Data is a collection of facts, numbers, observations, and opinions. Statistic researchers use this information to think, calculate, and make conclusions. Because data can be broad, it is divided into two types of data: qualitative and quantitative. Although both types are different, they still work together to help conduct research.
In this article, you will learn the different types of data in statistics. This beginner's guide covers qualitative and quantitative, their subtypes, their differences, and their importance in research.
What are the two main types of data?
The two main types of data in statistics are qualitative and quantitative. Both inputs serve as the foundation of research in every industry, including science, health, engineering, business, and much more. Without the two, it will be hard to conduct research that can help in understanding, analysing, and concluding research.
Quantitative and qualitative have differences; quantitative is about numbers, and qualitative is about descriptions. While they serve different purposes, together, they help achieve a common goal: to provide a comprehensive understanding of a subject by combining numerical analysis with descriptive insights.
Qualitative
Qualitative is one of the two types of data that focuses on descriptions and provides information about meanings, characteristics, opinions, and decisions. Since it uses descriptive words, this type of data can answer questions like what, where, when, why, who, and how. However, it cannot answer questions that ask how many or much because it does not deal with numbers.
Qualitative assessment is important because it gives a deeper understanding of certain subjects that can help guide decision-making, conclusions, and planning. Now, to gather this information, professionals such as scientists, doctors, and researchers use methods such as interviews, focus groups, surveys, observations, and reviews.
Under qualitative, there are also two types of data: nominal and ordinal. Both types are still descriptive. However, they differ in how they organise the information. Read down below for an in-depth explanation of nominal and ordinal data.
Nominal
Nominal data categorise information by label or description but not by order or rank. This indicates that the information serves to identify, analyze, or name categories. These types of data can be distinct or subjective as they are based on facts, experience, opinion, and observation. Examples of nominal information are:
- Gender: Gender categories like Male, Female, and Non-binary are labels used to classify individuals. There is no inherent ranking or order between these categories; they simply identify different groups.
- Sexuality: Categories such as Heterosexual, Homosexual, Bisexual, and Asexual are labels that describe someone's sexual orientation. These types of data are distinct but not ordered or ranked in any way.
- Colour: Colours like red, blue, and green are categories that describe different hues. Again, there is no order or ranking between colours; each one is a unique label.
- Yes/No: This is a binary choice that identifies two categories or labels. There is no ranking, just two distinct options: Yes or No.
Ordinal
Ordinal data is arranged based on ranking or order. This grouping makes ordinals helpful for learning how things compare to each other. However, each rank's difference or "distance" isn't always the same. To understand more, read below the examples:
- Ratings: Categories like Poor, Fair, Good, Very Good, and Excellent show a ranking from lowest to highest, but the difference between these types of data isn’t the same.
- Educational level: High School, College, and Graduate School are ranked from lowest to highest education, but the time or effort between each level can vary.
- Income levels: Categories like Low, Medium, and High show income levels in order, but the exact variation between them can differ.
- Likert scale: Choices like Strongly Disagree, Disagree, Neutral, Agree, and Strongly Agree are ordered by agreement, but the difference between the types of data may not be the same.
- Rankings: 1st place, 2nd place, and 3rd place are ranked based on performance, but the difference between 1st and 2nd place might not be the same as the difference between 2nd and 3rd.
Quantitative
The quantitative type is information that uses numbers to describe things. It answers questions like how many or how much. Since it's based on numeric values, quantitative is usually objective. This means that the inputs are clear and not influenced by personal opinions. However, personal choices or mistakes can sometimes affect it, such as when someone makes an incorrect measurement or counts wrong.
These types of data are important. They give clear, measurable information. With numbers, it's easier to compare things and make decisions. Gathering quantitatives has some similarities with qualitative, but not all of them. This includes surveys, experiments, counting, and tools or devices.
Just like qualitative, quantitative also has subtypes. These are discrete and continuous data. Both types provide numerical information. But they differ in how you measure and represent the numbers.
Discrete
Discrete data is information about the quantity of a subject. These types of data are measurable by counting and can only be distinct in whole numbers. This means you cannot break numbers down into fractions or decimals. Below are the examples of discrete information.
- People: People are counted as whole numbers because they cannot be represented in fractions or decimals. For example, you can have 1, 2, or 3 people, but never 1.5 or 2.3 people.
- Floor levels: Multiple floor levels in a building are counted as whole numbers. You can have one storey, two storeys, and so on and so forth, but you can’t have 2.5 floors. This is taken as discrete input because the levels are distinct and counted without fractions.
- Food items: When counting whole apples, you can count 1, 2, or 3 apples. However, if you include half an apple, it transitions into continuous information because you're now counting fractions or parts of the whole item, not just the whole units.
Continuous
Continuous data, in contrast, includes any value within a range and allows measurement to any degree of precision. These types of data deal with measurements and can have an infinite number of possible values. For example:
- Height: Height is a type of length measurement, typically expressed in units like feet or centimetres. Often, height isn’t a whole number. It includes in-between values, such as 5 feet 8 inches. Expressing height in fractions of an inch or centimetres enables precise values, which classifies it as continuous data.
- Temperature: Temperature can be measured to any decimal point, such as 72.5°F or 72.55°F. Measuring temperature to various decimal places qualifies it as continuous data.
- Weight: Weight is a continuous statement because it can be calculated with great precision. For example, a person might weigh 150 pounds, 150.5 pounds, or 150.75 pounds. The value can include decimal points, making it a variable that can take many values between whole numbers.
Conclusion: Data is the key to progress
Qualitative and quantitative are important in statistical research. Together, they help researchers learn from the past, understand the present, and predict the future. Using the two types of data helps experts make better decisions and find solutions to problems in science, health, and many other fields. By combining both, they can understand things more clearly and make progress in many areas.
Just like research, learning is a never-ending process. The College of Contract Management believes in always learning and growing. The college aims to help students keep up with new ideas and skills so they can do well in multiple industries, both now and in the future.
