| 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.
| Theoretical goal / data structure | Design that fits |
|---|
| Build a new process or construct from the field | Inductive qualitative (grounded theory, ethnography, comparative cases) |
| Test a cross-level mechanism in nested data | Multilevel / HLM with explicit composition or contextual logic |
| Trace organizational founding/failure over time | Event-history / survival; panel |
| Isolate a behavioral mechanism | Lab or field experiment, vignette/conjoint |
| Explore adaptation, learning, search dynamics | Agent-based / NK simulation or formal model |
| Characterize an interfirm or intra-org structure | Network analysis (ERGM, centrality, brokerage) |
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
