name: aos-data-analysis
description: Use when analyzing the material of an Accounting, Organizations and Society (AOS) manuscript — coding and interpreting qualitative field data, estimating experimental and survey models, or running theory-laden archival analyses, with an audit trail appropriate to each tradition. Analyzes and reports; it does not design the study (aos-methods).
Analysis & Evidence Craft (aos-data-analysis)
When to trigger
- Interviews, observations, and documents are collected and must become findings
- Experimental or survey data are in and the estimation plan is unsettled
- A mixed-methods paper needs its qualitative and quantitative strands reconciled
- Reviewers will probe the traceability of interpretations or the fit of the statistics
Qualitative analysis (field / historical material)
- Code with the lens on. First-order codes stay close to informants' language; second-order categories translate them into the paper's theoretical vocabulary; the movement between the two is the analysis. Keep the codebook versioned so you can show how categories evolved.
- Work the anomalies. The abductive engine: catalog episodes the current theory cannot absorb, and let them force conceptual revision — this is where AOS papers earn their contribution.
- Build the evidentiary chain. Every conceptual claim should trace to identifiable material (interview number, meeting observed, document). Maintain a claim → evidence register; reviewers increasingly expect a data-structure or evidence table.
- Weigh counter-evidence. Report material that resists the interpretation and say why the reading survives; interpretive rigor at AOS is demonstrated, not asserted.
- Historical work: corroborate across independent archives; date claims precisely; distinguish what the sources show from what the genealogy argues.
Experimental and survey analysis
- Match the model to the randomized design: ANOVA/ANCOVA with planned contrasts for factorial experiments; report cell means, standard deviations, per-cell n, and effect sizes, not p-values alone.
- Test the theorized process: mediation with bootstrapped confidence intervals; moderation exactly as predicted, with simple-effects follow-ups.
- Respect the randomization unit; report manipulation-check results and pre-specified exclusions transparently.
- Surveys: reliability and validity evidence (alpha/CR, factor structure), common-method-bias diagnostics, and models matched to the nesting of the data.
Archival-with-theory analysis
- Standard panel hygiene (fixed effects suited to the institutional claim, standard errors clustered to the data structure, documented sample screens) — but keep the estimand tied to the organizational/institutional construct, and interpret magnitudes in the theory's terms rather than as pricing effects.
Execution bridge (StatsPAI / Stata MCP)
For the quantitative lane of an AOS paper — experiments, surveys, and archival-with-theory
designs — execute and audit rather than only specify. Full map:
execution-with-mcp.
AOS is mixed-methods: route only the statistical strand through this bridge, and let the
qualitative strand keep its own audit trail (codebook, evidence register) outside it.
detect_design → recommend → fit with as_handle=true → audit_result to enumerate
the checks the design owes before a reviewer asks.- Experiments / many outcomes: randomization-based inference plus
romano_wolf or
benjamini_hochberg for the multi-outcome families behavioral reviewers flag.
- Surveys / nested data: cluster at the right level;
wild_cluster_bootstrap when
clusters are few.
- Institutional-shift panels:
callaway_santanna / sun_abraham with
bacon_decomposition and pre-trend evidence if a staggered adoption carries the claim;
oster_delta / sensemakr for OVB sensitivity.
- Exhibits:
etable / plot_from_result straight from the fitted handle — never
retype numbers into tables.
Keep decisive checks in the body, the battery in an appendix, and reconcile every printed
number with the script that produced it.
Reproducibility and the audit trail
- Qualitative: retain the coded corpus, codebook versions, and the claim–evidence register for the life of the project (subject to consent terms); describe the analytic process in the paper concretely enough to be assessed.
- Quantitative: scripts regenerate every exhibit from raw data; exclusions, transformations, and winsorizing documented; share instruments and code where consent and confidentiality allow, and state any restrictions honestly.
Checklist
Anti-patterns
- Quote-stitching: colorful excerpts arranged to illustrate a story decided in advance.
- Counting qualitative data as if frequency were meaning ("mentioned in 63% of interviews").
- Stars without process in experiments — a significant main effect with no mediator evidence.
- Untraceable interpretation: findings a skeptic cannot follow back to specific material.
Output format
【Strand(s)】qualitative / experimental / survey / archival — analysis state ...
【Evidence chain】codebook + claim-evidence register OR scripts + audit status ...
【Process tests】mediation / moderation / pre-trends as applicable ...
【Counter-evidence】weighed and reported? ...
【Reproducibility】what regenerates, what is restricted and why ...
【Next step】aos-contribution-framing