What is a key difference between qualitative and quantitative data analysis?

The statement that qualitative analysis interprets themes while quantitative analysis uses statistics highlights a fundamental distinction between the two types of data analysis.

Qualitative analysis is primarily focused on understanding the meaning and context behind the data. This involves interpreting themes, patterns, and narratives that emerge from non-numerical data such as interviews, open-ended survey responses, and observations. The aim is to gain insights into people's thoughts, feelings, and behaviors, providing a rich, contextual understanding of the subject being studied.

On the other hand, quantitative analysis deals with numerical data and employs statistical methods to test hypotheses, identify relationships, and make generalizations based on the data. This approach relies on structured data collection techniques (like surveys with closed-ended questions) and often aims to establish relationships between variables or measure outcomes in a measurable way.

Thus, the emphasis on theme interpretation in qualitative analysis versus the reliance on statistical analysis in quantitative research captures the core difference in methodologies, objectives, and approaches used in handling different types of data.