Qualitative research data deals with human behavior, which might be harder to analyze than numerical quantitative research data. It may take a lot of time to read long transcripts and extensive field notes and decide what details matter and what can be skipped. Also, participants' feedback can be conflicting, and researchers should remain objective and try not to ignore viewpoints that don't fit their beliefs. One of the best methods of breaking down and organizing rich data is thematic analysis.

Thematic analysis groups the collected data into themes by tagging individual observations and quotations with appropriate codes (like labels or keywords). While coding, researchers review each text segment and give it a name that describes the data. As they look for themes, some codes can be collapsed or expanded.

The last step involves evaluating your themes — a belief, practice, need, or another phenomenon that appears multiple times across data findings and can be supported with multiple instances.^[1]

Thematic analysis can be performed using software, journaling (which involves manual coding and essential thought processes by researchers), or affinity diagramming techniques.