Skill UI

A comprehensive guide for developing high-rigor, reader-ready tables and figures for Human Resource Management (HRM) manuscripts. This skill covers the strict conventions required for presenting descriptive statistics, correlation matrices, nested model build-up (HLM/SEM), interaction plots, and theoretical model mapping. It ensures that the visual exhibits not only present numbers but also clearly carry the research argument, adhering to top-tier academic standards.

This guide provides comprehensive guidelines for designing, conducting, and auditing empirical studies in software engineering. It emphasizes achieving scientific rigor by matching every claim to specific, measured evidence, ensuring fair baselining (equal budget), and applying appropriate advanced statistics (non-parametric tests, effect sizes). Essential reading for researchers submitting papers based on performance evaluations.

This guide outlines the rigorous standards for handling primary sources, evidence, and field data in humanities scholarship, particularly in religious studies. It provides detailed methodologies for citing original texts, establishing provenance, managing translations, and integrating fieldwork ethics, ensuring academic claims are well-situated and defensible without relying on quantitative statistics.

This guide outlines the rigorous standards for preparing empirical tables and figures for top-tier academic journals, such as the Journal of Accounting and Economics (JAE). It covers the entire research workflow, including variable definition, sample construction, descriptive statistics, correlation matrices, advanced regressions, and event studies. The goal is to ensure all exhibits are self-contained, reproducible, and meet strict academic reviewer expectations.

A comprehensive guide for authors preparing medical manuscripts for high-impact journals. It outlines rigorous standards for statistical reporting, emphasizing the use of effect sizes with 95% Confidence Intervals (CIs) over p-values alone. Key topics include pre-specification, intention-to-treat analysis, multiplicity control, handling missing data through imputation, and reporting both relative and absolute effects to ensure maximum statistical rigor and reproducibility.

This guide details the rigorous standards for creating empirical tables and figures in academic marketing science (JAMS style). It covers building conceptual models, structural path coefficient tables, measurement validity checks, and creating highly actionable 'managerial exhibits' that translate statistical estimates into decision-making insights, ensuring all work is self-contained and adheres to APA guidelines.

A comprehensive guide for finalizing the core evidence—tables and figures—for high-stakes empirical accounting or economics papers (e.g., Journal of Accounting Research). It outlines the mandatory 'exhibit set,' covering sample construction, descriptive statistics, correlation matrices, main regression results, and crucial identification plots (DiD, RD). Focuses on adhering to rigorous house styles and ensuring all claims are data-supported.

A structured guide for academic authors to sharpen the core methodological contribution of statistics or econometrics papers. It ensures the claim explicitly links a novel method (methodological delta) to a demonstrable practical application (empirical consequence), avoiding overly theoretical or purely descriptive arguments.

A comprehensive guide for advanced quantitative research, focusing on demonstrating the validity of econometric methods. It outlines best practices for combining rigorous Monte Carlo simulations (stress-testing DGPs, baselines) with substantive real-world data applications, essential for publication in high-impact economics and statistics journals.

This guide provides a structured protocol for authors writing methods papers in statistics or econometrics. It teaches how to define the precise 'delta' (incremental contribution) of a new method by explicitly referencing 3-6 existing state-of-the-art techniques. It emphasizes positioning the work across both traditional econometric theory and modern machine learning/data science literature, ensuring the contribution is clearly stated against named prior methods, not as a general survey.

A comprehensive guide designed for authors submitting to the Journal of Business & Economic Statistics (JBES). It details the unique multi-Co-Editor model, how referees scrutinize submissions (focusing on statistical validity, empirical relevance, and reproducibility), and the conventions of the discussion-paper tradition. This resource helps authors set expectations and anticipate critiques before drafting the formal response letter.

A comprehensive preflight checklist designed for submitting manuscripts to the Journal of Business & Economic Statistics (JBES). This guide ensures authors have verified all necessary components—including the data/code supplement, conflict disclosures, and cover letter targeting—against the latest academic standards. It functions as a critical final review step before formal submission, emphasizing the need to cross-reference live journal guidelines.