Skill UI

This skill guides authors to evaluate if their ecology or biology manuscript meets the rigorous standards of Global Change Biology. It focuses on the necessity of linking research findings to explicit global-change drivers (e.g., warming, elevated CO2) and providing robust biological evidence, from molecular mechanisms to ecosystem-scale consequences.

A comprehensive guide for researchers in Human Relations to structure and report analyses, ensuring the direct, auditable link between raw evidence and theoretical claims. It covers best practices for qualitative coding (Gioia-style), critical interpretation, and advanced quantitative modeling, ensuring the final contribution is substantive and transparent.

This guide provides advanced methodological guidance for establishing causality and identifying policy effects within complex international macro datasets. It covers best practices for cross-country panel analyses, high-frequency policy surprise detection, crisis event studies, and structural modeling, ensuring that observed variation is rigorously linked to policy-relevant factors, respecting cross-country dependence and global shocks.

This guide helps researchers assess the scope and fit of their international macro-finance topics for the IMF Economic Review (IMFER). It emphasizes the necessity of linking rigorous academic findings to explicit, global policy relevance, especially concerning open economies, capital flows, and global spillovers.

A comprehensive framework for strengthening International Relations (IR) manuscripts, particularly for top-tier journals. It guides researchers through rigorous design requirements, including causal identification in dyadic/network data, structured case selection and process tracing, experimental planning (surveys/conjoint), and formal-empirical linkages. The focus is on ruling out rival explanations and establishing robust empirical claims.

A comprehensive guide for designing rigorous experimental studies in behavioral science. It covers establishing causal links, testing psychological mechanisms (mediation and moderation), controlling confounding variables, conducting power analysis, and adhering to modern academic standards like pre-registration, ensuring research meets the high bar of top-tier journals.

A comprehensive guide to establishing rigorous identification for advanced economic papers, targeting general-interest academic journals. It covers causal inference techniques (DID, IV, RDD), structural parameter identification, and theoretical mechanism rigor, ensuring the link between data/assumptions and results is explicitly defensible.

A comprehensive guide for establishing robust causal links in environmental economics. It covers advanced econometric techniques for policy evaluation, including staggered Difference-in-Differences (DiD), Instrumental Variables (IV), and Regression Discontinuity Designs (RDD). It specifically addresses challenges in non-market valuation, such as spatial sorting, hypothetical bias, and separating weather shocks from long-term climate trends, ensuring the recovered parameter is welfare-relevant.

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 assists authors in preparing research materials for submission to the Journal of Financial Intermediation (JFI), adhering to Elsevier Option C. It provides detailed instructions on managing, depositing, citing, and linking research data, code, and algorithms—whether the data is public, proprietary, or restricted (e.g., central bank registers). The goal is to ensure methodological rigor and reproducibility through the Data Statement.

A comprehensive guide for researchers tackling endogeneity in market microstructure. It outlines advanced econometric designs—such as Difference-in-Differences (DiD), Regression Discontinuity Design (RDD), and rigorous event studies—necessary to establish credible causal links between market variables (like liquidity and price impact) and macro-level events for top-tier finance journals.

A comprehensive guide for researchers conducting advanced data analysis and reporting for top marketing journals. It details selecting the appropriate econometric estimator (e.g., ANOVA, DiD, IV, mediation models) based on the study design (experimental, observational, panel). It emphasizes meeting rigorous journal mandates, including exact p-values, standard errors, and effect sizes, and linking statistical findings to practical managerial claims.