![]() Now that we have broken down the bread and butter of qualitative and quantitative data, it’s time to consider which type is better for data analysis. Which data type is better for data analysis? Continuous dataĬontinuous data is data that can be infinitely broken down into smaller parts or data that continuously fluctuates.Ī few examples of continuous data would be the speed of your train during the morning commute, the time you take to write an article, your weight, and your age. For example, the number of baseball players in a team born in Mexico is whole and discrete. ![]() This type of data consists of integers (positive and negative numbers, e.g., -100, 10, 100, and so on) and is finite (meaning it reaches a limit).Ī few examples of discrete data would be how much change you have in your pocket, how many iPhones were sold last year, and how much traffic came to your website today.Īnother important note is that discrete data can technically be categorical. Discrete dataĭiscrete data is just data that cannot be broken down into smaller parts. These categories are called discrete and continuous data. Quantitative data can be broken into further sub-categories. Quantitative data can be generated through: Thus, qualitative data identifiers can be subjective, making qualitative data analysis a complex process with numerous possibilities and structures. Likewise, we can also discuss what type of oak it's made of or what part of Italy it came from. If someone doesn't have specific details on the bookcase, they can assume that it's made of teak, not oak and that it has a rough surface, not smooth. With a bookcase, there are many ways to get different qualitative information from different observers. When discussing qualitative data, we talk about a specific object’s characteristics. Qualitative data is derived through qualitative analysis of the detailed information about the matter at hand. ![]() The following characteristics of this bookcase determine the quality of the information that's available to us about it: To better understand qualitative data, let's take the example of a bookcase. Surprisingly enough, identification numbers like a social security number (SSN) or driver’s license are also considered qualitative because they're categorical and unique to one person. Qualitative data can be generated through: Generating this data from qualitative research is used for theorizations, interpretations, developing hypotheses, and initial understandings. Qualitative data can be used to ask the question “why.” It is investigative and is often open-ended until further research is conducted. Instead, it is categorized based on properties, attributes, labels, and other identifiers. This data isn’t necessarily measured using hard numbers used to develop graphs and charts. ![]() Qualitative data is non-statistical and is typically unstructured or semi-structured. Now that we got the differences out of the way let’s dive into each type of data using real-world examples. The other type is subjective, interpretive, and exploratory. One type of data is objective, to the point, and conclusive. Qualitative data can be categorized based on traits and characteristics. ![]() Qualitative data is descriptive and conceptual. Quantitative data can be counted, measured, and expressed using numbers. What is the difference between quantitative and qualitative data? ![]()
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