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# Data Types: Qualitative and Quantitative

Before we get into this topic, let us first understand what data attributes are. They are a characteristic of any information that sets it apart from other statistics. They are basically the features of a dataset.

There are two forms of data types:

1. Qualitative
2. Quantitative

As we go deeper into these two types, we will encounter more classifications.

Let’s start by defining what these terms actually represent.

When we mention qualitative data, you should be able to tell that qualitative means “quality” just by looking at the word. It’s an attribute because it includes values that express a quality or state.

This type of data is impossible to count or quantify. Gender, feelings, and so on are some examples.

Quantitative data, on the other hand, is concerned with quantity. This type of statistics is numerical in nature, which means you can count or measure them. Income, age, and so on are few examples.

Let us clarify this with a more general example. Consider feelings; you cannot quantify someone’s emotions. Because it is impossible to quantify how sad or happy someone is, we consider feelings to be qualitative data because they express quality. However, if you were to count how many people are sad or how many people are happy, you would be able to call it quantitative data derived from qualitative data.

## Types of Qualitative Data

When we delve deeper into qualitative data, we can further divide it into two types.

The following are the types:

### Nominal Variable

Nominal variables are figures that cannot be easily classified and ordered into hierarchies.

Flowers, for example, cannot have a hierarchical order ranking. You cannot say that lily is superior to rose. The same is true for colors, gender, race, country, and so on. Nominal variables are what they’re termed as.

On such variables, you can experiment four types of tests which are:

• McNemar
• Fisher’s Exact
• Cochran Q
• Chi-Square

### Ordinal Variable

Ordinal variables, on the other hand, can be ordered and scaled. It also possesses all of the properties of a nominal variable. Therefore, the ability to be able to measure ordinal variable and to be able to rank it in hierarchical order is a plus.

However, we should not assume that it can be numbered because it is still qualitative data!

So, if I were to create a Google form to collect feedback on, say, a webinar I hosted on Zoom application, I would insert the following question: “How informative did you find the webinar?” I’d also offer them the following marking options:

• A little informative
• Exceptionally Informative
• Not at all informative.

These options, which I just listed, are examples of ordinal variables.

On such variables, you can experiment four types of tests which are:

• Kruskal-Wallis 1-way
• Wilcoxon signed-rank
• Wilcoxon rank-sum
• Friedman 2-way ANOVA

It is important to note that both nominal and ordinal variables are non-parametric variables. The only difference between them is their ability to rank information based on their position.

## Types of Quantitative Data

Quantitative data can be classified into 2 types:

### Interval

We know both the order and the exact difference between the values in interval scales. For example, the difference between 20 and 10 degrees is at the same magnitude as the difference between 30 and 20 degrees.

### Ratio

In contrast, ratio data is an interval scale with a natural 0 point. This basically means that negative values cannot exist in ratio data. Height measurements in centimeters, meters, inches, and so on are an example of it.

Quantitative data can also be:

• Discrete: You can easily count discrete variables within a limited amount of time. You can, for example, count the money in your bank account or the number of times you drank a glass of water in a day. Essentially, if a variable is discrete, you will be able to count them, though it may take longer at times to count completely. They can only accept certain values that do not include decimal form.
• Continuous: Continuous variables, on the other hand, are “continuous” in nature, as the name implies. However, you can never completely count them because they are an on-going process. For example, if I asked you to tell me how many times you drank a glass of water in this century, you would be unable to do so because a century has not passed, so you would end up counting until you died. Continuous variables can have any value within a certain range of values.
• Continuous: Continuous variables, on the other hand, are “continuous” in nature, as the name implies. However, you can never completely count them because they are an on-going process. For example, if I asked you to tell me how many times you drank a glass of water in this century, you would be unable to do so because a century has not passed, so you would end up counting until you died. Continuous variables can have any value within a certain range of values.