Skill UI

This guide details the rigorous data analysis and reporting standards required for top-tier academic journals, such as AJPS. It focuses intensely on reproducibility, ensuring that all results—from primary findings to exploratory models—are generated by a single, seeded master script. Key requirements include reporting uncertainty intervals, proving robustness, adhering to pre-registration discipline, and accommodating mandatory third-party code verification. Use this when conducting complex empirical research in social sciences or political science, needing to withstand intensive methodolo...

Provides a detailed guide on the mandatory pre-publication replication and verification process for American Journal of Political Science (AJPS). It instructs authors on building a robust, reproducible package—including code, data, and documentation—that an independent third party can re-run to confirm all numerical findings match the main text before publication.

This guide outlines the stringent standards for creating tables and figures (exhibits) for the American Journal of Political Science (AJPS). It emphasizes that exhibits must be self-contained, highly accessible, and, critically, regenerated exactly by deposited code for third-party verification. It provides best practices for structuring research results, managing word counts by differentiating between core arguments in the main text and supporting details in the Supporting Information (SI).

This guide outlines the rigorous execution and reporting standards required for quantitative research intended for top-tier academic journals, such as APSR. It covers best practices for ensuring reproducibility, assessing robustness, reporting uncertainty honestly (using credible intervals), and correctly applying advanced statistical inference (e.g., clustering, randomization inference). It is essential for researchers conducting empirical work in political science, economics, or social sciences.

This guide assists researchers in strengthening the methodological rigor of academic work, particularly in political science. It provides structured methods for defending research design, covering rigorous causal identification for quantitative work (e.g., DiD, IV, RDD) and systematic case selection/process tracing for qualitative analysis. Use this when facing reviewer skepticism or preparing a pre-analysis plan to ensure the evidence credibly connects to the theoretical argument.

A comprehensive guide for authors submitting to the American Political Science Review (APSR). It outlines best practices for creating self-contained, accessible, and statistically rigorous tables and figures. The resource emphasizes principles like maintaining word-count compliance, ensuring reproducibility, and presenting complex results (like treatment effects) using appropriate visualizations.

This guide details the strict data and transparency policy required by the American Political Science Review (APSR). It outlines how to build a comprehensive reproducibility package—including raw data, master scripts, and detailed documentation—to allow the journal to verify that all reported tables and figures can be regenerated before publication. It covers quantitative methods, qualitative evidence sharing, and necessary exemption procedures.

A comprehensive guide for executing and reporting quantitative, computational, or experimental analyses for top-tier academic journals (e.g., BJPS). It outlines stringent norms for ensuring methodological rigor, transparency, and reproducibility, covering topics like uncertainty reporting, robustness testing, and measurement equivalence. This skill is essential when structuring the results section of a manuscript, particularly when facing expert peer review or preparing for data deposition.

A comprehensive guide for authors preparing replication and transparency packages for submissions to the British Journal of Political Science (BJPS). It outlines requirements for depositing quantitative and qualitative data and code into the BJPolS Dataverse, ensuring that research findings are fully reproducible and adhering to the Data Access and Research Transparency (DA-RT) statement.

This guide outlines the best practices and norms for executing and reporting empirical analyses in governance and comparative political science. It covers essential methods including cross-national inference, clustering uncertainty, robustness checks, multi-method triangulation, and ensuring reproducibility, guiding researchers to conduct rigorous, publication-ready studies.