Quality Checklist

Before Moving to Stakeholder Engagement

This checklist helps you catch common issues early. You don't need a "perfect" Problem Tree—stakeholder validation will refine it. But you do need a solid preliminary analysis that demonstrates rigor and prepares meaningful questions.

Quality Assurance Decision Flow

graph TD
    START["🌳 Preliminary Problem Tree<br/>from AI-Assisted Research"]

    %% Checkpoint 1: Core Problem Review
    CP1{"✓ CHECKPOINT 1<br/>Core Problem<br/>Quality Check"}
    CP1_PASS["✅ Specific, observable,<br/>no embedded causes"]
    CP1_FAIL["❌ Generic, vague, or<br/>has embedded solutions"]
    CP1_FIX["🔧 Revise Core Problem<br/>Refer to Step 2<br/>Define clear scope"]

    %% Checkpoint 2: Root Causes Review
    CP2{"✓ CHECKPOINT 2<br/>Root Causes<br/>Quality Check"}
    CP2_PASS["✅ 2-3 levels deep,<br/>(E) and (A) tagged,<br/>multi-dimensional"]
    CP2_FAIL["❌ Shallow, untagged,<br/>single-dimension"]
    CP2_FIX["🔧 Deepen Cause Analysis<br/>Refer to Step 8<br/>Add (E) and (A) tags"]

    %% Checkpoint 3: Effects & Research Review
    CP3{"✓ CHECKPOINT 3<br/>Effects & Research<br/>Quality Check"}
    CP3_PASS["✅ Time horizons mapped,<br/>sources verified,<br/>gaps acknowledged"]
    CP3_FAIL["❌ No time dimension,<br/>unverified sources,<br/>no gaps identified"]
    CP3_FIX["🔧 Strengthen Effects<br/>& Research<br/>Refer to Steps 6 & 9"]

    %% Final Outputs
    READY["🎯 READY FOR<br/>STAKEHOLDER<br/>VALIDATION<br/>(Lesson 1.2)"]
    QUESTIONS["📝 8-10 Validation<br/>Questions Prepared<br/>from (A) items"]

    %% Flow
    START --> CP1
    CP1 -->|Pass| CP1_PASS --> CP2
    CP1 -->|Fail| CP1_FAIL --> CP1_FIX --> CP1

    CP2 -->|Pass| CP2_PASS --> CP3
    CP2 -->|Fail| CP2_FAIL --> CP2_FIX --> CP2

    CP3 -->|Pass| CP3_PASS --> QUESTIONS
    CP3 -->|Fail| CP3_FAIL --> CP3_FIX --> CP3

    QUESTIONS --> READY

    %% Festa Colors
    style START fill:#F59E0B,stroke:#D97706,stroke-width:2px,color:#1F2937
    style CP1 fill:#10B981,stroke:#059669,stroke-width:2px,color:#fff
    style CP2 fill:#10B981,stroke:#059669,stroke-width:2px,color:#fff
    style CP3 fill:#10B981,stroke:#059669,stroke-width:2px,color:#fff
    style CP1_PASS fill:#72B043,stroke:#5A8F36,stroke-width:1px,color:#fff
    style CP2_PASS fill:#72B043,stroke:#5A8F36,stroke-width:1px,color:#fff
    style CP3_PASS fill:#72B043,stroke:#5A8F36,stroke-width:1px,color:#fff
    style CP1_FAIL fill:#E12729,stroke:#B91C1C,stroke-width:1px,color:#fff
    style CP2_FAIL fill:#E12729,stroke:#B91C1C,stroke-width:1px,color:#fff
    style CP3_FAIL fill:#E12729,stroke:#B91C1C,stroke-width:1px,color:#fff
    style CP1_FIX fill:#F37324,stroke:#E05C1B,stroke-width:1px,color:#fff
    style CP2_FIX fill:#F37324,stroke:#E05C1B,stroke-width:1px,color:#fff
    style CP3_FIX fill:#F37324,stroke:#E05C1B,stroke-width:1px,color:#fff
    style QUESTIONS fill:#72B043,stroke:#5A8F36,stroke-width:2px,color:#fff
    style READY fill:#10B981,stroke:#059669,stroke-width:3px,color:#fff

How to use this diagram: Follow the decision flow through three quality checkpoints. If you pass all three, you're ready to convert assumptions into stakeholder questions and proceed to validation. If you fail any checkpoint, the diagram guides you to specific steps for improvement.

Core Problem Quality

Your core problem statement is the anchor for everything else. It should be crystal clear.

Specific population and geographic scope defined

Can you answer: Who exactly? Where exactly? Not "youth everywhere" but "youth aged 18-25 in rural Nyanza region, Kenya."

Problem stated without embedded causes or solutions

Bad: "Youth are unemployed because they lack skills." Good: "Youth have limited access to employment opportunities" + separate cause "Skills-market mismatch."

Observable, measurable, and clearly urgent

You can verify this problem exists through data (unemployment rates, income levels) or direct observation. It affects people's lives significantly.

Connects to your organization's mission and capacity

Your organization has the mandate, expertise, or partnerships to credibly address this problem area.

Root Causes Analysis

Strong root cause analysis goes deep, covers multiple dimensions, and distinguishes evidence from assumptions.

Causes answer "why" the problem exists (not just describe it)

Test: Does each cause explain why the problem happens? If you removed this cause, would the problem still exist?

2-3 levels of depth with sub-causes

You have direct causes, underlying causes, and structural causes. Each level answers "Why?" about the level above it.

Mix of evidence-based (E) and assumption (A) items

Every cause tagged (E) or (A). You have at least 3-5 items marked (A) that will become stakeholder questions. Not everything is (E)—that would mean you don't need validation.

Cover multiple cause categories (economic, social, policy, etc.)

Problems are rarely uni-dimensional. You should see causes across economic, social, cultural, geographic, policy, and institutional dimensions.

Are potentially addressable through interventions

While you identify structural causes, at least some causes are within reach of nonprofit/NGO interventions. Not everything requires national policy change.

Effects Mapping

Effects demonstrate why solving this problem matters—to individuals, families, communities, and systems.

Realistic consequences across different time horizons

You have immediate (0-6 months), medium-term (6 months-2 years), and long-term (2+ years) effects. Not all effects happen instantly.

Individual, community, and system-level impacts considered

Effects aren't just personal—they ripple through families, communities, and broader systems (economic, social, political).

Clear connection between problem and effects

Each effect logically follows from the core problem. You can explain: "Because [problem] exists, [effect] happens."

Demonstrate why solving this problem matters

Effects are significant enough to justify intervention. They show urgency and importance to funders and partners.

Research Quality

Your Problem Tree should be backed by credible research while acknowledging knowledge gaps.

Major claims supported by credible, recent sources

Items tagged (E) have citations from UN agencies, government statistics, peer-reviewed research, or established NGOs. Sources are recent enough for your context.

Evidence clearly separated from assumptions

(E) and (A) tags used consistently throughout. No unmarked causes or effects. This transparency builds credibility.

Knowledge gaps and uncertainties acknowledged

You explicitly note what you don't know and need to learn from stakeholders. This isn't weakness—it's intellectual honesty.

8-10 stakeholder validation questions prepared

You have open-ended, non-leading questions ready that target your (A) assumptions. You're ready to engage stakeholders meaningfully.

Ready to learn from and be surprised by community insights

Mindset check: Are you approaching stakeholders to confirm what you think, or to learn what you don't know? The latter is the right approach.

Red Flags: Pause Before Moving Forward

If you see these issues, revise before engaging stakeholders:

❌ No (A) tags: If everything is (E), you're claiming perfect knowledge—unlikely and not credible
❌ Generic problem: Could describe any country/population—too vague to guide intervention
❌ Single-cause thinking: Only 1-2 causes identified—problems are complex
❌ No source verification: You haven't checked any sources from AI outputs
❌ Leading validation questions: Questions like "Don't you think..." or "Isn't it true..."

Scoring Your Problem Tree

Count your checkmarks across all four sections:

16-18 checks: Excellent! Your Problem Tree is ready for stakeholder validation.
13-15 checks: Good foundation. Address gaps before engaging stakeholders.
10-12 checks: Needs work. Revisit the Step-by-Step Guide and strengthen weak areas.
Below 10: Go back to Phase 2 or 3 of the implementation process. Your analysis needs more development.

Ready to Engage Stakeholders?

If you've checked most items in this list, congratulations! You have a solid preliminary Problem Tree that demonstrates rigor, acknowledges uncertainties, and prepares you for meaningful community engagement in Lesson 1.2: Stakeholder Mapping & Engagement.

Common Questions

"How many causes and effects should I have?"

Quality over quantity. Aim for 4-6 direct causes (with 2-3 underlying causes each = 8-18 total causes) and 4-6 effects across time horizons. More isn't better—clarity is.

"What if I can't verify sources from AI?"

If sources don't check out, tag those findings (A) and note "AI-suggested, needs verification" in your documentation. Be transparent about it.

"Should everything be validated before I write proposals?"

No. You'll never have 100% certainty. Stakeholder validation refines and strengthens—it doesn't have to eliminate all (A) tags. Be honest about remaining uncertainties in proposals.

Next Steps

After completing this checklist:

If ready: Move to Lesson 1.2: Stakeholder Mapping & Engagement to plan your validation approach
If gaps remain: Revisit Step-by-Step Guide or Templates & Tools to strengthen your analysis
Want inspiration: Check Examples to see completed Problem Trees from real projects