Sample Size Calculator

This page is scoped to Emerging Household Energy monitoring operations. It covers sample-size design for surveys and stove-use monitoring evidence quality.

Quick Start

curl -X POST https://api.emerging.eco/v1/sampling \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "projectId": "did:ixo:project/789",
    "population": 5000,
    "confidence": 0.95,
    "precision": 0.05,
    "expectedMean": 0.8,
    "expectedSD": 0.2,
    "dropoutRate": 0.15,
    "stratification": {
      "enabled": true,
      "strata": [
        {"name": "urban", "weight": 0.6},
        {"name": "rural", "weight": 0.4}
      ]
    }
  }'

from emerging import SampleCalculator

calculator = SampleCalculator(
    project_id="did:ixo:project/789",
    confidence=0.95,
    precision=0.05
)

sample_size = calculator.compute(
    population=5000,
    expected_mean=0.8,
    expected_sd=0.2,
    dropout_rate=0.15,
    stratification={
        "enabled": True,
        "strata": [
            {"name": "urban", "weight": 0.6},
            {"name": "rural", "weight": 0.4}
        ]
    }
)

Core Requirements

Statistical Parameters

Confidence/Precision:
- 95% confidence level mandatory
- 5% precision requirement
- Factor imprecision into ER calculations
Sample Parameters:
- Justify expected mean/SD with credible data
- Document ER coverage percentage
- Account for realistic dropout rates
Adaptation Requirements:
- Recalculate after each monitoring period
- Use actual variation from monitoring data
- Adjust size up/down based on real data

Stratified Sampling

Independent Strata:
- Calculate size for each stratum separately
- Maintain confidence/precision per stratum
- Document stratum-specific parameters
Stratification Criteria:
- Based on baseline survey data
- Account for socio-economic factors
- Consider geographical variations

Random Selection

Initial Phase:
- Document selection methodology in MADD
- Ensure verifiable randomness
- Include non-adopting households
Verification Phase:
- Submit project list to FOEN
- Include active and planned projects
- Allow external random selection

Data Models

Sample Design

{
  "designId": "sample-123",
  "projectId": "did:ixo:project/789",
  "parameters": {
    "confidence": 0.95,
    "precision": 0.05,
    "population": 5000,
    "calculatedSize": 357,
    "adjustedSize": 420,
    "dropoutRate": 0.15,
    "erCoverage": 0.85
  },
  "stratification": {
    "method": "proportional",
    "strata": [{
      "name": "urban",
      "population": 3000,
      "sampleSize": 252,
      "mean": 0.8,
      "sd": 0.2
    }, {
      "name": "rural",
      "population": 2000,
      "sampleSize": 168,
      "mean": 0.75,
      "sd": 0.25
    }]
  },
  "verification": {
    "status": "approved",
    "verifier": "did:ixo:validator/456",
    "timestamp": "2024-02-20T10:00:00Z"
  }
}

from emerging import CDMCalculator, StratificationEngine

# Initialize CDM calculator
calculator = CDMCalculator()

# Calculate base sample size
base_size = calculator.compute_base_size(
    mean=0.8,
    sd=0.2,
    confidence=0.95,
    precision=0.05
)

# Adjust for finite population
adjusted_size = calculator.adjust_for_population(
    base_size=base_size,
    population=5000
)

# Account for dropout
final_size = calculator.adjust_for_dropout(
    sample_size=adjusted_size,
    dropout_rate=0.15
)

# Stratify if needed
stratification = StratificationEngine()
strata_sizes = stratification.allocate(
    total_size=final_size,
    strata=[
        {"name": "urban", "weight": 0.6},
        {"name": "rural", "weight": 0.4}
    ]
)

Random Selection

from emerging import RandomSelector

selector = RandomSelector(seed=20240220)

# Generate random sample
sample = selector.select(
    population_list="household_registry.csv",
    sample_size=420,
    stratification={
        "column": "region",
        "sizes": strata_sizes
    }
)

# Validate selection
validation = selector.validate_randomness(
    selected_sample=sample,
    confidence=0.95
)

Monitoring & Adaptation

from emerging import SampleMonitor

monitor = SampleMonitor("did:ixo:project/789")

# Analyze current data
stats = monitor.analyze_current_period(
    start_date="2024-01-01",
    end_date="2024-01-31"
)

# Recalculate sample size
new_size = monitor.recalculate_size(
    current_mean=stats.mean,
    current_sd=stats.sd
)

# Check if adjustment needed
if monitor.requires_adjustment(new_size):
    adjustment = monitor.generate_adjustment_plan()

External Verification

from emerging import VerificationSubmission

submission = VerificationSubmission(
    project_id="did:ixo:project/789"
)

# Prepare project list
project_list = submission.prepare_project_list(
    active_projects=True,
    planned_projects=True
)

# Submit for sampling
response = submission.submit_to_foen(
    project_list=project_list,
    monitoring_period="2024-Q1"
)

Error Handling

error

Invalid sampling parameters

error

Insufficient population data

error

Conflicting stratum definitions

Best Practices

Sample Design

Document all assumptions
Justify stratification choices
Consider seasonal variations
Plan for dropouts
Maintain stratum independence

Data Quality

Validate input parameters
Monitor dropout rates
Track response rates
Document non-responses
Verify random selection

Adaptation

Regular recalculation
Document changes
Update stratification
Maintain precision levels
Track ER coverage

Verification

Prepare FOEN submissions
Document selection process
Maintain project lists
Track verification status
Archive supporting data

Next Steps

Survey Integration

Apply sampling to surveys

SUM Integration

Sensor deployment sampling

CDM Tools

Official calculators

Verification Guide

FOEN submission process

Sample Size Adaptation

Monitoring Period Updates

from emerging import AdaptiveSampling

adaptive = AdaptiveSampling(project_id="did:ixo:project/789")

# Analyze current monitoring data
current_stats = adaptive.analyze_period(
    start_date="2024-01-01",
    end_date="2024-01-31"
)

# Recalculate using actual data
new_sample_size = adaptive.recalculate(
    actual_mean=current_stats.mean,
    actual_sd=current_stats.sd,
    current_dropout_rate=current_stats.dropout_rate
)

# Generate adaptation plan if needed
if adaptive.requires_adjustment(new_sample_size):
    plan = adaptive.create_adjustment_plan(
        current_size=current_stats.sample_size,
        required_size=new_sample_size,
        implementation_date="2024-03-01"
    )

Dropout Management

# Configure with realistic dropout rate
calculator = CDMCalculator(
    base_confidence=0.95,
    base_precision=0.05,
    dropout_buffer=0.15  # 15% dropout rate
)

# Track actual dropouts
dropout_analysis = calculator.analyze_dropouts(
    historical_data="2023",
    current_period="2024-Q1"
)

# Adjust sample size if needed
adjusted_size = calculator.compensate_for_dropouts(
    current_size=400,
    actual_dropout_rate=dropout_analysis.rate
)

FOEN Submission Process

from emerging import FOENSubmission

submission = FOENSubmission(project_id="did:ixo:project/789")

# Prepare project list for random selection
project_list = submission.prepare_list(
    active_projects=True,
    planned_projects=True,
    monitoring_period="2024-Q2"
)

# Submit for random sampling
response = submission.submit_to_foen(
    project_list=project_list,
    er_coverage_percentage=0.85,  # Document ER coverage
    supporting_data={
        "mean_justification": "historical_data.pdf",
        "sd_calculation": "variance_analysis.pdf"
    }
)

Get Started

Platforms

Articles

Sample Size Calculator

Quick Start

Core Requirements

Data Models

Sample Design

Sample Size Calculator

Random Selection

Monitoring & Adaptation

External Verification

Error Handling

Best Practices

Sample Design

Data Quality

Adaptation

Verification

Next Steps

Survey Integration

SUM Integration

CDM Tools

Verification Guide

Sample Size Adaptation

Monitoring Period Updates

Dropout Management

FOEN Submission Process

​Quick Start

​Core Requirements

​Data Models

​Sample Design

​Sample Size Calculator

​Random Selection

​Monitoring & Adaptation

​External Verification

​Error Handling

​Best Practices

​Sample Design

​Data Quality

​Adaptation

​Verification

​Next Steps

Survey Integration

SUM Integration

CDM Tools

Verification Guide

​Sample Size Adaptation

​Monitoring Period Updates

​Dropout Management

​FOEN Submission Process

Quick Start

Core Requirements

Data Models

Sample Design

Sample Size Calculator

Random Selection

Monitoring & Adaptation

External Verification

Error Handling

Best Practices

Sample Design

Data Quality

Adaptation

Verification

Next Steps

Sample Size Adaptation

Monitoring Period Updates

Dropout Management

FOEN Submission Process