Social Impact Measurement

GATE: Confirm the brief before proceeding.

Phase 2: Framework Design

Logic Model

## Logic Model: [Program Name]

**Inputs** → **Activities** → **Outputs** → **Outcomes** → **Impact**

**Inputs:** [Resources invested — staff, money, materials, time]
**Activities:** [What the program does — training, services, events]
**Outputs:** [Direct products — people served, sessions delivered, materials distributed]
**Outcomes:** [Changes in participants — knowledge gained, behavior changed, conditions improved]
**Impact:** [Long-term change in the community or system]

Indicator Selection

For each outcome, define measurable indicators:

| Outcome | Indicator | Data Source | Collection Method | Frequency |
|---------|-----------|-------------|------------------|-----------|
| [Outcome 1] | [Measurable indicator] | [Where the data comes from] | [Survey, observation, records] | [Monthly, quarterly, annually] |
| [Outcome 2] | [Measurable indicator] | [Source] | [Method] | [Frequency] |

Indicator Types

• Quantitative: Numbers, percentages, counts (e.g., "85% of participants report improved confidence")
• Qualitative: Stories, quotes, observations (e.g., participant testimonials about life changes)
• Leading: Early signals of progress (e.g., workshop attendance rate)
• Lagging: Long-term results (e.g., employment rate 6 months after program)

GATE: Present the logic model and indicators for approval.

Phase 3: Build

Data Collection Tools

For each indicator, create or recommend a collection tool:

Surveys:

## Pre/Post Survey Template
Administer at program start and end to measure change.

1. On a scale of 1-5, how confident are you in [skill]? (Pre and Post)
2. How often do you [desired behavior]? (Pre and Post)
3. What is your biggest challenge related to [topic]? (Pre — open-ended)
4. What changed for you as a result of this program? (Post — open-ended)

Tracking Sheets:

## Output Tracking
| Date | Activity | Participants | Hours | Notes |
|------|----------|-------------|-------|-------|

Interview Guide:

## Beneficiary Interview (15 minutes)
1. What was your situation before the program?
2. What did you learn or gain from participating?
3. How has your [specific area] changed since the program?
4. What would you tell someone considering this program?
5. What could we do better?

Reporting Template

## Impact Report: [Period]

### Summary
[One-paragraph overview of impact during the period]

### Outputs
| Metric | Target | Actual |
|--------|--------|--------|
| People served | [X] | [Y] |
| Sessions delivered | [X] | [Y] |

### Outcomes
| Indicator | Baseline | Current | Change |
|-----------|----------|---------|--------|
| [Indicator 1] | [X] | [Y] | [+/-Z%] |

### Stories
[1-2 beneficiary stories illustrating the data]

### Lessons Learned
[What the data tells you about how to improve]

Phase 4: Polish

1. Measurement Calendar

| When | What | Who |
|------|------|-----|
| Program start | Pre-survey, baseline data | Program staff |
| Monthly | Output tracking update | Program staff |
| Quarterly | Outcome review, beneficiary interviews | Impact lead |
| Program end | Post-survey, final data collection | Program staff |
| Annually | Annual impact analysis and report | Leadership |

2. Data Quality Checklist

- [ ] Baseline data is collected before the program starts
- [ ] Surveys use validated questions where possible
- [ ] Sample size is large enough to draw conclusions
- [ ] Data is collected consistently (same method, same timing)
- [ ] Qualitative data supplements quantitative data
- [ ] Data is stored securely and ethically

3. Right-Sizing the Framework

Match measurement effort to organizational capacity:

**Lightweight (1-2 hours/month):** Track 3-5 output metrics + annual survey
**Moderate (4-6 hours/month):** Outputs + quarterly outcome surveys + beneficiary interviews
**Comprehensive (10+ hours/month):** Full logic model tracking + comparison groups + longitudinal data

Example 1: Youth Mentorship Program

Logic model: Trained mentors (input) → weekly meetings (activity) → 100 youth matched (output) → improved academic confidence (outcome) → higher graduation rate (impact)
Key indicator: Pre/post survey on academic confidence (1-5 scale)
Target: 80% of participants show improvement of 1+ point

Example 2: Small Business Incubator

Logic model: Training curriculum + mentors (inputs) → 12-week program (activity) → 30 businesses launched (output) → increased revenue (outcome) → community economic growth (impact)
Key indicator: Average revenue change 6 months post-program
Target: 70% of participants increase revenue by 25%+

Anti-Patterns

• Measuring only outputs — counting people served is not impact. Impact is whether their lives actually changed.
• Over-measuring — collecting data you never analyze wastes everyone's time. Only measure what you will use.
• No baseline — you cannot prove change without knowing where people started. Always collect pre-program data.
• Ignoring qualitative data — numbers tell what happened, stories tell why it matters. You need both.
• Confirmation bias — designing measurements to prove your program works instead of honestly assessing it.
• One-time measurement — impact happens over time. Build in follow-up measurement at 3, 6, and 12 months.

Recovery

• No baseline data for current participants: Start collecting now for future cohorts. For current participants, use retrospective surveys ("Thinking back to before the program...").
• Organization has no measurement capacity: Start with the lightweight option — 3 output metrics and one annual survey. Build from there.
• Funders want specific metrics you do not track: Be honest about what you can measure now, commit to adding their priority metrics, and provide qualitative data in the interim.
• Impact is hard to attribute to your program: Acknowledge external factors honestly. Use comparison data where possible and focus on the change you can credibly claim.