Skill UI

A comprehensive guide for conducting advanced quantitative research using Structural Equation Modeling (SEM) on questionnaire data. It details rigorous procedures for testing complex causal relationships, including mediation, moderation, and moderated mediation, alongside handling nested data via Hierarchical Linear Modeling (HLM). Essential for academic papers requiring robust inference.

This skill acts as a central router for manuscripts targeting The New England Journal of Medicine (NEJM). It does not replace specialized skills but rather diagnoses the current stage of a clinical manuscript—from initial significance testing to response to reviewers—and directs the user to the correct subsequent skill (e.g., nejm-fit, nejm-study-design). It outlines the rigorous workflow, necessary checks, and structural differences required for submission to top-tier clinical journals.

A comprehensive guide for stress-testing the evidence supporting ML claims before submitting to top-tier academic conferences. Covers rigorous auditing of baselines, ablation studies, robustness checks, data leakage, and calibrating claims against evidence strength, ensuring scientific reproducibility.

A detailed guide for authors submitting review essays to highly selective journals, outlining the double-anonymous review model. It teaches authors how to anticipate expert critiques—moving beyond simple summaries to demonstrate critical intervention, conceptual clarity, and forward contribution. Covers desk-reject triggers and best practices for pre-submission stress-testing.

This guide helps scholars assess whether their literary or language-studies project meets the broad, general-interest standards of PMLA. It provides a detailed "fit test" framework, ensuring the work focuses on a significant problem, maintains broad scholarly interest, and presents a sustained argument rather than a descriptive survey. Useful for framing academic essays or deciding between different publication venues.

This guide provides a comprehensive checklist for designing, executing, and presenting empirical evidence for academic papers in Pattern Recognition and AI. It covers critical areas like establishing fair baselines, ensuring statistical rigor (mean ± std dev, significance testing), conducting sufficient ablation studies, preventing data leakage, and reporting computational costs. It helps authors make their findings withstand rigorous external review.

This comprehensive guide details the rigorous standards required to defend the identification of parameters, estimands, or causal effects in quantitative economic research. It provides specific, advanced methodologies and checklists for three major branches: structural modeling, empirical causal design (e.g., DID, IV, RDD), and experimental studies. It ensures the work meets the high quantitative bar of top-tier journals by demanding explicit data-to-object mapping and robust diagnostic testing.

This tool guides researchers through pressure-testing their economic research ideas against the standards of top-tier, general-interest journals (like QJE). It helps diagnose whether a study's 'big idea' and broad implications are compelling enough, ensuring the focus is on the general lesson rather than just the technical method or narrow finding. Ideal for structuring academic arguments and improving manuscript scope.

This skill guides the execution and stress-testing of empirical analysis for innovation studies (Research Policy). It details how to build reproducible bibliometric and patent variables, select appropriate econometric models (like Poisson/Negative Binomial for counts), and establish robust evidence. The focus is on addressing methodological challenges, ensuring transparent variable construction, and performing rigorous sensitivity checks to withstand expert scrutiny.

This comprehensive guide details advanced econometric methodologies required for rigorous causal inference studies. It covers how to defend both the causal identification strategy (DiD, RD, IV) and the quality of variables (handling measurement error, index construction). Learn to meet the stringent standards of top-tier journals by applying techniques like staggered DiD estimators, density testing, and attenuation correction.

This specialized router guides the entire manuscript development process for submissions to The Review of Economics and Statistics (REStat). It does not replace specialized skills but systematically directs users through the full research pipeline—from initial topic selection and causal identification to robustness testing, figure creation, and rebuttal strategy—ensuring a structured approach to applied econometrics research.

This guide provides a rigorous framework for stress-testing and implementing state-of-the-art causal identification strategies required for high-impact empirical economics. It covers advanced methodologies including Difference-in-Differences (DID), Instrumental Variables (IV), Regression Discontinuity (RDD), Synthetic Control, and Structural Modeling. It emphasizes modern estimators (e.g., Callaway-Sant'Anna, effective F) and robust diagnostic checks to ensure the causal claims are methodologically sound and defensible.