Asq Data Analysis

Data Analysis & Evidence (asq-data-analysis)

When to trigger

• You have data but the path from data to theory is opaque
• Qualitative: your quotes are decorative, not evidentiary; coding is undocumented
• Quantitative: main results exist but robustness/alternative explanations are thin
• Reviewers ask "how did you get from your data to these constructs?"

Branch A — Qualitative analysis (the data-to-theory link)

ASQ expects readers to see how raw data became theory — its guidelines stress that helping readers understand how the research was performed and ensuring the trustworthiness of published work are explicit aims (verify at journals.sagepub.com/author-instructions/asq). Qualitative rigor is judged on its own terms here, not held to a quantitative yardstick. Make the analytic ladder visible.

• Transparent coding. Describe first-order codes (informant terms), second-order themes (researcher constructs), and aggregate dimensions — the Gioia-style data structure — or an equivalent (Eisenhardt cross-case, Langley process bracketing). State who coded, how disagreements were resolved, and how iteration proceeded.
• Data-to-theory table. Provide a table linking representative raw evidence → codes → constructs, so the inference is auditable (see asq-tables-figures).
• Power quotes vs. proof quotes. Use a few vivid "power quotes" in the body; place corroborating "proof quotes" in tables/appendix. Quotes must carry the claim, not illustrate it after the fact.
• Evidence for each construct. Every theoretical construct should be backed by patterned evidence across informants/cases, with counts or prevalence where appropriate.
• Negative cases. Report disconfirming instances and how they refined the theory.
• Process display. For process theory, show the temporal/event structure (timeline, phase model, visual mapping) — as Barley (1986, ASQ) did in tracing how CT scanners restructured radiology departments over time.

Branch B — Quantitative analysis

• Main models match the design (FE/RE, event-history, multilevel, network models); report clearly with appropriate standard errors (clustering at the right level).
• Robustness that targets the theory's threats: alternative measures, alternative samples, alternative specifications, endogeneity checks, and modern staggered-DiD diagnostics if relevant.
• Mechanism evidence. Don't stop at the reduced-form relationship — provide mediation/moderation or supplementary tests that probe why.
• Effect interpretation. Report and interpret magnitudes in organizational terms, not just significance stars.
• Alternative explanations are tested, not waved away.

Either branch — the "so what" of the evidence

• Tie every analytic result back to the mechanism and the surprise.
• Distinguish what the data can and cannot establish — overclaiming is a fast path to rejection.
• Prepare the exhibits jointly with asq-tables-figures.

Execution bridge (StatsPAI / Stata MCP)

Run the battery, don't just enumerate it. Full map: execution-with-mcp. ASQ wants a clean causal or well-identified observational design behind an organizational-theory contribution; reduced-form estimation fits the chain below, interpretive work does not.

• 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

• Qual: data structure (first-order → second-order → dimensions) is documented
• Qual: a data-to-theory / evidence table is built; quotes carry (not decorate) claims
• Qual: negative cases reported and used to refine theory
• Quant: standard errors clustered at the appropriate level
• Quant: robustness targets the theory's threats; effect magnitudes interpreted
• Mechanism is probed, not just the headline relationship
• Claims are matched to what the evidence can actually support

Anti-patterns

• "Anecdotal" qualitative work: a few cherry-picked quotes with no coding transparency
• Quotes that illustrate a pre-set conclusion rather than generating/supporting it
• Quantitative robustness theater: many tables that never address the real threat
• Reporting significance with no interpretation of organizational magnitude
• Stopping at the X→Y relationship without evidence on the mechanism
• Overclaiming causality or generalizability beyond the design

Output format

【Branch】qualitative / quantitative
【Data-to-theory link】data structure / mechanism tests done
【Key evidence】power quotes or main estimates
【Robustness/trustworthiness】checks completed + gaps
【What evidence cannot show】explicit limits
【Next step】asq-contribution-framing

Source: brycewang-stanford/Awesome-Journal-Skills → Administrative-Science-Quarterly-Skills/skills/asq-data-analysis/SKILL.md