Skill UI

This guide outlines the rigorous quantitative standards required for publishing comparative and longitudinal studies in high-impact sociological journals. It covers essential techniques such as multilevel modeling, handling small macro-N inference, calculating marginal effects, and ensuring strict reproducibility. It teaches researchers how to design analyses that withstand expert double-blind review.

Guides authors on meeting the rigorous transparency standards required by the European Sociological Review (ESR). It details how to prepare a Data Availability Statement (DAS) and a replication package for statistical and computational manuscripts. The guide covers everything from documenting data sources and versions to handling restricted administrative data, ensuring that the research process is fully reproducible and traceable.

This package guides researchers on assembling a comprehensive deposit to meet the high standards of Experimental Economics (ESA) journals. It is crucial for academic submissions, ensuring that the materials—including participant instructions, experiment software, raw data, and master analysis code—are complete enough for others to both reproduce the statistical findings and replicate the entire economic experiment.

This router guides researchers through the systematic process of preparing a manuscript for Experimental Economics (ExpEcon), the ESA's flagship journal. It identifies the correct specialized skills (e.g., theory modeling, robustness checks, referee strategy, rebuttal) needed at each stage, ensuring adherence to methodological rigor, including incentive compatibility and the no-deception norm. It is designed to help teams structure their research from initial design to final submission.

This tool guides authors through the rigorous standards required for publishing in top experimental economics journals. It serves as a comprehensive checklist, evaluating a manuscript's fit, methodological strength, design transparency, and structural requirements (e.g., pre-registration, incentive-compatibility) against best practices in behavioral and experimental economics.

Provides rigorous guidance and structure for statistical analysis in Field Crops Research (FCR). Focuses on implementing linear mixed models that respect complex experimental designs (e.g., split-plot, block, environment). Essential for proper G×E analysis, robust means separation, and accurate evaluation of crop models, ensuring results are methodologically sound and actionable.

Provides essential guidelines for designing rigorous, generalizable field experiments in crop research. Covers multi-environment trials (MET), proper randomization, blocking strategies, split-plot designs, and Genotype-by-Environment (G×E) analysis. Use this tool when planning or defending the scientific rigor of an agricultural manuscript.

This skill provides a comprehensive guide for authors targeting the high-level academic journal, *Finance and Trade Economics*. It helps users assess if their manuscript's scope, methodology, and core contribution align with the journal's specific focus areas (e.g., fiscal policy, budget governance, international trade). It outlines strict submission formats, common rejection pitfalls, and alternative journal suggestions, ensuring academic rigor and proper positioning.

A comprehensive guide to assessing manuscript suitability for 'Financial Regulation Research.' It outlines the journal's scope (systemic risk, macroprudential policy, financial technology), required academic structure, acceptable methodologies (DID, VAR, etc.), and common pitfalls to avoid. Use this tool to refine your paper's narrative and ensure alignment with niche academic standards.

This guide outlines the rigorous methodological standards required for empirical finance manuscripts. It covers essential details from sample construction (attrition tables, survivorship bias) to variable definition, merge integrity, and appropriate statistical inference (clustering, panel structure). Use this to harden your data layer and ensure your methodology is robust enough to pass peer review in top finance journals.

This guide outlines advanced econometric strategies and best practices for establishing causal identification in corporate finance and asset pricing. It addresses common methodological pitfalls, such as endogeneity, spurious correlation, and weak designs, providing specific techniques (e.g., staggered DiD, robust IV inference) required to defend a research paper's claims to top-tier academic journals.

This guide instructs authors on crafting highly targeted and convincing robustness sections for empirical finance papers. Instead of merely adding volume, it teaches the concept of 'subtraction, then precision'—identifying specific alternative explanations (threats) a referee might raise (e.g., reverse causality, omitted variables) and selecting the minimal, most effective check to neutralize each threat. This aligns with the philosophy of top-tier financial journals that value new ideas over superficial testing.