Build an Organization Evidence Model by recording five connected layers: the change your organization is committed to, the framework explaining how that change happens, the evidence standard used to judge claims, the governed definitions that keep evidence comparable, and the rules for aligning with funders or external standards. Use this shared record in proposals, delivery, learning and reporting, with a named owner and a version history.
Your next step in the reporting course
Goal: Keep your outcome logic, definitions and evidence rules in one usable place.
Start with: Bring the pathway, model and evidence gaps you have already worked on. The “evidence model” is the shared record of that work, not another competing framework.
Carry forward: Leave with an owned, versioned evidence plan. Next, compare it with what the intended report reader actually requires.
In this course, “organization evidence model” is Sopact’s working name for connecting those records; it is not an external certification or a replacement for an established impact framework. Start with the evidence review from the related lesson. The task here is to agree how the organization will maintain and use it.
- State the organization’s convictions and non-negotiables.
- Choose or reconcile the framework that explains change.
- Define what evidence is credible enough for each decision.
- Encode outcomes, indicators, categories, and rules in a governed data dictionary.
- Crosswalk the model to funder requirements and external standards without erasing differences.
What is an Organization Evidence Model?
An Organization Evidence Model is the governed expression of what an organization believes should change, how its work may contribute, what evidence it will trust, and which definitions or ethical boundaries it will not rewrite for each funding opportunity.
A mission statement explains why the organization exists. A Theory of Change, Logic Model, Logframe, or Results Framework organizes its causal logic. A data dictionary defines the evidence fields. The Organization Evidence Model connects these artifacts so that the same organizational position survives across programs, people, systems, proposals, and reports.
This is especially important for organizations working across sites, partners, programs, or donors. Without an explicit model, each application becomes an opportunity to rename outcomes, change denominators, accept a new reporting category, or overstate what the available evidence can prove. The drift often becomes visible only when numbers cannot be reconciled at reporting time.
Five questions to keep together in your evidence plan
These are aspects of one working plan, not additional course stages or five separate documents you must create. A field definition can describe a value without explaining its source, its place in the framework or the decision it informs. Link those details so another team can interpret the result.
| Layer | Question | Output |
|---|---|---|
| Conviction | What change matters, what have we learned, and what will we not distort? | Mission commitments, field beliefs, hypotheses, cautions, and non-negotiables |
| Framework | How could activities contribute to change? | Outcomes, pathways, assumptions, external factors, risks, and contribution boundaries |
| Evidence standard | What evidence is credible enough for this decision? | Decision-specific rules for quantitative, qualitative, longitudinal, comparative, and participant evidence |
| Dictionary | What does every concept, category, and calculation mean? | Stable definitions, formulas, sources, timing, disaggregation, ownership, permissions, and versions |
| Alignment | Where do our model, the funder’s context, and a standard genuinely match? | A sourced crosswalk of matches, translations, extensions, gaps, conflicts, and non-negotiables |
How do you state organizational conviction without turning it into marketing?
Separate what the organization values from what it has demonstrated. Preserve both, but label them differently.
Ask five questions in plain language:
- What problem was the organization created to address, and who has authority to define that problem?
- What changes for people, communities, systems, or institutions when the work succeeds?
- What has field experience taught the organization that common practice misses?
- What will the organization not claim, count, exclude, or redefine merely to qualify for funding?
- Which statements are commitments, which are causal hypotheses, and which are supported findings?
The last distinction protects integrity. “We believe accessible employment should include job quality” is a conviction. “Our program improves job quality” is a claim requiring evidence. “Participants were employed at the 90-day checkpoint” is a finding only when its population, source, missingness and calculation are defined. Claiming continuous retention requires evidence about the intervening period as well.
How do you connect different impact frameworks?
Keep the framework the organization actually uses, then crosswalk equivalent elements. Do not rename everything into one format before checking whether the meanings match.
A Theory of Change may emphasize pathways and assumptions. A Logic Model may organize inputs, activities, outputs, and outcomes. A Logframe may connect objectives, indicators, verification sources, and assumptions. A Results Framework may organize results and indicators at several levels. The model should govern at least:
- population and context;
- needs or conditions;
- activities, outputs, and intended outcomes;
- assumptions, external factors, and risks;
- contribution versus attribution boundaries;
- learning questions and decisions.
If several teams or partners use different frameworks, record the correspondence and the unresolved differences. A crosswalk is more honest than declaring that two boxes are equivalent because their labels sound similar.
What should an organizational evidence standard contain?
An evidence standard defines what is credible enough for a particular use. Operational learning, participant support, funder reporting, board oversight, public communication, and causal claims do not require identical evidence thresholds.
For a weekly service decision, a recent case note and current attendance record may be sufficient. A public causal claim requires a much stronger design. A participant story can explain mechanism or context, but should not be presented as prevalence without a defensible sampling method.
How does the data dictionary make the model operational?
The dictionary converts organizational intent into fields that can be collected and interpreted consistently. It must govern qualitative concepts and longitudinal links as well as numeric indicators.
For each shared concept, record the fields needed for its intended use. The following list is a reference, not a requirement to populate every field for every local question: a stable ID, canonical name, plain-language definition, concept type, framework position, calculation or interpretation rule, evidence source, method, disaggregation, collection timing, owner, permissions, quality rule, limitations, version, and external mappings. The linked data dictionary lesson provides the implementation detail; here, the essential point is that the dictionary carries the organization’s model into actual workflows.
Worked example: when “successful employment” means three different things
Fictional worked example. A funder counts a placement when a participant starts any paid job. The organization’s program model treats success as accessible, adequately paid work retained for 90 days. Suppose a delivery partner submits only a monthly aggregate placement count. That count cannot establish person-level retention or job quality. In this example, 36 of 60 people with known status were employed 90 days after exit; the other 20 of 80 starters have unknown status. Twelve of 18 interviewees mentioned transport difficulties, but those interviews were not selected to estimate prevalence across the cohort.
| Model layer | Employment example | Decision protected |
|---|---|---|
| Conviction | A placement is not sufficient if the work is inaccessible or unstable. | Do not equate start date with durable outcome. |
| Framework | Placement, job quality and sustained employment, with accessibility and transport assumptions. | Track the pathway, not one terminal count. |
| Evidence standard | Separate start dates, wage/quality fields, checkpoint status, evidence of continuity, and participant accounts of barriers. | Combine outcome level with mechanism and context. |
| Dictionary | placement_start, job_quality, employment_90d, access_barrier_theme | Keep each construct and missing state distinct. |
| Alignment | Report starts under the agreed funder definition; retain job quality and retention as separate outcomes. Explain where placements sit in each framework. | Meet a valid requirement without weakening the core model. |
Where do SDG, IRIS+, and GRI mappings belong?
External standards belong after the organization defines its own outcome, population, unit, period, boundary, and calculation. A related label is not automatically an exact mapping.
The UN maintains the official SDG indicator framework. IRIS+ provides qualitative and quantitative metrics for impact investors through its catalog of metrics. GRI uses Universal, Sector, and Topic Standards to report an organization’s significant impacts; see the official GRI Standards. Each serves a different purpose. Record the exact version and whether the relationship is exact, partial, or contextual.
Build a source register before approving claims
A source register is an index, not a copy of every private document. Give each approved source an ID, date, owner and location. Record who may access it and whether it supports a policy, a finding or a hypothesis. A file that could not be read belongs in the missing-information list, not in the evidence column.
| Source in the fictional exercise | What it supports | What it cannot establish |
|---|---|---|
| Cohort register: 80 starters | The defined starting population | Employment status for everyone |
| 90-day follow-up: 60 known, 36 employed | 60% employed among known statuses; 75% coverage | Continuous retention, job quality or program causality |
| 18 interviews; 12 mention transport | Transport is a theme within this interview set | That two-thirds of all starters face the same barrier |
For each report sentence, ask whether a colleague can find the source and reproduce the interpretation. If the answer changes after a definition update, retain the previous version and explain the change. Do not silently restate last quarter’s result using this quarter’s rules.
Give each change an owner and an effective date
Start with a small approval record. A data owner maintains definitions; a program lead reviews the intended outcome; the person responsible for the report approves its use. One person may fill several roles in a small team, but the responsibilities should still be explicit.
| Change | Decision to record | Reporting effect |
|---|---|---|
| Rename employment_90d as employment_status_90d | Clarifies a checkpoint, not continuous retention | Existing numbers remain; misleading retention language is corrected |
| Add a later follow-up | Approve timing, consent, collection method and owner | New evidence may support duration; do not backfill an invented history |
| Receive a different funder denominator | Check population and eligibility rules | Keep a separate calculation if meanings differ |
For a proposed change, record its reason, reviewer, approval date, effective reporting period and treatment of prior results. An unresolved disagreement stays visible until the responsible person decides. This is a practical way for program data owners to maintain control without reopening every definition whenever a new report is requested.
Use this evidence-model review before approving the artifact
Using only the supplied strategy, program, framework, interview, learning, and governance sources, draft an Organization Evidence Model. Return: (1) mission commitments and field convictions; (2) intended outcomes and causal assumptions; (3) evidence rules by decision; (4) candidate governed concepts and definitions; (5) non-negotiables; (6) contradictions and missing information; and (7) human decisions required. Cite the source, date, and location for every populated statement. Label each as documented policy, observed practice, hypothesis, disputed interpretation, or missing information. Do not convert an aspiration into a demonstrated outcome, infer participant experience, or resolve a framework conflict without an owner’s decision.
How does Sopact Sense support the model?
A team can build the five layers manually with a source register, framework crosswalk, evidence-policy document, and data-dictionary spreadsheet. The difficulty begins when programs, partners, funders, and versions multiply.
In a configured Sopact workflow, teams can collect partner responses and supporting documents, analyze new submissions against agreed definitions, and keep successive records connected. Use the assistant to review candidate definitions and conflicting language with their source records. Confirm the required sources, permissions and reporting setup before relying on a particular workflow. People still approve convictions, causal assumptions, definitions, standards mappings, access rules, and consequential decisions. AI assists with retrieval and comparison; it does not establish truth or organizational authority.
Watch: why clean data still needs context
Watch the 6-minute 7-second explainer on the context behind a useful report. Use it to discuss how the evidence, framework and audience affect interpretation. Browse the video library.
Frequently asked questions
What are the steps for building an Organization Evidence Model?
State organizational convictions and non-negotiables; choose or reconcile the framework of change; define evidence thresholds for real decisions; encode approved concepts in a governed data dictionary; and crosswalk the model to funder context and external standards. Preserve sources, owners, versions, disagreements, and missing information throughout.
Is an Organization Evidence Model the same as a Theory of Change?
No. A Theory of Change is one possible framework for explaining how change may happen. The Organization Evidence Model also governs what counts as evidence, how concepts are defined in data, how versions are controlled, and how funder or standards mappings are handled.
Can an organization use a Logic Model or Logframe instead?
Yes. Use the framework that fits the organization’s decisions and obligations. The model can contain a Theory of Change, Logic Model, Logframe, Results Framework, or a crosswalk among several frameworks. The important requirement is that outcomes, assumptions, evidence, and definitions remain explicit.
Should a funder’s metrics become the organization’s metrics?
Only when the underlying construct, population, timing, calculation, and use genuinely align. Otherwise retain the organization definition and record the funder measure as a translated view, valid extension, or conflict requiring discussion.
Does the data dictionary replace the impact framework?
No. The framework explains intended change; the dictionary governs the fields used to observe and discuss it. A dictionary without outcome logic can make inconsistent data cleaner without making the evidence more meaningful.
Can AI build the model automatically?
AI can inventory documents, extract candidate statements, compare frameworks, draft dictionary records, and flag contradictions. People must decide what the organization believes, which causal claims are defensible, what evidence is sufficient, and which ethical boundaries or definitions are non-negotiable.
How often should the model be updated?
Review it when strategy, program design, population, evidence, regulation, standards, or funding commitments materially change. Version the model rather than overwriting history, and record which definitions and rules applied to each reporting period.
Practice and continue
Bring together one outcome definition, one source register and one unresolved crosswalk. Ask a colleague to reproduce the employment calculation and explain what remains unknown. Save the approved version and list the questions to take into the funder discussion.
Related practice: Build a sourced Funder Context Profile →
Then work through the data dictionary lesson. For the reporting output, use How to Write an Impact Report and report examples.
By Sopact Academy · Revised September 12, 2026. The worked dataset is fictional; the five-layer organization method is a course practice, not an external standard.