Survey Design

Academic Survey/Questionnaire Design

Design questionnaires that meet academic publication standards, with built-in self-evaluation based on COSMIN, Dillman, and psychometric best practices.

Input

Collect the following from the user before starting. If any required field is missing, ask for it.

Required:

1. Research purpose — What research question does this questionnaire answer?
2. Target construct — What are you measuring? (one or more constructs)
3. Target population — Who will fill out this questionnaire?
4. Language — Questionnaire language (default: same as conversation language)

Optional:

• Existing theory or model
• Expected sub-dimensions
• Item count limit
• Special needs (cross-cultural, specific population, etc.)

Process

The workflow has two phases: an alignment phase (interactive) and an execution phase (autonomous).

Phase 1: Align — Construct Definition (interactive)

This is the only phase that requires user confirmation. Getting the construct definition and sub-dimensions right is critical because everything downstream depends on it. If this is wrong, the entire questionnaire is wasted effort.

Step 1: Load Reference Frameworks

Read all four reference files:

• COSMIN content validity framework — evaluation criteria for measurement instruments
• Dillman survey design principles — item writing and questionnaire structure rules
• Common pitfalls checklist — defects to actively avoid
• Self-evaluation rubric — scoring rubric for the finished questionnaire

Step 2: Propose Construct Definition

Based on the user's research purpose, present the following to the user for confirmation:

1. Operational definition of the construct (cite supporting literature/theory)
2. Distinction from related concepts — how it differs from similar constructs
3. Existing validated scales — list them, compare, explain why a new instrument is needed (or recommend adopting an existing one if it fits)
4. Sub-dimension decomposition with definition and source for each
5. Item blueprint — sub-dimension × planned item count

Present it in this format:

## Construct Definition
[Operational definition]

## Distinction from Related Concepts
[How this construct differs from similar concepts]

## Existing Scale Review
| Scale | Author | Features | Reason for not adopting |
|-------|--------|----------|----------------------|

## Sub-dimension Decomposition
| Sub-dimension | Definition | Source | Planned items |
|---------------|-----------|--------|---------------|

Then ask the user: "Does this decomposition look right? Any sub-dimensions to add, remove, or rename?"

Wait for the user to confirm or adjust before proceeding. If the user changes the sub-dimensions, update the blueprint accordingly.

Phase 2: Execute — Design & Output (autonomous)

Once the user confirms the construct definition, execute Steps 3–7 without stopping.

Step 3: Item Design

Design items for each sub-dimension following Dillman principles. Actively check against the common-pitfalls checklist.

Each item must include:

• Item ID (dimension code + number, e.g., A1, A2, B1...)
• Item text
• Response format (scale type and options)
• Sub-dimension assignment
• Forward/reverse scoring indicator

Design rules:

• One idea per item (no double-barreled questions)
• Neutral wording (no leading language)
• Clear time frame where applicable
• Simple language appropriate for the target population
• Reverse items should not exceed 20-30% of total items
• Each factor needs at least 3-5 items for factor analysis

Step 4: Questionnaire Structure

1. Write questionnaire instructions (opening statement for respondents)
2. Arrange item order following Dillman ordering principles:
   • Start with easy, engaging items
   • Place sensitive items later
   • Demographics at the end
3. Design section structure with section titles and section instructions
4. Add filter questions and skip logic if needed
5. Arrange demographic items at the end

Step 5: Self-Evaluation & Auto-Remediation Loop

5a. Run self-evaluation

Read references/rubric.md and score each of the 24 items honestly. Be honest — the purpose is to find weaknesses, not inflate scores. Write the justification for each score specifically (why this score, not 4 or 2).

5b. Auto-remediation loop (only if any item scored below 3)

The rubric defines the 5/5 state for each criterion. If an item is below 3, apply the specific fix to move it toward the 5/5 definition. Do NOT modify items that are already ≥3 — don't let chasing a higher total score create new problems.

Loop structure:

iteration = 0
MAX_ITERATIONS = 3

while any item < 3 AND iteration < MAX_ITERATIONS:
    iteration += 1
    
    For each item scoring < 3:
      - Identify the specific fix based on the rubric's 5/5 definition
        and relevant entries in references/common-pitfalls.md
      - Apply the fix (edit items, add reverse items, add a note, etc.)
      - Record what changed (item IDs affected, before/after text if applicable)
    
    Re-score the items that were below 3, plus any items whose criteria are
    affected by the fixes (e.g., adding reverse items → re-score C4 and D3).
    Do not re-score the rest of the rubric.
    
    Announce to user:
      "Iteration [N]:
       - Fixed: F2 (2→4) — added 2 reverse items (A5R, C5R)
       - Fixed: C4 (3→4) — same reverse items serve as attention checks
       - Remaining below 3: [list] / none"

5c. Termination

• All items ≥3 within 3 iterations → proceed to Step 6 with a changelog
• Still below 3 after 3 iterations → stop the loop. Proceed to Step 6 but flag the unresolved items in the delivery summary with an explanation (possible reasons: the criterion conflicts with user requirements, the construct is fundamentally hard to test on this dimension, etc.). Ask the user how to proceed.

5d. Guardrails

• Don't over-optimize. If a fix for one criterion would genuinely hurt another (e.g., adding reverse items to satisfy F2 but pushing C4 out of its optimal range), note the trade-off rather than chasing both scores.
• Don't invent new items or sub-dimensions in the loop. Only refine what's already there. Structural changes (new sub-dimensions, different theoretical framework) require going back to Phase 1.
• Keep the changelog factual. "Added 2 reverse items" not "Improved the questionnaire."

Step 6: Output

Produce 3 files in an output/ folder under the user's current working directory (never inside the skill directory) — one per audience.

File 1: questionnaire.md — For respondents

Clean questionnaire only. No metadata, no variable names, no scoring information.

# [Questionnaire Title]

[Opening instructions — purpose, anonymity, time estimate, response format]

## Part 1: [Section Title] ([N] items)

> [Section instructions]

| # | Item | 1 [anchor] | 2 [anchor] | 3 [anchor] | 4 [anchor] | 5 [anchor] |
|---|------|---|---|---|---|---|
| 1 | [item text] | ○ | ○ | ○ | ○ | ○ |

## Part N: Demographics

1. [demographic item with options]

File 2: documentation.md — For the researcher

Combines codebook and methods into a single researcher reference. Use this structure:

# [Questionnaire Title] — Documentation

## 1. Construct Definition
[Operational definition, distinction from related concepts]

## 2. Existing Scale Review
| Scale | Author | Features | Reason for not adopting |
|-------|--------|----------|----------------------|

## 3. Sub-dimension Decomposition & Item Blueprint
| Sub-dimension | Definition | Source | Items |
|---------------|-----------|--------|-------|

## 4. Design Decisions
[Item writing principles applied, response format rationale, ordering logic, bias control measures. Cite references/ frameworks.]

## 5. Codebook

### Variable Table
| Variable | Survey# | ItemID | Sub-dimension | Direction | Scoring |
|----------|---------|--------|---------------|-----------|---------|

### Scoring Instructions
[Sub-dimension scores, composite scores, reverse scoring, missing data handling]

## 6. References

File 3: expert-review-form.md — For domain experts

Read the template at assets/expert-review-template.md. Fill in all {{placeholder}} fields with the actual questionnaire content:

• Replace {{QUESTIONNAIRE_NAME}}, {{CONSTRUCT}}, {{POPULATION}} with questionnaire info
• For each sub-dimension, create a section with its name, definition, and item table
• Fill {{ID}} and {{ITEM_TEXT}} for every item
• Keep the CVI calculation tables at the bottom intact — those are for the researcher to fill after collecting expert ratings
• Adjust the number of expert columns and the pass threshold to the actual number of experts (with ≤5 experts, I-CVI must equal 1.00 to pass; ≥0.78 applies only with 6+ experts)
• Translate the template into the questionnaire language; keep the table structures and formulas intact

Step 7: Delivery

Present results directly in the conversation — do not save self-evaluation as a file.

7a. Final self-evaluation

## Self-Evaluation: [XX]/120 (Grade [X])

| Dimension | Max | Score |
|-----------|-----|-------|
| A. Construct Definition | 15 | [X] |
| ... | | |
| **Total** | **120** | **[X]** |

7b. Revision changelog (only if the auto-remediation loop ran)

## Revisions Made

| Iteration | Item | Before | After | Change |
|-----------|------|--------|-------|--------|
| 1 | F2 | 2/5 | 4/5 | Added 2 reverse items (A5R, C5R) for attention checking |
| 1 | C4 | 3/5 | 4/5 | Same reverse items serve C4's purpose |
| 1 | F3 | 2/5 | 4/5 | Added within-section randomization note to instructions |

7c. Unresolved items (only if loop terminated with items still below 3)

## Unresolved Items After 3 Iterations

| Item | Final Score | Why Still Below 3 | Options for You |
|------|------|------|------|
| [ID] | [X]/5 | [explanation] | [A: accept as trade-off / B: restart with different approach / C: other] |

7d. Output summary & next steps

## Output Summary

| File | Audience | Next Action |
|------|----------|-------------|
| `questionnaire.md` | Respondents | Review wording, upload to survey platform |
| `documentation.md` | You (researcher) | Reference during analysis and paper writing |
| `expert-review-form.md` | Domain experts | Send to 5-7 experts for CVI assessment |

## Recommended Next Steps
1. [If unresolved items exist: address them first]
2. Send `expert-review-form.md` to 5-7 domain experts
3. After collecting expert ratings, calculate CVI using the form's built-in tables
4. Pilot test with 30-50 members of your target population
5. Run item analysis and exploratory factor analysis on pilot data

Adjust the next steps based on the final state — if there are unresolved items, that's the top priority, not expert review.

Scope Boundaries

This skill ends at a design draft ready for expert review. Out of scope — these are follow-up steps the user performs after delivery:

• Statistical validation (EFA/CFA, reliability analysis, item analysis)
• Data collection (expert ratings, cognitive interviews, pilot testing)
• Survey platform deployment

For future maintainers: do not add automated CVI calculation or statistical analysis to this skill — it deliberately stops at the design stage.

Important Notes

• All design decisions must be traceable to principles in references/
• If the user's research purpose is unclear, clarify before proceeding
• Do not add unnecessary items just to increase count
• If an existing validated scale fits the user's needs well, recommend adopting it instead of designing from scratch — explain how to properly cite and adapt it
• Use the conversation language for the questionnaire; keep technical terms in their original language