Data Analysis + Synthesis
Start
Create analysis/synthesis schedule + strategy
- The Project Lead determines the analysis strategy and shares it via email or Slack with the team
- Larger projects may require an Analysis Strategy meeting to align goals and delegate tasks
- Who is doing what aspect(s) of the analysis?
- It is best practice to have more than one set of eyes on any dataset.
- Split out a portion of interviews for another researcher to analyze
Plan
Prepare for analysis analysis
Prepare interview data
- Download interview data from Qualtrics and Save As, adding "RAWDATA" to the file name. Save in file path: DORIS Box> Projects> ProjectName> Challenge Defining> Analysis> Qualitative Analysis
- Open the dataset in a blank spreadsheet and SaveAs
- Delete irrelevant columns
- Prep for Maxqda
- Save As .xlsx
Execute + Monitor
Analyze data
Qualitative Data
- Identify the important questions to answer
- Code segments, assigning value to long-form qual answers
- Use a Mural board, spreadsheet or go analogue to group and organize data based on themes. Label groups and name relationships between them when known
Analyze quantitative data
Interpret usability data
Synthesize data
Conduct individual synthesis
- Synthesis must be external. Use Mural, a whiteboard, paper, etc.
- Write what stands out
- What seems like the biggest challenge?
- What are some other top challenges people experience?
- Identify under 10 challenge categories that stand out and show how they are related
Considerations:
Conduct project team meeting for group synthesis
The Ops Lead facilitates the team through discussion of their individual analysis and synthesis in order to organize the insights into the Challenge Defining document.
Finish
Write document outline
The document outline comes out of the synthesis meeting. The Ops Lead converts the team's synthesis into the structure for the Challenge Defining document.
Outline Components
Once the initial outline is complete, the Ops Lead shares it with the team for review and addition of supporting data. All team members are expected to contribute quotes, insight, or quantitative supporting data depending on what analysis they did.