When you’re working with data, it can feel like there are a million different ways to categorise. Much of the time, this comes down to whether the information is qualitative or quantitative in nature.
While both of these terms might seem like they’d be used almost interchangeably in the world of analytics and statistics, there’s actually quite a bit of nuance between them. Understanding the difference between qualitative and quantitative data can help you make sense of which pieces of information fall into which category and why that matters when analysing data sets.
What is qualitative data?
Qualitative data is information that is not quantifiable. This means that it is not measured in numbers and does not follow a numerical scale — for example, a person’s age, their hair colour and height. Instead, qualitative data is non-numerical data that describes or explains a person’s experience or opinion.
Qualitative data is subjective, meaning that it reflects the opinions, feelings, and experiences of the person who provided it. Qualitative data can be used in analysis to explain why people think or feel the way that they do, but it’s generally not used to make conclusions or generalisations about a larger group of people.
What is quantitative data?
Quantitative data is data that can be measured, is often numerical, and follows a scale. This can include things like the number of page views on a website, the amount of money made through a product, or the weight of an object.
Quantitative data can be used to make inferences about a larger group of people and is often used to make conclusions and generalisations about human behaviour in the population. It’s objective, meaning that each data point was measured without any opinion or feelings on the part of the person who measured it.
The difference between the two
Generally speaking, qualitative data is subjective and doesn’t follow a numerical scale, while quantitative data is objective and often numerical. When you’re analysing data, it’s important to keep these two terms in mind.
If you’re working with mostly qualitative data, it can be difficult to make generalisations about a larger group of people, whereas if you’re working with mainly quantitative data, it can be difficult to explain why people have certain traits. As opposed to quantitative research, qualitative research is more focused on understanding the opinions of those involved.
Quantitative studies look at the number of views and likes on a given video or the number of comments left on a blog post. Qualitative research looks at the feelings and motivations of those involved in an issue. By asking questions such as, “Why did you like this video?” or “Why did you share this article?” you can begin to understand what drives individuals to engage with certain content.
Types of quantitative research
Quantitative research is the most common type of research in psychology, economics, and other social sciences. It involves collecting and analysing large amounts of data. Quantitative researchers usually use statistical tests to analyse the data. They may also collect data by conducting surveys, observations, or field studies.
Correlation: Correlation research is a form of scientific research in which researchers measure the relationship between two or more variables. In correlational studies, the researchers look for correlations between two or more variables in order to determine whether one variable causes another. Correlation is the extent to which two variables are associated with each other.
Surveys: Survey research is often used to study consumer behavior, attitudes, or opinions. It can help organisations understand the needs and preferences of their customers and target groups, identify areas for improvement, or test marketing strategies.
Econometric: In econometric research, researchers use mathematical formulas and equations to calculate the relationship between two or more variables (such as money spent on advertising and sales). When this type of research is done properly, it can provide strong evidence for or against the existence of any relationship between those variables.
Types of qualitative research
Qualitative research focuses on the collection and analysis of data from qualitative sources. It can be analysed using various statistical techniques such as correlation and regression analysis, which can provide valuable insights into the underlying causes of human behaviour and experiences.
Case studies: Case studies are one of the most common types of qualitative research that explore the experiences of one person or a group of people. Typically, case studies explore a specific situation or decision, and they focus on the thoughts and feelings that went into that situation or decision.
Focus groups: Focus groups are another common form of qualitative research and are often used alongside other types of research. Focus groups involve bringing a group of people together and discussing their thoughts and feelings about a specific topic. This can be helpful for understanding what people like and don’t like about a product or service.
Interviews: Interviews are a type of qualitative research that involves speaking one-on-one with people and exploring their thoughts and feelings about a specific topic. Interviews can be done in person or over the phone and are most often recorded so that the researcher can refer back to the conversation and write down specific quotes and ideas.
Participant observation: Participant observation is a type of qualitative research that is done over a period of time and involves observing the daily lives of people or a specific group of people. This type of research is often used in psychology and anthropology.
Which type is better for data analysis?
Generally speaking, quantitative data is better for analysis and making conclusions about a larger group of people.
Qualitative data is largely unstructured, and cannot be analysed through conventional methods. NoSQL databases does make collecting and storing qualitative data more manageable, but it’s still more difficult and time-consuming than its counterpart.
Quantitative data is better for analysis mainly because it can easily be standardised and put on a scale. This means that you can compare it across different situations, which is incredibly important when trying to make conclusions about a larger group of people. It also allows you to use statistical analysis, which allows you to make inferences and come to conclusions about a larger group of people.
Turn data into information and make better decisions
Insightful data analysis can help you discover the gaps in your digital marketing strategies, identify where there is room for improvement, and support training and development efforts.
The marketing strategists at LeftLeads can help you use data to look at past trends and predict future outcomes. For example, an analysis of sales data could show how prices affect demand over time. Our expertise can give you a deeper understanding of your business, allowing you to make more informed decisions that will ultimately lead to better results for your company.