--- name: user-research-analysis description: > Analyze user research data to uncover insights, identify patterns, and inform design decisions. Synthesize qualitative and quantitative research into actionable recommendations. --- # User Research Analysis ## Table of Contents - [Overview](#overview) - [When to Use](#when-to-use) - [Quick Start](#quick-start) - [Reference Guides](#reference-guides) - [Best Practices](#best-practices) ## Overview Effective research analysis transforms raw data into actionable insights that guide product development and design. ## When to Use - Synthesis of user interviews and surveys - Identifying patterns and themes - Validating design assumptions - Prioritizing user needs - Communicating insights to stakeholders - Informing design decisions ## Quick Start Minimal working example: ```python # Analyze qualitative and quantitative data class ResearchAnalysis: def synthesize_interviews(self, interviews): """Extract themes and insights from interviews""" return { 'interviews_analyzed': len(interviews), 'methodology': 'Thematic coding and affinity mapping', 'themes': self.identify_themes(interviews), 'quotes': self.extract_key_quotes(interviews), 'pain_points': self.identify_pain_points(interviews), 'opportunities': self.identify_opportunities(interviews) } def identify_themes(self, interviews): """Find recurring patterns across interviews""" themes = {} theme_frequency = {} for interview in interviews: for statement in interview['statements']: theme = self.categorize_statement(statement) theme_frequency[theme] = theme_frequency.get(theme, 0) + 1 # Sort by frequency // ... (see reference guides for full implementation) ``` ## Reference Guides Detailed implementations in the `references/` directory: | Guide | Contents | |---|---| | [Research Synthesis Methods](references/research-synthesis-methods.md) | Research Synthesis Methods | | [Affinity Mapping](references/affinity-mapping.md) | Affinity Mapping | | [Insight Documentation](references/insight-documentation.md) | Insight Documentation | | [Research Validation Matrix](references/research-validation-matrix.md) | Research Validation Matrix | ## Best Practices ### ✅ DO - Use multiple research methods - Triangulate findings across sources - Document quotes and evidence - Look for patterns and frequency - Separate findings from interpretation - Validate findings with users - Share insights across team - Connect to design decisions - Document methodology - Iterate research approach based on learnings ### ❌ DON'T - Over-interpret small samples - Ignore conflicting data - Base decisions on single data point - Skip documentation - Cherry-pick quotes that support assumptions - Present without supporting evidence - Forget to note limitations - Analyze without involving participants - Create insights without actionable recommendations - Let research sit unused