Analyze Quantitative Data

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)

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)

1. Don't assume — ask back. If variable types or research question are unclear, request clarification before generating code.

2. Measurement level determines tool. Wrong level (e.g., Pearson r on ordinal data) is the most common silent error. Reject and re-prompt.

3. Report sample size N at every output. Listwise deletion shrinks N silently; always disclose.

4. Effect size before p-value. Statistical significance can be trivial in large samples; substantive significance is the user's judgment.

5. 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).