You are an expert M&E specialist and data analyst. Draft a composite indicator definition that scores Excellent (5) on every dimension of the Composite Indicator Design rubric for the construct and context described in the brief below.
QUALITY REQUIREMENTS:
Component Selection: Choose component sub-indicators that are theoretically grounded in the named construct. Cover the construct with no obvious dimension missing. Avoid redundant components measuring the same underlying thing twice. Cite the source for the component set (literature, framework, expert consensus, or prior validated index).
Weighting Rationale: State the weighting scheme explicitly. If using differential weights, name the basis (theory, expert judgment, statistical method, prior validated index). If using equal weights, justify the choice rather than defaulting. Note any sensitivity check against alternative weights.
Aggregation Logic: Write the aggregation formula in a reproducible form (sum, weighted sum, average, weighted average, geometric mean, min, threshold-and-count). Specify any scaling or normalization of components before aggregation. State the score range and the direction (higher is better, or higher is worse).
Missing-Data Handling: Specify the rule for incomplete components. Name the minimum number of components required to compute a valid score. Choose an imputation method, a proration method, or a rule to mark the unit as missing. Document how missing-data cases will be flagged.
Interpretability: Make the score meaningful to a non-specialist. State what high and low values mean substantively. Provide thresholds or bands tied to substantive categories where appropriate. Add a worked example showing how the score is computed for one unit.
FORMAT: Present the composite indicator as a structured definition: (1) Construct and purpose, (2) Component sub-indicators (table: component name, measurement, source, weight), (3) Aggregation formula, (4) Score range and direction, (5) Missing-data rule, (6) Interpretation guide (bands or thresholds), (7) Worked example.
BRIEF:
[Paste your construct, target population, available data sources, and any constraints here]
Quality bar: The result must meet these criteria. It will be reviewed against them afterwards.
- Component Selection: Whether the component sub-indicators are well-chosen, theoretically grounded in the construct the index claims to measure, and collectively cover the construct without obvious gaps or redundant proxies.
- Weighting Rationale: Whether the weighting scheme across components is justified, with either an explicit rationale for differential weights or an explicit justification for equal weighting rather than a default fallback.
- Aggregation Logic: Whether the mathematics for combining components into a single score is transparent, reproducible, and stated in a way that a second analyst could replicate the calculation from the definition alone.
- Missing-Data Handling: Whether the rule for handling incomplete or missing components is specified, so the index can be computed consistently when one or more sub-indicators are not available for a given unit.
- Interpretability: Whether a non-specialist can make sense of the resulting score, including what high and low values mean substantively and how the score relates to the underlying construct.
Anything still in [brackets] is not known yet: ask me, or mark it clearly in your answer.