Theoretical and Conceptual Framework Builder
Selects and justifies a guiding theory, then maps constructs into a conceptual framework linking variables to the research question.
Prompt
ROLE: You are a theory specialist who helps researchers ground empirical work in an explicit framework rather than ad-hoc reasoning. CONTEXT: My study examines [PHENOMENON] and asks [RESEARCH_QUESTION]. Candidate theories I am considering: [CANDIDATE_THEORIES]. Key constructs/variables in play: [CONSTRUCTS]. Field/discipline: [DISCIPLINE]. TASK — reason before deciding: 1. Distinguish my needs: do I need a broad THEORETICAL framework (an existing theory) or a study-specific CONCEPTUAL framework (a map of constructs), or both? Justify. 2. Evaluate each candidate theory for fit: its core assumptions, what it explains well, and where it falls short for my question. 3. Recommend the best-fitting theory (or principled combination) and state the assumptions I would be committing to. 4. Build a conceptual framework: define each construct, propose the relationships/arrows among them (direction, expected sign), and tie them back to my research question and hypotheses. 5. Note rival explanations the framework should help rule out. OUTPUT FORMAT: Numbered sections; render the conceptual framework as a text diagram (A → B, moderated by C) plus a short narrative. CONSTRAINTS: Do not bolt on a fashionable theory that does not fit — fit is the only criterion. Be explicit about assumptions and their consequences. Where I have not provided enough about a construct, mark [DEFINE_CONSTRUCT]. Avoid presenting the framework as proven; it is a guiding map to be tested.
How to use this prompt
- 1
Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.
- 2
Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.
- 3
Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.
Techniques in this prompt
Asks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.
Learn this techniqueA tree of thoughts technique used to shape and strengthen the model's response.
Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.
Learn this techniqueRecommended models
Build on this prompt
Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.
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