Skill UI

This guide provides comprehensive methodological standards for conducting high-quality human-subjects studies in Human-Robot Interaction (HRI). It covers the entire research lifecycle, including choosing optimal designs (within/between-subjects), ensuring statistical power, reporting effect sizes, and adhering to ethical guidelines (e.g., Wizard-of-Oz disclosure). It ensures research meets the rigorous academic standards required for top-tier publications.

This comprehensive guide provides best practices for designing, running, and reporting computational experiments for academic journals. It teaches authors how to make performance claims scientifically defensible by addressing potential referee criticisms (e.g., tuning artifacts, seed luck). It covers advanced statistical techniques (performance profiles, nonparametric tests) and details the full structure required for a complete, verifiable GitHub data deposit.

A comprehensive guide to designing rigorous, reproducible, and fair experimental protocols for computational science research, particularly for top-tier academic journals. It covers critical elements such as selecting standard benchmark instances, establishing strong baselines, ensuring fair parameter tuning, reporting hardware constraints, and performing robust statistical validation, helping researchers avoid common pitfalls and maximize the scientific validity of their claims.

This framework provides a comprehensive methodology for designing, auditing, and reporting empirical measurements about large-scale systems like the Internet. It guides users to ensure measurements are scientifically rigorous, by matching claims to evidence, quantifying coverage bias, designing for temporal changes, and adhering to strict ethical guidelines and data provenance standards.

Comprehensive guidelines for preparing and reporting complex empirical evidence across various methodologies (SEM, econometrics, design science, qualitative). This guide ensures results meet the high academic standards of top-tier journals by emphasizing theoretical traceability, statistical rigor, and required data transparency, including full correlation/covariance matrices.

This comprehensive guide helps researchers evaluate whether a clinical or health-services study is appropriate for publication in JAMA Internal Medicine. It details the journal's high bar for methodological rigor, including requirements for randomized trials (CONSORT), observational studies (STROBE), and systematic reviews (PRISMA). It covers everything from study design and statistical reporting to addressing potential desk-reject triggers, ensuring authors submit high-quality, impactful research.

A comprehensive guide for authors to select, defend, and strengthen the internal validity of clinical study designs (RCT, Cohort, Diagnostic, etc.). It provides detailed safeguards—covering topics like intention-to-treat analysis, confounding control, blinding, and power calculations—to ensure manuscripts meet the rigorous reporting standards of top-tier medical journals like JAMA.

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.

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 to constructing rigorous empirical datasets for academic papers in finance and banking. It covers best practices for handling complex data sources (e.g., bank micro-data, central bank reports, macro series), addressing measurement biases, and ensuring full replicability, adhering to the standards of top-tier economic journals. Focus areas include merger tracking, regime transitions, and variable definition consistency.

A comprehensive guide for researchers conducting advanced data analysis and reporting for top marketing journals. It details selecting the appropriate econometric estimator (e.g., ANOVA, DiD, IV, mediation models) based on the study design (experimental, observational, panel). It emphasizes meeting rigorous journal mandates, including exact p-values, standard errors, and effect sizes, and linking statistical findings to practical managerial claims.

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.