social-impact-measurement

name	social-impact-measurement
description	Designs social impact measurement frameworks with indicators, data collection methods, and reporting templates.
allowed-tools	Read Write Glob
metadata	{"author":"matthewhitcham","version":"1.0"}

Social Impact Measurement

When to Use This Skill

Use this skill when you need to:

• Design a framework to measure the social impact of programs or initiatives
• Define indicators, data collection methods, and reporting structures
• Create a logic model or theory of change for impact tracking
• Build reporting templates that communicate impact to funders and stakeholders

DO NOT use this skill for business KPI dashboards, financial ROI calculations, or customer satisfaction measurement. This is for measuring social, community, or environmental impact.

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Core Principle

MEASURING IMPACT IS NOT ABOUT PROVING YOU DID SOMETHING — IT IS ABOUT UNDERSTANDING WHETHER WHAT YOU DID ACTUALLY CHANGED LIVES, AND USING THAT UNDERSTANDING TO DO IT BETTER.

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Phase 1: Brief

Required Inputs

Input	What to Ask	Default
Program or initiative	"What program are you measuring impact for?"	No default — must be provided
Intended impact	"What change are you trying to create in the world?"	No default — must be provided
Stakeholders	"Who needs to see these impact measurements? (funders, board, public)"	Funders and board
Current tracking	"What data are you already collecting?"	Minimal or none
Resources for measurement	"What time and budget can you dedicate to data collection?"	Low — needs to be lightweight

GATE: Confirm the brief before proceeding.

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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.

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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]

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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

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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%+

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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.

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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.