---
name: research-question-framing-framework
description: "Transforms a vague research topic or business question into a scoped, answerable research question with a clear construct definition, unit of analysis, decision it will inform, and go/no-go feasibility check — before any study design begins. Produces a primary question, 3-5 supporting sub-questions, an explicit scope boundary, and a hypothesis or expected-findings map. Use when a research topic is too broad to design against, when a stakeholder request needs sharpening into a studyable question, or when scoping a research initiative ahead of methodology selection."
argument-hint: "[research-topic] [decision-context]"
allowed-tools: ["Read"]
---

# Research Question Framing Framework

## Purpose
Convert a fuzzy topic or stakeholder request into a precisely scoped, answerable research question — with defined constructs, a clear unit of analysis, and an explicit link to the decision it will inform — so study design starts from a sharp target instead of a moving one. Prevents the most common and expensive research failure: running a well-executed study that answers the wrong question.

## When to Use
Invoke this skill when the user asks to:
- Sharpen a broad topic ("does our product help students learn?") into a studyable question
- Translate a stakeholder or executive request into a research question
- Scope a research initiative before choosing methodology
- Separate one overloaded question into a primary question plus sub-questions
- Decide whether a proposed question is even answerable with available data and access
- Align a team on what a study will and won't cover before design kickoff

## Inputs Required
Before starting, collect:
- **Raw topic or request**: The question or topic as it currently exists, in the requester's words
- **Decision it informs**: What action or choice the findings will drive, and who makes it
- **Population/context**: Who or what the question is about (learners, teachers, cohorts, segments, product surfaces)
- **Timeline and resources**: How long the team has and what data, access, or budget exists
- **Prior work**: What's already known or previously studied on this topic
- **Stakeholders**: Who requested this and who will consume the findings
- **Constraints**: Access limits, ethical/consent considerations, or off-limits data

If the decision it informs is missing, treat that as the first thing to reconstruct — an unlinked question is the primary risk this skill exists to catch. State the assumed decision explicitly so the requester can correct it.

## Instructions

### Step 1: Reconstruct the Decision Behind the Request
Before touching the question wording, surface the decision.
- Name the specific choice the findings will inform (ship/kill, redesign X, invest in Y, target segment Z).
- Identify the decision-maker and the threshold that would change their mind.
- Apply the **"So what?" test**: if you had a perfect answer, what would the team do differently? If nothing, the question isn't worth studying yet — flag it and stop.
- Distinguish **research questions** (answerable with evidence) from **strategy questions** (require judgment calls) and **operational questions** (already answerable from existing data). Route non-research questions out.

### Step 2: Diagnose the Raw Question
Score the raw question against five failure modes and note which apply:
- **Too broad**: no bounded population, timeframe, or outcome
- **Compound/overloaded**: two or more questions fused into one
- **Assumption-laden**: presumes an unproven causal or definitional premise
- **Unmeasurable construct**: relies on a term ("engagement," "learning," "success") with no operational definition
- **Undecidable**: no realistic data or access could answer it

### Step 3: Define the Constructs
For each fuzzy term in the question, produce an operational definition.
- State the **construct** (e.g., "learning gain"), the **observable indicator** (e.g., "pre/post assessment score delta on standards-aligned items"), and the **boundary** (what counts and what doesn't).
- Flag any construct that has multiple defensible definitions and pick one deliberately, noting the trade-off.
- For EdTech/product contexts, explicitly separate **behavioral proxies** (time-on-task, sessions) from **outcome constructs** (mastery, retention) — do not let a proxy silently stand in for the outcome.

### Step 4: Set the Unit of Analysis and Scope
- Fix the **unit of analysis**: student, session, class, cohort, teacher, school, account, or feature.
- Define the **population and timeframe** explicitly.
- Write a one-line **scope boundary**: "This study covers ___ and explicitly does not cover ___." Name at least two adjacent questions being deliberately excluded.

### Step 5: Draft the Primary Question and Sub-Questions
Frame the question type deliberately using the appropriate frame:
- **Descriptive** ("What is happening / who / how much"): frame around measurement and prevalence
- **Relational** ("Is X associated with Y"): name both variables and the population
- **Causal** ("Does X cause Y"): name the intervention, comparison, outcome, and confound risk — and confirm the design could support a causal claim
- **Exploratory** ("Why / how / what patterns"): frame around discovery, not confirmation

Produce one **primary question** and 3-5 **supporting sub-questions** that decompose it. Each sub-question must be independently answerable and ladder up to the primary.

### Step 6: State Hypotheses or Expected Findings
- For confirmatory questions: state a directional **hypothesis** and its **null**, plus what result would falsify it.
- For exploratory questions: state **expected findings or working assumptions** the study could confirm or overturn.
- Name the **result that would surprise the team** — this exposes hidden bias and sharpens the design.

### Step 7: Feasibility Go/No-Go
Run a final gate before handing off to design:
- **Data**: Does the needed evidence exist or can it be collected in the timeframe?
- **Access**: Are the participants, records, or consent obtainable?
- **Answerability**: Could any realistic finding actually move the decision?
- Return a verdict: **Go**, **Go with narrowed scope**, or **No-go (reason)** — with the single change that would flip a no-go to a go.

## Output Format
- **Structure**: Structured document with labeled sections; primary question and sub-questions rendered as a clearly set-off block
- **Length**: 700-1,000 words
- **Format**: Structured prose with short bulleted lists; construct definitions in a compact table (Construct / Indicator / Boundary)
- **Sections**: Decision Behind the Question → Diagnosis of Raw Question → Construct Definitions → Unit of Analysis & Scope Boundary → Primary Question + Sub-Questions → Hypotheses / Expected Findings → Feasibility Verdict
- **Tone**: Research director — precise, skeptical, decision-first; comfortable telling the requester their question isn't ready

## Quality Checklist
Before delivering output, verify:
- [ ] The primary question passes the "So what?" test — a named decision changes based on the answer
- [ ] Every fuzzy construct has an operational definition with an observable indicator (count them against the raw question's vague terms)
- [ ] The question type (descriptive/relational/causal/exploratory) is named and matches what a feasible design could support
- [ ] The scope boundary explicitly names at least two excluded adjacent questions
- [ ] 3-5 sub-questions are present and each is independently answerable
- [ ] The feasibility verdict is stated as Go / Go-with-narrowed-scope / No-go — not left ambiguous

## Notes
- The most common defect in EdTech research is a behavioral proxy standing in for a learning outcome — catch this in Step 3 every time.
- When a request arrives as a compound question, splitting it is usually more valuable than answering it; resist the urge to fuse sub-questions back together for tidiness.
- A "No-go" verdict is a successful output, not a failure — it saves a study's worth of effort. Always pair it with the one change that would make it a Go.
- Causal framings are the easiest to write and the hardest to answer; if the available design is observational, reframe the question as relational rather than overclaiming.

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## How to use this skill

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