Skill UI

A comprehensive guide detailing the rigorous methodologies for conducting and reporting high-level academic research. It covers norms for quantitative, comparative-historical, and computational analyses, emphasizing transparency, reproducibility, robustness checks (e.g., confounders, alternative specifications), and linking claims directly to evidence, suitable for top-tier social science journals.

A comprehensive guide for structuring and communicating robustness checks in economic policy papers. It teaches authors how to organize tests not as a checklist of alternative specifications, but by the specific threat (e.g., sample size, functional form, pre-trends) they neutralize. The focus must remain on defending the policy-relevant magnitude and confidence interval in the main text, preventing the paper from becoming a mere appendix dump.

This guide outlines the mandatory structure for creating a reproducible research package, required by academic journals like the European Economic Review (EER). It details how to properly assemble raw data, master analysis scripts, environmental specifications, and comprehensive documentation (README) to ensure that reported results can be independently verified by others. It is essential for any paper involving empirical analysis or simulations.

This comprehensive guide outlines the process for packaging artifacts from Human-Robot Interaction (HRI) research. Recognizing that HRI artifacts are complex, involving human subjects, robot behaviors, and extensive study materials, this skill provides guidelines for honest, ethical, and reproducible dissemination. It covers structuring de-identified data, analysis code, and behavioral specifications while adhering to participant consent and academic archiving standards.

This guide details the necessary steps for achieving and reporting reproducibility in Human-Robot Interaction (HRI) studies. Since HRI involves unique elements like human participants and physical environments, reproducibility focuses on making the study protocol, robot behavior specifications, analysis code, and de-identified data transparent. Learn how to properly assemble a comprehensive replication package and ethically disclose limitations to enhance academic credibility in top-tier HRI publications.

A comprehensive toolkit designed for international macroeconomics research, ensuring that headline estimates are stable and reliable. It systematically tests the robustness of findings against common methodological threats, such as leaving out individual countries, changing sample periods, controlling for global common shocks, or varying estimation specifications. Essential for submitting high-impact academic papers.

A comprehensive guide for assembling a complete and reproducible materials package for experimental economics research, specifically for JEBO submissions. It details required components, including participant instructions, z-Tree/oTree code, anonymized raw data, detailed analysis scripts, and environment specifications, ensuring rigorous academic reproducibility.

A comprehensive guide for structuring and presenting robustness checks in high-impact economics journals, such as JEEA. It advises authors to organize checks around the *threat* to the claim (e.g., omitted variables, weak instruments) rather than simply adding more specifications. Focus on stable magnitude and reproducibility, ensuring the main finding survives credible perturbations.

Provides a comprehensive framework for constructing a robust battery of empirical tests, tailored for high-impact academic journals like JFE. This guide covers alternative measures, specifications, subsamples, and falsification tests, ensuring research findings are highly credible and capable of withstanding rigorous academic scrutiny.

A comprehensive guide for planning and stress-testing empirical analysis in financial intermediation, specifically for high-impact journals. It covers the construction of bank/loan-level panel datasets, defining variables (growth vs. levels), implementing advanced econometric specifications (e.g., fixed effects), and executing substantial robustness checks (placebo periods, alternative clustering). It also guides the planning of reproducible numerical illustrations for theoretical models.

Provides a comprehensive framework for conducting and reporting robustness checks in health economics studies. It guides authors to organize findings by addressing specific threats to the core claim (e.g., selection bias, concurrent policy changes, measurement error) rather than simply appending multiple specifications. Essential for ensuring results are stable and defensible for top-tier journal submissions.

A comprehensive guide for executing and reporting quantitative analyses for high-stakes public policy research. This skill emphasizes methodological rigor, demanding transparency in uncertainty reporting (effect size, intervals), robust checks (heterogeneity, alternative specifications), and ensuring full reproducibility through master scripts and pinned versions for journal submission.