Argument Development & Logic Check (amr-data-analysis)

SkillDev tools

Use when stress-testing the LOGIC of an Academy of Management Review (AMR) theory manuscript, checking logical coherence, running thought experiments and counterfactuals, addressing alternative explanations and disconfirming cases, and verifying each proposition follows from its argument. This is ARGUMENT DEVELOPMENT, NOT data analysis; AMR publishes no datasets, no statistics, and no empirical results.

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the Argument Development & Logic Check (amr-data-analysis) skill

What this skill tells your AI

The instructions your AI receives, as published by brycewang-stanford/awesome-journal-skills in Academy-of-Management-Review-Skills/skills/amr-data-analysis/SKILL.md and read by ahel’s review.

AMR publishes NO empirical data. There is nothing to estimate, plot, or test. The "analysis" in an AMR paper is the analysis of the argument itself: does each proposition follow logically from the constructs and mechanisms? At AMR, logical soundness plays the role that statistical rigor plays at empirical journals.

The empirical-analog reframe (keep the folder, change the content)

This skill replaces an empirical "identification + robustness" stage. The mapping:

Empirical sibling (AMJ/ASQ/SMJ)AMR theory analog
Identification strategy (IV, DiD, RD, matching)Generative mechanism — the why (Whetten 1989, DOI 10.5465/amr.1989.4308371)
Robustness checks / alternative specificationsInternal consistency + counterfactual probes on premises
Ruling out confoundersEngaging and bettering the strongest rival theory
Replication package (data + code)Transparent reasoning — premises and derivations a reader can re-derive
"Estimates are significant and robust"Propositions are falsifiable in principle (AMR's "testable knowledge-based claims")

There is no instrument, no parallel-trends test, no placebo here; their presence signals a misfiled empirical paper.

When to trigger

  • Propositions are written but you are not sure they actually follow from the argument
  • The theory "feels right" but has not been adversarially tested
  • A reviewer would raise an alternative explanation you have not addressed
  • The argument chain has hidden leaps between premises

The four logic tests

Run every proposition through these before drafting.

1. Premise-to-conclusion check (per proposition)

For each Pn, write the chain explicitly: premise → premise → mechanism → conclusion. If any step is missing, the proposition is asserted, not derived. Use a Toulmin frame: claim / grounds / warrant / backing / rebuttal. The warrant (the mechanism that licenses the inference) is where most theory papers are thin.

2. Thought experiment / counterfactual

Manipulate the focal construct in your head and trace the consequence: "If construct X rose sharply while everything else held, what does the theory predict for Y, and is that prediction sensible?" Then run the counterfactual: "Under what condition would X move and Y not follow?" If the counterfactual is plausible and unexplained, you are missing a boundary condition (route back to amr-theory-development).

3. Alternative-explanation audit

For each proposition, name the strongest rival theoretical account of the same relationship. Then either (a) show why your mechanism is more complete/parsimonious, or (b) integrate the rival as a boundary condition. Ignoring rivals is the fastest path to a reject — reviewers are the rival theorists.

4. Disconfirming-case search

Actively look for a case where the proposition should fail. A theory that "explains everything" explains nothing. Either the disconfirming case is covered by a stated boundary condition, or the proposition needs to be narrowed.

Internal-coherence checks across the whole theory

  • Consistency: no two propositions contradict each other (unless the tension is the point and is theorized). Constructs mean the same thing throughout — no concept drift (a core Suddaby construct-clarity criterion, AMR 2010, DOI 10.5465/amr.2010.0419).
  • Non-circularity: a construct is not defined by its effects, then used to explain those effects.
  • Sufficiency: the constructs and mechanisms are enough to generate the propositions — nothing is smuggled in mid-argument.
  • Parsimony: every construct earns its place; drop any that does no logical work.

Exemplar: Oliver (AMR 1991, DOI 10.5465/amr.1991.4279002) "analyzes" by argument — deriving a typology and propositions from antecedent conditions and addressing why organizations might resist rather than conform (the rival expectation) — all logic, no data.

Checklist

  • Each proposition has an explicit premise → mechanism → conclusion chain
  • The warrant (mechanism) for each inference is stated, not assumed
  • A thought experiment has been run on each focal relationship
  • Counterfactuals are addressed by boundary conditions, not ignored
  • The strongest alternative explanation for each proposition is named and handled
  • A disconfirming case has been sought for each proposition
  • The theory is internally consistent, non-circular, sufficient, and parsimonious
  • No empirical evidence is invoked as proof (AMR has none)

Anti-patterns

  • Propositions presented as self-evident, with the argument left to the reader
  • Hand-waving the mechanism ("it stands to reason that...")
  • Defending the theory by asserting it would be "supported by data" — there are no data
  • Ignoring the obvious rival theory the reviewers hold
  • A theory that cannot be wrong: no boundary, no disconfirming case, no rebuttal addressed
  • Circular reasoning: defining a construct by the outcome it is meant to explain

Output format

【Per-proposition logic】P1: chain ok? / gap at warrant? ... Pn
【Thought experiments run】[focal construct → predicted consequence]
【Counterfactuals → boundary conditions】[...]
【Alternative explanations handled】[rival → resolution]
【Disconfirming cases】[case → covered by boundary / narrow proposition]
【Coherence】consistent / non-circular / sufficient / parsimonious : pass/fix
【Next step】amr-contribution-framing

Signals

GitHub stars
1k
Forks
155
Last commit
Sep 2026
Advanced
Item type
skill
Key
amr-data-analysis
Source
github.com/brycewang-stanford/awesome-journal-skills