Quantitative Findings Writer

Draft Results/Findings sections for quantitative sociology articles using structural patterns discovered in 83 Social Problems and Social Forces articles.

Project Integration

This skill reads from project.yaml when available:

From project.yaml

type: quantitative # This skill is for quantitative projects paths: drafts: drafts/sections/ tables: output/tables/ figures: output/figures/

Project type: This skill is designed for quantitative projects.

Consumes output from r-analyst or stata-analyst (tables, figures, interpretation memos from Phase 5).

Updates progress.yaml when complete:

status: results_draft: done artifacts: results_section: drafts/sections/results-section.md

Connection to Other Skills

Skill Relationship Details

r-analyst Upstream Produces tables, figures, interpretation memos (Phase 5 output)

stata-analyst Upstream Same as r-analyst but for Stata

article-bookends Downstream Takes results section as input for framing

methods-writer Parallel Methods section written alongside or before results

lit-synthesis Upstream Provides theoretical framework for theory-linking

prose-craft Craft guide Sentence/paragraph benchmarks (evaluative mode); tone, anti-LLM rules

File Management

This skill uses git to track progress across phases. Before modifying any output file at a new phase:

• Stage and commit current state: git add [files] && git commit -m "quant-findings-writer: Phase N complete"

• Then proceed with modifications.

Do NOT create version-suffixed copies (e.g., -v2 , -final , -working ). The git history serves as the version trail.

Workflow

Phase 1: Orient

Gather from the user:

• Method type: secondary-survey-analysis, administrative-data, or content-analysis

• Key results: tables, model output, or thematic findings to present

• Theoretical predictions: hypotheses or expectations the results address

• Target length: typical is 12-25 paragraphs (2,000-5,000 words)

If the user has already written a draft, read it and assess which cluster it most resembles before suggesting revisions.

Phase 2: Select Cluster

Present the 7 clusters with their canonical arcs. Recommend 1-2 based on method type and analytic strategy:

Cluster Best for Arc

Progressive Model Builder Regression-heavy papers building from simple to complex specs DESCRIBE → BASELINE → ELABORATE → MECHANISM → ROBUSTNESS

Hypothesis Tester Papers with numbered H1/H2/H3 predictions SETUP → BASELINE → ELABORATE → SUBGROUP → SUMMARY

Decomposition Analyst Gap/disparity papers using Oaxaca-Blinder, mediation DESCRIBE → BASELINE → DECOMPOSE → MECHANISM → ROBUSTNESS

Subgroup Comparator Heterogeneity-focused papers (by race, gender, class) DESCRIBE → BASELINE → SUBGROUP → COMPARISON → ROBUSTNESS

Temporal Tracker Event studies, trend analysis, periodization TEMPORAL → BASELINE → TEMPORAL → SUBGROUP → ROBUSTNESS

Thematic Explorer Content analysis with qualitative themes/frames THEMATIC → THEMATIC → THEMATIC → SUMMARY

Causal Inference Specialist DiD, IV, RDD, matching designs SETUP → BASELINE → ELABORATE → ROBUSTNESS → MECHANISM

Selection heuristics:

• Survey data + model progression → Progressive Model Builder

• Admin data + quasi-experimental design → Causal Inference Specialist

• Admin data + inequality decomposition → Decomposition Analyst

• Any method + explicit hypotheses → Hypothesis Tester

• Any method + group comparisons as central question → Subgroup Comparator

• Content analysis + thematic coding → Thematic Explorer

• Panel/longitudinal + change over time → Temporal Tracker

After the user selects a cluster, read the matching guide from clusters/{cluster-name}.md for detailed arc, paragraph budget, signature techniques, and exemplar patterns.

Phase 3: Build the Arc

Using the cluster guide, construct a section outline:

• Map each major finding/table to a MOVE from the standardized vocabulary

• Sequence moves following the cluster's canonical arc

• Allocate paragraphs using the cluster's paragraph budget

• Identify the opening and closing moves

Standardized move vocabulary:

Move Function

DESCRIBE Descriptive statistics, sample overview, bivariate patterns

SETUP Methodological restatement, analytic strategy recap

BASELINE Initial/simple models, main effects without interactions

ELABORATE Add complexity: interactions, nonlinearities, mediators

DECOMPOSE Formal decomposition (Oaxaca-Blinder, mediation, etc.)

SUBGROUP Heterogeneity by subgroups (race, gender, class)

MECHANISM Mediation, mechanism tests, process tracing

ROBUSTNESS Sensitivity analysis, alternative specs, placebo tests

THEMATIC Substantive theme/topic analysis

TEMPORAL Over-time patterns, periodization, event studies

COMPARISON Cross-group or cross-context comparison

VISUAL Key figure/visualization driving the narrative

SUMMARY Brief recap paragraph

TRANSITION Bridge to discussion section

Present the arc to the user as a numbered outline with paragraph counts per move.

Phase 4: Draft

Write each move following corpus norms. Consult techniques/techniques.md for the full technique catalog.

Opening paragraph (choose one based on cluster):

• Table reference (58% of corpus): "Table 2 presents results from..."

• Sample description (20%): "Before turning to multivariate models, I describe..."

• Hypothesis restatement (14%): "Recall that H1 predicted..."

• Methodological setup (5%): "To estimate the causal effect, I use..."

Body paragraphs:

• Lead with the finding, not the method

• Translate every key coefficient into substantive terms (85% of corpus does this)

• Use attenuation tracking when adding controls: "the coefficient falls from .34 to .21"

• Connect to theory at moderate density: ~1 theory reference per 3-4 paragraphs for most clusters

• Report null findings transparently (45% of corpus does this)

Closing paragraph (choose one):

• Robustness cascade (18%): "Results are robust to..."

• Strongest finding (18%): save the most important result for the end

• Subgroup analysis (17%): end with heterogeneity

• Supplemental reference (14%): "Additional specifications in Appendix Table A3..."

• Summary (11%): brief recap of all findings

Cross-cutting norms:

• Median section length: ~18 paragraphs, 3 tables/figures referenced

• 75% use hybrid table strategy: tables anchor the narrative but prose interprets

• 55% link results to theory heavily; 40% moderately; only 5% minimally

• Distinguish statistical from practical significance when warranted

Phase 5: Calibrate

After drafting, check against cluster norms:

• Does the arc match the canonical sequence?

• Is the paragraph budget balanced?

• Are tables referenced with interpretive guidance, not just pointed at?

• Is theory linking at the right density for the cluster?

• Are robustness checks present if the cluster expects them?

• Are null findings acknowledged rather than buried?

Present the draft with a brief calibration note.

Reference Files

• Cluster guides (read the one matching the selected cluster):

• clusters/progressive-model-builder.md

• clusters/hypothesis-tester.md

• clusters/decomposition-analyst.md

• clusters/subgroup-comparator.md

• clusters/temporal-tracker.md

• clusters/thematic-explorer.md

• clusters/causal-inference-specialist.md

• techniques/techniques.md — 20 writing techniques with descriptions and frequency data

• references/corpus-statistics.md — summary statistics from the 83-article analysis corpus