MMIntegrator™
AI-powered mixed methods integration engine for synthesising quantitative and qualitative findings into publication-ready outputs
AI-Assisted. Researcher-Led.
Learn moreThis tool provides: structured relationship mapping between your quant and qual strands — integration matrices, convergence maps, and meta-inference starting points — as scaffolding for your own synthesis. Your role: critically evaluate every mapped relationship, develop the synthesis in your own scholarly voice, and ensure all integration claims are independently justified in your thesis. Always follow your institution’s AI-use and disclosure policies.
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MMIntegrator™ is available to paid Commacad learners. Log in to your portal to access this tool.
Go to Learner PortalHow It Works
Three steps to a structured integration analysis of your mixed methods findings — scaffolding for your own scholarly synthesis.
01
Enter your findings
Input your quantitative findings (variable, finding, significance, effect size) and qualitative themes (theme, description, frequency) into structured tables. Add optional research context for a more tailored analysis.
02
AI builds your Integration Matrix
The AI maps each quant finding to each qual theme, classifies the relationship (Convergence, Expansion, Complementarity, or Divergence), and provides an academically grounded interpretation for each link.
03
Review your integration report
Receive a structured integration report: Integration Matrix, Convergence Map, Divergence Map, Interpretation Support paragraph, and Meta-inference starting point — for you to critically evaluate and develop into your own synthesis.