Qualitative data is collected by interviewing and observing. Quantitative data is analyzed using statistical analysis, while qualitative data is analyzed by grouping it in terms of meaningful categories or themes.
If you’re considering a career in data—or in any kind of research field, like psychology—you’ll need to get to grips with two types of data: Quantitative and qualitative. Quantitative data is anything that can be counted or measured; it refers to numerical data. Qualitative data is descriptive, referring to things that can be observed ...
Let’s summarize the key differences before exploring each aspect in more detail: Quantitative data is countable or measurable, relating to numbers. Qualitative data is descriptive, relating to language.
Qualitative data analysis. With qualitative data analysis, the focus is on making sense of unstructured data (such as large bodies of text). Given that qualitative data cannot be measured objectively, it is open to subjective interpretation and therefore requires a different approach to analysis.
Ordinal data is when the categories used to classify your qualitative data fall into a natural order or hierarchy. For example, if you wanted to explore customer satisfaction, you might ask each customer to select whether their experience with your product was “poor,” “satisfactory,” “good,” or “outstanding.”.
A big advantage of quantitative data is that it’s relatively quick and easy to collect, meaning you can work with large samples. At the same time, quantitative data is objective; it’s less susceptible to bias than qualitative data, which makes it easier to draw reliable and generalizable conclusions.
However, qualitative data can be harder and more time-consuming to collect , so you may find yourself working with smaller samples. Because of its subjective nature, qualitative data is also open to interpretation, so it’s important to be aware of bias when conducting qualitative analysis. 6. Key takeaways.