misq-data-analysis
GitHub用于MISQ实证核心分析，涵盖行为、经济、设计科学及定性研究的测量与结构模型评估。执行并报告分析结果，准备符合传统的透明性材料，但不涉及研究设计或贡献框架构建。
Trigger Scenarios
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill misq-data-analysis -g -y
SKILL.md
Frontmatter
{
"name": "misq-data-analysis",
"description": "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)."
}
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) orbenjamini_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 exactsuggest_functionfor each. - Exhibits:
etable/did_summary_to_latexfrom 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
Version History
- 1839142 Current 2026-07-05 14:02
