| name | orgstud-data-analysis |
| description | Use when executing and reporting the analysis for an Organization Studies (OS) manuscript — qualitative coding and the data-to-theory ladder, process analysis, or quantitative estimation and robustness. Makes the evidence-to-theory link transparent; it does not design the study (see orgstud-methods). |
Data Analysis & Evidence (orgstud-data-analysis)
When to trigger
- You have data but the path from raw material to theory is opaque
- Qualitative: your quotes are decorative, not evidentiary; the coding is undocumented
- Process: you have events but no visible analytic structure turning them into a model
- Quantitative: main results exist but robustness and alternative explanations are thin
- A reviewer asks "how did you get from your data to these constructs?"
OS expects readers to see how data became theory
OS's interpretive, European tradition makes analytic transparency a first-class criterion — qualitative rigor is judged on its own terms, not against a quantitative yardstick. The reader must be able to audit the inference from raw data to theoretical claim. Make the analytic ladder visible.
Branch A — Qualitative analysis (the data-to-theory ladder)
- Transparent coding. Show first-order codes (informant terms), second-order themes (researcher constructs), and aggregate dimensions — the Gioia data structure — or an equivalent (Eisenhardt cross-case tables, Langley process bracketing). State who coded, how disagreements were resolved, and how iteration with theory proceeded.
- Data-to-theory table. A table linking representative raw evidence → codes → constructs, so the inference is auditable (build it with
orgstud-tables-figures).
- Power quotes vs. proof quotes. A few vivid "power quotes" in the body carry the argument; corroborating "proof quotes" sit in tables/appendix. Quotes must carry the claim, not illustrate a conclusion reached elsewhere.
- Evidence for each construct. Every construct backed by patterned evidence across informants/cases, with prevalence where appropriate.
- Negative cases. Report disconfirming instances and how they refined the theory — central to trustworthiness at OS.
- Process display. For process theory, show the temporal/event structure (timeline, phase model, visual mapping); make the transitions between phases analytically explicit, not just narrated.
Branch B — Process analysis (when the contribution is a process model)
- Choose a process strategy explicitly: narrative, temporal bracketing, visual mapping, grounded theory, or alternate templates (Langley). Say why it fits.
- Identify events, sequences, and turning points; show what triggers each transition and what each phase accomplishes that the prior could not.
- Distinguish real-time from retrospective data and address the recall/hindsight risks of each.
- The output is a process model figure plus the analytic account that earns it.
Branch C — Quantitative analysis
- Main models match the design (FE/RE, event-history, multilevel, network); standard errors clustered at the right level.
- Robustness that targets the theory's threats — alternative measures, samples, specifications, endogeneity checks, modern staggered-DiD diagnostics if relevant — not a wall of tables that never address the real threat.
- Mechanism evidence. Don't stop at the reduced-form relationship; probe why (mediation/moderation or supplementary tests).
- Effect interpretation in organizational terms — magnitudes, not just significance.
Either branch — the "so what" of the evidence
- Tie every analytic result back to the mechanism and the theoretical puzzle.
- Distinguish what the data can and cannot establish — overclaiming is a fast OS rejection.
- Prepare exhibits jointly with
orgstud-tables-figures.
Execution bridge (StatsPAI / Stata MCP)
Run the battery, don't just enumerate it. Full map:
execution-with-mcp. Organization Studies is largely qualitative/theoretical; use the chain below only for its quantitative-empirical papers, and say so when a study is interpretive.
- Many outcomes / specifications:
romano_wolf (step-down FWER) or
benjamini_hochberg — report the adjusted threshold.
- OVB sensitivity:
oster_delta / sensemakr.
- Inference:
wild_cluster_bootstrap (few clusters), twoway_cluster / conley;
multilevel data → cluster at the right level.
- Re-fit off one handle:
audit_result(result_id) lists the missing checks and the
exact suggest_function for each.
- Exhibits:
etable / did_summary_to_latex from the handle — no retyped numbers.
Keep the decisive checks in the body and the exhaustive battery in the appendix. See the
executed chain in the JF execution walkthrough.
Checklist
Anti-patterns
- "Anecdotal" qualitative work: cherry-picked quotes with no coding transparency
- Quotes that illustrate a pre-set conclusion rather than generating/supporting it
- A process "model" that is really a narrative with no analytic structure or transition logic
- Robustness theater: many tables that never address the real identification threat
- Reporting significance with no interpretation of organizational magnitude
- Overclaiming causality or generalizability beyond what the design supports
Output format
【Branch】qualitative / process / quantitative
【Data-to-theory link】data structure / process strategy / mechanism tests done
【Key evidence】power quotes, the process model, or main estimates
【Trustworthiness/robustness】checks completed + gaps (negative cases, clustering, alt explanations)
【What evidence cannot show】explicit limits
【Next skill】orgstud-contribution-framing
