Skill UI

This guide details the necessary steps for achieving and reporting reproducibility in Human-Robot Interaction (HRI) studies. Since HRI involves unique elements like human participants and physical environments, reproducibility focuses on making the study protocol, robot behavior specifications, analysis code, and de-identified data transparent. Learn how to properly assemble a comprehensive replication package and ethically disclose limitations to enhance academic credibility in top-tier HRI publications.

A guide for designing and structuring empirical tables and figures for submissions to International Organizations (IOs). Focuses on meeting strict word limits, ensuring exhibits are self-contained, reproducible, and clearly separating core arguments (main text) from supporting details (supplementary material). Best practices include explicit unit labeling, prioritizing effect plots over dense tables, and guaranteeing that all displayed numbers match the original analysis code.

A comprehensive guide for executing and reporting the full empirical robustness battery required by top accounting journals. It covers modern econometric techniques like standard error clustering, advanced Difference-in-Differences (DiD) estimators for staggered timing, Regression Discontinuity (RD) diagnostics, and Instrumental Variables (IV/2SLS). The guide emphasizes rigorous validation of accounting constructs, executing multiple sensitivity tests, and ensuring full data-and-code reproducibility for academic publication.

This comprehensive guide assists users in analyzing and reporting data for high-impact academic journals, specifically following the rigorous standards of the Journal of Consumer Research (JCR). It covers advanced statistical process evidence (mediation, moderation, effect sizes) for behavioral experiments, as well as methods for establishing trustworthiness in interpretive (CCT) research. It emphasizes transparency, replication code, and adherence to modern research reporting mandates.

A comprehensive guide for assembling a complete and reproducible materials package for experimental economics research, specifically for JEBO submissions. It details required components, including participant instructions, z-Tree/oTree code, anonymized raw data, detailed analysis scripts, and environment specifications, ensuring rigorous academic reproducibility.

Provides comprehensive guidelines for meeting modern academic publishing standards, particularly those required by educational psychology journals. It outlines mandatory requirements for ensuring research reproducibility, including depositing data, materials, and analysis code in trusted repositories with persistent identifiers (DOIs), adhering to JARS reporting standards, and documenting preregistration status.

A mandatory guide for authors submitting to the Journal of Financial and Quantitative Analysis (JFQA). This policy dictates the required archive structure, ensuring that all reported findings can be fully reproduced by depositing source code, raw or pseudo datasets, and a detailed execution roadmap in the JFQA Dataverse. It guarantees the highest standards of academic transparency and reproducibility in financial research.

This comprehensive guide serves as a preflight checklist for authors submitting manuscripts to the Journal of Financial and Quantitative Analysis (JFQA). It details stringent requirements, including double-anonymous formatting, specific layout standards (8.5x11, 12pt Times New Roman, double-spaced), the 100-word abstract limit, and mandatory disclosure rules regarding prior rejections and code-sharing. Use this checklist before submitting via Editorial Manager to ensure compliance and avoid desk rejection.

A comprehensive guide for assembling reproducible research packages using restricted health data, such as Medicare claims or EHR data. This package focuses not on running the analysis, but on establishing a credible data access path, creating detailed Data Availability Statements (DAS), and ensuring code is shared and reproducible, which is crucial for publishing in health economics journals.

This guide helps researchers meet rigorous open science and transparency requirements, such as those mandated by journals like JAP. It details the process of depositing data, analysis code, and materials with persistent identifiers (DOIs), drafting comprehensive data-availability statements, and correctly managing prior-use disclosures and pre-registrations. Essential for ensuring research reproducibility and publication compliance.

A comprehensive guide for conducting and reporting data analysis for submissions to the Journal of Public Administration Research and Theory (JPART). It covers advanced statistical methods, addressing common-method bias, ensuring robustness, and meeting mandatory data and code release standards to withstand expert scholarly review.

This guide outlines the rigorous open science and transparency requirements for submissions to the Journal of Personality and Social Psychology (JPSP). It details compliance with APA's TOP Guidelines at Level 2, mandating the proper disclosure of data, analysis code, and materials in a trusted repository. It also covers JARS reporting standards and guides authors on stating the preregistration status, ensuring research reproducibility.