Skill UI

A comprehensive guide detailing the conventions for constructing high-quality empirical tables and figures, especially for corporate finance and econometrics journals. Covers best practices for reporting summary statistics, fixed effects, clustering, event-study/CAR plots, and ensuring self-contained notes for academic publication.

A comprehensive guide for advanced statistical analysis in consumer psychology, moving beyond simple p-values. It details how to rigorously test complex processes like mediation and moderation (measured and experimental). This resource emphasizes reporting standards for high-impact journals, covering effect sizes, confidence intervals, pre-registration, and establishing causal evidence.

This guide details advanced best practices for creating publication-ready, APA-formatted tables and figures for academic research, particularly in behavioral sciences. It moves beyond simply presenting numbers, focusing instead on visualizing the underlying research mechanism, such as mediation paths, moderated effects, and multi-study comparisons. Mastery ensures rigor and clarity for top-tier submissions.

This comprehensive guide assists users in analyzing and reporting data for high-impact academic journals, specifically following the rigorous standards of the Journal of Consumer Research (JCR). It covers advanced statistical process evidence (mediation, moderation, effect sizes) for behavioral experiments, as well as methods for establishing trustworthiness in interpretive (CCT) research. It emphasizes transparency, replication code, and adherence to modern research reporting mandates.

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.

Provides comprehensive guidelines for meeting modern academic publishing standards, particularly those required by educational psychology journals. It outlines mandatory requirements for ensuring research reproducibility, including depositing data, materials, and analysis code in trusted repositories with persistent identifiers (DOIs), adhering to JARS reporting standards, and documenting preregistration status.

This guide provides expert standards for creating rigorous, self-contained tables and figures for top-tier academic journals (like JFE). It covers detailed conventions, such as structuring exhibits to convey a single, clear message, consistently reporting statistical metrics (SEs/t-stats), and ensuring figures visualize patterns rather than merely decorate. Essential for submitting polished, publication-ready empirical research.

A comprehensive guide to designing highly defensible and causal research studies for top-tier finance journals, such as the Journal of Financial and Quantitative Analysis (JFQA). It covers advanced econometric methods for corporate finance, asset pricing, and policy shocks, including Fixed Effects, Staggered Difference-in-Differences (DID), Instrumental Variables (IV), and Regression Discontinuity Design (RDD). Focuses on addressing endogeneity, reporting economic magnitude, and ensuring robust inference.

Provides a comprehensive framework for conducting and reporting robustness checks in health economics studies. It guides authors to organize findings by addressing specific threats to the core claim (e.g., selection bias, concurrent policy changes, measurement error) rather than simply appending multiple specifications. Essential for ensuring results are stable and defensible for top-tier journal submissions.

This comprehensive guide provides rigorous methodological guidelines for executing and reporting empirical analysis on cross-national, multi-level, or panel data, specifically tailored for top-tier international business journals. It covers essential checks like measurement invariance (configural to scalar), Common Method Variance (CMV), dynamic endogeneity, and appropriate estimation techniques (e.g., Multilevel SEM, GMM), ensuring research meets the highest academic standards.

A comprehensive guide for conducting and reporting rigorous statistical analysis for academic marketing manuscripts, particularly tailored for the Journal of Marketing (JM) standards. It covers selecting appropriate estimators for various designs (e.g., panel, choice, field experiments), managing identification, testing robustness, and adhering to the mandatory reporting of p-values, standard errors, and managerial effect sizes. Also covers data replication and advanced econometric techniques.

A comprehensive guide detailing the quantitative and econometric standards required for submissions to top-tier monetary and macroeconomic journals. It covers advanced techniques like VAR/SVAR, Local Projections, and DSGE modeling, focusing on proper inference, robustness checks, and diagnostic reporting to ensure scholarly rigor.