Skill UI

A comprehensive framework for conducting rigorous empirical data analysis required for top-tier academic journals. It guides users through building transparent archival samples, specifying appropriate panel data estimators (like firm/year fixed effects), ensuring correct standard error clustering, and executing sophisticated identification strategies (e.g., Difference-in-Differences, Instrumental Variables). Essential for addressing endogeneity and passing deep reviewer scrutiny.

This guide details the rigorous standards for creating empirical tables and figures in academic marketing science (JAMS style). It covers building conceptual models, structural path coefficient tables, measurement validity checks, and creating highly actionable 'managerial exhibits' that translate statistical estimates into decision-making insights, ensuring all work is self-contained and adheres to APA guidelines.

A comprehensive guide for finalizing the core evidence—tables and figures—for high-stakes empirical accounting or economics papers (e.g., Journal of Accounting Research). It outlines the mandatory 'exhibit set,' covering sample construction, descriptive statistics, correlation matrices, main regression results, and crucial identification plots (DiD, RD). Focuses on adhering to rigorous house styles and ensuring all claims are data-supported.

This guide outlines the rigorous method for developing econometrically testable hypotheses for accounting research, particularly for high-tier journals. Instead of relying on vague constructs, it teaches how to ground predictions in core economic theories like information economics, agency theory, and contracting frictions. It helps researchers build a clear mechanism by specifying the agent's optimization problem, identifying the core friction, and deriving conditional, testable predictions.

This comprehensive guide provides a structured framework for building and auditing empirical data and estimation pipelines in finance and banking research. It covers data construction from various sources (e.g., CRSP, Call Reports), advanced econometric techniques like fixed effects and winsorization, conducting stress tests (e.g., crisis periods, large banks), and ensuring full reproducibility. It emphasizes reporting results with meaningful economic magnitudes.

Use this skill when you observe a consumer behavior effect but lack a deep psychological explanation. It guides the construction of a robust causal chain (antecedent → mediator → outcome) by explicitly naming the core psychological process (mediator), deriving theoretical moderators, and pre-empting rival hypotheses, adhering to high academic standards in consumer psychology.

A comprehensive guide for developing the conceptual core of academic manuscripts in consumer research, aligning with high standards like JCR. It instructs users on defining psychological mechanisms (mediators/moderators) for experimental papers, or building robust conceptual frameworks for interpretive/CCT papers, ensuring the theory advances, deepens, or repudiates existing knowledge.

This guide helps authors elevate their literature review or related work section from a simple list of papers to a structured, high-impact academic argument. It provides methodologies for building multi-dimensional classification frameworks, defining the technical 'delta' (unique contribution) in original research, and ensuring systematic coverage of both domestic and international literature for top-tier academic journals.

This guide provides a comprehensive framework for building verifiable reproducibility evidence for academic manuscripts. It instructs authors on critical engineering disciplines, including freezing environment dependencies, controlling random seeds, versioning datasets and models, and creating anonymous, self-contained running materials that stand up to rigorous external peer review.

Provides a comprehensive, structured workflow for submitting academic papers to JCRD. It guides users through all milestones, from initial topic selection and evidence building to double-blind submission, multi-round revisions, and final camera-ready proofing. Ideal for managing the entire publication lifecycle of a computer science manuscript.

A comprehensive guide detailing the mandatory replication and data policy required by journals like JDE. It outlines how to build a self-contained, reproducible package containing master scripts, documented data provenance, pinned environments, and strategies for handling restricted or proprietary data, ensuring readiness for review and acceptance.

This guide helps researchers structure and articulate the unique contribution of their work, specifically tailored for high-impact journals like JEBO. It focuses on building the 'what we knew / what was missing / what this adds' framework, ensuring that the contribution is positioned at the mechanism level, whether the work is novel, a reconciliation, or a bounded replication.