misq-data-analysis

Research

Use when running and reporting the empirical core of a MIS Quarterly manuscript — measurement and structural models (PLS/CB-SEM) for behavioral IS, causal identification and robustness for economics-of-IS, artifact evaluation for design science, or trustworthiness for qualitative IS — and assembling the genre-appropriate transparency materials. Executes/reports the analysis; it does not design the study (misq-methods) or frame the contribution (misq-contribution-framing).

QUICK START

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Source SKILL.md: https://github.com/brycewang-stanford/Awesome-Journal-Skills/blob/HEAD/MIS-Quarterly-Skills/skills/misq-data-analysis/SKILL.md

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Data Analysis, Evaluation & Transparency (misq-data-analysis)

When to trigger

Data are collected (or the artifact is built) and it is time to estimate, evaluate, and report
A reviewer probes measurement validity, identification, artifact utility, or replicability
You must prepare the pluralistic transparency materials uploaded at submission

Analyze by tradition — there is no single MISQ estimator

Tradition	What to report
Behavioral	Reliability (alpha/CR), CFA or PLS measurement model, AVE, discriminant validity (Fornell-Larcker / HTMT); structural paths with effect sizes; mediation via bootstrap CIs; moderation via simple slopes
Economics of IS	The identifying variation, parallel-trends/exogeneity evidence, clustered SEs, and a battery of robustness checks (alternative specifications, placebo/event-time tests, sensitivity to assumptions)
Design science	Artifact performance against credible baselines on held-out data; ablations; field/A-B or expert evaluation tied to the design propositions; cost/utility discussion
Organizational / qualitative	A transparent data structure (codes → themes → dimensions), an audit trail, and representative quotations so the path from raw data to constructs is traceable

Behavioral IS: defend measurement before structure

IS reviewers expect the measurement model first. PLS-SEM is common in IS for predictive/formative models; covariance-based SEM for theory-testing with reflective constructs — justify the choice. Report reliabilities, AVE, and discriminant validity, and address common-method bias beyond a single-factor test (marker variable, unmeasured method factor, or showing interactions survive). Then report structural paths with effect sizes, not just significance.

Economics of IS: make the causal claim earn its keep

Lead with the identification logic, then stress-test it: alternative specifications, placebo and event-study plots, sensitivity to the key assumption, and clustering that matches the data structure. Report magnitudes and their economic meaning, not just stars.

Design science: evaluate the artifact, not just the math

Demonstrate utility for the real problem: benchmark against the baselines a skeptic would name, run ablations to show which design principles matter, and connect each result back to a design proposition. Where possible, evaluate in a realistic field setting.

Assemble the pluralistic transparency materials

MISQ's research-transparency policy is genre-appropriate, not a single template. Document the study's design, data, and analysis to the standard of your tradition, and include procedures and/or code sufficient to permit replication. The transparency commitment is declared and uploaded at submission (Step 2, Miscellaneous). Consider replication badges and the AIS Transactions on Replication Research collaboration. Plan code/data sharing within confidentiality and platform terms.

Execution bridge (StatsPAI / Stata MCP)

Run the battery, don't just enumerate it. Full map: execution-with-mcp. MISQ is empirical IS — surveys, econometric panels, experiments, and design science; the chain below serves the causal / econometric lane, while design-science artifacts use their own evaluation standards.

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

Analysis matches the tradition (SEM / causal econometrics / artifact evaluation / qualitative)
Behavioral: reliability, AVE, discriminant validity, CMB beyond single-factor; effect sizes
Economics: identification defended, robustness/placebo tests, clustered SEs, magnitudes
Design science: baselines, ablations, field/expert evaluation tied to design propositions
Qualitative: traceable data structure and audit trail
Genre-appropriate transparency package with procedures/code for replication prepared

Anti-patterns

Single-factor test as the sole common-method-bias defense.
A causal claim with no identification and no robustness battery.
A design-science "evaluation" that benchmarks against no credible baseline.
Reporting p-values with no effect sizes or practical/economic interpretation.
Treating transparency as an afterthought rather than genre-appropriate documentation.

Output format

【Tradition & analysis】SEM / DiD-IV-RD / artifact eval / qualitative
【Validity or identification】measurement + CMB / identification + robustness / baselines + ablations
【Effect sizes / utility】magnitudes and meaning
【Transparency package】procedures/code for replication: ready/gaps
【Next step】misq-contribution-framing