Researchers use qualitative methods to understand users in depth. These methods answer open-ended “why” questions, like “Why do users behave this way?” Data is collected by observing or directly speaking with users. For example, in a contextual inquiry, researchers watch users interact with a product and ask follow-up questions to understand their actions. The analysis is usually non-mathematical and based on patterns in behavior or feedback. Qualitative methods are good for exploring why a problem exists or how to solve it. But the results can be skewed by poor questions, misunderstandings, or researcher bias.

Quantitative research collects numerical data and analyzes it using statistics. It’s used to measure user behavior and attitudes indirectly, through tools like surveys, experiments, or analytics. These methods answer questions like “What do users do?”, “How many use this feature?”, or “How often does this error occur?” Quantitative data is useful for spotting trends, testing hypotheses, or comparing options. But it doesn’t explain why users behave a certain way. It lacks the depth of qualitative research.