orgsci-methods

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](../../../shared-resources/empirical-methods/execution-with-mcp.md). 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](../../../Journal-of-Finance-Skills/resources/worked-examples/02-execution-walkthrough.md).

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