Jms Data Analysis

Data Analysis (jms-data-analysis)

When to trigger

• Estimates are in but reviewers question endogeneity, robustness, or the indirect-effect claim
• A qualitative analysis reaches findings but the path from data to constructs is not auditable
• Effects hinge on a single specification with no robustness
• A mediation/moderation result is reported without the analysis JMS expects
• A reviewer says "the analysis does not support the claim" or "I can't see how you got here"

The JMS analysis bar — two idioms, one standard

JMS judges analysis by whether it credibly supports the theoretical claim, in whichever idiom the study uses. Quantitative work is held to identification and robustness standards; qualitative work is held to trustworthiness and transparency standards. Use the path that matches your design; do not import quant criteria (p-values, effect sizes) to judge a qualitative paper, or qualitative looseness into a quantitative one.

Quantitative path

• Specification & estimator: match the estimator to the data structure (OLS/GLM, fixed effects for panels, SEM for latent constructs and full mediation models, multilevel models for nested data). State why.
• Mediation done right: test indirect effects with bootstrapped confidence intervals (not Baron–Kenny steps alone); but remember an indirect effect is evidence for a theorised mechanism, not a substitute for theorising it.
• Moderation: plot the interaction; report simple slopes and the region of significance; do not over-read a marginal interaction.
• Endogeneity & robustness: run the identification strategy planned in jms-methods (FE, IV/2SLS, DiD, matching) and a robustness battery — alternative measures, alternative samples, controls in/out — each tied to a named threat, not a fishing expedition.
• Measurement evidence: report reliability (alpha/CR), convergent/discriminant validity (AVE), and CFA fit; address CMB with a designed test, not only Harman.

Qualitative path

• Coding transparency: show the move from first-order codes → second-order themes → aggregate dimensions; a reader should be able to trace a quote to a construct.
• Abductive logic: make the iteration between data and theory explicit — surprising observations, the candidate explanations considered, why the retained one fits best. JMS rewards visible abduction, not a tidy after-the-fact story.
• Evidentiary support: a representative-quotes table tying each theme to data; report disconfirming/negative cases and how they refined the model.
• Trustworthiness: state the procedures used (audit trail, member checking, inter-coder reliability where appropriate, prolonged engagement) so credibility is demonstrable.
• From narrative to mechanism: for process work, show what drives the transitions across phases, not just the sequence.

Execution bridge (StatsPAI / Stata MCP)

Run the battery, don't just enumerate it. Full map: execution-with-mcp. JMS mixes qualitative and quantitative management research; the chain below is for the quantitative-empirical lane.

• 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

• Path chosen (quantitative / qualitative) and matched to the design
• Quant: estimator fits the data; mediation via bootstrapped CIs; interactions plotted with simple slopes
• Quant: each robustness check tied to a named threat; CMB addressed by design; CFA/validity reported
• Qual: first-order → second-order → aggregate-dimension chain is auditable
• Qual: abductive reasoning visible; representative quotes table; negative cases reported
• Qual: trustworthiness procedures stated
• The claim never exceeds what the analysis supports

Anti-patterns

• Mechanism by mediation: claiming a process exists only because the indirect effect is significant
• Robustness theatre: a wall of checks that never names the threat each one rules out
• p-hacking / specification mining: the one significant model among many, presented as the model
• Quote-mining: cherry-picked quotes with no systematic coding behind them
• Tidy abduction: a too-clean narrative that hides the messy data-theory iteration reviewers want to see
• Idiom confusion: judging a qualitative paper by sample size and significance, or a quant paper by "richness"

Output format

【Path】quantitative / qualitative
【Quant】estimator + why; mediation (bootstrap CI); moderation (simple slopes); robustness→threats; CMB/CFA
【Qual】coding chain (1st→2nd→dimensions); abduction made visible; quotes table; negative cases; trustworthiness
【Claim support】does the analysis carry the theoretical claim? gaps …
【Next step】jms-tables-figures