Skill UI

A comprehensive methodological guide for conducting rigorous statistical analysis in Operations Management (OM). It covers establishing measurement validity (CFA, reliability), selecting appropriate estimators (SEM, HLM, DiD, IV) for survey and archival data, and addressing critical issues like common-method bias and endogeneity. Essential for academic research reporting.

This comprehensive suite provides systematic checks to ensure spatial estimates are robust against methodological choices. It addresses core econometric issues such as Modifiable Areal Unit Problem (MAUP), boundary arbitrariness, spatial sorting, and spillover effects. Use this guide when submitting research to high-impact journals (like JUE) to demonstrate the reliability and stability of spatial relationships.

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.

This guide provides comprehensive standards for achieving statistical rigor in scientific research reporting. It mandates that every quantitative claim must include effect sizes, uncertainty, sample size (n), and justified statistical tests. Key areas covered include distinguishing biological vs. technical replication, conducting power analyses, implementing multiple comparison corrections, and utilizing Registered Reports to prevent publication bias, ensuring maximum transparency and reliability in scientific findings.

A comprehensive guide detailing the open science and reproducibility standards required for academic publications, especially those utilizing original data or conducting meta-science. It outlines critical steps such as data repository deposit, protocol preregistration, and reporting inter-rater reliability to ensure research methods are fully auditable.

This skill details the rigorous methodology for defining study eligibility criteria and coding data for comprehensive meta-analyses. It emphasizes pre-specifying inclusion/exclusion rules in a protocol, developing a detailed codebook, performing double-coding, and calculating inter-rater reliability (e.g., Kappa, ICC) to ensure the reproducibility and trustworthiness of synthesis findings.

A comprehensive skill for advanced meta-analysis in psychology, designed to assess the reliability and sources of heterogeneity. It performs moderator/subgroup analyses (meta-regression) to explain effect size variation and runs multiple publication-bias diagnostics (e.g., Funnel plot, Egger's test, p-curve, selection models). Essential for rigorous academic reporting requiring robust evidence and sensitivity checks.

Provides a comprehensive guide for authors submitting to the Review of Educational Research (RER). It helps authors anticipate reviewer and editor expectations, framing their work as a significant advance rather than a simple summary. The guide covers rigorous methodological standards, addressing predictable objections (e.g., search incompleteness, coding reliability) and ensuring the manuscript is appropriately positioned relative to other academic venues.

A comprehensive guide detailing how to make systematic reviews and meta-analyses fully auditable and reproducible for academic publishing. It emphasizes adhering to standards like PRISMA and MARS, quantifying inter-coder reliability (e.g., Kappa/ICC), and sharing all open materials, including search strings, coding datasets, and analysis code (via OSF/DOI).

This workflow performs a comprehensive review against the Azure Well-Architected Framework (WAF) using your existing Infrastructure as Code (IaC) and deployed resources. It systematically checks the five pillars—Reliability, Security, Cost Optimization, Operational Excellence, and Performance Efficiency—identifying architectural risks, drift, and compliance gaps. Findings are automatically logged and converted into actionable GitHub issues for streamlined remediation.