In our increasingly digital world, data is created faster than ever before, offering priceless insights for companies around the world.
However, it’s important to understand what we’re collecting and how that data is managed and classified. Companies must also be able to interpret the data and turn it into information that can be used in order to improve operations, increase sales and grow the business.
At Bocasay, our offshore outsourcing company, dealing with data is part of our daily routine. In this article, we help you understand and analyze the different available types of data categories.
Why are data types important?
Data types are important because they are essentially attributes of data that inform a computer system how to interpret its value. Understanding the different data types helps users choose the type that fits their needs and goals.
When working with datasets, data scientists use data types to determine which statistical analyses can be applied to the data in order to achieve the best results.
Understanding data types is also essential for successful exploratory data analysis (EDA), which is one of the key elements of any machine learning project. To summarize, a data type represents a means of classification, indicating what kinds of mathematical operations and analyses are needed in order to transform it into valuable information.
Within machine learning, knowing the appropriate data types for independent and dependent variables is the basis for choosing an appropriate data analysis method.
Improperly identified data types can yield incorrect or useless information.
Different Data Types
Today, business revolves around data, and most companies use data in order to create and launch campaigns, formulate strategies, launch products and services…
There are several categories of data, which can be grouped in different ways. Today, Bocasay tackles the definition of qualitative and quantitative data. Here are the main types of data:
- Nominal data.
- Ordinal data.
- Interval data.
- Ratio data.
Qualitative and Quantitative Data
Qualitative data ⭐
Qualitative data can be observed and recorded. This type of data is not numerical in nature. They are collected through observation methods, individual interviews, focus groups, and other similar methods.
In statistics, qualitative data is also called categorical data. Data that can be classified according to the attributes and properties of objects and phenomena. In other words, this type of data is categorical because it is difficult to count or measure numerically.
🔎 Qualitative data collection methods:
- Individual interviews.
- Focus groups.
- Observational processes.
- Longitudinal studies.
- Case studies.
Quantitative data 📈
As quantitative data can be represented numerically, and counted, it allows, among other things, a statistical analysis of this type of data. That’s why this data type is also called numerical data. Numerical data has the capacity to answer questions like “how much? ” and “how often? “.
Quantitative data can be used for statistical manipulation and is often displayed though various graphs and tables such as:
- Bar graphs.
- Scatter plots.
- Pie charts.
- Line graphs.
For example, phone prices, computer memory, and people’s height and weight are all examples of quantitative data.
🔎 Quantitative data collection methods:
- Longitudinal studies.
- Individual interviews.
𝔹𝕠𝕠𝕤𝕥 𝕪𝕠𝕦𝕣 𝕓𝕦𝕤𝕚𝕟𝕖𝕤𝕤 𝕨𝕚𝕥𝕙 𝕥𝕙𝕖 𝔹𝕠𝕔𝕒𝕤𝕒𝕪 𝕠𝕗𝕗𝕤𝕙𝕠𝕣𝕖 𝕠𝕦𝕥𝕤𝕠𝕦𝕣𝕔𝕚𝕟𝕘 𝕔𝕠𝕞𝕡𝕒𝕟𝕪! 𝔼𝕩𝕡𝕖𝕣𝕥𝕤 𝕚𝕟 𝔸𝕘𝕚𝕝𝕖 𝕀𝕋 𝕡𝕣𝕠𝕕𝕦𝕔𝕥𝕚𝕠𝕟, 𝕨𝕖 𝕕𝕖𝕧𝕖𝕝𝕠𝕡 𝕎𝔼𝔹 𝕒𝕟𝕕 𝕄𝕆𝔹𝕀𝕃𝔼 𝕤𝕠𝕝𝕦𝕥𝕚𝕠𝕟𝕤 𝕗𝕠𝕣 𝕤𝕥𝕒𝕣𝕥𝕦𝕡𝕤, 𝕊𝕄𝔼𝕤 𝕒𝕟𝕕 𝕝𝕒𝕣𝕘𝕖 𝕒𝕔𝕔𝕠𝕦𝕟𝕥𝕤. 𝔻𝕠 𝕪𝕠𝕦 𝕙𝕒𝕧𝕖 𝕒 𝕢𝕦𝕖𝕤𝕥𝕚𝕠𝕟? ℂ𝕠𝕟𝕥𝕒𝕔𝕥 𝕦𝕤 𝕟𝕠𝕨!
Other Data Categories
All statistical techniques fall into two broad categories: descriptive statistics (which summarize the characteristics of a data set) and inferential statistics (which help make predictions based on that data).
Here is a list of the main measurement data:
👉 Nominal data
Nominal data is a type of qualitative data that groups variables into categories. You can think of these categories as names or labels.
They are purely descriptive, they have no quantitative or numerical value, and the different categories cannot be placed in any meaningful order or hierarchy.
Examples of nominal data:
- Hair color.
- Marital status (single, widowed, married).
👉 Ordinal data
Ordinal data is a type of qualitative data that groups variables into descriptive categories. A distinguishing feature of ordinal data is that the categories they deploy are arranged in a sort of hierarchical scale, such as top to bottom. At the level of measurement, ordinal data are second only to nominal data in complexity, but are still relatively simple.
Ordinal data helps to give a rough idea, but they are ultimately imprecise measures. Nevertheless, ordinal data are a good way to “get started” when you have a general measure in your sample group and do not require fine tuning.
Examples of ordinal data:
- Opinion, feedback on a scale of 1 to 10.
- Economic status (high, medium, and low).
- Education level (higher, secondary, primary) …
👉 Interval data
Interval data is defined as the type of data measured along a scale with regularly spaced points. It is always displayed as a number or by using numerical values where the distance between the two points is normalized and equal.
This type of data cannot be multiplied or divided, but can be added or subtracted. Interval data is measured on an interval scale, it is a type of quantitative data, and therefore can be analyzed with the same methods.
Examples of interval data are:
- Temperature (Farenheit, Celcius).
- pH Levels
- IQ tests…
👉 Ratio data
Ratio data are a form of quantitative data. They measure variables on a continuous scale with equal spacing between adjacent values.
Although they share these characteristics with interval data, ratio data are distinguished by their “absolute zero.”
In other words, a zero measurement on a ratio scale is an absolute value. Ratio data can never be negative. This property is important because it allows us to apply all sorts of mathematical operations (addition, subtraction, multiplication, and division) when performing statistical analysis.
Examples of ratio data:
- The number of vehicles owned over a period of time.
- The number of years of work experience.
- Driving speed.
Each data type serves a different purpose or functionality. Understanding the benefits and purposes of each type of data will help your company collect data, analyze it and make strategic decisions that have a positive impact on your business.