| name | comparative-formulation |
| description | Strategy: Construct comparative research questions — systematic comparison of A vs B |
| version | 1.0.0 |
| category | hypothesis-formation |
| type | strategy |
| campaign | research-question |
| tactics | ["framework-selection-and-application"] |
| sops | ["framework-matching","pico-application","finer-criteria-check","success-criteria-definition"] |
| dependencies | {"tactics":["framework-selection-and-application"],"sops":["finer-criteria-check","success-criteria-definition"]} |
Comparative Formulation
Construct comparative research questions — when research requires comparing A vs B, systematically construct a fair, meaningful comparison.
When to Use
- Need to compare two methods/conditions/groups
- The hypothesis involves "X is better than / different from Y"
- Need to ensure the fairness and validity of the comparison
Thinking Framework
Core logic: a good comparative research question requires clarifying four elements — what is compared (objects), along what dimension (metrics), under what conditions (controls), and what counts as "different" (threshold).
Comparison Design Principles
- Fairness: the comparison conditions are fair to both sides (not a strawman)
- Clear dimensions: along which dimension(s) the comparison is made
- Controlled variables: all conditions are the same except the compared objects
- Effect size: not just "whether there is a difference" but "how large a difference is meaningful"
Comparison Types
| Type | Example | Key considerations |
|---|
| Method comparison | Method A vs Method B | Implementation fairness, dataset selection |
| Condition comparison | With X vs Without X | Controlled variables, confounding factors |
| Group comparison | Group A vs Group B | Matching, selection bias |
| Temporal comparison | Before vs After | History effects, maturation effects |
Budget Gate
| Tier | Comparison design | Fairness argument | Output |
|---|
| S | Comparison objects + clear dimensions | Basic fairness statement | ≥1 comparative RQ |
| M | + controlled variables + effect size | Fairness argument + identification of potential bias | ≥2 comparative RQs |
| L | + multi-dimensional + sensitivity | Full fairness analysis + bias mitigation strategy | ≥3 comparative RQs |
Default Reference Flow
- Determine the comparison objects (what A and B are)
- Determine the comparison dimensions (along what metrics to compare)
