to identify a variable.

For example, you ask for a respondent’s gender, and the options are ‘Male’ or ‘Female.’

Here, you can use the variable to identify whether a respondent is male or female. However, you cannot perform any arithmetic operations on it.

*A nominal scale is used to measure such variables.*

### Magnitude

Magnitude refers to the ability to rank or order the variables.

For example, you’re ranking the contestants in a race.

You’ll start with assigning the contestants positions based on how they finished the race. Say, 1st, 2nd, or 3rd, and so on.

*An ordinal scale is used to measure such variables, as they consist of both: identity and magnitude.*

### Equal Intervals

An equal interval refers to the difference between any two particular numbers on the scale.

These intervals are always equal and cannot be measured using an ordinal scale.

For example, consider the previous scenario.

Assume that the individual in the 1st position completed the race in 20 seconds. The 2nd individual took 20.8 seconds, the 3rd individual in 30 seconds, and the 4th individual took 31.3 seconds.

Basically, there are no equal intervals between each position’s differences.

*An interval scale is used to measure variables that have identity, magnitude, and equal intervals.*

### Absolute Zero

Absolute zero refers to a true zero point on the scale.

This means that the zero point represents the complete absence of the measured variable.

For example, if you’re measuring weight using the kilogram scale, an object weighing 0 kg has no weight.

## Comparison – Four Measurement Scale Types

Here’s a go-to chart that you can refer to any time you like:

Offerings | Nominal | Ordinal | Interval | Ratio |

Can you establish a sequence of variables? | No | Yes | Yes | Yes |

Can you calculate the mode? | Yes | Yes | Yes | Yes |

Can you calculate the median? | No | Yes | Yes | Yes |

Can you calculate the mean? | No | No | Yes | Yes |

Can you calculate the difference between variables? | No | No | Yes | Yes |

Can you add and subtract two variables? | No | No | Yes | Yes |

Can you multiply and divide two variables? | No | No | No | Yes |

Is absolute zero present? | No | No | No | Yes |

### Conclusion

Measurement scales are an essential concept in statistics. This helps you determine the type of data you’re collecting and perform analysis on it.

Hence, it is essential to know the characteristics of each measurement scale, as they help you choose the appropriate statistical method for analyzing data.

To summarize this blog, nominal, ordinal, interval, and ratio are the four statistical measurement scales. Each scale has its own unique characteristics and purposes.

The nominal scale is the simplest and most basic, while the ratio scale is the most advanced and informative.

Always remember to choose the right measurement scale, as it will assist you in making accurate conclusions and predictions.

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