Skill UI

This guide provides a rigorous methodological checklist for conducting data analysis suitable for top-tier academic journals, such as JMIS. It covers advanced techniques across econometrics (addressing identification and endogeneity), behavioral modeling (SEM/PLS, common-method bias), and ML/analytics, ensuring that research findings meet the highest standards of robustness and theoretical grounding.

This guide assists researchers in aligning their study design—whether behavioral (lab/field experiments) or modeling-econometric (IV/DiD/RDD)—with the rigorous standards required by the Journal of Marketing Research (JMR). It covers best practices for causal identification, power analysis, structural estimation, and meeting journal-level expectations like replication and Web Appendix planning.

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.

A comprehensive guide for presenting complex statistical evidence in academic manuscripts, specifically tailored for journals like JAP. It covers APA 7th-edition standards for creating self-contained exhibits, including correlation matrices, CFA/SEM path diagrams, multilevel modeling (HLM) results, and meta-analytic plots. Emphasis is placed on reporting effect sizes, confidence intervals, and reliabilities.

This guide provides a rigorous framework for structuring academic arguments and building robust theoretical models, particularly for I-O psychology manuscripts adhering to standards like JAP. It teaches users how to identify core mechanisms, specify levels of analysis (individual, team, etc.), derive directional hypotheses, and clearly delineate between confirmatory and exploratory findings, ensuring the research contribution is theory-first.

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.

Provides comprehensive guidelines for authors submitting to the Journal of Econometrics (JoE). It details the required best practices for data citation (using the Elsevier `[dataset]` format), ensuring methodological reproducibility (e.g., Monte Carlo simulations), and preparing estimators as runnable code packages (R, Python, etc.). This guide is essential for meeting rigorous academic publication standards and passing peer review.

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.

This tool guides researchers in assessing the methodological fit of their empirical findings for top-tier journals like the Journal of Econometrics (JoE). It pressures-tests the proposed work against rigorous academic standards, checking for mathematical novelty, clear economic motivation, and methodological advancement. Use it when unsure of the journal scope or the true contribution type.

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.