User Experience Strategist
How do I analyze my results?
Qualitative analysis often requires a significant time commitment, due to the need to transcribe material and then apply a variety of analytic strategies, which can include coding, writing memos, and “connecting strategies” like narrative analysis (Maxwell 2013).
When should I start?
It is easy for material to pile up and become overwhelming; analysis shouldn't wait until data collection is complete. You can begin analysis after completing your first interview or observation, and should continue it throughout the research process (Maxwell 2013; Braun and Clarke 2013).
How should I do it?
You should consult works that describe and demonstrate analytic approaches in detail. A few that could get you started:
Qualitative Inquiry and Research Design by Call Number: AC Frost Stacks H61 .C73 2013Explores key elements of each of five qualitative inquiry traditions: narrative research, phenomenology, grounded theory, ethnography, and case study. Relates research designs to each of the traditions of inquiry and compares theoretical frameworks, ways to employ standards of quality, and strategies for writing introductions to studies, collecting data, analyzing data, writing a narrative, and verifying results.
The SAGE Handbook of Qualitative Data Analysis by Call Number: AC Frost Stacks H62 .S25 2014Presents general analytic strategies used in qualitative research, to approaches specific to particular types of qualitative data, including talk, text, sounds, images and virtual data. Includes traditional analytic strategies such as grounded theory, content analysis, hermeneutics, phenomenology and narrative analysis, as well as coverage of newer trends like mixed methods, reanalysis and meta-analysis.
The Coding Manual for Qualitative Researchers by Call Number: AC Frost Stacks H62 .S343 2016Comprehensive assessment of different coding types, examples and exercises. Guides you through the multiple approaches available for coding qualitative data. Provides information about the method's origin, gives a detailed description of the method, demonstrates its practical applications, and sets out a clearly illustrated example with analytic follow-up.
Common processes and strategies
Data cleaning/reducing
Regardless of your specific approach, you will need to do some amount of reviewing your raw data and transforming it into a form suited to analysis. This may happen throughout the project, as you'll want to review your data in full, as well as in process.
Coding
- A process of categorizing information by applying codes to specific chunks of material. Codes can be a single word, or a phrase, that describes what is significant or interesting about a section of your material (Braun and Clarke 2013).
- Codes can be based on prior theory, specific concepts relevant to your concerns, characteristics or aspects, or terminology or keywords. They can be descriptive or interpretive, and may use participants’ own words, as well as your own (Asher and Miller 2016).
- You can use a combination of approaches with codes, both applying pre-developed codes based on your research questions or hypotheses, and being open to new themes and relationships that emerge from the material (Taylor, Lewins, and Gibbs 2010).
Memos
- Making notes of your own perceptions of and analytic thinking about your data, throughout the process, both in order to trace the shifts in your own approaches and understanding and to spur deeper investigation (Maxwell 2013).
- More in-depth than codes, and can be used as a form of pre-writing for your final analysis (Braun and Clarke 2013)
Connecting Strategies
- Narrative analysis is a type of analytic strategy that focuses on the material holistically, focusing on the relationships between different elements and the entire “story” in context (Maxwell 2013)
- Other approaches include thematic analysis, which works to identify broader, unifying concepts that are illustrated or demonstrated across the data (Braun and Clarke 2013)
How long will this take?
This depends on the specifics of your project, but generally, transcribing and analyzing your data will take a long time. Asher and Miller (2016) describe a four-to-one relationship: four hours of transcription needed for one hour of interview material.
For data analysis, Braun and Clarke (2013) recommend allocating over half of the total research project timeline to data analysis, in a timeline with overlapping stages.