Categories of Data

Quantitative Data - This type involves numerical measurements and can be counted or measured, providing amounts, sizes, or other numeric values. Examples include the number of books you read last year or the distance you run each morning. This data can be easily analysed statistically to identify patterns or trends.

Qualitative Data - This type captures descriptive attributes that are not numerical. It could include the genres of books you enjoy or the colours you prefer for your clothing. This data is about characteristics and qualities that help us understand 'what' something is like, rather than 'how much' or 'how many'.

Sub-Categories

Quantitative

Discrete Data - A sub-type of quantitative data, discrete data can only take certain values. For example, the number of students in a class is discrete because you can't have half a student. It is counted in whole numbers.

Continuous Data - Another sub-type of quantitative data, continuous data can take any value within a range. Examples include the weight of a person or the temperature outside. This type of data is measured and can have decimal points.

Qualitative

Nominal Data - A sub-type of qualitative data, nominal data is used for labelling variables without any quantitative value. Examples include types of fruit or names of countries. There is no inherent order in nominal data.

Ordinal Data - Another sub-type of qualitative data, ordinal data deals with order or rank. For example, movie ratings (like 1-star, 2-star, etc.) or positions in a race (1st, 2nd, 3rd, etc.). Though the data is qualitative, it shows a clear order or ranking.

Example

Let’s consider an example from a retail company that wants to analyse its sales data.

Data analysis usually starts with quantitative data. The company can look at the total number of products sold (discrete data) and the revenue generated each day (continuous data). By analysing this data, they can identify trends such as the most popular products or the best sales days.

This can enhanced or supplemented with insight from qualitative data. The company might collect customer feedback in the form of reviews. These reviews can be categorised into nominal data, such as different types of feedback (e.g., compliments, complaints). They may also use ordinal data, where customers rate their shopping experience on a scale from 1 to 5 stars.

By using both types of data, the company can gain a comprehensive understanding of their sales performance. Quantitative data helps them see the big picture in terms of numbers and trends, while qualitative data provides deeper insights into customer satisfaction and areas for improvement.

Types of Data

Want More Resources Like This?