Skills Data Science Research Design and Methods for CAR

Research Design and Methods for CAR

v20260724
car-methods
This guide provides comprehensive guidelines for selecting, validating, and defending rigorous research designs for Contemporary Accounting Research (CAR) manuscripts. It covers various approaches—including panel archival, controlled experiments, analytical models, field studies, and surveys—while stressing critical rigor checks like identification strategy, internal validity, and model discipline. It also mandates adherence to ethical review processes and data integrity policies.
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Overview

Research Design & Methods (car-methods)

When to trigger

  • The design may not deliver the inference the question needs (identification, internal validity, equilibrium logic)
  • An archival causal claim rests on an endogenous regressor with no strategy
  • An experiment's manipulation may not isolate the theorized construct
  • The study involves human participants and ethics-approval verification is unprepared
  • A reviewer says "the design cannot support this claim"

Match the design to the question (CAR is method-agnostic)

CAR welcomes any appropriate method; the bar is fit and rigor, not a preferred method. Pick the design that earns the claim:

Claim Design that earns it
Capital-market/contracting effect of reporting Panel archival with fixed effects + an identification strategy
Causal effect of an information feature on judgment Controlled experiment (lab/online/professional subjects)
Existence/optimality of an equilibrium or contract Analytical model: primitives, equilibrium concept, proofs
Mechanism inside firms, audits, or standard-setting Field study / interviews with an explicit coding protocol
A new construct's measurement and external validity Survey with a validated instrument; or multi-method

A two-study design (e.g., an experiment isolating the mechanism behind an archival association) is a recognized CAR strength.

Design against the threats CAR reviewers probe

  • Identification (archival). Anticipate omitted variables, reverse causality, and selection; plan a strategy (natural experiment, difference-in-differences with a credible parallel-trends argument, instrument, entropy balancing/matching, firm/year fixed effects) and state the assumptions each requires.
  • Internal validity (experimental). Design manipulation and attention checks; randomize; pre-specify the predicted mediator; rule out demand effects and confounds; justify the participant pool (student, online, or professional) for the inference.
  • Model discipline (analytical). Justify each assumption and the equilibrium concept; show which results are robust to relaxing assumptions.

CAR-specific design requirements

  • Ethics-approval verification (mandatory). For any research involving human participants — experiments, interviews, surveys, including secondary human-participant data — you must obtain and upload institutional REB/IRB clearance, an REB-issued exemption, or a senior-administrator letter where no review board exists. A bare assertion is not accepted, and failure is grounds for withdrawal by the EIC. Plan this before data collection.
  • Instrument capture. Surveys/experiments must submit the full research instrument with the manuscript (Data Integrity policy, item 1).
  • Proprietary/field data. If you use proprietary organizational data, plan a credible means of verifying the data source/site on editor request and disclose any non-disclosure restrictions (policy item 2).

Execution bridge (StatsPAI / Stata MCP)

For the empirical / causal lane, estimate and audit rather than only specify. Full map: execution-with-mcp. CAR is archival/empirical accounting; the DiD / IV / RDD chain serves its causal designs around reporting and regulation.

  • 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.

Checklist

  • Design can support each prediction (identification / internal validity / equilibrium logic)
  • (Archival) endogeneity strategy specified with its assumptions
  • (Experimental) manipulation/attention checks, randomization, predicted mediator, pool justified
  • (Analytical) assumptions and equilibrium concept justified; robustness mapped
  • Ethics-approval verification secured for any human participants
  • Full instrument prepared; proprietary-data verification/NDA plan in place

Anti-patterns

  • Cross-sectional causal claims from one-period archival correlations with no strategy.
  • Confounded manipulations that move more than the theorized construct.
  • Assumption-driven results (analytical) never tested for robustness.
  • Treating ethics approval as a formality — CAR requires documented verification, not a statement.

Output format

【Design】panel-archival / experiment / analytical / field / survey / multi-method
【Inference fit】each prediction supportable? notes ...
【Identification / internal validity / equilibrium】strategy + assumptions ...
【Ethics】REB/IRB clearance, exemption, or senior-admin letter secured?
【Instrument & proprietary data】full instrument; verification/NDA plan ...
【Next step】car-data-analysis
Info
Category Data Science
Name car-methods
Version v20260724
Size 5.91KB
Updated At 2026-07-28
Language