Questionnaires
Design comprehensive surveys with 10+ question types for quantitative and qualitative insights.
Questionnaires - Structured Research
Questionnaires in OpinioAI enable you to conduct structured research with synthetic personas using a comprehensive suite of question types. Perfect for quantitative analysis, comparative studies, and large-scale insights.
Overview
OpinioAI's questionnaire system provides powerful tools for creating sophisticated surveys that can be answered by synthetic personas at scale. This feature is ideal for:
- Quantitative Research: Generate statistical data and measurable insights
- Comparative Analysis: Compare responses across different segments or personas
- Structured Data Collection: Gather consistent, organized responses
- Large-Scale Studies: Collect hundreds or thousands of responses quickly
- Statistical Analysis: Perform advanced analytics on response data
Question Types Available
1. Text Questions
Open-ended questions that allow for detailed, qualitative responses.
Single Line Text
- Use Case: Short answers, names, simple responses
- Example: "What is your primary occupation?"
- Best For: Demographic data, brief opinions, categorization
Multi-Line Text
- Use Case: Detailed explanations, stories, comprehensive feedback
- Example: "Describe your ideal shopping experience in detail."
- Best For: Qualitative insights, detailed feedback, storytelling
Text with Character Limit
- Use Case: Controlled-length responses, social media-style feedback
- Example: "In 280 characters or less, describe this product."
- Best For: Concise feedback, headline testing, brief reactions
2. Choice-Based Questions
Structured questions with predefined response options.
Multiple Choice (Single Select)
- Use Case: Exclusive choices, preferences, classifications
- Example: "Which age group best describes you?"
- 18-24
- 25-34
- 35-44
- 45-54
- 55+
- Best For: Demographics, preferences, categorization
Multiple Choice (Multi-Select)
- Use Case: Multiple applicable options, feature preferences
- Example: "Which features are most important to you? (Select all that apply)"
- Price
- Quality
- Brand reputation
- Customer service
- Convenience
- Best For: Feature prioritization, behavior patterns, multiple preferences
Dropdown Selection
- Use Case: Long lists of options, location selection, categorization
- Example: "Select your country of residence"
- Best For: Geographic data, extensive option lists, clean interface design
3. Rating and Scale Questions
Measure intensity, satisfaction, agreement, and preferences.
Likert Scale
- Use Case: Agreement measurement, attitude assessment
- Example: "Rate your agreement with this statement: 'This product meets my needs'"
- Strongly Disagree
- Disagree
- Neutral
- Agree
- Strongly Agree
- Best For: Attitude measurement, opinion polling, satisfaction assessment
Numeric Rating Scale
- Use Case: Satisfaction ratings, performance evaluation
- Example: "Rate your satisfaction with our customer service (1-10)"
- Best For: Performance metrics, satisfaction scores, comparative ratings
Star Rating
- Use Case: Product reviews, service evaluation, user experience
- Example: "Rate this product (1-5 stars)"
- Best For: Product feedback, service quality, user experience assessment
Slider Scale
- Use Case: Continuous measurement, precise positioning
- Example: "How likely are you to recommend this product? (0-100)"
- Best For: Net Promoter Score, precise measurements, probability assessment
4. Ranking Questions
Understand priorities and preferences through ordering.
Rank Order
- Use Case: Priority ranking, preference ordering
- Example: "Rank these factors in order of importance when choosing a smartphone"
- Price
- Camera quality
- Battery life
- Brand
- Storage capacity
- Best For: Priority assessment, feature importance, decision factors
Drag and Drop Ranking
- Use Case: Interactive ranking, visual preference ordering
- Example: "Drag these brands in order of your preference"
- Best For: Brand preference, visual ranking, interactive engagement
5. Grid Questions
Efficiently collect multiple ratings or responses in a matrix format.
Rating Grid
- Use Case: Multiple item evaluation, comparative assessment
- Example: "Rate each of these brands on the following attributes"
- Brands: Apple, Samsung, Google
- Attributes: Innovation, Value, Design, Reliability
- Best For: Brand comparison, attribute assessment, efficiency
Choice Grid
- Use Case: Multiple choice responses across several items
- Example: "For each product category, select your preferred brand"
- Categories: Smartphones, Laptops, Tablets
- Options: Apple, Samsung, Microsoft, Google
- Best For: Category preferences, brand mapping, systematic choices
6. Advanced Question Types
MaxDiff Analysis
- Use Case: Preference measurement, feature importance
- Example: "From this list, select the MOST important and LEAST important factors"
- Features: Price, Quality, Service, Convenience, Brand
- Best For: Feature prioritization, preference measurement, trade-off analysis
Van Westendorp Price Sensitivity
- Use Case: Pricing research, price optimization
- Questions:
- "At what price would this product be too expensive?"
- "At what price would this product be too cheap to trust?"
- "At what price would this product be expensive but still worth buying?"
- "At what price would this product be a good value?"
- Best For: Pricing strategy, market positioning, value perception
Image Choice
- Use Case: Visual preference, design testing, concept evaluation
- Example: "Which design do you prefer?" (with image options)
- Best For: Design testing, visual preferences, concept selection
Video Response
- Use Case: Rich media feedback, detailed explanations
- Example: "Watch this video and provide your feedback"
- Best For: Content evaluation, detailed feedback, multimedia research
Questionnaire Design Best Practices
Structure and Flow
Logical Organization
- Introduction: Brief explanation of the survey purpose
- Warm-up Questions: Easy, engaging questions to start
- Core Content: Main research questions organized by topic
- Demographics: Personal information questions at the end
- Conclusion: Thank you message and next steps
Question Sequencing
- General to Specific: Start broad, then narrow focus
- Easy to Difficult: Begin with simple questions
- Logical Grouping: Group related questions together
- Sensitive Questions Last: Place personal/sensitive questions at the end
- Randomization: Consider randomizing question or option order when appropriate
Question Writing Excellence
Clarity and Precision
- Simple Language: Use clear, everyday language
- Single Concept: Ask about one thing at a time
- Specific Terms: Define technical or ambiguous terms
- Appropriate Length: Keep questions concise but complete
- Cultural Sensitivity: Consider cultural context and language
Avoiding Bias
- Neutral Wording: Don't lead respondents toward specific answers
- Balanced Options: Provide balanced response choices
- No Assumptions: Don't assume prior knowledge or experience
- Objective Language: Use neutral, unbiased terminology
- Complete Options: Include all relevant response options
Response Quality Optimization
Option Design
- Comprehensive Coverage: Include all relevant response options
- Mutually Exclusive: Ensure options don't overlap
- Balanced Scales: Use balanced rating scales
- "Other" Options: Provide "Other" or "None of the above" when appropriate
- Logical Order: Arrange options in logical sequence
Engagement Techniques
- Varied Question Types: Mix different question formats
- Visual Elements: Use images and media when relevant
- Progress Indicators: Show completion progress
- Conditional Logic: Show relevant questions based on previous answers
- Interactive Elements: Use engaging question formats
Advanced Features
Conditional Logic and Branching
Skip Logic
- Purpose: Show questions only when relevant
- Example: "If you selected 'Yes' to owning a car, answer questions about your vehicle"
- Benefits: Improved relevance, shorter surveys, better experience
Display Logic
- Purpose: Show/hide questions based on previous responses
- Example: Show brand-specific questions only for users of that brand
- Benefits: Personalized experience, relevant content, efficient data collection
Piping
- Purpose: Insert previous answers into subsequent questions
- Example: "You mentioned [previous answer]. How satisfied are you with this choice?"
- Benefits: Personalization, context continuity, engagement
Randomization and Testing
Question Randomization
- Purpose: Reduce order bias, test question effects
- Application: Randomize question order within sections
- Benefits: Unbiased responses, methodological rigor
Option Randomization
- Purpose: Eliminate position bias in multiple choice questions
- Application: Randomize order of response options
- Benefits: Fair option presentation, reduced bias
A/B Testing
- Purpose: Test different question versions or formats
- Application: Show different question versions to different personas
- Benefits: Optimization insights, methodological improvement
Integration with Personas
Persona-Specific Customization
Demographic Matching
- Approach: Tailor questions to persona demographics
- Example: Age-appropriate language and references
- Benefits: Increased relevance, authentic responses
Experience-Based Questions
- Approach: Leverage persona backgrounds and experiences
- Example: Ask tech personas about advanced features
- Benefits: Deeper insights, expert perspectives
Cultural Adaptation
- Approach: Adapt questions for persona cultural backgrounds
- Example: Region-specific product references
- Benefits: Cultural relevance, authentic responses
Response Quality Assurance
Persona Consistency
- Monitoring: Ensure responses align with persona characteristics
- Validation: Check for consistency across similar questions
- Quality Control: Flag inconsistent or unrealistic responses
Characteristic Alignment
- Demographics: Verify responses match persona demographics
- Psychographics: Ensure responses reflect persona values and attitudes
- Behavioral Patterns: Check responses align with persona behaviors
Analysis and Reporting
Quantitative Analysis
Descriptive Statistics
- Frequency Distributions: Count and percentage of responses
- Central Tendency: Mean, median, mode calculations
- Variability: Standard deviation, range analysis
- Cross-Tabulation: Compare responses across different groups
Comparative Analysis
- Segment Comparison: Compare responses across persona segments
- Demographic Analysis: Analyze patterns by age, location, etc.
- Trend Analysis: Track changes over time or conditions
- Statistical Testing: Perform significance tests when appropriate
Qualitative Analysis
Text Analysis
- Thematic Coding: Identify common themes in open-ended responses
- Sentiment Analysis: Assess emotional tone of responses
- Content Analysis: Categorize and count response elements
- Quote Extraction: Identify representative or notable quotes
Mixed Methods
- Quantitative + Qualitative: Combine numerical and text analysis
- Triangulation: Use multiple data sources for validation
- Sequential Analysis: Use quantitative findings to guide qualitative exploration
- Integrated Reporting: Present combined insights effectively
Common Use Cases
Market Research
- Brand Awareness Studies: Measure recognition and recall
- Competitive Analysis: Compare brands and products
- Market Segmentation: Identify distinct customer groups
- Trend Analysis: Track market changes and preferences
Product Development
- Concept Testing: Validate product ideas and features
- Usability Assessment: Evaluate user experience and interface design
- Feature Prioritization: Understand which features matter most
- Price Sensitivity: Determine optimal pricing strategies
Customer Experience
- Satisfaction Measurement: Track customer satisfaction levels
- Journey Mapping: Understand customer experience touchpoints
- Service Quality: Evaluate service delivery and support
- Loyalty Assessment: Measure customer retention factors
Marketing and Communications
- Message Testing: Evaluate marketing copy and creative assets
- Campaign Effectiveness: Measure campaign impact and recall
- Audience Insights: Understand target audience preferences
- Content Strategy: Optimize content for different segments
Best Practices Summary
Design Excellence
- Clear Objectives: Define what you want to learn before designing
- Logical Flow: Organize questions in a logical, engaging sequence
- Quality Questions: Write clear, unbiased, relevant questions
- Appropriate Length: Balance comprehensiveness with respondent fatigue
- Testing: Test questionnaires before full deployment
Implementation Success
- Persona Selection: Choose appropriate personas for your research goals
- Sample Size: Ensure adequate sample sizes for statistical confidence
- Quality Monitoring: Watch for response quality and consistency issues
- Iterative Improvement: Refine questionnaires based on performance
- Validation: Confirm key findings with additional research methods
Analysis and Reporting
- Appropriate Methods: Use analysis methods suited to your data types
- Statistical Rigor: Apply appropriate statistical techniques
- Clear Presentation: Present findings clearly and actionably
- Context Provision: Provide appropriate context and limitations
- Actionable Insights: Focus on findings that can inform decisions
Ready to create your first questionnaire? Start designing structured research that will generate valuable quantitative and qualitative insights with OpinioAI!