Skill UI

This skill details the rigorous methodology for defining study eligibility criteria and coding data for comprehensive meta-analyses. It emphasizes pre-specifying inclusion/exclusion rules in a protocol, developing a detailed codebook, performing double-coding, and calculating inter-rater reliability (e.g., Kappa, ICC) to ensure the reproducibility and trustworthiness of synthesis findings.

Provides rigorous statistical guidance for conducting meta-analyses in academic settings. This skill covers the calculation of standardized effect sizes (e.g., Hedges' g, log odds ratio), selecting appropriate models (defaulting to random-effects or multilevel models), and accurately quantifying between-study heterogeneity (Q, I², τ², and prediction intervals). It ensures the analysis meets state-of-the-art standards for high-impact psychological journals.

This tool serves as an essential orchestrator for crafting and submitting research syntheses (such as meta-analyses and systematic reviews) for Psychological Bulletin. It guides users through the entire rigorous research lifecycle—from initial topic selection and literature searching (PRISMA) to data coding, meta-analysis, theoretical integration, and final manuscript preparation. It ensures that the project is correctly framed as a synthesis of existing work, not a primary study.

This comprehensive framework guides the rigorous design and defense of research in public administration. It covers essential causal inference methodologies, including Difference-in-Differences (DiD), Instrumental Variables (IV), Regression Discontinuity (RD), and various experimental designs on bureaucrats and citizens. Use this guide when defining identification, responding to reviewer critiques regarding causality, or pre-registering a study, ensuring the design is both academically sound and practically relevant to public policy.

This guide provides detailed best practices for presenting empirical results, specifically tailored for top-tier economics journals like the QJE. It emphasizes shifting from 'table-heavy' to 'figure-forward' visualization, covering techniques for event-study plots, RDD discontinuity plots, and ensuring all exhibits are self-contained, readable, and follow strict academic formatting standards.

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 guide provides best practices for building self-explanatory, high-impact exhibits for interdisciplinary academic manuscripts. It covers the structured design of complex visualizations, including regression tables, bibliometric maps, event-study plots, and qualitative data structures. The goal is to ensure every figure and table carries the core innovation mechanism and is immediately interpretable by diverse readers (e.g., policymakers, managers, and economists).

This guide assists researchers in scoping their academic questions to ensure alignment with specialized journals like Research Policy. It provides detailed criteria for determining if a study focuses on the emergence, diffusion, or governance of innovation systems, rather than purely economic or strategy-focused outcomes. Use it to frame your work to speak directly to innovation scholars and policymakers.

This skill guides researchers through the rigorous process of refining a preliminary accounting idea into a sharp, contribution-driven research question suitable for top academic journals like Review of Accounting Studies (RAST). It helps users identify the core accounting construct, define the mechanism (friction/channel), and ensure the study's focus is on accounting theory, rather than merely using accounting data in a general finance context.

A comprehensive guide for executing and stress-testing empirical analyses in Industrial Organization (IO). It covers structural and reduced-form model diagnostics, advanced inference techniques (e.g., wild-cluster bootstrap), counterfactual validation, and meeting the rigorous robustness standards demanded by top-tier academic journals. Focuses on methodology, not study design.

This guide provides an in-depth breakdown of the academic research paper review cycle, using top-tier database conferences (like SIGMOD) as a case study. It covers the double-anonymous review process, the author feedback phase, interpreting the Accept/Reject/Revise verdicts, and navigating the post-decision revision window and embargo rules. Essential knowledge for authors preparing submissions to top-tier conference proceedings.

This guide helps researchers determine if their academic idea constitutes a genuine theoretical problem suitable for a specialized theory journal, rather than an empirical study or a simple literature review. It screens for conceptual advances, teaching users to distinguish between mere topics, data-driven findings, and deep theoretical gaps. Use this when you have an intuition or critique but are unsure of the theoretical depth or appropriate academic venue.