Study Design Selector AI Prompt

Help me choose the right study design for my research question.

Research question: {{research_question}}
Available resources: {{resources}} (time, budget, sample access)
Field/domain: {{field}}

1. Classify my research question:
   - Is this a question about description (what is the prevalence or distribution of X)?
   - Is this a question about association (is X related to Y)?
   - Is this a question about causation (does X cause Y)?
   - Is this a question about mechanism (how or why does X cause Y)?
   The appropriate study design depends critically on this classification.

2. Present the candidate designs:

   For descriptive questions:
   - Cross-sectional survey: snapshot of a population at one point in time. Pros: fast, cheap. Cons: no temporal information, cannot establish causality.
   - Case series / case report: detailed description of a small number of cases. Pros: useful for rare phenomena. Cons: no comparison group, cannot generalize.

   For association questions:
   - Observational cohort: follow a group over time and measure exposures and outcomes. Pros: can assess temporality (X precedes Y). Cons: expensive, slow, confounding.
   - Case-control: compare people who have the outcome to those who do not and look back at exposures. Pros: efficient for rare outcomes. Cons: recall bias, cannot estimate prevalence.
   - Cross-sectional: measure exposure and outcome at the same time. Pros: fast. Cons: cannot determine temporal order.

   For causal questions:
   - Randomized controlled trial (RCT): gold standard for causality. Randomly assign participants to treatment or control. Pros: eliminates confounding. Cons: expensive, ethical constraints, artificial settings.
   - Quasi-experiment: exploit natural variation in treatment assignment (difference-in-differences, regression discontinuity, instrumental variables). Pros: more realistic than RCT. Cons: requires strong assumptions.
   - Natural experiment: an external event creates as-if random assignment. Pros: high external validity. Cons: rare and not controllable.

3. Apply the evidence hierarchy:
   - Rank the feasible designs for my question from strongest to weakest causal inference
   - Identify which designs are feasible given my resources and constraints

4. Recommendation:
   - Recommend the strongest feasible design
   - State clearly: what causal claims can this design support, and what claims it cannot
   - Identify the top 2 threats to validity in the recommended design and how to mitigate them

Return: design recommendation with full rationale, validity threat analysis, and a one-paragraph justification suitable for a methods section.