# Types of Data

Data is the most powerful force in the world today. When used correctly, it has the potential for making revolutionary changes. Hence, there is an imminent need to understand what data is and the various types of data.

## Data

In the simplest terms possible, Data is any kind of information. Yes, that’s it. There is no obligation for data to be useful to us. You could just write down the times at which the pressure cooker’s whistle goes off and that would be considered data. It’s that simple!

## Hierarchical view of Types of Data

Now comes the fun part. Take a look at the hierarchy below.

Seems a little complex? Guess what? It’s not! Let me show you how! Every possible kind of data can be classified under one of the following nodes in the diagram. Let us now go through each of these nodes:

## Quantitative Data vs Qualitative Data

An easy trick I use to remember the differences between these two is to separate the first 2 syllables from the rest of the word. That would leave us with “Quali” and “Quanti”. “Quali” suggests Quality. Qualitative data deals with types and categories of data. Categorical data answering questions like how? and why? fall under this category. “Quanti” suggests Quantity. Quantitative data deals with numbers. Numeric data answering questions like how many? and how much? fall under this category.

## Types of Qualitative Data

#### Nominal Data

Categorical data that cannot be compared and has no order falls under this category. For example, consider gender. Gender is categorical data and Female, Male, Transgender are its categories. These categories cannot be compared to each other – No category is greater than the other. Another good example is the jersey numbers on the backs of cricketers. They only serve the purpose of differentiating between the players and in no way reflect their skills in the game.

#### Ordinal Data

Categorical data that can be compared and ordered in a meaningful manner falls under this category. For example, consider grades. Grades are categorical data and A, B, C, D are its categories. Assuming the conventional notation, these grades can be ordered in a meaningful way – A>B>C>D.

## Types of Quantitative Data

##### Discrete Data

Any numeric variable that is discrete or distinct and can be counted is called Discrete data. For example, the number of integers between 1-10 is discrete, i.e., it can be counted. It is a finite value -There are 10 integers between 1-10.

##### Continuous Data

Any numeric variable that is continuous and can be measured is called Continuous data. For example, the number of rational numbers between 1-10 is continuous, i.e., it can be measured. It is an infinite value – There are infinite rational numbers between 1-10 (1.23, 5.989, and so on).

### Types of Continuous Data

##### Interval Data

Just like ordinal data, interval data is also ordered. However, unlike ordinal data, interval data requires the presence of equal intervals between adjacent categories. For example, consider temperature. The difference between 5 degrees and 10 degrees is the same as 105 degrees and 110 degrees.

##### Ratio Data

Ratio data has all the features of Interval data along with something called a True Zero Point.  For example, consider weight. The difference between 5kg and 10kg is the same as 105kg and 110kg. Moreover, 0kg would mean an absence of weight. But 0 degrees does not mean there is no heat.

## Exercise

Let’s how well I trained you! Categorize the following variables into one of these categories – Nominal, Ordinal, Interval, Ratio.

1. The Marital status
2. The positions finished by players in a 100-meter sprint
3. The results of IQ scores
4. The Age of a person
5. The Year

## Statistical Tests and Measures

For any of you who are willing to learn more, here’s the section for you. I have displayed brief definitions on the statistical tests associated with the variables and sources to learn more about them.

#### ANOVA Test

Short for Analysis of Variance, it is used as a significance testing method to see how Nominal variables affect a Quantitative Variable. This is a good place to dive a little deeper into this – Here

#### Chi-Square Test

This is also a significance testing method that uses the difference between the expected and observed values to make conclusions when the variables at hand are Qualitative. This is a good place to learn more about it – Here

#### Correlation

This is a statistical measure that tells us the relationship between variables and how they affect each other. It can be used to understand the relationship between the interval and ratio data. For a more mathematical understanding of this, refer here.