orgsci-methods

name: orgsci-methods description: Use when choosing and defending the research design for an Organization Science manuscript — matching one of the journal's eclectic methods (qualitative/inductive, quantitative/archival, experimental, computational/simulation, formal-analytical) to the question and level of analysis, and justifying design without requiring causal identification.

Research Design & Methods (orgsci-methods)

When to trigger

• You are choosing a method, or a reviewer says the method does not fit the question
• A reviewer demands causal identification you cannot obtain
• Your level of analysis and your data source do not line up
• You are mixing methods and need to justify the combination

Match the method to the question — the journal is pluralistic

Organization Science is methodologically eclectic: it publishes qualitative and inductive fieldwork, quantitative and archival studies, experiments, computational/simulation models, and formal-analytical theory, and it does not privilege one. The design must fit the theoretical contribution and the level of analysis, not signal methodological fashion.

Causal inference is valued but not required

A defining stance: causal inference is valued but "not necessary and often impossible" at this venue. Do not abandon a strong organizational question because clean identification is unavailable. Instead, support inference with research design, theoretical logic, institutional/field knowledge, and mechanism evidence — triangulation, process tracing, placebo and falsification logic, and ruling out alternative explanations. This distinguishes Organization Science from identification-first, economics-leaning venues: a transparent design with a credible mechanism beats a thin paper with a clever instrument.

Design quality that reviewers check

• Fit: the method can actually deliver the theoretical claim and operates at the right level.
• Transparency: sampling, case selection, coding scheme, manipulation, model assumptions, or parameter ranges are fully specified.
• Trustworthiness (qualitative): purposive sampling rationale, saturation, audit trail, member checks where relevant.
• Replicability: enough detail and references that others could reproduce the study; appendices carry the design detail.

Execution bridge (StatsPAI / Stata MCP)

For the empirical / causal lane, estimate and audit rather than only specify. Full map: execution-with-mcp. Org Science spans field studies, experiments, and computational/qualitative work; the chain below is for its empirical/causal lane — simulation and qualitative work are outside it.

• detect_design → recommend → fit with as_handle=true → audit_result to enumerate the checks the design owes.
• Panel / staggered DiD: callaway_santanna / sun_abraham + bacon_decomposition
  • honest_did_from_result. IV: effective_f_test + anderson_rubin_ci. RDD: rdrobust + mccrary_test.
• Experiments: randomization-based inference and romano_wolf for 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.

Anti-patterns

• Reaching for an instrument or quasi-experiment the setting cannot support, when mechanism evidence would serve better.
• Aggregating individual data to organizational claims with no composition justification.
• A simulation with no empirical anchor or unjustified parameter ranges.
• Method chosen to look rigorous rather than to test the theory.

Methods pass for Organization Science

Use this as a second-pass capability check. First lock a level map, a mechanism paragraph, and the cover-letter contribution statement; then test whether the manuscript addresses interdisciplinary organization reviewers who ask whether the mechanism travels across levels of analysis.

• Primary move: Name assumptions, diagnostics, robustness, falsification, and failure modes; do not accept a method section that hides the decisive validity threat.
• Decision ledger: return claim / evidence / blocker / next edit rows so the next pass can patch the manuscript directly.
• Neighbor test: compare against AMJ for empirical management framing, ASQ for organization-theory depth, Management Science for formal/quantitative operations; if the neighboring outlet has the stronger audience claim, recommend re-routing before polishing.
• Submission-ready gate: before final advice, re-open resources/official-source-map.md for upload-week rules and name the one live-check item that could change the recommendation.

Output format

【Design】qualitative-inductive / multilevel / panel-EH / experiment / simulation / formal
【Level fit】matches the theoretical claim's level? cross-level logic stated?
【Inference strategy】design + logic + institutional knowledge + mechanism (not identification-only)
【Transparency/trustworthiness plan】sampling, coding, assumptions, audit trail
【Next step】orgsci-data-analysis