Qualitative Surveys

Implement qualitative survey workflows for the Emerging Household Energy solution.

This guide is scoped to Emerging Household Energy. It explains how to run baseline, follow-up, and monitoring surveys that complement solution dMRV evidence.

Survey Types

Baseline Survey

Pre-distribution assessment and eligibility verification

Follow-up Survey

Early adoption and performance evaluation

Monitoring Survey

Ongoing SDG impact and emission reduction validation

Survey Stages and Objectives

Baseline Survey (Pre-Project)

• Timing: Before stove distribution/sales
• Target Population:
  • Potential adopters
  • Non-adopting households for comparison
• Objectives:
  • Assess community perception of new stoves
  • Gather socio-economic baseline data
  • Verify household eligibility
  • Validate inclusion criteria
  • Inform sampling design
• Data Collection:
  • Household demographics
  • Current cooking practices
  • Economic indicators
  • Technology preferences

Follow-up Survey (Post-Installation)

• Timing: Shortly after stove deployment
• Objectives:
  • Identify performance strengths
  • Detect early adoption issues
  • Enable rapid problem resolution
  • Assess user satisfaction
• Focus Areas:
  • Installation quality
  • Initial user experience
  • Technical challenges
  • Usage patterns

Monitoring Survey (Ongoing)

• Purpose:
  • Validate emission reduction calculations
  • Verify SDG impacts
  • Cross-reference with KPT/SUM data
• Key Parameters:
  • Stove usage patterns
  • Fuel consumption
  • User satisfaction
  • SDG indicators
  • Inclusion criteria validation

Implementation Requirements

Survey Design

• MADD Integration:
  • Complete questionnaire in MADD
  • Regular suitability assessment
  • Adaptation based on monitoring feedback
• Data Collection Protocol:
  • Defined collection methodology
  • Specified storage solutions
  • Clear responsibility assignment
• Quality Control:
  • Validation procedures
  • Cross-reference mechanisms
  • Update protocols

Data Management

• Collection Responsibility:
  • Designated data collectors
  • Training requirements
  • Quality assurance roles
• Storage Solutions:
  • Secure data repositories
  • Access control
  • Backup procedures
• Data Flow:
  • Collection to storage pipeline
  • Verification process
  • Integration with other data sources

Sampling Design

• Methodology:
  • Statistical approach
  • Population coverage
  • Stratification criteria
• Sample Size:
  • Calculation methodology
  • Confidence levels
  • Margin of error
• Selection Process:
  • Random selection methods
  • Bias prevention
  • Documentation requirements

Quick Start

Create Survey

curl -X POST https://api.emerging.eco/v1/surveys \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "type": "BaselineSurvey",
    "projectId": "did:ixo:project/789",
    "template": {
      "eligibilityCriteria": true,
      "socioEconomicData": true,
      "cookingPatterns": true,
      "stovePerceptions": true
    },
    "sampling": {
      "method": "stratified",
      "size": 100,
      "confidence": 0.95
    }
  }'

from emerging import Survey, SamplingDesign

# Create a new survey campaign
survey = Survey.create(
    type="BaselineSurvey",
    project_id="did:ixo:project/789",
    template=Survey.Templates.BASELINE,
    sampling=SamplingDesign(
        method="stratified",
        size=100,
        confidence=0.95
    )
)

Data Models

Survey Template

{
  "templateId": "baseline-v1",
  "sections": [{
    "id": "eligibility",
    "questions": [{
      "id": "current_cooking",
      "type": "multiple_choice",
      "required": true,
      "options": ["wood", "charcoal", "lpg", "electric"]
    }]
  }, {
    "id": "socio_economic",
    "questions": [{
      "id": "household_size",
      "type": "number",
      "validation": {
        "min": 1,
        "max": 20
      }
    }]
  }],
  "validations": {
    "requiredSections": ["eligibility"],
    "completionThreshold": 0.8
  }
}

Survey Response

{
  "responseId": "survey-123",
  "surveyId": "baseline-v1",
  "householdId": "did:ixo:household/123",
  "timestamp": "2024-02-20T10:00:00Z",
  "responses": {
    "eligibility": {
      "current_cooking": ["wood", "charcoal"]
    },
    "socio_economic": {
      "household_size": 5
    }
  },
  "verification": {
    "status": "verified",
    "verifier": "did:ixo:validator/456",
    "timestamp": "2024-02-20T10:15:00Z"
  }
}

Survey Management

from emerging import SurveyManager, DataValidator, SampleCalculator

# Create survey campaign with CDM-compliant sampling
calculator = SampleCalculator(
    project_id="did:ixo:project/789",
    confidence=0.95,
    precision=0.05
)

sampling = calculator.compute(
    population=1000,
    expected_mean=0.8,
    expected_sd=0.2,
    dropout_rate=0.15
)

# Create and deploy survey
manager = SurveyManager("did:ixo:project/789")
campaign = manager.deploy_survey(
    template_id="baseline-v1",
    sampling=sampling,
    collectors=["did:ixo:agent/123"]
)

For detailed information on sampling methodology and CDM compliance, see the Sample Size Calculator guide.

Data Handling

from emerging import DataProcessor, SDGValidator

# Process survey responses
processor = DataProcessor()
results = processor.analyze_survey_data(
    survey_id="baseline-v1",
    validation_rules={
        "completeness": 0.8,
        "consistency": True
    }
)

# Validate SDG impacts
sdg_validator = SDGValidator()
sdg_impacts = sdg_validator.assess_impacts(
    survey_data=results,
    sdg_targets=["SDG7", "SDG13"]
)

Integration with Monitoring Tools

from emerging import MonitoringIntegration

# Create integrated analysis
integration = MonitoringIntegration(
    project_id="did:ixo:project/789"
)

# Cross-validate data sources
validation = integration.cross_validate(
    survey_data="baseline-v1",
    sum_data="sum-456",
    kpt_data="kpt-789"
)

# Generate comprehensive report
report = integration.generate_report(
    period_start="2024-01-01",
    period_end="2024-01-31"
)

Error Handling

400 · error

Invalid survey configuration

404 · error

Survey or response not found

409 · error

Conflicting response data

Best Practices

Survey Design

• Align with project objectives
• Include all MADD requirements
• Enable data cross-validation
• Support multiple languages
• Allow for periodic updates

Data Collection

• Train survey collectors
• Implement quality controls
• Ensure consistent methodology
• Document collection process
• Maintain chain of custody

Data Analysis

• Cross-reference with SUMs/KPTs
• Validate response consistency
• Track temporal changes
• Generate actionable insights
• Monitor inclusion criteria

Documentation

• Record methodology
• Track survey updates
• Document responsibility assignments
• Maintain data flow records
• Archive survey versions

Next Steps

Survey Templates

Standard questionnaire formats

Data Collection

Mobile survey tools

Protocols

Impact assessment methods

Integration Guide

Cross-validation with SUMs

Related Guides

Household Monitoring

Track complete household transitions

Stove Use Monitoring

Validate with sensor data

Kitchen Performance Tests

Measure fuel consumption

Household Reporting

Generate comprehensive reports