| name | analyze-quantitative-data |
| description | Use when analyzing quantitative social science data following Neuman's Social Research Methods Ch 12 — codebook creation, data cleaning, univariate descriptive statistics, bivariate association, multivariate analysis (elaboration paradigm + multiple regression), and inferential statistics (significance testing, Type I/II errors). Trigger on requests like "analyze this survey data", "run regression on X", "check bivariate relationship between A and B", "what does this codebook need", "interpret these statistics", "我要做定量分析", "帮我跑回归", "分析这份调查数据". |
Analyze Quantitative Data · Neuman Ch 12 Workflow
This skill guides users through systematic quantitative data analysis based on
W. Lawrence Neuman, Social Research Methods: Qualitative and Quantitative Approaches, 7e, Chapter 12 (p.393-430).
The chapter's procedure is decomposed into 7 sequential steps, each with a
dedicated prompt template that enforces methodological rigor.
When to invoke
Invoke this skill when the user:
- Has a survey, experiment, or numerical dataset to analyze
- Asks "how do I analyze X?" with quantitative data
- Wants to run a regression, t-test, ANOVA, chi-square
- Needs to interpret statistical output
- Is preparing a Methods or Results section for a quantitative paper
Do not invoke for:
- Pure data engineering / ETL (no analysis intent)
- Machine learning prediction (different methodology)
- Qualitative analysis (use
analyze-qualitative-data instead)
The 7-step procedure (Neuman Ch 12)
| # | Step | Prompt file | Neuman pages |
|---|
| 1 | Build codebook | prompts/01-codebook.md | 393-394 |
| 2 | Clean data (wild code + contingency) | prompts/02-clean-data.md | 397 |
| 3 | Univariate analysis (frequency, central tendency, variation) | prompts/03-univariate.md | 397-403 |
| 4 | Bivariate analysis (scattergram, percentaged tables, association measures) | prompts/04-bivariate.md | 403-416 |
| 5 | Multivariate elaboration (5 patterns) | prompts/05-multivariate-elaboration.md | 417-420 |
| 6 | Multiple regression | prompts/06-multivariate-regression.md | 420-422 |
| 7 | Inferential statistics | prompts/07-inferential.md | 422-426 |
How to use
Sequential mode (full study, beginner)
Walk through 1 → 2 → 3 → 4 → 6 → 7 in order. Step 5 (elaboration) only if user
wants to test for spurious relationships before regression.
Targeted mode (specific question)
Jump directly to the relevant step. Common patterns:
- "I need to interpret a regression" → step 6 → 7
- "Is my X-Y relationship spurious?" → step 5
- "Just describe my variables" → step 3
Audit mode (review existing analysis)
Run user's analysis through steps 2 → 3 → 4 to check for missed cleaning, lurking
distributional issues, or wrong-level association measures.
Cross-cutting principles (apply at every step)
-
Don't assume — ask back. If variable types or research question are
unclear, request clarification before generating code.
-
Measurement level determines tool. Wrong level (e.g., Pearson r on ordinal
data) is the most common silent error. Reject and re-prompt.
-
Report sample size N at every output. Listwise deletion shrinks N silently;
always disclose.
-
Effect size before p-value. Statistical significance can be trivial in
large samples; substantive significance is the user's judgment.
-
Surface uncertainty. When AI is unsure (e.g., whether a missing pattern is
random), say so explicitly and propose ways for user to verify.
What this skill does NOT do
- Decide research questions or hypotheses — that's research design (Neuman Ch 6)
- Choose methodology — handled in earlier course content
- Verify causal identification — Mill's methods or experimental design needed
- Replace methodological consultation — for complex designs (mediation, SEM,
multilevel models, IV/DID/RDD), recommend the user find a domain expert after
steps 1-5 to verify key judgments
Required outputs at each step
Every step's prompt enforces:
- Sample size N (with listwise deletion accounting)
- Variable measurement levels (validated against statistical choice)
- Suspect patterns flagged (collinearity, outliers, severe skew, near-zero
variance, etc.)
- Code reproducible by the user (no placeholders, runnable)
- Plain-language interpretation alongside numerical output
Companion skill
For mixed-methods projects, see analyze-qualitative-data (Neuman Ch 14).
