Qualitative data analysis
Qualitative data analysis is the systematic process of making sense of non-numerical data, such as interviews, open-ended answers, observations and documents, by organizing, coding and interpreting it to find patterns, themes and explanations.
How it works
Most approaches follow a similar arc. The analyst becomes familiar with the data by reading it repeatedly, codes meaningful passages, groups codes into candidate themes, reviews those themes against the data, defines and names them, and writes up the findings with evidence. That arc follows the six phases of reflexive thematic analysis set out by Virginia Braun and Victoria Clarke.
Other approaches suit other questions: grounded theory builds a theory from the data, narrative analysis studies whole stories, and discourse analysis studies how language is used. Whatever the method, quality depends on transparency: showing which data supports each finding and how the analysis reached it.
A worked example
Permission settings are the topic when a software company's research team interviews 28 IT administrators. Familiarization and coding produce 150 codes; theme work reduces them to 6. The report leads with "fear of locking myself out", supported by quotes from 19 of the 28 administrators.
Related
Automate this in Pulse
Familiarization, reading the data again and again, is where qualitative data analysis begins, and in Pulse it can start with the first interview, since each transcript is ready while the rest of the study is still in the field.