misq-methods
GitHub为MISQ论文选择并辩护研究设计，匹配行为、设计科学、IS经济学或组织传统。评估方法适用性，规划设计科学效用验证，消除共同方法偏差和因果识别威胁，并管理页面限制。
Trigger Scenarios
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill misq-methods -g -y
SKILL.md
Frontmatter
{
"name": "misq-methods",
"description": "Use when choosing and defending the research design for a MIS Quarterly manuscript — a behavioral survey\/experiment, an economics-of-IS identification strategy, a design-science build-and-evaluate cycle, or an organizational\/qualitative design. Matches the method to the IS claim and the manuscript category; it designs the study and hands estimation\/evaluation to misq-data-analysis."
}
Research Design & Methods (misq-methods)
When to trigger
- You have a theory or design propositions but no defensible way to test/evaluate them
- The method may not match the tradition or the claim (e.g., a causal claim with a correlational design)
- A reviewer asks "how do you know the artifact works?" or "what identifies this effect?"
- You need to decide what fits inside the category page limit (which counts everything)
Match the design to the tradition and claim
MISQ spans four traditions, so there is no single mandated method. Pick the design that the claim requires.
| Tradition | Typical designs | The design must establish |
|---|---|---|
| Behavioral | Lab/online experiment, field experiment, multi-wave survey, panel | Internal validity, construct validity, and (for surveys) procedural remedies for common-method bias |
| Design science | Build-and-evaluate of an IT artifact | That the artifact is novel and useful for a real problem — utility demonstrated, not asserted |
| Economics of IS | Natural experiment, DiD, IV, RD, structural model | A credible identification strategy for the causal/economic effect |
| Organizational | Case study, ethnography, mixed methods, longitudinal field | Trustworthiness, rich context, and a transparent path from data to constructs |
Design science: plan the evaluation up front
A MISQ design-science paper lives or dies on evaluation. Following Hevner et al. (2004), decide before building how you will demonstrate utility: held-out benchmarks against credible baselines, a controlled experiment or A/B field deployment, simulation, or expert evaluation — and tie each back to the design propositions. State the problem's relevance, the artifact's novelty, and the evaluation criteria so reviewers can judge rigor and relevance. "We built it and it ran" is not an evaluation.
Behavioral and economics: design out the threats early
- Behavioral surveys: build in procedural separations against common-method bias — temporal/psychological/source separation, validated scales, attention/manipulation checks — because statistical fixes alone will not convince reviewers later.
- Economics of IS: anchor identification in a real source of exogenous variation (a platform/policy change, staggered rollout, a breach, a system go-live). Pre-commit the comparison and the assumptions you will defend.
Mind the page budget
Because supplementary materials are discouraged and the page limit counts text, tables, figures, references, and appendices, design a study whose evidence fits the chosen category. Scope the design to the page budget rather than planning to offload it to an online appendix.
Execution bridge (StatsPAI / Stata MCP)
For the empirical / causal lane, estimate and audit rather than only specify. 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.
detect_design→recommend→ fit withas_handle=true→audit_resultto enumerate the checks the design owes.- Panel / staggered DiD:
callaway_santanna/sun_abraham+bacon_decompositionhonest_did_from_result. IV:effective_f_test+anderson_rubin_ci. RDD:rdrobust+mccrary_test.
- Experiments: randomization-based inference and
romano_wolffor the many-outcome family-wise correction reviewers expect.
Match the toolchain to the reviewer pool, and report the effect size the venue wants. A run end-to-end (synthetic data, real returns) is in the JF execution walkthrough.
Checklist
- Design matches the tradition and the strength of the claim
- Behavioral: validity threats and CMB designed out, not just measured
- Economics: a named source of exogenous variation and a defensible identification logic
- Design science: an evaluation plan tied to design propositions and credible baselines
- Qualitative: a transparent, traceable path from data to constructs
- The evidence fits the category page limit
Anti-patterns
- A causal IS claim resting on a cross-sectional correlation.
- A design-science artifact with no comparison and no real-problem evaluation.
- Single-source, single-wave self-report with no procedural CMB remedies.
- A design that only "fits" by exporting half the evidence to discouraged supplements.
Output format
【Tradition & design】experiment / survey / DiD-IV-RD / build-and-evaluate / qualitative
【Identification or evaluation】source of variation OR evaluation plan + baselines
【Validity threats handled】CMB / confounds / trustworthiness
【Fits page budget?】yes / trim
【Next step】misq-data-analysis
Version History
- 1839142 Current 2026-07-05 14:02
