Skill UI

A comprehensive guide for authors submitting empirical or quantitative manuscripts to the Journal of Monetary Economics (JME). It details mandatory requirements for data deposition, model code (Dynare, MATLAB, Stata, R), master scripting, and adherence to Elsevier's generative AI declaration, ensuring maximum reproducibility and meeting high academic standards.

A comprehensive guide detailing the data sharing and transparency requirements for submitting manuscripts to the Journal of Marriage and Family (JMF). It specifically addresses the challenge of restricted-use family datasets, teaching researchers how to document access paths and ensure replication-level detail without direct data redistribution.

A comprehensive guide for generating rigorous, APA-compliant tables and figures for top-tier academic journals (e.g., JOM). Covers the creation of crucial exhibits, including descriptive statistics, CFA, SEM/HLM results, interaction plots, and meta-analysis forest plots. It ensures findings are presented clearly, adhere to strict methodological standards, and fit within tight page limits.

This guide provides comprehensive standards for defending rigorous data analysis in academic submissions, particularly for the Journal of Management Studies (JMS). It details specific requirements for both quantitative work (e.g., robustness checks, SEM, endogeneity) and qualitative work (e.g., coding transparency, abductive logic, trustworthiness), ensuring the analysis credibly supports the theoretical claims.

This guide details the rigorous statistical reporting standards required for submitting manuscripts to the Journal of Applied Psychology (JAP). It covers advanced techniques such as SEM, Multilevel Models (HLM), mediation, and meta-analysis, emphasizing the reporting of effect sizes with confidence intervals, model fit indices, and full analytical transparency.

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.

This skill guides the execution and reporting norms for advanced data analysis in communication studies, specifically adhering to the rigorous standards of the Journal of Communication (JoC). It emphasizes transparency, reproducibility, honest reporting of uncertainty (beyond p-values), rigorous robustness checks, and proper handling of computational data, ensuring that analyses withstand expert and peer review.

A comprehensive guide for authors preparing manuscripts for the Journal of Communication (JoC). It details the requirements for achieving open science and transparency, covering essential elements like the Data Availability Statement (DAS), Open Science Badges (Open Data, Open Materials, Preregistration), and best practices for ensuring reproducibility across quantitative, computational, and qualitative methodologies. Learn how to build materials as you write.

A comprehensive guide for authors responding to highly technical referee reports, particularly for submissions to top econometrics journals like the Journal of Econometrics (JoE). It details strategies for addressing objections concerning assumptions, asymptotic gaps, and Monte Carlo simulations, ensuring the revised manuscript and response letter are technically airtight and methodologically rigorous.

A comprehensive guide detailing the rigorous standards for generating tables and figures intended for top-tier econometrics journals, such as the Journal of Econometrics (JoE). It covers best practices for Monte Carlo simulations, separating size and power reporting, comparing multiple estimators, and ensuring all exhibits are statistically rigorous, reproducible, and legible in a single-PDF print format.

A comprehensive guide for conducting reproducible empirical analysis in labor economics, adhering to high academic standards (e.g., JOLE norms). It covers essential steps including sample construction from CPS/ACS/registers, advanced wage decompositions (Oaxaca/RIF), proper handling of standard errors, and robust sensitivity checks to ensure results are fully replicable.

This guide provides comprehensive standards for establishing credible causal claims in labor economics research, specifically tailored to the rigor of journals like JOLE. It covers advanced econometric techniques (DID, RDD, IV, RCT, etc.) and outlines necessary robustness checks, bias corrections, and methodological defenses to ensure research findings are statistically sound and replicable.