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

  1. Introduction: Brief explanation of the survey purpose
  2. Warm-up Questions: Easy, engaging questions to start
  3. Core Content: Main research questions organized by topic
  4. Demographics: Personal information questions at the end
  5. 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

  1. Clear Objectives: Define what you want to learn before designing
  2. Logical Flow: Organize questions in a logical, engaging sequence
  3. Quality Questions: Write clear, unbiased, relevant questions
  4. Appropriate Length: Balance comprehensiveness with respondent fatigue
  5. Testing: Test questionnaires before full deployment

Implementation Success

  1. Persona Selection: Choose appropriate personas for your research goals
  2. Sample Size: Ensure adequate sample sizes for statistical confidence
  3. Quality Monitoring: Watch for response quality and consistency issues
  4. Iterative Improvement: Refine questionnaires based on performance
  5. Validation: Confirm key findings with additional research methods

Analysis and Reporting

  1. Appropriate Methods: Use analysis methods suited to your data types
  2. Statistical Rigor: Apply appropriate statistical techniques
  3. Clear Presentation: Present findings clearly and actionably
  4. Context Provision: Provide appropriate context and limitations
  5. 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!