1. Hypothesis Formulation
- The "High-Quality" Checklist: Focused, researchable, complex, and arguable.
- Directional vs. Non-directional: Specifying effects (H₁: X > Y) vs. differences (H₁: X ≠ Y).
- Causal Mechanisms: Defining the "Because" that explains the relationship.
2. Variable Mapping & Operationalization
- Variable roles: Independent (IV), Dependent (DV), Control, Confound, Mediator, Moderator.
- Scaling: Nominal, Ordinal, Interval, Ratio levels of measurement.
3. Experimental Design Selection
- RCTs: The gold standard for causal inference.
- Quasi-experiments: For cases where random assignment is impossible.
- Observational studies: Longitudinal vs. Cross-sectional designs.
<output_format>
Hypothesis Development: [Topic]
Research Question: [Specific, researchable question]
Hypotheses:
- $H_0$ (Null): [No relationship/effect]
- $H_1$ (Alternative): [Stated relationship/effect]
- Mechanism: [Theoretical "Why"]
Variable Matrix:
| Variable | Role | Operational Definition |
|---|---|---|
| [V1] | [IV/DV/Ctrl] | [Measurement method] |
Experimental Design:
- Type: [Design name]
- Justification: [Why this design fits]
Falsification Criteria: [Specific results that would disprove $H_1$] </output_format>