Create from my documents: Design an Evaluation Matrix

You are a senior MEAL specialist designing an evaluation matrix for "program name" (from the material, if given), assessed against "criteria" (from the material, if given).

The matrix is the operational backbone of the evaluation: it shows how each evaluation question will be answered.

Requirements:

1. Evaluation questions. List the prioritized evaluation questions (typically 5-8). Each must be answerable, not rhetorical.
2. Sub-questions and judgment criteria. For each question, define 2-4 sub-questions and the judgment criteria that will determine 'good' vs. 'weak' performance (e.g., "criterion" (from the material, if given)).
3. Methods. Specify the methods for each question (e.g., document review, KIIs, FGDs, survey, secondary data analysis). At least one question must triangulate two or more methods.
4. Data sources. Identify the specific data sources and informant categories (e.g., "informants" (from the material, if given)).
5. Analysis approach. State how data from each method will be analyzed and how results will be brought together (e.g., qualitative coding frame, descriptive statistics, contribution analysis).
6. Limitations and mitigation. For each question, flag the most likely limitations (e.g., access, recall bias, sample size) and how they will be mitigated.

Output Format:

Produce:

1. A 1-paragraph framing.
2. An evaluation matrix table with columns: evaluation question, sub-questions, judgment criteria, methods, data sources, analysis approach, limitations/mitigation.
3. A short note on triangulation strategy across the full matrix.
4. A list of inputs the evaluation team will need to begin (documents, contacts, datasets).

Quality bar: The result must meet these criteria. It will be reviewed against them afterwards.

• Methodology Justification and Method-Question Fit: Whether the methodology is explicitly justified relative to research questions, each major method maps to specific questions, alternatives have been considered, and the choice is appropriate to the context.
• Sampling and Data Source Adequacy: Whether sample size or data source coverage is justified, the sampling method is appropriate to the question, inclusion and exclusion criteria are clear, and the sample frame is documented.
• Triangulation and Multi-Method Strategy: Whether multiple data sources or methods are used for key questions, the triangulation strategy is explicit, different methods address different aspects, and sources complement rather than duplicate.
• Data Quality Safeguards: Whether quality assurance procedures are documented, consistency checks are specified where relevant, pretesting or piloting is planned for primary data, and data cleaning protocols are specified.
• Transparency, Limitations, and Bias Mitigation: Whether limitations are explicitly stated and not generic, biases are acknowledged with mitigation strategies, assumptions are documented, and replication is feasible from what is documented.

Where the material is silent, write "Not in the material" rather than guessing.

Source material: Attached, or pasted below. Base everything above on it and do not invent details that are not in it.