Skill UI

A comprehensive guide for authors on creating APA 7th edition compliant tables and figures for scientific manuscripts, specifically tailored for the Journal of Personality and Social Psychology (JPSP). This resource emphasizes best practices for presenting multi-study data, including self-contained exhibits, cross-study summary tables, and internal meta-analysis forest plots, ensuring the findings are clear and impactful.

This guide provides comprehensive methodology for advanced empirical analysis in Public Economics, particularly for manuscripts using large administrative or register microdata. It covers advanced topics like estimating behavioral elasticities, calculating welfare metrics (MVPF, DWL), and ensuring robust causal identification strategies. It emphasizes translating raw coefficients into policy-relevant quantities with rigorous uncertainty quantification.

This advanced analytical skill focuses on rigorously identifying and testing the specific transmission channels through which external shocks affect an economy. Essential for high-impact international economics research, it moves beyond descriptive mechanism narratives by focusing on testable proxies across trade, investment, finance, and expectation channels. It requires advanced econometrics (e.g., mediation analysis, sample splitting) and careful identification strategy to ensure causal claims are robust.

This skill guides the planning and auditing of analysis for linguistic manuscripts to ensure evidence supports theoretical claims. It covers quantitative modeling like mixed-effects models in R, phonetic measurement, and corpus statistics. It helps establish the analytic trail from raw data to generalization without fabricating results. It ensures proper statistical specification, effect size reporting, and handling of uncertainty, aligning with academic standards for credibility in language research.

A comprehensive guide for executing and reporting advanced analysis for high-level academic manuscripts, particularly in management science. It mandates rigorous verification across two lanes: the analytical lane (proving theorems and comparative statics) and the empirical lane (identifying and estimating causal effects using methods like DiD, IV, and RDD). It emphasizes adherence to Data-and-Code-Disclosure policies, ensuring all results are reproducible, verifiable, and meet high methodological standards.

A comprehensive guide for executing, evaluating, and reporting the empirical core of MIS Quarterly manuscripts. It covers specialized methods for Behavioral IS (PLS-SEM), Economics of IS (causal identification), Design Science (artifact evaluation), and qualitative studies, ensuring full transparency and replicability for top-tier academic submissions.

This guide outlines the rigorous process for estimating, validating, and interpreting structural models in Marketing Science manuscripts. It covers advanced econometric techniques (GMM, MLE, Bayesian), demonstrating empirical identification, assessing model fit (in-sample/out-of-sample), and computing robust counterfactuals. It ensures the final results are statistically sound, economically plausible, and prepared for replication.

This guide serves as a comprehensive workflow router for authors preparing a Marketing Science manuscript. It dictates the sequential steps, from identifying a modeling-worthy topic and developing a structural or analytical model, through data analysis, contribution framing, and final submission. It ensures the paper maintains a focus on formal modeling and managerial counterfactuals, adhering to the journal's rigorous standards.

A comprehensive guide for designing and reporting rigorous computational studies in Operations Research (OR) manuscripts. It covers best practices for selecting benchmark instances and strong baselines, applying statistical methods to stochastic output (e.g., confidence intervals), and adhering to mandatory code and data deposit standards (ORJournal workflow) to ensure full reproducibility.

This guide outlines the rigorous analytical methodology required for Operations Research (OR) manuscripts. It instructs users on selecting the appropriate mathematical machinery—such as duality, dynamic programming, convergence analysis, or concentration inequalities—to establish provable guarantees, approximation factors, and algorithmic complexity. It ensures that claims are backed by rigorous proofs, moving beyond mere heuristics.

Provides comprehensive guidelines for conducting highly rigorous, reproducible statistical analysis for complex survey data (e.g., weights, strata, clusters). This guide ensures that analyses meet the standards of top academic journals, emphasizing design-based inference, honest uncertainty reporting (CIs, MOE), and thorough robustness checks. Use this when you need to build a master script that regenerates every published table and figure.

This skill guides authors in assembling a comprehensive transparency package for academic submissions (especially meta-analyses). It ensures adherence to Open Science principles by detailing requirements for protocol preregistration (PROSPERO/OSF), depositing the coded database, codebook, and analysis scripts. It is essential for proving the full reproducibility of quantitative research findings.