Choose the synthesis target before the model

Name what the summary is supposed to describe

The metafor model introduction, checked 7 November 2026, distinguishes an equal-effects model with a common effect from a random-effects model describing a distribution of effects with an average. Its terminology is deliberate. The analyst must identify the intended target and assumptions rather than relying on an ambiguous model label.

Begin after the review has defined which studies answer a meaningful comparison. State the decision the summary will inform and the set of settings to which the team wants its conclusion to apply. A board considering one standardized procedure has a different question from a research group describing several deliberately varied implementations. Neither question is answered merely by selecting the software’s default option.

Write the target in a sentence that contains no statistical abbreviation. Ask a business reader what that sentence would let them conclude. If they hear a promise about every future implementation while the analyst means a summary of a specified evidence base, resolve the mismatch before fitting or presenting the model.

Keep substantive assumptions visible

List the differences among studies that matter to the business interpretation: audience experience, implementation intensity, comparator and outcome window. Do not turn this into another eligibility exercise. The studies may already belong in the comparison while still requiring a reasoned account of what their summary represents.

Ask the analyst which assumptions make the proposed estimate interpretable. Record those assumptions beside the target rather than burying them in a technical appendix. Do not choose a model solely because a diagnostic crosses a conventional threshold or because one interval happens to exclude zero. The practical question must survive a disappointing result. If the assumptions cannot be defended, the appropriate next step may be a different summary or a narrower claim, not another search for a favorable specification.

Compare outputs without changing the question silently

In a fictional methods discussion, an analyst presents two possible summaries of the same approved instruction studies: one estimate is 1.4 percentage points and another is 2.1. These invented numbers are supplied outputs, not a worked fit. The review owner should ask what each estimate represents and why its assumptions differ, rather than declaring the larger value the better answer. The first is offered as a common-effect estimate; the second as a mean across varying effects.

Trace any resulting change in the business recommendation to the target, assumptions and uncertainty, not merely the decimal difference. If the analyst presents a sensitivity analysis, label it as such and explain what would make the primary interpretation less secure. Preserve the original question and any legitimate revision. Changing the stated target after seeing the outputs is a methodological change that readers deserve to understand.

Write the claim the chosen model can support

Prepare a short claim template containing the comparison, outcome scale, intended scope and important qualification. Test it with an informed reader who was not involved in the modeling discussion. Can they distinguish what the evidence summarizes from what the organization proposes to do next? The recommendation may incorporate cost, reversibility and operational constraints, but those judgments should not be smuggled into the statistical estimate.

Retain the target contract, approved model rationale and sensitivity questions with the analysis. If the evidence base later expands to materially different implementations, revisit the contract rather than assuming that an old label remains sufficient. The deliverable is an interpretable relationship between question and model. It does not establish that all assumptions are true, eliminate study bias or guarantee the result of the organization’s next campaign.

Make the reasoning concrete.

A fictional professional-learning publisher receives two model outputs for a review of instruction studies. The editor prefers the larger estimate because it would make a stronger headline, but the team has not agreed what population of implementations the claim describes.

The publisher approves the target contract before choosing its headline. The invented output comparison is not presented as empirical evidence that one method performs better.

Take the questions into your next brief.

Use these prompts in your own notes. Nothing is submitted from this page.

1. Target

   What quantity should this summary describe?

2. Scope

   Which settings belong to that interpretation?

3. Assumptions

   What must hold for the proposed model to answer the question?

4. Sensitivity

   Which alternative specification probes a meaningful concern?

5. Claim

   What exact sentence can the accepted output support?

Watch for the wrong conclusion.

• Selecting a method by the most attractive estimate.
• Treating a diagnostic threshold as the research question.
• Using an ambiguous model label instead of explaining the target.

Sources & scope

Original adult business decision scenarios are explicitly fictional. Methodological claims and their applications remain source-derived. Qualified analysts own formal calculations. Cochrane-specific guidance is not a universal mandate. No actual research operations or publication occur.

• metafor: A Meta-Analysis Package for R →Primary technical documentation · Supports: Supports the documented equal-effects and random-effects distinction, not a universal model-selection rule.