Skill UI

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 provides a rigorous framework for designing academic studies, particularly for high-impact psychological journals. It guides researchers to proactively address common methodological pitfalls such as common-method variance (CMV), unmodeled nesting, and construct validity gaps. Topics covered include temporal separation, multi-level modeling, advanced causal inference techniques (DiD, IV), and pre-registration best practices, strengthening the plan before data collection.

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.

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.

This guide assists researchers in transforming narrowly focused, technically correct findings into broadly significant methodological contributions suitable for high-impact econometrics journals (e.g., JoE). It outlines strategies for framing the work by identifying the general problem class, stating the advance as a property (rather than a procedure), and anchoring the contribution within broader economic settings. Use this when your paper needs to be elevated from an incremental fix to a field-changing principle.

A comprehensive guide for designing rigorous Monte Carlo simulations and empirical illustrations for methodological papers, particularly in econometrics. It details how to stress assumptions, report necessary metrics (size, power, bias), and structure the evidence to prove a method's theoretical validity and practical utility.

This guide outlines the rigorous formal requirements for econometric papers, focusing on achieving the level of mathematical rigor expected by top academic journals. It covers proving identification, verifying primitive assumptions, constructing asymptotic derivations (consistency, rate, distribution), and structuring proofs for maximum verifiability in causal inference and time series analysis.

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.

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.

This guide provides a rigorous structural and strategic framework for responding to a 'Revise and Resubmit' (R&R) decision, specifically tailored for the Journal of Labor Economics (JOLE). It emphasizes drafting concise, point-by-point rebuttals, addressing complex identification challenges with robust diagnostics, reconciling conflicting referee demands, and strictly adhering to the journal's word economy and single-blind submission rules. It is a critical tool for refining advanced academic manuscripts.

A comprehensive methodological guide for conducting rigorous statistical analysis in Operations Management (OM). It covers establishing measurement validity (CFA, reliability), selecting appropriate estimators (SEM, HLM, DiD, IV) for survey and archival data, and addressing critical issues like common-method bias and endogeneity. Essential for academic research reporting.