Skill UI

A comprehensive skill for constructing robust theoretical arguments for entrepreneurial phenomena, specifically tailored for high-level academic journals. It guides the user in identifying the core causal mechanism, selecting the appropriate multidisciplinary lens (economics, psychology, or sociology), and choosing between deductive hypothesis testing or interpretive narrative theory. This ensures the theory is specific to the entrepreneurial context, avoiding generic organizational models.

A comprehensive guide for designing and presenting rigorous experiments for top-tier computer science journals. It covers best practices for ensuring reproducibility, matching evidence to claims, selecting appropriate baselines, performing statistical significance testing, and handling modern challenges like LLM contamination and system scalability measurements. Essential for passing double-blind peer review.

A comprehensive guide detailing the advanced statistical standards required for publishing in top educational psychology journals. It covers modeling nested data (multilevel/SEM), reporting educationally meaningful effect sizes with confidence intervals, testing theoretical mechanisms (mediation/moderation), and ensuring full methodological disclosure (JARS). This resource is crucial for researchers aiming for high-impact, reproducible empirical studies.

This comprehensive guide details the rigorous identification and argumentation strategies required for high-level growth and dynamic macroeconomics research. It covers both empirical tracks (System GMM, IV, DID, RDD, addressing endogeneity) and theoretical tracks (justifying assumptions, proving existence, demonstrating generality). Essential for stress-testing manuscripts for specialized journals.

A comprehensive guide for designing and stress-testing asset-pricing empirical models, tailored for submissions to top-tier finance journals. Covers methodologies like Fama–MacBeth regressions, factor model comparisons, handling standard-error subtleties, and rigorous out-of-sample validation. Essential for ensuring results survive academic scrutiny, addressing issues like multiple testing and economic magnitude.

A comprehensive guide for empirical finance researchers on structuring robustness checks and handling multiple testing concerns for top-tier journals like The Journal of Finance. It teaches how to distinguish load-bearing checks for the main text versus exhaustive analyses relegated to the Internet Appendix, ensuring methodological rigor and maximizing impact.

This tool guides researchers in scoping and rigorously testing a research topic for submission to top-tier finance journals, such as the Journal of Financial Economics (JFE). It helps users refine an economic question, establish a novel contribution, identify plausible causal links, and assess the paper's depth against high academic standards (e.g., Jensen or Fama-DFA prizes). It is crucial for transforming a general academic idea into a publishable, high-impact manuscript.

This guide provides advanced methodological principles for conducting theoretically grounded heterogeneity analysis in financial research. It emphasizes that any dimension used for splitting the data (e.g., ownership, bank type, financial constraint) must be predicted by the underlying economic mechanism, rather than being an arbitrary data exploration. It details best practices, statistical rigor (group coefficient difference testing), and common pitfalls to avoid.

A comprehensive guide for constructing and auditing empirical data pipelines, specifically tailored for high-impact journals. It covers advanced causal inference techniques (DID, RDD, IV, RCT), detailed sample management, robustness testing, and creating structured comparative estimates to withstand intense academic scrutiny.

A comprehensive guide for authors preparing a major revision and response letter after a top-tier academic journal review (e.g., JIBS). It details the necessary workflow, including prioritizing feedback from the area editor, executing complex methodologies (cross-national measurement, CMV, endogeneity, invariance testing), and structuring a precise, point-by-point response to demonstrate rigorous compliance with journal standards.

A guide for formalizing complex institutional arguments into rigorous, testable economic models, specifically tailored for submission to top journals like JLEO. It covers foundational lineages—such as transaction-cost economics, property rights, and principal-agent problems—teaching researchers how to right-size models, justify assumptions institutionally, and ensure comparative statics are the primary deliverable for empirical testing.

A comprehensive guide for conducting and reporting rigorous statistical analysis for academic marketing manuscripts, particularly tailored for the Journal of Marketing (JM) standards. It covers selecting appropriate estimators for various designs (e.g., panel, choice, field experiments), managing identification, testing robustness, and adhering to the mandatory reporting of p-values, standard errors, and managerial effect sizes. Also covers data replication and advanced econometric techniques.