Quantitative and Qualitative Data: Differences and Benefits
Qualitative Data
Quantitative Data
What is it?
Descriptive, language-based data with categorization [5]
Number-based data [5]
What kind of information does each type of data provide?
Qualitative data (qualities/types) answers the question “what type/category” and give us information about the different types of attributes [5].
Counts and/or numeric variables; quantitative data answers the question “how much?” or “how many?”
Quantitative data gives us information about the quantity of an attribute [5].
How is data analyzed and interpreted?
Qualitative data is more open to interpretation by the individuals analyzing it.
Qualitative data itself is often in the form of text. Methods of analyzing this kind of data might vary between different people, leading to different conclusions based on their perspectives [6].
Quantitative data has more standardized analysis methods and reporting standards.
Analyzing quantitative data is often very methodical and sequential. It involves statistical, or other (mathematical) methods that can be applied to identify and visualize trends and patterns in the data [7].
Benefits
Qualitative data is more often used to describe observations about complex phenomena that cannot be easily measured.
We get more details and insight, even if the conclusions aren’t as robust and have a lower applicability to wider populations.
More holistic in nature: can remove the need to categorize before collecting data, very flexible.
Qualitative data is analyzed in a way that actively considers the pre-existing perspective of the researcher!
Collecting this data is often simple, and there are established routines in the scientific community for collection of this kind of data.
Many different methods of statistical analyses can be used on the same set of data to show different ideas.
More often leads to “black and white” interpretations. However, it is important to note that outcomes from these analyses may lead to different conclusions based on the type of statistical analysis used.
For example, this SfE character wants to understand the satisfaction of the reader with this learning module. Hover over each box below to see an example of qualitative and quantitative data that can be collected through a survey to answer the same research question. Notice how each has its strengths and weaknesses, and can be used together to achieve the goal.
Survey question: Explain what aspects of the learning module you enjoyed the most.
Collecting qualitative data, in this case, allows us to get a holistic view of what readers enjoyed the most, which can be incorporated into future lessons. This type of data is less restrictive, more open ended, and therefore open to interpretation. This factor also makes it harder to analyze due to the variability in answers that can be given.
Survey question: On a scale of 1-10, how satisfied were you with this learning module?
This is a quantitative measure of the readers experience with the learning module. It's helpful as it's a simple way to get information, and its analysis is more straightforward. However, it is less descriptive, and the data is less specific.
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