Skill UI

A comprehensive guide for conducting and reporting rigorous experimental research in social and behavioral sciences. It covers various design types (vignette, lab, field), statistical requirements (effect sizes, power analysis), quality control measures (manipulation checks, confound control), and advanced techniques for establishing causality, ensuring high internal validity for academic publication.

A comprehensive academic guide for validating survey instruments. This resource details required standards for reliability (Cronbach's α, Composite Reliability), validity (AVE, Fornell-Larcker, HTMT), and diagnosing common method bias (CMV) in self-report studies. It outlines best practices for cross-cultural adaptation, pre-testing, and advanced statistical reporting necessary for high-impact academic publications.

A comprehensive guide for qualitative theory-building, covering multi-case comparison, grounded theory, and advanced methodological rigor. Learn how to structure your study by defining case selection logic, conducting multi-level coding (open, axial, selective), ensuring theoretical saturation, and establishing trustworthiness through triangulation, member checking, and audit trails. Ideal for papers built inductively from interviews or archives.

A comprehensive guide for conducting advanced quantitative research using Structural Equation Modeling (SEM) on questionnaire data. It details rigorous procedures for testing complex causal relationships, including mediation, moderation, and moderated mediation, alongside handling nested data via Hierarchical Linear Modeling (HLM). Essential for academic papers requiring robust inference.

This comprehensive guide outlines best practices for creating key clinical display items, including Table 1 (baseline characteristics), Kaplan-Meier curves, Forest Plots, and CONSORT diagrams. It emphasizes statistical rigor (e.g., using standardized differences instead of P values for baseline), ensuring numbers-at-risk, and maintaining consistency across all reporting elements for high-impact medical publications.

This skill rigorously stress-tests a clinical study's potential impact against the standards of top-tier medical journals (e.g., NEJM, Lancet, JAMA). It evaluates three critical gates: clinical importance (patient outcomes), methodological rigor (study design), and generalizability (broader applicability). Use this to determine if a study is merely incremental or if it genuinely sets a new standard of care, helping authors decide the best publication venue.

This guide details best practices for rigorous statistical reporting in clinical trials, following guidelines from top medical journals like NEJM. It emphasizes using confidence intervals (CIs) over bare P-values, defining primary analysis populations (ITT), controlling for multiplicity, and reporting absolute risks (NNT). It helps researchers ensure their findings are statistically robust, transparent, and defensible.

This tool provides a comprehensive checklist and rigor check for writing clinical studies for top-tier journals like NEJM. It emphasizes the mandatory requirements for prospective trial registration (ICMJE), pre-specification of Statistical Analysis Plans (SAP), and adherence to design principles (e.g., randomization, blinding, ITT). It helps authors identify critical methodological gaps before submission.

This comprehensive master checklist serves as the final gate before submitting clinical manuscripts to top-tier journals like NEJM. It verifies adherence to stringent requirements across significance, statistical rigor, reporting guidelines (e.g., CONSORT, PRISMA), ethical disclosures (ICMJE, data-sharing), and structural integrity. Use it to ensure nothing is missed.

A comprehensive guide for stress-testing the evidence supporting ML claims before submitting to top-tier academic conferences. Covers rigorous auditing of baselines, ablation studies, robustness checks, data leakage, and calibrating claims against evidence strength, ensuring scientific reproducibility.

A comprehensive guide for authors submitting to machine learning conferences like NeurIPS. It details the rigorous standards for data splitting, hyperparameter reporting, random-seed protocols, and compute transparency required for a main-track paper. It also helps authors strategically decide between the main track and the MLRC/TMLR routes based on their core contribution (replication vs. novelty).

This guide provides methodological standards for interpreting and reporting findings in New Media & Society research. It emphasizes analytical transparency, robustness, and credibility across qualitative, discourse, and computational methods, ensuring all inferences are rigorously defensive and fully traceable to the evidence.