Skill UI

This guide provides best practices for preparing code, data, and supporting artifacts for academic submissions, focusing on achieving reproducibility for peer review (especially in double-blind settings). It covers crucial steps including artifact anonymization, establishing clear data availability statements, structuring the repository (README, env, data, src), ensuring licensing compliance, and planning for permanent post-acceptance archiving.

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.

This comprehensive guide establishes the rigorous methodological standards for developing credible causal identification strategies in development economics. It details best practices for modern quasi-experimental methods, including RCTs, DID, IV, and RDD. Authors learn how to meet the high empirical norms required by top-tier journals by ensuring robustness, addressing biases (e.g., TWFE), and validating external validity.

A comprehensive guide detailing the mandatory replication and data policy required by journals like JDE. It outlines how to build a self-contained, reproducible package containing master scripts, documented data provenance, pinned environments, and strategies for handling restricted or proprietary data, ensuring readiness for review and acceptance.

This guide provides best practices for presenting empirical results in development economics, emphasizing a 'figure-forward' approach suitable for top-tier journals. It teaches researchers how to transform complex statistical models (like DID, RDD, and ITT) into clear, self-contained, and impactful visual exhibits (plots and maps). The goal is to ensure that the headline findings are immediately legible to expert referees, moving beyond traditional, complex coefficient tables.

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.

This guide provides best practices for creating high-impact tables and figures (exhibits) for academic journals, especially in behavioral economics. It emphasizes visualizing the core behavioral comparison—such as treatment effects, dynamic changes over rounds, or underlying distributions—so the main finding is immediately visible before reading any text. Key practices include using confidence intervals over significance stars, ensuring grayscale readability, and providing self-contained captions.

A comprehensive guide for structuring and justifying behavioral or bounded-rationality models for high-impact academic journals. It helps researchers 'right-size' their theory, ensuring that models generate sharp, testable predictions and explicitly define behavioral primitives, moving beyond mere notation to true empirical insight.

A comprehensive guide detailing the advanced statistical standards required for publishing in top educational psychology journals. It covers modeling nested data (multilevel/SEM), reporting educationally meaningful effect sizes with confidence intervals, testing theoretical mechanisms (mediation/moderation), and ensuring full methodological disclosure (JARS). This resource is crucial for researchers aiming for high-impact, reproducible empirical studies.

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 guide for structuring rigorous academic manuscripts in educational psychology. It teaches researchers how to state a clear psychological mechanism, derive directional and testable hypotheses, and properly distinguish between confirmatory (preregistered) and exploratory analyses, adhering to high academic standards.

A comprehensive guide to establishing rigorous identification for advanced economic papers, targeting general-interest academic journals. It covers causal inference techniques (DID, IV, RDD), structural parameter identification, and theoretical mechanism rigor, ensuring the link between data/assumptions and results is explicitly defensible.