Questionnaire Codebook Maker

Turn questionnaire items into clean research codebooks, scoring rules, reverse-scoring checks, variable names, and analysis-ready TSV/Markdown tables.

Purpose

Use this skill when the user needs to organize questionnaire items, scale dimensions, variable names, scoring rules, reverse-scored items, or data-entry specifications for psychology, education, public-health, social-science, or student research projects.

The core output is a clean codebook that can be copied into Word, Excel, SPSS, R, Mplus, or a research protocol.

When to activate

Activate this skill when the user asks for any of the following:

• Convert questionnaire items into a codebook.
• Generate variable names for survey items.
• Mark reverse-scored items and compute scoring rules.
• Build a table for dimensions, item numbers, response anchors, and total scores.
• Prepare data-entry rules for Excel/SPSS/R/Mplus.
• Check whether questionnaire scoring is internally consistent.
• Convert a messy scale description into an analysis-ready table.

Required output behavior

When the user provides questionnaire items, output the following sections unless they request a different format:

1. 量表代码本 / Codebook
   Provide a TSV table with these columns: variable, item_id, dimension, item_text, response_range, reverse_scored, scoring_note, missing_rule.

2. 计分规则 / Scoring rules
   Explain how to compute dimension scores and total scores. State whether to use sum scores or mean scores. If missing-value rules are not provided, recommend a transparent rule such as "calculate the mean score only when at least 80% of items in the dimension are non-missing".

3. 反向计分检查 / Reverse-scoring check
   List reverse-scored variables and give the formula. For a 1–5 item, use reversed = 6 - original. For a 0–4 item, use reversed = 4 - original.

4. 分析软件变量建议 / Analysis-ready variable names
   Provide short, readable variable names. Avoid spaces, Chinese punctuation, hyphens, and overly long names. Use prefixes such as dep_, anx_, smu_, sleep_, neuro_, or eant_ when relevant.

5. 质量控制提示 / QC checklist
   Mention duplicate item IDs, inconsistent response ranges, missing dimensions, and reverse-scoring ambiguity.

Variable-naming rules

• Use lowercase letters, numbers, and underscores only.
• Start with a letter.
• Keep names under 20 characters when possible.
• Preserve scale order using two-digit item numbers: dep_01, dep_02, anx_01.
• Use dimension prefixes when multiple dimensions exist.
• For Mplus compatibility, avoid names longer than 8 characters if the user explicitly asks for legacy Mplus-safe names.

Reverse-scoring formulas

For an item with minimum min and maximum max:

reversed_score = min + max - original_score

Common examples:

• 1–5 scale: reverse = 6 - original.
• 1–7 scale: reverse = 8 - original.
• 0–4 scale: reverse = 4 - original.
• 0–10 scale: reverse = 10 - original.

Missing-value rule recommendations

Use the user's stated rule when available. If no rule is given:

• Dimension mean score: compute if at least 80% of dimension items are valid.
• Total mean score: compute if at least 80% of all scale items are valid.
• Never silently impute missing values unless the user explicitly asks for imputation.

Optional helper script

This skill includes scripts/make_codebook.py, which converts a simple CSV item file into a Markdown codebook and a TSV variable map. It uses only Python standard-library modules.

Input CSV columns:

item_id,item_text,dimension,scale_min,scale_max,reverse

Example command:

bash

python3 scripts/make_codebook.py examples/demo_items.csv --out-dir output

If python3 is not available, try:

bash

python scripts/make_codebook.py examples/demo_items.csv --out-dir output

Safety and integrity

• Do not invent item wording or scoring rules.
• Mark uncertain reverse-scoring decisions as uncertain.
• Do not change original item meaning when shortening labels.
• Do not assume a clinical cutoff unless the user provides the scale manual or asks for verified lookup.
• When converting scoring rules, distinguish between item-level reverse scoring, dimension score calculation, and total score calculation.