# Scale of Measurement in SPSS

In this section, we will learn about the **measure option** in SPSS. The measure is a property that is used to define the **label of measurement** used in a variable. Itâ€™s a very important property. The treatment of data and the choice of the right statistical test depend upon the kind of measure we have taken for the variable. If we select any **variable**, we can see **three** types of **measure**, as shown in the following diagram:

So we have **Nominal, Ordinal**, and **Scale** type of measurement. These are the different types of measures, but all in all, if we have to understand the measures, there are **four** types of **measurement** in social science research. They are referred to as **Nominal, Ordinal, Interval**, and **Ratio scales** of measurement. So we will quickly try to understand what they are and how we can use them for the SPSS. Measure and scale of measurement both are the same things.

The **Measure** is used to measure something or something that refers to the property that we have focusing upon. **For example**, if we are from an **anthropology background**, we must be interested in measuring **blood glucose levels**. If we are from a **psychology background**, we must be interested in measuring the **motivation** and **personality** of the individual. If we are from a **management background**, we must be interested in the **management process**. These are different types of variables. For studying the variables, we need to measure them. We need to exact quantify them. The **ordinal, nominal**, and **scale** measurements are used for **quantification**.

### Scale of Measurement

The **Scale of measurement** refers to the measurement scales that can be used for measuring any **socio** or **psychometric property** or any variable that we are studying. All the scales of measurement can be categorized into **two parts**. The first one is a **Categorical scale** of measurement, and the second one is a **Continuous scale**.

The **Categorical scale** is also known as **Discrete scales**. So we need a categorical scale to measure the categorical variable. The **categorical variable** comes into two formats as **Nominal** variables and **Ordinal** variables.

So, **Nominal variables** are those variables that come in the format of **perfect** categories or **mutually exclusive** categories. It means if we are part of one category, we cannot be part of another category. **For example**, the **Gender** variable has two labels **male** and **female**. So if we are part of one category, we are already excluded from other categories. So these categories are mutually exclusive. Thatâ€™s why in SPSS, this scale has been shown through a **Venn diagram** with different colors like this:

In the above diagram, we can see a Venn diagram of a **nominal scale** with three different colors **green, blue**, and **red**, indicating the mutual exclusiveness of the categories that we are defining.

If we look at the **Ordinal variables** in the following diagram, we can see three different colors again, but they seem to be ranked order like bars.

So **Ordinal scale** refers to the scale in which the variables can be categorized, but they can also be ranked order. **For example**, the **height** of a student in the class can be measured in the **meters**. So, in this case, it will be a **continuous measure**. But if we measure the **height** of a student in the class as **short** height, **medium** height, and **tall** height, we can arrange short, medium, and tall in **ascending** or **descending** order. So **ordinal scales** are basically those nominal scales that can be **ranked order**. **For example**, the height of a student, socioeconomic status.

There are **two** types of **Continuous scales**, which are **Interval** scale and **Ratio** scale. **Interval scale** refers to the liquor type of scale which ask for the opinion on certain topic and responses vary from **Strongly disagree** to **Strongly agree**, and in between, we can have other options. So we will define a value of **0** to **strongly disagree**. We will also define **1 2**, and **3** as **undecided** and **4** to **strongly agree**. It is known as the **interval scale** because the idea is that the measurement between two points of the scale is the same throughout the scale. So if the value between **0** to **1** is **x**, it means the value between **1** to **2, 2** to **3, 3** to **4** is also **x** like this:

So, this is a scale that divides our entire measurement into an equal number of parts, which typically happens in the liquor scale. So that is our **interval scale**. This scale superficially seems that it has a 0, but truly, it does not have an absolute zero. **For example**, suppose we measure the **attitude** of individuals or **intelligence** of the individual or **personality** treats of the individual. In that case, it is impossible for us to find the person who has zero personality treats or 0 attitudes or 0 motivations. However, we are assigning a 0 value here. So whatever the case may be, we are not going to have an individual who is 0 in terms of the trade that we measure, especially when they are the **social trades** like **personality, attitude, motivation**, and **leadership**. So the internal scale has one limitation, i.e., the lack the absolute zero.

This thing is available in the case of a **Ratio scale**, which contains an absolute zero. So **absolute zero** is the point where the movement among the ion stops, and we have the 0 temperature at that point. So absolute zero means the property that we are measuring is absent. **For example**, if we measure blood glucose level, and if we say that **blood glucose level** was **0**, it means blood glucose was totally **absent**. Similarly, if we measure the **income** and report income as **zero**, it means the person had **no income**. It means absolute zero is possible in case of a ratio scale.

Now in SPSS, if we look at the **processor**, we will see **nominal, ordinal**, and **scale** variables.

In SPSS, for all practical purposes, it combines the **Interval** and **Ratio scale** into one and called **Scale variable**. We can see the meter scale kind of symbol for the scale variable, so it is showing that itâ€™s a **quantitative variable**. However, the **quantitative variables** are either **interval** variables or **ratio** variables.