Skill UI

This guide outlines rigorous standards for conducting heterogeneity analysis in economic research. It stresses that heterogeneity should not merely involve slicing data by region or size, but must be theoretically driven, linking multiple dimensions (e.g., ownership, financing constraints, industry type) to core economic mechanisms. The analysis must demonstrate significant group differences and clearly connect findings to differential policy implications, elevating the discussion from mere observation to actionable policy science.

This guide provides rigorous methodological standards for conducting mechanism analysis (mediation and moderation) in academic economics research. It strongly cautions against the traditional three-step mediation approach due to identification issues, recommending preferred alternatives like split-channel regression and causal moderation analysis to enhance the robustness of causal claims.

This comprehensive router guides authors through the entire academic submission process for top-tier journals. It maps out the necessary steps—from initial topic selection and literature review to advanced econometric techniques (DID, mechanism, robustness) and handling reviewer rebuttal—ensuring the manuscript meets the rigorous standards of empirical research.

Comprehensive guidelines for conducting rigorous empirical validation in financial forecasting and econometrics manuscripts. This framework covers essential steps such as ensuring data provenance, performing robust out-of-sample testing (rolling windows), establishing multi-layered benchmark models, and conducting significance tests (e.g., DM/MCS). It helps authors meet the high academic standards required for top-tier journals.

This guide outlines the rigorous, model-based standards required for data analysis and statistical reporting in Cognitive Psychology manuscripts. It covers essential techniques such as model comparison (using AIC/BIC or Bayes factors), demonstrating parameter and model recovery, implementing mixed or hierarchical Bayesian models, and ensuring all findings are fully reproducible from shared code.

This guide details the rigorous requirements for achieving research reproducibility in academic publishing. It moves beyond simple data sharing, mandating that authors deposit model-fitting code, analysis scripts, and materials with persistent identifiers (DOI). Key steps include ensuring reported results can regenerate in a fresh session, completing declarations of competing interests, and linking pre-registrations, ensuring compliance with strict journal standards.

This guide outlines the rigorous standards for presenting tables and figures in Cognitive Psychology manuscripts. It emphasizes moving beyond simple bar charts of means, requiring authors to overlay the model's predicted curve onto observed data, reveal distributions and uncertainty intervals, and accurately report parameter estimates with confidence intervals. The goal is to ensure reproducible, comprehensive, and highly informative scientific communication.

Provides comprehensive guidelines for executing and reporting advanced statistical analyses (e.g., ANOVA, SEM, multilevel models) for academic manuscripts. It mandates adherence to rigorous APA reporting standards, including reporting effect sizes, confidence intervals, and ensuring methodological transparency and reproducibility to withstand expert scientific scrutiny.

A comprehensive guide for developing scientifically rigorous, APA-formatted tables and figures for quantitative communication research. This skill emphasizes that exhibits must be self-contained, displaying effect sizes and uncertainty (not just significance stars), and following strict reporting standards for maximum clarity for reviewers.

A comprehensive guide for authors preparing manuscripts for journals requiring high standards of openness and rigor. It details how to prepare required materials, including data-availability statements, open data/materials deposits, pre-registration plans, and handling restricted data paths, ensuring submitted research is fully reproducible and verifiable.

This comprehensive guide outlines the strict analytical and reporting standards required for top-tier conservation biology manuscripts. It covers everything from selecting appropriate statistical models (e.g., hierarchical, spatial) to honestly quantifying uncertainty, ensuring full reproducibility, and translating complex findings into actionable, decision-relevant conservation insights. Use this skill when performing main analyses or preparing manuscripts for expert peer review.

A comprehensive guide for creating self-contained, accessible, and budget-compliant scientific exhibits (figures, tables, and maps) for manuscripts, particularly in conservation biology. It covers best practices for data visualization, adhering to journal standards, ensuring accessibility (e.g., grayscale compatibility), and masking sensitive geographical data.