jmis-data-analysis

Data Analysis (jmis-data-analysis)

When to trigger

• A regression "works" but the identifying variation and threats are not pinned down
• A survey model is run but reliability, validity, and common-method bias are not established
• An ML/analytics artifact reports accuracy but no credible-baseline comparison or robustness
• A reviewer says results are "not robust," "not identified," or "could be reverse causality"

Run the analysis to the JMIS standard for your evidence type

JMIS reviewers are method-literate across econometrics, psychometrics, analytical modeling, and data science. The bar is that the analysis credibly supports the verb in your claim.

Empirical IT-value / platform econometrics

• Identification, made visible. Show the source of variation (shock/rollout/breach), not just the regression. With staggered timing, use Callaway–Sant'Anna / Sun–Abraham / de Chaisemartin–D'Haultfœuille and report a clean event study with leads; for naive TWFE, run a Goodman-Bacon decomposition to expose contamination.
• Endogeneity head-on. For IT investment and platform participation, address reverse causality and selection: IV with a defended exclusion restriction and a strong first stage (weak-IV-robust inference if not), or a credible quasi-experiment; do not lean on "we control for X."
• Inference. Cluster at the assignment/treatment level; handle few clusters (wild-cluster bootstrap); report robustness to specification, sample, and functional form.

Behavioral IS (survey / experiment)

• Measurement first. Report reliability (composite reliability / α), convergent and discriminant validity (AVE, HTMT), and confirm the measurement model before the structural model.
• Common-method bias. Go beyond Harman's single-factor test — use a marker/measured latent-method variable and report whether structural paths survive.
• Structural model. SEM (CB-SEM) or PLS as the theory and sample justify; for moderation/mediation use proper interaction or indirect-effect tests (bootstrapped indirect effects), not a chain of separate regressions.

Analytical / economic models

• State assumptions; prove the propositions; present comparative statics as the result. Sketch proofs in the body, full proofs in an appendix. Show the insight is not an artifact of a knife-edge assumption (robustness to functional form / parameter ranges).

Design-science / ML artifacts

• Benchmark against credible baselines (strong incumbents, not strawmen); report appropriate metrics with variance/uncertainty; validate out-of-sample; tie performance back to the managerial decision the artifact serves.

Report effects so a manager can read them

JMIS values managerial relevance: report economic magnitude (elasticities, marginal effects, dollar value, lift), not only significance. Translate the headline coefficient into what it means for a firm, platform, or decision.

Worked vignette: making robustness answer a threat (illustrative)

A referee writes "the result is not robust." A weak reply adds ten specifications and reports they are "all significant." A JMIS-grade reply names the threat each check defends against: a placebo on a period before the platform change rules out a secular trend; an alternative control group rules out a coincident shock; a sensitivity analysis (e.g., Oster-style bounds on selection) shows the estimate survives plausible unobserved confounding; an alternative measure of the construct rules out operationalization artifacts. Robustness is not a quantity of regressions; it is a mapping from each surviving threat to the check that kills it.

Translate the headline into a managerial number

Suppose the main coefficient implies the ranking redesign cut marginal-seller retention by 6 percentage points. State it that way, then carry it to the platform decision: at the observed seller base that is roughly N exits per quarter and a Y% variety reduction (illustrative). A JMIS reader wants the economic magnitude and its managerial reading, not just p < 0.01.

Checklist

• Empirical: identifying variation named; modern estimator where TWFE would bias; endogeneity addressed, not just controlled
• Behavioral: reliability + convergent/discriminant validity reported; CMB tested beyond Harman's
• Mediation/moderation tested with proper indirect-effect / interaction methods
• Analytical: assumptions stated, propositions proven, robustness to functional form shown
• ML: credible baselines, uncertainty reported, out-of-sample validation
• Inference: clustering correct; robustness to spec/sample shown
• Magnitudes, not just significance, are reported and interpreted for managers
• Reverse causality/selection pre-empted (timing, within-unit, sensitivity bounds)
• Body is self-contained; sample construction, estimator, and inference are traceable

Pre-empt the reverse-causality reflex

On IT-value and platform papers, the most common first-round attack is "this could be reverse causality or selection." Do not wait for it — pre-empt it in the analysis. Show the timing (the cause precedes the effect), use within-unit variation that differences out fixed selection, lean on a quasi-experimental shock where you have one, and where you must use an instrument, defend the exclusion restriction on institutional grounds and report weak-IV-robust inference if the first stage is not strong. Then bound what remains: a selection-sensitivity analysis (e.g., Oster-style δ/bounds) tells a referee how much unobserved confounding it would take to overturn the result. Anticipating the endogeneity objection inside the paper is worth more than answering it in a rebuttal.

Anti-patterns

• "The coefficient is significant" presented as if it were identification
• Endogenous IT/platform variables left to control variables
• Reporting only Harman's single-factor test as the CMB defense
• Discriminant validity skipped; constructs that empirically overlap treated as distinct
• ML accuracy against strawman baselines, or with no out-of-sample check
• Significance stars with no economic magnitude or managerial interpretation
• Waiting for the reverse-causality objection instead of pre-empting it with timing/within-unit/sensitivity evidence
• A robustness section that adds specifications without naming the threat each one defends against
• A load-bearing analysis that exists only in the online appendix, not the body

Keep the analysis reproducible and self-contained

JMIS submissions are double-anonymized and capped at 50 pages, which shapes how you present analysis. Online appendixes are permitted, but the body must stand on its own: a reviewer should be able to follow the identification and the headline result without the appendix, and should never find that a load-bearing robustness check exists only there. Document the sample construction, the estimator, the software/version, and the inference choices clearly enough that the result is traceable; if you reference your own prior code or data, phrase it so it does not de-anonymize you. Tighten the analysis narrative — full diagnostic batteries and secondary specifications belong in the appendix, while the body carries the chain that establishes the contribution.

Hand a stable headline to the next stage

Do not move to jmis-contribution-framing while the headline number is still drifting across specifications. The contribution sentence and the exhibits both depend on a settled effect size and a settled inference, so lock the preferred specification, confirm it survives the robustness battery, and fix the economic-magnitude interpretation before framing the claim. A contribution built on a coefficient that later moves forces a rewrite of the intro, the discussion, and the abstract.

Output format

【Evidence type】firm/platform econometrics / survey-SEM / experiment / analytical / ML
【Identification or validity】variation source + diagnostics / measurement model + CMB test / proof + robustness
【Inference】clustering / weak-IV-robust / bootstrap as applicable
【Magnitude】economic/managerial interpretation of the headline effect
【Robustness done】spec / sample / functional-form / out-of-sample
【Next step】jmis-contribution-framing