Skill UI

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.

Provides a comprehensive guide for designing rigorous academic studies for high-impact journals. Whether planning multi-study behavioral experiments, interpretive Consumer Culture Theory (CCT) fieldwork, or mixed designs, this resource ensures that the evidence collected is optimally structured to support the conceptual claim, emphasizing internal validity and transparency.

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 designed to stress-test the causal identification of behavioral mechanisms for top-tier economic journals (e.g., JEBO). It provides stringent criteria across lab/field experiments, observational data (DiD, IV, RDD), and simulations, ensuring the design isolates the claimed psychological or institutional channel, not just a correlation. Use this when standard econometric methods are insufficient to prove mechanism causality.

This resource simulates the rigorous peer review process of a top journal, focusing on educational relevance, theoretical mechanism, design rigor (especially nested data analysis), and transparency standards (JARS, preregistration). Use this guide to stress-test your manuscript before submission, preemptively address common rejection patterns, or interpret a complex decision letter, ensuring your work meets the highest academic standards.

This skill assists researchers in creating methodologically rigorous study designs for educational psychology manuscripts, especially when dealing with nested data structures (e.g., students within classes within schools). It guides users in matching the unit of randomization, power, and analysis to the correct cluster level. Focus areas include ensuring robust cluster-level power, establishing ecological validity, developing pre-analysis plans, and adhering to preregistration standards.

A comprehensive guide for structuring quantitative exhibits for scholarly manuscripts, specifically tailored for educational psychology journals. It focuses on adhering to APA 7th edition standards, accurately presenting model-based results, effect sizes with confidence intervals (CIs), growth trajectories, and ensuring full anonymization for masked review. It teaches researchers how to make complex statistical arguments legible and reproducible.

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.

A comprehensive framework designed to ensure environmental economics manuscripts meet rigorous data-availability and reproducibility standards, especially for journals like JEEM. This package guides users to document the entire research pipeline, handling complex issues like data versioning, proprietary satellite/admin data, and multi-step spatial operations by establishing a credible end-to-end reproduction path.

This tool provides a comprehensive pre-submission checklist designed for authors submitting survey papers (either a proposal or a full draft) to the Journal of Economic Literature (JEL) via the AEA portal. It ensures compliance by verifying JEL codes, disclosure statements, data availability, and adherence to style guides, helping authors finalize their submission package.

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 methodological guide for conducting rigorous quantitative research in finance, covering asset pricing, corporate finance, and econometrics. This skill defines the necessary standards for factor construction, portfolio sorting, advanced estimators (Fama-MacBeth, GMM), robust standard error clustering, and multiple-testing discipline. It ensures research designs are academically sound, reproducible, and suitable for top-tier financial journals.